《1 Engineering research fronts》
1 Engineering research fronts
《1.1 Trends in Top 10 engineering research fronts》
1.1 Trends in Top 10 engineering research fronts
The global top ten engineering research fronts of engineering management field in 2021 include: “research on the human– computer trust and collaboration mechanism in human– computer collaborative decision-making”, “research on blockchain-based data security management”, “research on the low-carbon transition management and driving mechanism of energy system”, “research on the sustainable development of construction industry based on intelligent technologies”, “research on the risks and security management of cyber physical systems (CPS) ”, “research on network-based platform governance methods”, “research on the impact of artificial intelligence (AI) on industrial transformation and factors distribution”, “research on the modeling and prediction of major infectious disease epidemics”, “research on the human–vehicle–road–network–cloud integrated traffic management under the Internet of Everything (IoE) ”, and “research on the management of complex systems on the whole industrial chain for strategic mineral resources”. The publication status of the core papers on the research fronts above is shown in Tables 1.1.1 and 1.1.2 below. “Research on the human–computer trust and collaboration mechanism in human–computer collaborative decision-making”, “research on blockchain-based data security management”, and “research on the low-carbon transition management and driving mechanism of energy system” will be prioritized in interpretation. Both current and future development trends of the three will be interpreted in detail later.
(1) Research on the human–computer trust and collaboration mechanism in human–computer collaborative decision- making
In the current era, the rapidly developing information technology (IT) has been followed by in-depth integration of IT with our daily life and production, interconnection
Table 1.1.1 Top 10 engineering research fronts in engineering management
|No.||Engineering research front||Core papers||Citations||Citations per paper||Mean year|
|1||Research on the human-computer trust and collaboration mechanism in human-computer collaborative decision-making||39||1121||28.74||2017.1|
|2||Research on blockchain-based data security management||27||1793||66.41||2018.9|
|3||Research on the low-carbon transition management and driving mechanism of energy system||66||6 203||93.98||2016.7|
|4||Research on the sustainable development of construction industry based on intelligent technologies||7||17||2.43||2020|
|5||Research on the risks and security management of cyber physical systems (CPS)||37||1597||43.16||2017.4|
|6||Research on network-based platform governance methods||24||1359||56.62||2017.5|
|7||Research on the impact of artificial intelligence (Al) on industrial transformation and factors distribution||4||627||156.75||2017.5|
|8||Research on the modeling and prediction of major infectious disease epidemics||11||670||60.91||2017.2|
|9||Research on the human-vehicle-road-network-cloud integrated traffic management underthe Internet of Everything (loE)||30||901||30.03||2016.6|
|10||Research on the management of complex systems on the whole industrial chain for strategic mineral resources||15||795||53||2016.5|
Table 1.1.2 Annual number of core papers published for the Top 10 engineering research fronts in engineering management
|No.||Engineering research front||2015||2016||2017||2018||2019||2020|
|1||Research on the human-computer trust and collaboration mechanism in human-computer collaborative decision-making||8||9||7||5||7||3|
|2||Research on blockchain-based data security management||0||0||1||9||11||6|
|3||Research on the low-carbon transition management and driving mechanism of energy system||17||14||16||13||5||1|
|4||Research on the sustainable development of construction industry based on intelligent technologies||0||0||0||0||0||7|
|5||Research on the risks and security management of cyber physical systems (CPS)||4||3||13||10||6||1|
|6||Research on network-based platform governance methods||3||1||5||11||3||1|
|7||Research on the impact of artificial intelligence (Al) on industrial transformation and factors distribution||0||0||2||2||0||0|
|8||Research on the modeling and prediction of major infectious disease epidemics||1||4||1||3||1||1|
|9||Research on the human-vehicle-road-network-cloud integrated traffic management under the Internet of Everything (loE)||7||7||9||5||1||1|
|10||Research on the management of complex systems on the whole industrial chain for strategic mineral resources||5||4||3||0||3||0|
of everything in the world, and accumulation of massive amounts of global data. Human–computer interaction tends to be seen anytime anywhere, and humans and computers are collaborating with each other in making decisions. Human intelligence (AI) is reflected in intuition, reasoning, experience, and learning, among others; while computer intelligence is obviously advantageous in computing, storage, search, optimization, and other aspects. Intelligent computers will, in some aspects, come to own human-like intelligence and active cognition for rapid perception, analysis, decision- making, communication and action. Nevertheless, both human intelligence and computer intelligence appear weak when either of them acts alone. So, the two will be gradually integrated into a new human–computer hybrid social brain. Changes in the relationship between humans and intelligent computers will inevitably cause the formation of a new social pattern in which humans and intelligent computers coexist, game and interact with each other. Human–computer collaborative decision-making represents a key technology for enhancing human–computer hybrid intelligence. It aims to allow interaction, learning and collaborative decision- making between humans and computers, and exploit both strengths of humans and computers, finally contributing to human–computer hybrid intelligence. Currently, focuses of research on human–computer collaboration mainly include: dynamic modeling of human–computer system with partially observable information, human–computer interaction, data-driven human–computer hybrid adaptive learning, the human–computer collaborative decision-making and optimization control based on the gaming theory in uncertain environments, the human–computer trust and collaboration mechanism in human–computer collaborative decision- making, etc. Research on human–computer collaborative decision-making can combine the wisdom of both humans and computers, and provide an important technical support for the management and decision-making scenarios in complex human–computer social and engineering systems. Therefore, this research is of great strategic and scientific significance.
(2) Research on blockchain-based data security management
As the Internet of Everything (IoE) becomes popular, data has become an important carrier of information and resources. Meanwhile, concerns from all walks of life about data security arise following the surge of massive amounts of data. Blockchain technology and data coexist. The former refers to a new application mode of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, encryption algorithms, etc. As a set of mathematical data storage architecture constructed in a way that makes forgery or tampering almost impossible, it can be used to store various types of valuable data and ensure data security and credibility. Blockchain technology plays a key role in global big data reconstruction, and is necessary for digitalization of production technology, holographic economy, big data security and other aspects. Focuses of research on blockchain-based data security mainly include consensus mechanisms, privacy protection, smart contracts, and regulation, among others, and have gained extensive applications in the Internet of Things (IoT), healthcare, logistics, and other fields. Future research will mainly focus on the vertical and horizontal technical deepening of blockchain- based data security in the fields of authentication, access control, data protection, and so on. It is necessary to make efforts to solve the problem of big data security while ensuring data privacy, which can fully ensure security and compliance during data use and circulation.
(3) Research on the low-carbon transition management and driving mechanism of energy system
The energy system represents a vital sub-system of the socio-economic system. It covers the entire process during which natural resources are transformed into specific forms of energy services required in our daily life and production, generally including exploration, exploitation, transportation, processing, conversion, storage, transmission, distribution, use, environmental protection and other links. In the face of the challenge of global climate change and increasingly tight environmental constraints, the global energy system is increasingly showing the inevitable trends of low carbon, cleanliness and high efficiency on the path to its accelerated development. Among these trends, low carbon mainly refers to the substantial decrease in carbon dioxide emissions of the energy system, specifically through energy structure transformation, energy efficiency improvement, end-of-pipe control and other approaches. And these approaches, when implemented, normally involve multiple links in the energy system, and may even trigger fundamental changes of the entire energy system. The low-carbon transition management of energy system is a discipline that plans, designs, implements and optimizes the energy system by regarding the low-carbon energy system as its goal, and technology, economy, society and natural conditions, etc. as constraints. In recent years, the low-carbon transition path of energy system, low-carbon technology application and promotion model, supporting infrastructure and pipeline network planning, etc., have become the key focuses of research in the low-carbon transition management of energy system, and received extensive attention from both Chinese and foreign scholars. Meanwhile, some scholars have noticed that the low-carbon transition of energy system is closely correlated with other socio-economic and natural systems. Multiple factors such as policy guidance, public awareness, market environment and geographic resources jointly act on the low-carbon transition direction and process of energy system. Thus, the driving mechanism of low-carbon transition of energy system has also become a key research frontier prioritized by scholars in related fields of China and the rest of the world. Additionally, the rapid development of digital information technologies, including big data, cloud, IoT, AI and mobile internet, has meant new opportunities for energy system transition. How to couple new information technologies during the low-carbon transition process of energy system and build a more stable, reliable smart energy system is coming to attract scholars’ attention.
(4) Research on the sustainable development of construction industry based on intelligent technologies
Sustainable development means the development that can meet our current needs without weakening the ability of our future generations to meet their needs. As a fundamental industry closely linked to the national economy and people’s livelihood, the construction industry offers quite many job opportunities for our society and contributes a lot to economic growth. However, a lot of energy and natural resources would be consumed during the construction process, and the metabolites generated from this process may cause potential harm to the environment. Thus, faster sustainable development of the construction industry is crucial for achieving the overall sustainable development goal of mankind. Sustainable development of the construction industry has been discussed in the existing research, such as optimizing design for less building materials consumption, adopting new products and processes for less construction waste, optimizing management for higher production efficiency, and offering vocational education for greater environmental awareness of construction workers. With the advancement of science and technology, intelligent technologies have presented new opportunities for the sustainable development of construction industry. Focuses of the future research will tend to be placed on the coordinated management of cost, quality, schedule, carbon emissions and other aspects during the whole life cycle of a project; the on- and off-site management based on sensor monitoring, computer vision, the fifth-generation mobile communication technology (5G), cloud computing, Internet of Things (IoT), etc.; the application of virtual reality (VR), augmented reality (AR), mixed reality (MR), and other technologies for higher working efficiency and innovation capability; the use of 3D printing for less materials consumption and shortened production cycle; the adoption of drones, construction robots, etc. for safe and efficient construction; caring for both mental and physical health of construction workers through human factors engineering; and the application of blockchain technology for higher authenticity and security of project data.
(5) Research on the risks and security management of cyber physical systems (CPS)
Cyber physical systems (CPS) are intelligent systems that integrate computing, network and physical environment, and allow real-time perception, dynamic management and control, and information service of large-scale engineering systems. In the context of the new technological revolution mainly characterized by digitalization, cyberization and intelligence, big data, AI, and IoT have gained extensive applications, enabling the effective integration of cyber and physical elements for new infrastructures under the scenarios that have urban functions such as transportation, energy, education, healthcare, and finance. It seems hard to effectively respond to the threats from coordinated cyber-physical attacks, such as failures, natural disasters and cyber hackers through traditional risk analysis, system optimization as well as management theories and practice. To this end, scholars from many countries and regions have proposed their new system risk and resilience theories on various CPS systems, and discussed the corresponding security management strategies and solutions. The high-level integration of cyber and physical systems has changed the way in which humans interact with the physical world, and also posed new challenges against the management and control decision- making of humans under complex, risky environments. How to effectively prevent, control and respond to coordinated cyber-physical risks and emergencies has become a hot issue of common concern to both academic and industrial circles. The rapid development of CPS has brought about profound changes to related fields, including the discipline of engineering management. In this context, it is necessary to conduct effective risk management of new infrastructures represented by smart grids, smart buildings, and intelligent healthcare, intelligent transportation, smart water networks and industrial internet, improve intelligent system planning and operation, and ensure the safe operation of CPS. These moves will generate significant social and economic benefits and help form new frontiers and focuses for interdisciplinary international research.
(6) Research on network-based platform governance methods
Online social networks and mobile internet platforms have fully penetrated into different aspects of our work and life. Netizens are witnessing an increasing tendency towards online-offline interaction and cyber socialization. On one hand, due to the impacts of factors such as the selective release of opinion leaders on online platforms and the group psychology of audiences, information features fragmented communication and one-sided presentation. As a result, various types of false or distorted information spread fast via network-based platforms and continue to influence more people. On the other hand, owing to the malicious manipulation of personalized recommendation and cyber information on these platforms, it is common to see some phenomena such as information cocoons, digital echo, winner-take-all and big data-enabled price discrimination against existing customers. These new risks triggered by network-based platforms are intertwined with group and social events, regional economic development and other factors, taking on the feature of coupling and cascadability. The greater possibility for the risks to be partially turned into systematic risks has seriously damaged sound development of the society and greatly affected the marginal effect of social innovation. Currently, the boundary delineation and governance strategies for network-based platforms are being explored. And governments in all corners of the world are actively studying scientific methods on the governance of network-based platforms, and trying to empower the network-based platform governance system by applying AI and other advanced technologies. The key scientific issues can be summarized as follows: how to perceive in real time the evolution of cyber society and automatically identify hotspot events and abnormal information; how to fully interpret the network-based ecosystem and conduct quantitative analysis of the whole chain of network-based platform and its internal and external influencing factors; how to conduct system-level modeling under the human-network integration environment and obtain a panoramic understanding of system behaviors in the new social pattern of human-network integration; and how to explore the intelligent warning against social risks trigged by network-based platforms, enable the early detection of and rapid response to platform risks, and provide policy support for the healthy, orderly development of network-based platforms.
(7) Research on the impact of artificial intelligence (AI) on industrial transformation and factors distribution
Industrial transformation refers to the redistribution of production activities and production factors among industrial sectors. Factors distribution means the distribution of the proportions of production factors, such as labor, land, capital, technology, management, knowledge and data, in national income. As a general technology pushing for a new round of technological revolution and industrial transformation, AI will reshape the way we live and work, and affect industrial transformation and factors distribution on both supply and demand sides, thus facilitating the profound adjustment of the relationship between economic efficiency and equitable distribution. Following the booming development of AI worldwide in recent years, there has been a rapid expansion of market, and research regarding the impact of AI on economy management has gradually become popular. However, there is still a lack of comprehensive, systematic theoretical paradigm as well as rigorous, standardized empirical evidence in the existing research, and it is rather difficult to provide practical guidance for the formulation of macro industrial policies and the management of scientific and technological engineering projects. In terms of industrial transformation, what differentiated characteristics are exhibited in the AI research, development and application of different industrial sectors on varying supply sides? What are the differences and linkage between AI and other general technologies in research efficiency and production technology? How AI changes the consumer behavior and demand structure on the demand side? Can AI significantly affect the structures of investment and export? In terms of factors distribution, to what extent AI replaces different production factor types and they makes up for each other? How AI changes the supply and demand structures of different types of labor forces? How AI affects the allocation efficiency of production factors? And how is the distribution percentage of data elements increased? All these issues will become hot topics in the research on the impact of AI on industrial transformation and factors distribution.
(8) Research on the modeling and prediction of major infectious disease epidemics
Major infectious diseases refer to the public health events in which infectious diseases break out within a specific short period of time, affect a large population and involve a large number of infection or death cases. They include both epidemics caused by known infectious diseases with high incidence and prevalence rates (such as influenza, hepatitis A, and plague), and widespread transmission events formed by new, emergancy infectious diseases (such as SARS, Ebola, and COVID-19). In recent years, major new, sudden infectious disease epidemics have occurred frequently. And it is quite easy for the epidemics to spread across borders due to the modern convenient means of transport, thus posing a huge challenge to the health of global population. Additionally, major infectious disease epidemics have also greatly endangered the environment, politics and economy, and seriously affected social stability and economic growth. Thus, research on the modeling and prediction of major infectious disease epidemics is of important theoretical and practical value for improving the ability to assess and predict the risk of epidemic disease spread, monitoring and sending early warning on the breakout of these diseases, taking scientific epidemic control measures, and reducing the damage of major infectious disease epidemics. The traditional compartment models (SI, SIR, SEIR, etc.), which regard uniform mixing as a basic assumption, have large limitations in the modeling and prediction of major infectious disease epidemics due to their wide coverage, complex impact and diversified hazards, etc. And for the non-medical data based epidemic prediction models, represented by Google Trends, their training data deviate from the medical reality and the models are prone to be affected by hot events. In the context of the worldwide spread of COVID-19, how to acquire high- precision data of physical contacts among personnel based on the contact tracking technology and establish a more precise epidemic spread model by integrating diversified data such as demographic composition, social behaviors and environmental factors has become a key scientific problem to be urgently solved both at home and abroad. With the wide application and rapid development of intelligent sensors, mobile internet, and 5G communication technology, among others, it will be the key focuses for the future research to establish a more flexible monitoring and warning model and a comprehensive prediction model for the risks of exposure to and spread of infectious diseases that integrate both online features and offline behaviors, develop a digital twin simulation and inference platform for major infectious disease epidemics, and evaluate the control measures and their effects under complex social conditions by combining infectious disease monitoring data and non-medical data.
(9) Research on the human–vehicle–road–network–cloud integrated traffic management under the Internet of Everything (IoE)
As for integrated traffic management, the information of traffic system elements such as personnel, vehicles, roads and environment is dynamically acquired anytime anywhere under the support of IoT, big data, cloud computing and other information technologies. Also, information is enabled to play a key role in traffic management based on AI. Both dynamic and static elements in the traffic system are actively managed and served so that personnel and objects can move in a safe, efficient and environment-friendly way. Compared with the traditional traffic demand management practice (such as congestion charging and traffic control) and passive traffic control means (such as congestion guidance), the human–vehicle–road–network–cloud integrated traffic management under the Internet of Everything (IoE) will collect the digital information before, during and after the travel and give feedback to the traffic management system so as to achieve the goal of both optimal individual travel or service and transportation system. Currently, main focuses of research on the integrated traffic management include: collection and fusion of aggregate data and disaggregate data, coordination and fusion of moving subjects and static facilities, twinning and integration of physical and virtual systems, service and integration of traffic control and individual travel, and coordination and integration of human driving and self-driving vehicles. Following the automation, sharing, networking and motorization of the automobile industry and the promotion and popularization of the fifth- generation mobile communication technology (5G), AI technology is gradually improving, and the computing data, algorithms and power of the traffic system will be further enriched. Fundamental changes will occur to the connectivity, intelligence level and interaction scope of traffic participants, vehicles and infrastructure, thus making our travel safer, smoother, environment-friendly and humanized. And this traffic management mode will be generally believed to be the final solution to the traffic challenges against mankind.
(10) Research on the management of complex systems on the whole industrial chain for strategic mineral resources
As an important material guarantee for the high-quality development of national economy, strategic mineral resources play an irreplaceable role in emerging industries such as new energy, new materials and IT, as well as defense and military industries. The whole industrial chain of these resources, as a complex system, covers exploration, development, excavation, washing and other roughing processes in the upstream, processing and product manufacturing in the midstream, industrial application in the downstream, whole- process environmental impact and recycling, and other links. It involves supply chain, product chain, technology chain, value chain, capital chain and other chains. All these relational chains form a complex mechanism of interaction, and trigger a complex interaction between all links of the whole industrial chain and multiple subjects. It has become the priority for the breakthrough in future research to propose a set of modes of thinking, practice and research in relation to the management of complex systems on the whole industrial chain for strategic mineral resources at the theoretical and methodological levels and based on complex systems and big data thinking. Specifically, the reconfiguration and modeling of complex systems on the whole industrial chain for strategic mineral resources, the multi-subject compound dynamic mechanism on the whole industrial chain for mineral resources, the resilience of complex systems on the whole industrial chain for mineral resources, the risk assessment and warning of complex systems on the whole industrial chain for mineral resources, the optimized management of complex systems on the whole industrial chain for mineral resources, and the global governance of complex systems on the whole industrial chain for strategic mineral resources in the post-pandemic era, etc. have become multidisciplinary frontier research hotspots.
《1.2 Interpretations for three key engineering research fronts》
1.2 Interpretations for three key engineering research fronts
1.2.1 Research on the human–computer trust and collaboration mechanism in human–computer collaborative decision-making
Human–computer collaborative decision-making aims to explore the law of human–computer interaction, transmission mechanism and intelligent human–computer collaborative decision-making method in order to solve complex problems. From incomplete to complete information, from centralized to distributed structures, from optimization to gaming thinking, it has obvious intersections and applications in national social governance, complex engineering and management, social ecosystem, defense and military, and covers healthcare, intelligent manufacturing, business management, intelligent education, public safety, transport and vehicles, among others. A more in-depth analysis will be made on human–computer interaction, human–computer collaborative decision-making, and human–computer trust mechanism below.
(1) Human–computer interaction
With its rapid development, human–computer interaction (HCI) technology has produced a profound influence on our life and society. This technology offers strengths for the collaborative framework by combining the flexibility, perception and intelligence of humans, and the repeatability and precision of computers. It can contribute to higher efficiency, flexibility and productivity, and also lower pressure and workload based on ergonomic design. Early research on human–computer interaction was mainly conducted on remote operations and intelligent auxiliary equipment. Certain collaborative robots can share work space with humans and make physical contacts with them. In recent years, HCI research relate to human and computer safety, collaboration, teaching system, simulated learning system, visual guidance, voice interaction, tactile and physical human–computer interaction, human-robot task planning and coordination, demo learning, multimodal communication framework, cognitive system, physiological and psychological research in human–computer interaction process, etc.
(2) Human–computer collaborative decision-making
Human–computer collaborative decision-making promotes the mutual collaboration between humans and computers by optimizing the relationship between the two. In this way, both humans and robots can exploit their wisdom for the division of labor and execution of human–computer decision-making. This technology enables the two to make up for each other’s advantages, representing an extension of human behaviors and intelligence. Currently, research on this technology mainly includes basic theoretical research on collaborative perception, collaborative cognition, collaborative planning and control, etc. The application scenarios of this technology include rehabilitation medicine, shared control, business management, etc. Human–computer collaborative decision- making can help effectively allocate tasks to humans and computers, optimize the performance of human–computer system, and enable the mutual consultation and inclusiveness of humans and computers.
(3) Human–computer trust mechanism
Human–computer trust mechanism research aims to enable computers to perceive and respond to human trust, and tap the impact of trust on human–computer collaboration when humans and computers work together. Trust includes three aspects, namely character trust, context trust and learning trust. Character trust is based on human characteristics, such as culture, gender, age and personality. Context trust includes both external factors (such as task difficulty) and internal factors (such as domain knowledge) of humans. And learning trust affects the initial way in which humans think based on the experience accumulation of intelligent computers. In recent years, the evolutionary game theory, statistical methods, and AI methods have been mainly adopted in this field to study control-oriented dynamic human trust behavior models, the evolution and update of trust, the dynamic human trust behavior model, human trust behavior prediction, trust- based strategies, and the safety of task collaboration. Some scholars have also studied the factors affecting human– computer trust, and regard trust as an indicator for human– computer collaborative decision-making.
In terms of the output of core papers on the engineering research front of “research on the human–computer trust and collaboration mechanism in human–computer collaborative decision-making”, the top three countries are the USA, Germany, and China (Table 1.2.1); and the top three institutions are University Patras, Vienna University of Technology, and Aarhus University (Table 1.2.2). And according to the collaboration network among major countries in terms of core paper output (Figure 1.2.1), the cooperation among the USA, China, and Germany seems more frequent, and according to the network among major institutions of core paper, Aarhus University, Vienna University of Technology, Alexandra Institute, IT University Copenhagen and University Limerick cooperate closely (Figure 1.2.2).
According to Table 1.2.3, the USA ranks first in terms of the number of citing papers. And according to Table 1.2.4, East China University of Science and Technology, University College London, and ShanghaiTech University are top institutions in this respect.
1.2.2 Research on blockchain-based data security management
Blockchain technology represents a natural protective umbrella for data and information security management as it features decentralization, peer-to-peer transmission, transparency and traceability, and it cannot be tampered with and can ensure data security. In particular, this technology plays a vital role in solving the problem of “trust” between the platform and cooperation, which has aroused the enthusiasm of scholars towards research on it. High-quality core papers in the field of blockchain-based data security focus on not only the algorithm and architecture development in relation to this technology but also its commercial applications, such as in telecommunication, healthcare, automobile, payment, and other fields.
Table 1.2.1 Countries with the greatest output of core papers on “research on the human–computer trust and collaboration mechanism in human–computer collaborative decision-making”
|No.||Country||Core papers||Percentage of core papers||Citations||Citations per paper||Mean year|
Table 1.2.2 Institutions with the greatest output of core papers on “research on the human–computer trust and collaboration mechanism in human–computer collaborative decision-making”
|No.||Institution||Core papers||Percentage of core papers||Citations||Citations per paper||Mean year|
|1||University of Patras||2||5.13%||205||102.5||2016.5|
|2||Vienna University of Technology||2||5.13%||90||45||2016.5|
|4||Massachusetts Institute of Technology||2||5.13%||72||36||2017.5|
|8||IT University of Copenhagen||1||2.56%||59||59||2015|
|9||University of Limerick||1||2.56%||59||59||2015|
In terms of core papers published, China, USA, and India rank top three (Table 1.2.5). In terms of blockchain-based data security, China demonstrates a strong strength, ranking first in the number of core papers. In this country, blockchain technology is used to protect medical data hosted in cloud, and prevent malicious use of medical data in systems. And continuous efforts are made to protect patient privacy and maintain a good doctor-patient relationship. USA, only second to China in this respect, mainly focuses on the application of blockchain-based data security in the industries of healthcare, oil and gas industries. Pakistan and United Arab Emirates, with the papers they jointly publish frequently cited, focus
Figure 1.2.1 Collaboration network among major countries in the engineering research front of “research on the human– computer trust and collaboration mechanism in human–computer collaborative decision-making”
on the provision of solutions for the intelligent application of IoT using blockchain technology, laying an academic foundation for blockchain-based data security in the IoT field.
Figure 1.2.2 Collaboration network among major institutions in the engineering research front of “research on the human–computer trust and collaboration mechanism in human–computer collaborative decision-making”
Table 1.2.3 Countries with the greatest output of citing papers on “research on the human–computer trust and collaboration mechanism in human–computer collaborative decision-making”
|No.||Country||Citing papers||Percentage of citing papers||Mean year|
Table 1.2.4 Institutions with the greatest output of citing papers on “research on the human–computer trust and collaboration mechanism in human–computer collaborative decision-making”
|No.||Institution||Citing papers||Percentage of citing papers||Mean year|
|1||East China University of Science and Technology||21||12.80%||2017|
|2||University College London||20||12.20%||2019.3|
|4||University of Washington||17||10.37%||2018.5|
|5||University of Patras||15||9.15%||2017.8|
|7||University of Sussex||13||7.93%||2018.9|
|8||Politecnico di Milano||12||7.32%||2019.6|
|9||Xi'an Jiaotong University||12||7.32%||2018.8|
|10||Chinese Academy of Sciences||11||6.71%||2018.9|
Table 1.2.5 Countries with the greatest output of core papers on “research on blockchain-based data security management”
|No.||Country||Core papers||Percentage of core papers||Citations||Citations per paper||Mean year|
|6||United Arab Emirates||2||7.41%||582||291||2019|
South Korea attaches great importance to the application of blockchain technology in the telecommunications field. In terms of average year of publication, China and the USA started earlier in blockchain-based data security, representing leaders in this respect. According to Table 1.2.6, universities in all these countries become the main force for R&D in blockchain-based technical data security. Among them, Chinese universities show the greatest vitality in R&D. By combining blockchain technology with authentication, Changsha University of Science & Technology and Fujian University of Technology provide new solutions in access control mechanism design and use edge computing to improve the data storage capability of the system. Noticeably, Khalifa University of Science, Technology and Research and Bahauddin Zakariya University have a large number of cited core papers and they join hands in technical research on the safety construction of IoT. Their support for IoT construction with the aid of blockchain technology has become important academic achievements in this field and also a model for transnational cooperation in blockchain technology.
According to the network of cooperation among major countries in terms of core paper output (Figure 1.2.3), the countries see an unbalanced network of cooperation in terms of blockchain-based data security management. Among them, the length of arc between China and the USA is the largest, indicating the two countries’ valuing of blockchain- based data security cooperation, despite their different fields of cooperation. China has established close cooperation
Table 1.2.6 Institutions with the greatest output of core papers on “research on blockchain-based data security management”
|No.||Institution||Core papers||Percentage of core papers||Citations||Citations per paper||Mean year|
|1||Changsha University of Science & Technology||3||11.11%||78||26||2019.3|
|2||Fujian University of Technology||3||11.11%||78||26||2019.3|
|3||Jaypee University of Information Technology||2||7.41%||77||38.5||2020|
|5||Nanyang Technological University||2||7.41%||68||34||2017.5|
|6||Nanjing University of Information Science & Technology||2||7.41%||44||22||2019|
|7||Bahauddin Zakariya University||1||3.70%||548||548||2018|
|8||Khalifa University of Science, Technology and Research||1||3.70%||548||548||2018|
|9||The University of Hong Kong||1||3.70%||192||192||2018|
|10||University of Salerno||1||3.70%||192||192||2018|
Figure 1.2.3 Collaboration network among major countries in the engineering research front of “research on blockchain-based data security management”
relationships with Italy, Singapore, India, and the USA. Specifically, China cooperated with the USA and Italy in data security building in the healthcare field; with the USA in the application of blockchain technology to medical data sharing and oil & gas industries; with Italy in the data security of electronic medical record sharing; with Singapore in studying the use of blockchain technology in outsourcing service; with India in authentication research using blockchain. Additionally, USA, Singapore, and South Korea attach great importance to research on the impact of the blockchain’s consensus mechanism; China and Singapore highlight the fair payment framework for blockchain-based cloud computing outsourcing service so as to enable fair payment for outsourcing service. The expansion of research boundaries s by these countries and cooperation between different participants can contribute to diversified explorations in the field of blockchain-based data security.
According to Figure 1.2.4, the network of cooperation among institutions is becoming more and more obviously unbalanced in blockchain-based data security management. The Chinese universities represented by Changsha University of Science & Technology, Fujian University of Technology, and Nanjing University of Information Science & Technology lead the trend of institutional cooperation. Other countries except China are dominated by cooperation among domestic universities, with a certain gap from China in terms of the closeness and results of cooperation. Nevertheless, international cooperation and innovation will be bound to become an important paradigm for blockchain-based data security research.
According to Table 1.2.7, China ranks first in the number of core papers cited. And as we can see from Table 1.2.8, the top institutions in this respect include King Saud University, Nirma University, and Xi’an University of Posts & Telecommunications.
1.2.3 Research on the low-carbon transition management and driving mechanism of energy system
In the 1980s, the Institute for Applied Ecology, Germany proposed the concept of “energy transmission” to refer to the transition of the dominant energy from fossil energy
Figure 1.2.4 Collaboration network among major institutions in the engineering research front of “research on blockchain-based data security management”
Table 1.2.7 Countries with the greatest output of citing papers on “research on blockchain-based data security management”
|No.||Country||Citing papers||Percentage of citing papers||Mean year|
Table 1.2.8 Institutions with the greatest output of citing papers on “research on blockchain-based data security management”
|No.||Institution||Citing papers||Percentage of citing papers||Mean year|
|1||King Saud University||47||16.97%||2020|
|3||Xi'an University of Posts & Telecommunications||26||9.39%||2019.1|
|5||Chinese Academy of Sciences||24||8.66%||2020|
|6||Changsha University of Science & Technology||23||8.30%||2020|
|7||King Abdulaziz University||23||8.30%||2020.2|
|8||Beijing University of Posts and Telecommunications||22||7.94%||2020.1|
|9||JeJu National University||21||7.58%||2020.4|
to renewable energy in order to cope with the oil crisis. As climate change has been on the global agenda and related research is being deepened and extended, the concept of “low- carbon transition of energy system” has come to take shape, covering multi-link de-carbonization processes from energy production, storage, transportation to terminal consumption. It is reflected as multi-directional system replacement for dominant energy types, technologies and systems. Currently, global scholars in the energy field have reached a consensus that the low-carbon transition of energy system represents not only a key approach to coping with global climate change and achieving the goal of cutting greenhouse gas emissions, but also a key focus for maintaining national energy security and realizing the goal of sustainable development.
The path for low-carbon transition of energy system, low- carbon technology application and promotion models, supporting infrastructure and pipeline network planning, and research on the driving mechanism for the low-carbon transition of energy system, highlighted by domestic and international scholars in recent years, are further interpreted below, and the prospect for the future development is also described.
(1) Research on the path for low-carbon transition of energy system
Initially, the research on the path for the low-carbon transition of energy system resolved around three approaches, namely energy efficiency improvement, structural transformation and end-of-pipe control. The time-series changes in related indicators such as energy intensity, share of clean energy and carbon intensity at different levels (such as national, regional, and industrial levels) were planned. Then, the selection, entry and exist mechanisms of specific technical routes were further moved into. Research methods include comprehensive evaluation model, planning model, scenario analysis, system simulation, etc. How to design a path for the low-carbon transition of energy system based on local conditions and by taking into account the heterogeneity of different regions and industries (such as resource endowment, energy demand, as well as social and economic factors) has become a research hotspot and difficulty in recent years.
(2) Research on the application and promotion mode of low- carbon technology for energy system
The extensive utilization of low-carbon technology represents the key to the low-carbon transition of energy system. Chinese and foreign scholars have conducted series of research on the application methods, business models, diffusion laws, constraints and promotion policies, etc., in relation to technologies including clean energy (such as wind energy, solar energy, hydrogen energy, and nuclear energy), carbon capture and storage, and energy efficiency improvement. In particular, there has been an extremely high interest in hydrogen energy in recent years. Meanwhile, a number of emerging technologies and industries, such as new energy vehicles, energy storage systems and smart grids, have also been driven by the application and promotion of new energy technologies. The development of related technical industries and the coupling mechanism between them and new energy technologies have become research frontiers and difficulties in recent years.
(3) Research on the planning of supporting infrastructure and pipelines for energy system transition
To achieve effective operation, the energy system needs to be supported by its supporting infrastructure, such as power transmission and distribution networks, oil and gas pipelines, energy storage units, and energy charging stations, etc. In the low-carbon transition of energy system, it is necessary to make adjustments and optimization based on the existing infrastructure and pipeline layout (such as electricity-heat- gas network integration, reliability improvement of grid integration of renewable energy) and to build new facilities and networks to serve the industries that adopt new energy technologies (such as charging piles for electric vehicles and hydrogen refueling stations). How to construct a network planning model that takes comprehensive account of a variety of practical factors, accurately estimate the parameter value range, and design effective solution algorithms represent a long-term hotspot and difficulty in this focus of research.
(4) Research on the driving mechanism for the low-carbon transition of energy system
The actual progress of the low-carbon transition of energy system is jointly affected by several factors such as policies, market, society and resources. So, clarifying the driving mechanisms of all factors on the low-carbon transition of energy system is of vital significance for facilitating the low- carbon transition of energy system. In recent years, Chinese and foreign scholars have adopted the empirical analysis method to assess the impact of specific factors on the low-carbon transition of energy system based on observational data, and also the remodeling analysis method to explore the possible effects of different factors on the low-carbon transition of energy system. The comparison of the effects of varying types of driving factors, and the interaction of multiple factors represent the research frontiers and difficulties for this focus of research.
(5) The development trend of future research
Low-carbon transition is the general trend for the development of the existing global energy system, and this system engineering is strongly supported by the research on the low-carbon transition management and driving mechanism of energy system. How to take comprehensive account into the interactions among technologies, economy, society, natural systems and energy systems, give consideration to the heterogeneity of regions and industries, and design reasonable low-carbon transition paths and guarantee mechanisms will continue to be a research frontier and hotspot waiting for breakthrough in the energy engineering management field. Additionally, how to couple the low-carbon transition of energy system with the revolution of information technologies such as low-carbon transition of energy system and “big data, cloud, IoT, AI and mobile internet” and collaborate in building a more reliable intelligent energy system will gradually become the research frontier focused on by scholars in this field.
The top two countries in terms of the number of core papers and citations in the engineering research front of “research on the low-carbon transition management and driving mechanism of energy system” are the UK and the USA, followed by some European countries and China (Table 1.2.9). Among them, the USA cooperates more frequently with China, the Netherlands, Austria, the UK, and Germany (Figure 1.2.5). The top-ranked institutions in terms of the number of core papers are mostly distributed in Europe. University of California, Berkeley in the USA and Tsinghua University in China also rank among top ten (Table 1.2.10). Utrecht University in the Netherlands has the most frequent cooperation with other institutions and Potsdam Institute for Climate Impact Research in Germany ranks second in this respect (Figure 1.2.6). According to Table 1.2.11, China ranks top in terms of the number of citing papers. And according to Table 1.2.12, Tsinghua University and Imperial College London rank among the top.
《2 Engineering development fronts》
2 Engineering development fronts
《2.1 Trends in Top 10 engineering development fronts》
2.1 Trends in Top 10 engineering development fronts
The global top ten engineering development fronts of the engineering management field in 2021 include “big data based disease diagnosis and prediction system and technology”, “city information modeling (CIM) and systems”, “blockchain-based quality information tracking method and system”, “data-driven large-scale engineering construction environment risk technology and method”, “intelligent energy
Table 1.2.9 Countries with the greatest output of core papers on “research on the low-carbon transition management and driving mechanism of energy system”
|No.||Country||Core papers||Percentage of core papers||Citations||Citations per paper||Mean year|
Table 1.2.10 Institutions with the greatest output of core papers on “research on the low-carbon transition management and driving mechanism of energy system”
|No.||Institution||Core papers||Percentage of core papers||Citations||Citations per paper||Mean year|
|2||The University of Manchester||5||7.58%||467||93.4||2016.6|
|4||International Institute for Applied Systems Analysis||5||7.58%||302||60.4||2016.8|
|5||University of California, Berkeley||4||6.06%||684||171||2016|
|6||University College London||3||4.55%||957||319||2017.3|
|7||Imperial College London||3||4.55%||888||296||2017.7|
|9||University of Leeds||3||4.55%||253||84.33||2016|
|10||Potsdam Institute for Climate Impact Research||3||4.55%||243||81||2016.3|
optimization management method”, “supply chain based financial risk management and control platform”, “intelligent reconfigurable manufacturing technology and system”, “basic software development for intelligent planning and scheduling in the aerospace field”, “blockchain-based smart contract development”, and “intelligent warehouse management method and equipment”. The status of their core patents is available in Tables 2.1.1 and 2.1.2. The ten engineering development fronts relate to several disciplines such as energy, transportation, medicine, aerospace, and architecture. “Big data based disease diagnosis and prediction system and technology”, “city information modeling (CIM) and systems”, and “blockchain- based quality information tracking method and system” will
Figure 1.2.5 Collaboration network among major countries in the engineering research front of “research on the low-carbon transition management and driving mechanism of energy system”
be prioritized in interpretation. And their current and future development trends will be interpreted in detail below.
Figure 1.2.6 Collaboration network among major institutions in the engineering research front of “research on the low-carbon transition management and driving mechanism of energy system”
Table 1.2.11 Countries with the greatest output of citing papers on “research on the low-carbon transition management and driving mechanism of energy system”
|No.||Country||Citing papers||Percentage of citing papers||Mean year|
Table 1.2.12 Institutions with the greatest output of citing papers on “research on the low-carbon transition management and driving mechanism of energy system”
|NO.||Institution||Citing papers||Percentage of citing papers||Mean year|
|2||Imperial College London||129||13.30%||2019.2|
|4||Chinese Academy of Sciences||103||10.62%||2019.6|
|6||University College London||73||7.53%||2018.8|
|7||Technical University of Denmark||70||7.22%||2018.9|
|8||University of California, Berkeley||63||6.49%||2018.6|
|9||University of Sussex||62||6.39%||2019|
|10||Potsdam Institute for Climate Impact Research||60||6.19%||2018.5|
(1) Big data based disease diagnosis and prediction system and technology
The big data based disease diagnosis and prediction system and technology refers to a system for disease diagnosis or prediction built based on big data technology and by collecting medical data from millions of patients. The disease of a specific patient can be accurately diagnosed by typing the individual data of the patient into the diagnosis system. This can accurately diagnose the patient’s disease, get a better treatment solution, and increase the recovery rate of the patient. Meanwhile, the prediction system can help identify the risk factors of a specific individual or population and predict the probability of disease occurrence so as to further intervene in health risk factors and achieve the goal of disease prevention. With the integration of medicine and big data, healthcare big data has demonstrated its broad application prospects in pharmaceutical R&D, health service, health management, disease diagnosis and treatment, disease prevention, personalized precision medicine, and other fields. Additionally, with the proposing of the concept of precision medicine and the gradual introduction of big data related new technologies, new theories and new methods, research on the big data based disease diagnosis and prediction system and technology has become a hotspot for academic research. Big data processing technology occupies a central position in the disease diagnosis and prediction system. Nevertheless, healthcare big data involves massive amounts of data, uneven levels of data processing technologies, data barriers, privacy
Table 2.1.1 Top 10engineering development fronts in engineering management
|No.||Engineering development front||Published patents||Citations||Citations per patent||Mean year|
|1||Big data based disease diagnosis and prediction system and technology||127||129||1.02||2018.9|
|2||City information modeling (CIM) and systems||42||245||5.83||2016.9|
|3||Blockchain-based quality information tracking method and system||17||3||0.18||2019.9|
|4||Data-driven large-scale engineering construction environment risk technology and method||33||100||3.03||2017.3|
|5||Intelligent energy optimization management method||125||890||7.12||2016.7|
|6||Supply chain based financial risk management and control platform||106||496||4.68||2018.8|
|7||Intelligent reconfigurable manufacturing technology and system||39||1803||46.23||2016.5|
|8||Basic software development for intelligent planning and scheduling in the aerospace field||41||262||6.39||2017.2|
|9||Blockchain-based smart contract development||50||55||1.1||2019.8|
|10||Intelligent warehouse management method and equipment||75||370||4.93||2016.6|
Table 2.1.2 Annual number of core patents published for the Top 10 engineering development fronts in engineering management
|No.||Engineering development front||2015||2016||2017||2018||2019||2020|
|1||Big data based disease diagnosis and prediction system and technology||2||5||19||12||35||54|
|2||City information modeling (CIM) and systems||5||3||4||12||7||4|
|3||Blockchain-based quality information tracking method and system||0||0||0||0||1||16|
|4||Data-driven large-scale engineering construction environment risk technology and method||6||3||6||12||6||0|
|5||Intelligent energy optimization management method||32||32||25||22||12||2|
|6||Supply chain based financial risk management and control platform||1||4||6||12||36||43|
|7||Intelligent reconfigurable manufacturing technology and system||2||3||3||3||5||12|
|8||Basic software development for intelligent planning and scheduling in the aerospace field||9||3||9||3||8||6|
|9||Blockchain-based smart contract development||0||0||0||3||6||41|
|10||Intelligent warehouse management method and equipment||14||20||26||10||5||0|
protection related problems, among others, making the application of big data in medicine greatly challenged. In this context, the standardized medical data construction, R&D of multi-source heterogeneous data analysis technology, R&D of data sharing platforms and other emerging technologies, as well as optimization in the fields of privacy protection and security represent important focuses of future research.
(2) City information modeling (CIM) and systems
The city information modeling (CIM) is an organic city information complex built in three-dimensional digital space based on the building information model (BIM), geographic information system (GIS), Internet of Things (IoT), and other technologies. It integrates information model data and city perception data that involve multiple scales and dimensions, i.e. ground and underground, indoor and outdoor, history and the current days. As a fusion of the three-dimensional geographic information system (3D GIS) and the building information model (BIM), the CIM platform can be used to store massive amounts of city-scale information and to serve as a cloud platform capable of collaborative operation and data access. Through combination with IoT, big data, cloud computing and other technologies, this platform can offer an integrated management system that satisfies the needs for urban development. It connects the CIM model and the city with the aid of IoT to form a database that can be updated. Also, a work platform that enables information sharing and transmission is formed under the support of cloud computing, big data and other technologies to support various applications. Additionally, the CIM platform is formed for the same physical space and the information attached on the space. As an information platform constructed and managed under the organization of the government, it is open to targeted residents and enterprises, thus solving a series of problems that arise from urban development. Composed of physical characteristics and related information of all urban facilities, CIM can store, retrieve, update and modify all city-related information. A smart city platform oriented for urban governance is built to make full use of IoT, big data modeling, AI, 3D GIS visualization, BIM, CIM and other technologies. According to the design principle of high starting point, comprehensiveness and systematicness, a smart city governance pattern that combines three- dimensional transportation, environmental information, government service, economic operation, work safety, urban infrastructure and other aspects is built. Also, a city 3D GIS model is established to superimpose the city’s IoT aware data; warning in the city is integrated to form a CIM warning model; AI is used for intelligent governance of the city; the city’s operation management process is designed to intelligently identify and deal with urban events; and a smart city platform is established for intelligent governance of the entire city.
(3) Blockchain-based quality information tracking method and system
As an important advancement of next-generation information management technology, blockchain ensures that the data on the chain cannot be tampered with, is transparent and traceable by integrating cryptography, peer-to-peer networks, distributed consensus and other technologies. It is expected to provide a new idea for quality management through information tracking and value co-creation. The traditional information system is a centralized and asymmetrical structure that features low security and poor transparency. It seems difficult for the traditional system to satisfy the demand for quality accountability, when disputes can easily arise. The technical characters of blockchain make it suitable for the evidence tracking scenario. It can record in a true and reliable way the quality information that covers all factors of the product during its whole life cycle, and its information sources are clear and not deniable. Once any quality problem occurs, the responsible party can be quickly identified through tracing. Additionally, the on-the-chain information that is interconnected and consistent throughout the entire network breaks the “information island”, solves problems such as information asymmetry and low communication efficiency, facilitates the continuous improvement of engineering product quality, and pushes for quality value co-creation among participants. How to effectively integrate blockchain, IoT, cloud computing, big data, AI and other technologies for quality information acquisition, sharing, analysis, traceability and other applications need to be further explored. Additionally, quality information usually involves a wide range of sources and forms, and information volume will aggregate together as the project advances. So, how to improve the “scalability” of blockchain system while securing decentralization, security and reliability deserves our further attention.
(4) Data-driven large-scale engineering construction environment risk technology and method
Jim Gray, a Turing Award winner, considers “data-driven” as the fourth analytical paradigm in data science following experience, theory and computing. Therefore, in terms of data science, the data-driven large-scale engineering construction environment risk technology and method refers to the knowledge extraction from structural and non-structural engineering big data by using scientific methods, processes, algorithms and systems, and the application of knowledge to the recognition, assessment and control of and response to large-scale engineering construction environment risks. With the rapid evolution of information technology, the engineering construction field has entered an era of big data. In this context, the introduction of the data-driven based analytical paradigm into business fields such as engineering construction risk management represents an important engine for the transformation and upgrading of construction industry. Lots of explorations have been made in related theoretical and practical research. In terms of engineering issues, the large-scale engineering construction environment involves not only progress, cost, quality, safety, environment and other narrow-sense risks that may arise from the impacts to construction site structures, machinery, workers and other working environments, and also to the existing buildings (structures), pipelines, pedestrians and other surrounding environments due to the construction process, but also market, policy and other broad-sense environmental risks for construction projects. In terms of technical means, the mining and policy support for report text, monitoring data, streaming media and other engineering environment big data of various types using expert systems, machine learning, deep learning and other methods represent the current mainstream direction. However, due to the great fluidity and complex inline mechanism of engineering construction environment, it is necessary to combine different environmental factors organically and conduct dynamic analysis that covers all scenarios, the whole process and multiple participants of engineering construction. Additionally, different environmental risk factors may cause inconsistent data formats due to their varying perception means. In this context, it is necessary to interpret these factors by combining several technical means. So, how to dynamically recognize, analyze and predict the environmental risks that cover multiple scenarios and the whole process of engineering construction based on multi-source heterogeneous data, and provide data- driven based risk solutions for all participants will be a major trend for the future research.
(5) Intelligent energy optimization management method
Intelligent energy optimization management refers to the combination of traditional modeling practice, modern optimization algorithms, as well as prediction and intelligent technologies to have access to large amounts of data in real time for intelligent analysis; and the whole-process all-round energy management, optimization and control for scenario- based, intelligent and automated energy management. Different from the traditional optimization solutions such as the simplicity method and various gradient-based iterative algorithms, this intelligent optimization method can overcome the limitation that the traditional solutions can only solve structural problems. It adapts to the significantly complex and systematic modern energy system, and demonstrates higher management efficiency. Intelligent energy optimization management monitors, controls and predicts energy in an intelligent way in links such as production and conversion, transmission and distribution, storage and consumption, thus achieving the goal of energy structure optimization, energy conservation and consumption reduction, quality and energy efficiency improvement, cost control, and so on. The technical methods concerned do not only include traditional optimized decision-making (such as intelligent algorithms and deep learning) but also embody a combination of new information and communication technologies (such as IoT, big data, and AI) and energy technologies (such as energy storage, demand response, and multi-energy complementation). Through review of domestic and international patents, the intelligent energy optimization management method can be used for complex scenarios such as smart grids, energy internet, and energy-consuming terminals to facilitate the building of high-efficient smart energy systems, support multi-energy coordination, optimization and complementation, and improve the energy efficiency management of energy internet. The intelligent energy optimization management will, still based on information technology, promote the penetration of renewable energy and improve the utilization efficiency of traditional energy. With great adaptability and flexibility, the next-generation information technology can effectively solve the problems of non-linearity, high uncertainty, strong coupling and multiple variables. It is vital to the improvement of the energy system’s efficiency, safety, reliability and intelligence. This solution can be used to optimize the capabilities of resources allocation, safety guarantee and intelligent interaction in the links of energy production, transportation, transaction and consumption. And intelligent data- and information-based operation management of energy enterprises and intelligent development of energy industry will become a major trend for the future development.
(6) Supply chain based financial risk management and control platform
The supply chain based financial risk management and control platform refers to a digital platform that provides high-quality supply chain based financial services. It aims to evaluate and predict corporate credit, monitor business operations, improve risk control quality, and reduce risk control costs based on the real transaction data. Supply chain finance means comprehensive financial services provided to related upstream and downstream enterprises on the supply chain according to industry characteristics and based on credit data, with core enterprises or core enterprise groups or core data controllers as focus. Supply chain finance mainly involves risks such as policy-technology-economic cycle risk, uncertain supply-demand system risk, core corporate entity risk, business operation risk and false transaction risk, refinancing and self-financing risks. In the current stage, although the supply chain finance risk control platforms driven by fintech (such as AI, big data, cloud computing, IoT, block chain, and 5G communication, etc.) represents the development trend, most of them are underpinned by the credits of high-quality core enterprises or the government. The data barriers formed by scarce high-quality core enterprises and highly decentralized transaction credit data in small-, medium- and micro-enterprises have become major obstacles to their development. In this context, the use of fintech and anti-monopoly policies for further integration of government authorities, financial institutions, upstream and downstream enterprises on the supply chain and commercial platforms for the interconnection, sharing and mutual access of data have become the main solution to these bottlenecks. And evaluating the credit data of small-, medium- and micro-enterprises through tracing and regarding it as the asset available for pledging can help get rid of the rigorous guarantee requirements of core enterprises. In the future, the supply chain based financial risk management and control platform will, based on the unified credit system of the central government, synergize with the government, enterprises and banks. This can realize cross-validation, ensure data security, automatically identify risks and send warning, dynamically monitor post-loan supply chain transactions, stabilize industry and supply chains, and support the real economy in quality and efficiency improvement.
(7) Intelligent reconfigurable manufacturing technology and system
The reconfigurable manufacturing system is a manufacturing system capable of rapidly reorganizing or updating its structure or constituting units, timely adjusting its functions and production capacity so as to respond to market changes and other demands fast. Key enabling technologies include grouping, layout planning and optimization, online diagnosis, discrete event simulation, etc. It is necessary to explore intelligent reconfigurable manufacturing technologies and form an intelligent reconfigurable manufacturing system during the network-based digital, intelligent transformation and upgrading of the reconfigurable manufacturing system. The intelligent reconfigurable manufacturing technology and system adaptively regulate their own structure from physical/ logical aspects, at multiple levels and in other dimensions. It is one of the important theories that are quite likely to fundamentally optimize the manufacturing system and satisfy the uncertain, fluctuating personalized market demand. The main focuses of research include but are not limited to: intelligent reconfigurable manufacturing enabling technology, intelligent reconfigurable manufacturing system modeling and simulation, intelligent reconfigurable manufacturing system reconfiguration decision-making, intelligent reconfigurable manufacturing system performance evaluation, intelligent collaborative reconfiguration of network-based manufacturing system, data-driven intelligent reconfiguration, industrial robots and intelligent reconfigurable manufacturing systems, intelligent reconfiguration of multi-layered coupling, and AGV driven intelligent reconfigurable manufacturing systems. Since its initial design, the reconfigurable manufacturing technology has provided the manufacturing system with a high degree of flexibility for production. The intelligent research on the reconfigurable manufacturing technology represents the key to the diversified, personalized and customized intelligent manufacturing. As the division of labor and cooperation for the industry becomes increasingly clearer, network- based collaborative manufacturing has become a new trend, and there has formed a closely connected manufacturing network between key manufacturing enterprises and their supporting suppliers. In view of uncertain demands and the agile changes of the supply chain, how core manufacturing enterprises and multi-tier suppliers use next-generation information technologies (such as AI, big data and digital twin) for effective collaboration and reconfiguration and for higher manufacturing efficiency and quality represents an important trend for the future of the intelligent reconfigurable manufacturing technology and system.
(8) Basic software development for intelligent planning and scheduling in the aerospace field
The basic intelligent planning and scheduling software for the aerospace field refers to the computer simulation software that specifically solves the planning and scheduling problems arising from the launching and on-orbit running management of aircrafts such as satellites, space stations, manned spacecrafts, deep space probes, and assists the spacecraft control agency in formulating the launching and on-orbit operation plans and resolving the conflicts among plans. With the rapid development and popularization of aerospace industry, there has been a surging demand for planning and scheduling in the field of aerospace. It is urgent to develop specialized basic intelligent planning and scheduling software, absorb the latest research achievements in related fields such as operational research and intelligent optimization, and provide technical support for the design, R&D and flexible expansion of aerospace planning and scheduling systems such as satellites, space stations and deep space probes. It is found by reviewing domestic and international patents and articles that the following key technologies needs to be urgently solved in the development of basic intelligent planning and scheduling software in the field of aerospace: planning and scheduling modeling for various types of spacecrafts, high-performance planning and scheduling under complex aerospace constraints, real-time autonomous planning and scheduling under uncertain aerospace environments, and autonomous collaboration, planning and scheduling of spacecraft clusters for large-scale networking. In the future, in terms of problems, the single spacecraft planning and scheduling will be shifted to large-scale network-based heterogeneous spacecraft cluster planning and scheduling; in terms of organization, the unified planning and scheduling of the ground control center will develop towards the on- orbit autonomous collaboration, planning and scheduling of spacecraft clusters; in terms of method, the traditional operational research method will be changed to the planning and scheduling of multidisciplinary integration methods such as operational research, machine learning and game theory.
(9) Blockchain-based smart contract development
The smart contract is a computer protocol that spreads, verifies and executes contracts based on information technology, featuring high-efficient formulation, low-cost maintenance and high-precision execution. Blockchain technology provides a programmable environment for the smart contract, and pushes for its development and application. Characterized by decentralization, trustlessness, autonomous transactions, and non-tampering, the smart contract allows each party to the contract to conclude transactions without any basis of trust or trustable third party. Also, as an embedded programming contract, the smart contract can be built into any blockchain data, transaction or asset. It is expected to enable various types of programmable intelligent assets and systems and to promote the further reform of finance, IoT, medicine, and other traditional fields. With the further popularization and application of blockchain technology, the emerging smart contract technology has attracted wide attention from both academic and industrial circles. For example, the peer-to-peer, trustless transaction environment provided by the blockchain and its strong computing power can simplify financial transaction process, based on which the financial smart contract is used to enable the programmable currency and programmable financial system. Blockchain and the medical smart contract are combined to allow medical data sharing and drug traceability, automate the complex IoT process, promote resources sharing, ensure security and efficiency, and save cost. Additionally, the blockchain-based smart contract can also be used for real estate transactions, contract payment, government purchase, supply chain management, communication service, energy transactions, intellectual property management, voting management, intelligent manufacturing, digital asset transactions, electronic files and other fields. The existing smart contract development platforms mainly include: Ethereum, Hyperledger Fabric, NEM, Stellar and Waves, etc. However, due to the performance limitations of the blockchain system itself, there is no way for the system to deal with complex logic and high throughput data in traditional contracts. Also, there is a lack of privacy protection. And it is somewhat difficult to achieve cross-chain application. Therefore, in addition to possible application scenarios of the smart contract, how to solve the problems of privacy, performance, mechanism design, security and formal verification during the performance of smart contract will also become a frontier research hotspot of wide concern to both academic and industrial circles.
(10) Intelligent warehouse management method and equipment
The intelligent warehouse system is a technical ecosystem based on several interconnected intelligent warehouse equipment collaborative tasks. It can be used to automatically receive, sort, clarify, package and distribute goods and provide enterprises with high-efficient, low-cost intelligent logistic services. Due to the rapid development of IoT and AI, the intelligent warehouse management system, the intelligent inventory control platform, intelligent robots and other equipment have gained wide application. An intelligent warehouse system represented by the intelligent robot order execution system, the human–computer collaborative order sorting system, and the intelligent robot sorting system, etc. has been formed. Different from the traditional warehouse system, the intelligent equipment in the intelligent warehouse system can generate and receive real-time data. And it adopts the independent control mode for data-driven collaborative operation under the IoT environment. The existing intelligent warehouse management research mainly highlights the following aspects: first, system performance evaluation and structural design optimization, mainly through intelligent simulation, random queuing model and expected travelling time model, etc. second, operation strategy optimization for order scheduling, robot scheduling, order allocation, path optimization and other problems, mainly through the research paradigm that combines mixed integer programming (MIP) and intelligent optimization algorithms. Currently, the key issues to be explored in the intelligent warehouse management method include: data-driven intelligent equipment collaboration strategy, data-driven human– computer collaborative operation strategy, large-scale intelligent robot path planning and scheduling, the impact of robot path congestion, etc. With the improvement in its management level, the intelligent warehouse will greatly improve the intelligence level of corporate supply chain, and provide important conditions for supporting the intelligent upgrading and development of logistics system.
《2.2 Interpretations for three key engineering development fronts》
2.2 Interpretations for three key engineering development fronts
2.2.1 Big data based disease diagnosis and prediction system and technology
Following the development of IoT, internet, AI and other emerging technologies, big data has penetrated into all walks of life. In recent years, big data has gradually demonstrated its strengths in health management, individualized precision medicine, pharmaceutical R&D, disease diagnosis and prevention, among others. The big data based disease diagnosis and prediction system represents the achievements in multidisciplinary integration, making disease diagnosis not limited to electronic record data any longer. Instead, this system can further tap the value of multi-dimensional data related to patients, including living environment, public health, nutrition and health care, bio-omics, thus enabling more accurate disease diagnosis and prediction of disease occurrence.
The large data volume, multiple modalities, high generation speed, large value but low value density of healthcare big data has constrained the application of big data in the disease diagnosis and prediction system. Thus, standardized medical data construction, analysis and technical R&D of multi-source heterogeneous healthcare data, and R&D of the new big data acquisition, transmission, exchange and sharing platform represent the major future trends that drive the development of big data based disease diagnosis and prediction system and technology. According to patent analysis, the big data based disease diagnosis and prediction system and technology mainly cover big data acquisition, big data platform, and disease warning and monitoring.
(1) Big data acquisition
Big data acquisition refers to a series of technologies by which raw data is acquired from a certain data generation environment in multiple ways and is preprocessed, based on the characterization information that is abstracted according to the goal of disease diagnosis or prediction and that is required in data analysis and application. As the basis for big data analysis and application, this technology provides a data set necessary for the subsequent data processing and application. Comparatively, it is hard for the existing technologies to effectively acquire massive amounts of healthcare big data that features high generation speed, multiple sources, redundancy, privacy and other features. Therefore, research on standardized medical data construction and on the analysis and technical R&D of multi- source heterogeneous healthcare data needs to be prioritized in the future.
(2) Big data platform
The big data platform refers to a platform that integrates data acquisition, cleaning, fusion, analysis, management and quality control, etc. into one and that can support various applications. At present, the data processing and comprehensive service platform based on platform technology has become the best choice for big data processing in the medical field. However, there remain problems with big data processing, such as difficulty in data acquisition and sharing, confusion of modeling and analysis technologies, and a lack of effective promotion mechanism. The health management platform developed through the integration of data sharing interfaces, data interaction, cloud computing and other technologies, the big data processing platform based on the telemedicine system, and other platforms can enable the integration and processing of healthcare data, as well as big data modeling, analysis and application for disease diagnosis or prediction.
(3) Disease warning and monitoring
In disease warning and monitoring, big data analysis technology is used to integrate and analyze various medical data, including personal electronic medical records, the clinical treatment experience of hospitals, research and experimental achievements of experts and scholars, and a risk prediction model is established through Bayesian algorithms, neural network algorithms, among others. Then, a disease analysis and treatment plan model is established to predict the incidence rates of diseases, which effectively promotes the development of healthcare.
The top two countries in terms of the number of patents issued are China and South Korea (Table 2.2.1) and those in terms of the average number of citations are the USA and China (Table 2.2.1). No cooperation relationship has been formed among countries. The top three institutions in terms of the number of patents include Kangping Medical Health Co., Ltd., Sunshine Insurance Group Co., Ltd., and Shandong University (Table 2.2.2). As we can see from the network diagram for the cooperation among patent output institutions (Figure 2.2.1), regional cooperation has been initially formed but no cross-region cooperation has been started yet. Kangping Medical Health Co., Ltd., Sunshine Insurance Group Com., Ltd., and Shandong University collaborate with each other in disease risk prevention, data management, data acquisition, and other fields.
Countries show different research characteristics in terms of core patents on the big data based disease diagnosis and prediction system and technology. China attaches importance to the building of disease prediction systems and platforms. It has developed multiple prediction systems as well as big data management and application systems that cover different diseases, such as the heart disease data queue generation method and risk prediction system, the disease data structuring method and thyroid cancer risk prediction system, the disease data scheduling management method and bone cancer risk prediction system, a big data based health management platform, and a big data based system for students’ learning behavior analysis, etc. These systems skillfully apply big data, AI and other advanced technologies in disease monitoring and warning as well as health management. South Korea pays attention to data acquisition technology and platform building, such as the system that provides health information through plantar pressure measurement, the big data healthcare training AI system based on open APIs, a big data based medical consulting service method, and a hotel operating system based on medical service and its method. The number of citations of wearable personal digital devices developed in the USA is much higher than those of other patents, as it can facilitate the acquisition of personal health data. The electronic information processing device developed in Japan is provided with a platform for calculating the best drug dosage for patients. Recommendations are made to patients according to their information. The research focuses of different institutions also differ. For example, Kangping Medical Health Co., Ltd. focuses on the building of the risk prediction system for various diseases. It has proposed a step-by-step screening method for determining critical illness indicators in the urinary system and risk prediction system, a disease data structuring method and thyroid cancer risk prediction system, and a disease data structuring method and thyroid cancer risk prediction system, a disease data scheduling management method and bone cancer risk prediction system, and other systems that manage the data of different diseases and predict disease risks. Amobilepay Inc focuses on big data acquisition, and it has developed a wearable personal digital device for acquiring personal health data. Catholic Kwandong University in South Korea provides a mobile medical application system based on various collection components and component based mobile health application. This system can use several sets of medical equipment and multiple information acquisition servers that are compatible with different communication protocols to acquire information from medical equipment.
2.2.2 City information modeling (CIM) and systems
City information modeling (CIM) is an organic complex of three-dimensional city space model and city information built based on city information data. In terms of scope, it represents an organic combination of GIS data under big scenarios, and BIM data and IoT under small scenarios. With the advancement
Table 2.2.1 Countries with the greatest output of core patents on “big data based disease diagnosis and prediction system and technology”
|No.||Country||Published patents||Percentage of published patents||Citations||Percentage of citations||Citations per patent|
Table 2.2.2 Institutions with the greatest output of core patents on “big data based disease diagnosis and prediction system and technology”
|No.||Institution||Published patents||Percentage of published patents||Percentage of Citations||citations||Citations per patent|
|1||Kangping Medical Health Co., Ltd.||19||14.96%||5||3.88%||0.26|
|2||Sunshine Insurance Group Co., Ltd.||5||3.94%||0||0.00%||0|
|4||Shenzhen Qianhai Anycheck Information Technology Co., Ltd.||2||1.57%||15||11.63%||7.5|
|5||Beijing Tuoming Technology Co., Ltd.||2||1.57%||10||7.75%||5|
|6||Catholic Kwandong University||2||1.57%||2||1.55%||1|
|7||Chengdu Sunsheen Technology Co., Ltd.||2||1.57%||0||0.00%||0|
|8||Suhwooms Co., Ltd.||2||1.57%||0||0.00%||0|
|10||World Award Academy||1||0.79%||22||17.05%||22|
Figure 2.2.1 Collaboration network among major institutions in the engineering development front of “big data based disease diagnosis and prediction system and technology”
of global urbanization and wide application of information technology, smart cities are playing a more active role in sustainable socioeconomic development and micro-city management. China started 3D GIS research in the 1990s. The first step was digitalization alone, which means digitalizing buildings and scenarios and displaying them on the screen. In the early 21st century, digitalization was gradually shifted to information technology, and both attributes and correlative information were added when presentation was made. In recent years, information technology has seen an integration across sectors and disciplines and been applied to our life and production. In the future, several applications related to the acquisition and use of city information will be implemented on a large scale in combination with IoT, big data, AI, BIM and GIS.
There exists a nervous system for information transmission in a city, with invisible flow of personnel, information and capital, etc. The living things in the entire city are updated each day. Relative to the stable building system, the city changes with each passing day. The single-building management mode develops towards whole-system operation management in the city, and all intelligent buildings, awareness and construction are integrated into a new system. Community planning is shifted from “determining flow by shape” to “determining shape by flow”. All the existing IoT aware data in the city is combined to enable “city flow”, and the AI technology is also used for intelligent construction, operation and maintenance of the city. Overall, BIM is considered as a cell of CIM and buildings as cells of the city. Firstly, a city 3D GIS visualization data model is established. Then, the city material sub-system is remodeled, and the CIM template is established based on the city 3D GIS visualization model. Multiple types of data are imported for access and visualization of city spatiotemporal data in combination with IoT aware data. It is thus necessary to make full use of information and mobile technology to sense, analyze and integrate the city operation application system based on the next-generation information technologies including IoT, cloud computing and mobile internet and to respond to various demands for city management and development so as to improve the operating efficiency and management of city infrastructure and make our life better.
In terms of patent analysis, the engineering R&D of CIM and systems can be categorized from the perspectives of architecture and application. For the former, related R&D mainly covers IoT technology, CIM data management and visualization technology. For the latter, related R&D mainly covers the scheduling management system for smart city management, city 3D traffic management system, City environment information management system, city government service management system, city economic operation management system, city work safety management system, and city infrastructure management system.
The Internet of Things (IoT) plays an essential role in data acquisition. IoT is an extended application and network expansion that recognizes, positions, collects, processes and transmits object information through sensing equipment and communication modules based on communication network and internet technology. Based on information interaction, things and people are interconnected with each other. IoT extracts underlying data based on the overall network architecture that consists of the perception layer, network layer, platform layer and application layer, finally enabling its extensive application in multiple fields of urban construction. Among these layers, the perception layer monitors objects through the sensor and returns the collected information through the communication module; the network layer mainly transits data; the platform layer integrates and uses the collected data; and the application layer combines the internet technology with each field to achieve the ultimate goal of interconnection of everything. From information collection on the perception layer to the applications in all fields on the application layer, each layer represents a combination of multiple technologies. So, it can be said that IoT is a combination of a variety of advanced technologies.
(2) CIM data management
Since modern cities have a large number of infrastructure and equipment, which involve transportation, buildings, power grids, security, environmental protection, water affairs, etc., all the application systems adopted by these facilities are built based on a single independent project. Each CIM based application system has its own storage unit and database, and no resource sharing and access are available to any different systems, thus causing isolated data islands and complex management situation. The inclusion of new application methods and devices in the smart city application and integrated management system can enable fast and convenient update of the system, with higher reliability. Additionally, this system is committed to allowing the linkage and correlation of multiple urban applications, which solves the city’s problems of isolated data and management islands caused by the chimney-like application structure. It allows integrated management of individual systems on the same platform in the city. All data is stored in a cloud platform based data lake to facilitate the data integration of related applications and then in-depth analysis covering multiple dimensions and layers is made. A CIM is built based on container technology, and the corresponding container engine is used to operate the CIM aforementioned, with a great scalability and portability, safe and reliable. And a cloud computing operation platform is established to coordinate cloud on a large scale, manage computing resources, store resources, network resources and other underlying architectures.
(3) Visualization technology
A city is a living thing and a complex giant system with typical life characteristics. The urban cell elements carried by the city are reflected through multi-dimensional information data such s time, space and data type. CIM technology can be used to upgrade traditional data and drawings to multi- dimensional models. Digital twin technology is adopted to copy the entire city in a digitalized way. Due to the virtual realization of the physical status and spatial geographic information of buildings, we can have direct access to data. And along with the visualization of buildings and geographic information, digital technologies are adopted to visualize IoT data and connect the data and entities collected by IoT. A CIM where the city’s living things operate is formed through the fusion and flow of whole-life-cycle information data such as BIM+GIS+VR+IoT and cloud computing. Also, several intelligent terminal applications, such as desktop, web, mobile, large screens (circular screen, spherical surface, CAVE and sand table, etc.) and VR helmets, are combined. All CIM elements are presented from multiple aspects, such as BIM+GIS model, ground and underground, indoor and outdoor, past and future spatiotemporal models and multi-source data overlaying model. And multiple terminal applications are used to enable intelligent applications that feature one cloud and multiple terminals.
(4) Scheduling management system for smart city management
A mechanism that covers daily information receipt, coordinated processing and rapid response is required to be built and improved in smart city construction so as to improve the daily event handling and emergency response ability. The scheduling system development for smart city management runs through the entire management scheduling process, highly capable of acquiring and receiving information. The information received in a unified way is subject to intelligent data analysis and processing. The system is capable of daily affairs management and emergency response, and its functions cover the business demands for daily management and emergency response. The events are handled and a feedback of results is made in time according to the five-step closed-loop coordinated handling process, namely information reporting, commanding and dispatching, processing and feedback, task review, conclusion and filing. The system has functions of data acquisition, integrated business management, daily commanding and dispatching, collaborative operation, database management, resources management, and others.
(5) City 3D traffic management system
The city 3D traffic management system presents urban traffic operation conditions based on geospatial information, monitors and analyzes the conditions of integrated operation of regional traffic, intra-city traffic and intercity traffic. With one map incorporating all urban traffic conditions, the system provides information support for government decision- making, industry regulation, corporate operations and public travel. It mainly includes functions such as key road facilities and entities presentation, road traffic operation presentation, public transport operation presentation, new- energy vehicle traffic management, comprehensive conditions presentation analysis, public transport presentation and cross- department governance presentation and analysis. Through comprehensive operation monitoring and coordinated linkage of three transport networks (air, water and land) and four major intercity means of transport (passenger transport by air, passenger transport by water, passenger transport by road, passenger transport by railway), the system presents and analyzes urban traffic governance in terms of government decision-making, industry regulation, corporate operation and public travel.
(6) City environment information management system
Functions such as environment information access, integrated environment monitoring presentation, environmental pollution violation monitoring information presentation and analysis, simulated analysis and warning for pollution of key pollution sources, integrated urban ecological environment evaluation, emergency management of environmental risk control, atmospheric environment governance analysis, and river chief system management service should be included. One map for environmental regulation is generated based on the geospatial information system. Comprehensive situation research and judgment is made based on urban environment big data to provide visualized basis for formulating environmental policies and measures, predicting and sending warning against environmental risks and organizing key task consultation and evaluation. In this way, the system can demonstrate the performance of scientific integrated governance of urban ecological environment, and coordination of environmental protection and macro regulation capability improvement, thus enabling active, accurate, effective and innovative regulation for scientific haze control, monitoring, early warning and other government functions.
(7) City government service management system
Based on the unified application service of the intelligent governance center, the urban government ser vice management system gathers the government service and public service information from all related departments for centralized presentation and analysis so as to promote government reforms, transform the ruling idea, innovate governance methods, make e-government to be upgraded to intelligent government, and gradually improve the efficiency and effect of government service. The system covers functions such as themed information access, government service ecosystem development presentation, government service achievement management, and social and public service presentation and analysis. The system presents, in an integrated way, the government’s reforms, transformation of governance concepts and innovation of governance methods, analyze the achievements made in upgrading e-government to intelligent government, and gradually improve the efficiency and effect of government service. Also, the system provides an exchange system and feedback channel for different social participants, makes policies more accurate and targeted, and enables the public to obtain the senses of gain, belonging and happiness.
(8) City economic operation management system
Through economic operation management system building, the economic operations in key areas and in the fields of finance, taxation, consumption, etc. are tracked, presented, monitored and analyzed to accurately understand the development and fluctuations of major industries and key areas and make the economic situation analysis of related fields more scientific, promote economic operation scheduling monitoring and information sharing among administrative departments, and improve working efficiency and the decision-making ability of government leaders. The economic operation management system is composed of functions such as themed information access, enterprise service presentation, economic data management, digital economy evaluation result presentation, key industry development management, innovation and entrepreneurship environment evaluation and analysis, business environment evaluation and analysis, economic operation statistical analysis presentation, audit big data analysis and presentation, cross-border trade e-commerce analysis and presentation, etc.
(9) City work safety management system
Modern information technologies such as big data, IoT and cloud computing are applied on an in-depth basis to promote the interconnection of work safety data in all industries of the city, the multi-dimensional analysis and application of work safety data, and the data sharing of urban work safety resources. Regulation of the public’s work safety is innovated to enhance the ability to control work safety under new situations and build a highland of safety that is consistent with social and economic development. The system has one- map presentation of work safety situation of the entire city, safety accident changes analysis and presentation, two-key one-major situation analysis and presentation, potential hazard checking and governance analysis and presentation, work safety consequence simulated analysis, comprehensive governance analysis of dangerous chemical articles, VR simulated drill presentation, accident rescue and emergency response presentation, and other functions. The system will promote the interconnection of work safety data for all industries in the city, and the multi-dimensional analysis and application of work safety data and the data sharing of urban work safety resources, innovate public work safety regulation means, enhance the ability to control work safety under new situations and build a highland of safety that is consistent with social and economic development.
(10) City infrastructure management system
With typical spatial distribution characteristics, urban infrastructure information constitutes the basis for normal operation of the city. The construction of a city infrastructure management system by gathering the information of lifelines, utilities, sanitation, lighting and other facilities cannot only enable spatial, visualized management of the information of municipal facilities including roads, lights and bridges, but also presents the information of water, electricity and gas in an effective and aggregated way. This can not only improve the quality and efficiency of daily management and save management cost, but also lift the management level and assist with city leaders in making urban governance and planning decisions more reasonably, conveniently and accurately. The application scenarios mainly include urban water supply facilities presentation and analysis, urban gas supply facilities presentation and analysis, urban fire facilities presentation, urban flood control facilities management, real-time monitoring and presentation of urban bridges, integrated presentation of urban parts, presentation of urban construction facilities, city appearance facilities presentation, urban facilities co-governance application, and urban gardening and greening facilities presentation, etc.
The top two countries in terms of the number of patents issued are China and the USA (Table 2.2.3). China mainly focuses on urban work safety and urban three-dimensional traffic system management, while the USA attaches greater importance to economic operation platform management.
No cooperation relationship has been formed among these countries. The top two institutions in terms of the number of patents include State Grid Corporation of China and China Southern Power Grid (Table 2.2.4). It reflects that China’s power industry boasts high research and development capabilities in CIM and systems. According to network diagram for the cooperation among patent output institutions (Figure 2.2.2), there are close contacts among State Grid Corporation of China, China Southern Power Grid, and Information and Communication Branch of State Grid Jiangsu Electric Power Co., Ltd..
2.2.3 Blockchain-based quality information tracking method and system
Blockchain technology is a new application pattern based on computer technologies such as distributed data storage, peer- to-peer transmission, consensus mechanisms and encryption algorithms. It is tamper-proof on the chain, verifiable, traceable and automated in business execution. In view of
Table 2.2.3 Countries with the greatest number of core patents on “city information modeling (CIM) and systems”
|No.||Country||Published patents||Percentage of published patents||Citations||Percentage of citations||Citations per patent|
Table 2.2.4 Institutions with the greatest number of core patents on “city information modeling (CIM) and systems”
|No.||Institution||Published patents||Percentage of published patents||Citations||Percentage of citations||Citations per patent|
|1||State Grid Corporation of China||17||40.48%||84||34.29%||4.94|
|2||China Southern Power Grid||7||16.67%||19||7.76%||2.71|
|3||IN ESA (Group) Co., Ltd.||4||9.52%||0||0.00%||0|
|7||Automation Research and Design Institute of Metallurgical Industry||1||2.38%||14||5.71%||14|
|8||Everdisplay Optronics (Shanghai) Co.,Ltd.||1||2.38%||10||4.08%||10|
|9||Information and Communication Branch of State Grid Jiangsu Electric Power Co., Ltd.||1||2.38%||9||3.67%||9|
|10||Shanghai Jiao Tong University||1||2.38%||8||3.27%||8|
Figure 2.2.2 Collaboration network among major institutions in the engineering development front of “city information modeling (CIM) and systems”
the above characteristics, blockchain is expected to break the “isolated information islands” in multi-party cooperation, create a reliable cooperation and trust mechanism, and provide potential for solving the existing problems in the quality management of engineering products.
According to the analysis of related patents in the engineering product quality management field, the top three countries in terms of the number of core patents are China, South Korea, and the USA (Table 2.2.5). According to the institutions’ number of patents (Table 2.2.6), the top is Shandong iCity Information Technology Co., Ltd. (Table 2.2.6). In terms of the patent content studied by major countries and institutions, the quality information tracking method and system is mainly focused on for the research and development of engineering products in the quality management field. Specifically, the research and development mainly involve the following two types. ① Blockchain-based quality information traceability method and system. This kind of research and development aims to solve the difficulty in quality information traceability: quality information that involves the whole life cycle and full elements of engineering product manufacturing is discretely stored in the participants of industrial chain. It is easy for the information to be tampered with due to the fact it is neither safe, nor interconnected nor transparent. As a result, it is difficult to ascertain responsibility and cause sustained disputes if any engineering product quality issue arises. ② Blockchain-based quality value co-creation method and system. This type of research and development mainly aims to break the dilemmas of value co-creation in relation to engineering product quality: information asymmetry and low-efficiency communication would cause mistrust between supply and demand sides. End users are less willing to participate in value-creation, and there is a gap for quality information when it flows in the upstream and downstream of production to consumption, and the information becomes silent in the end. So, it is difficult to provide support for continuous quality improvements.
(1) Blockchain-based quality information traceability method and system
The blockchain-based quality information traceability method and system mainly includes but is not limited to the information acquisition module, the up-link module and the remote server module. Information acquisition module: IoT and other technologies are used to acquire quality information from the physical world. Up-link module: the distributed consensus mechanism is adopted to encapsulate quality information in blocks and distribute it to the blockchain system upon confirmation by related responsible parties, thus forming a evidence chain of quality information across the whole process and covering all factors. Remote server module: each party responsible for quality calls quality information from the blockchain system to allow real-time query and traceability of quality information. The blockchain- based quality information traceability method and system is committed to realize whole-process full-element trustable quality information, highly transparent circulation, and traceable quality responsibility. Once any quality problem occurs, related responsible parties can be quickly identified so as to avoid disputes and further improve quality management
Table 2.2.5 Countries with the greatest output of core patents on “blockchain-based quality information tracking method and system”
|No.||Country||Published patents||Percentage of published patents||Citations||Percentage of citations||Citations per patent|
Table 2.2.6 Institutions with the greatest output of core patents on “blockchain-based quality information tracking method and system”
|No.||Institution||Published patents||Percentage of published patents||Citations||Percentage of citations||Citations per patent|
|1||Shandong iCity Information Technology Co., Ltd.||3||17.65%||1||33.33%||0.33|
|2||Shenzhen Point Chain Technology Co., Ltd.||2||11.76%||0||0.00%||0|
|3||China Datang Corporation Ltd.||1||5.88%||1||33.33%||1|
|4||Beijing Technology And Business University||1||5.88%||1||33.33%||1|
|6||Guangdong Kechuang Engineering Technology Co., Ltd.||1||5.88%||0||0.00%||0|
|7||Sichuan Internet of Things Intelligent Technology Co., Ltd.||1||5.88%||0||0.00%||0|
|8||Sinochem Agriculture Holdings||1||5.88%||0||0.00%||0|
|9||Tianjin University of Science & Technology||1||5.88%||0||0.00%||0|
and service levels.
(2) Blockchain-based quality value co-creation method and system
Currently, different kinds of enterprises are transforming their production mode from large-scale mass production to personalized, customized production so as to fully satisfy the diversified needs of end users and consumers. During this process, whether the core competitiveness of enterprises can be improved or not depends on whether they can attract upstream and downstream clients to participate in the process of R&D, design and production, and realize value co-creation through interaction and adaptation so as to produce quality products satisfactory to clients.
The acquisition of product use information, unobstructed information flow and the cooperation environment that features mutual trust constitute the preconditions for quality value co-creation. Blockchain provides a necessary technical support for satisfying these conditions. Currently, the blockchain-based quality value co-creation method and system mainly focus on three links, namely information acquisition, upstream-downstream interaction and value creation. In terms of product use information acquisition, the blockchain system is integrated with the IoT for the products so that diversified quality information of the products can be acquired, stored, classified, transmitted and retrieved during their use. The authentication mechanism of the blockchain ensures the theft of user privacy will be impossible. In terms of upstream-downstream interaction, product information is fully recorded during the multi-party collaboration so that the interaction between the user and its producers and suppliers in the upstream of the industrial chain will be traced during the whole process. This can help clarify the added value of products and ownership of intellectual property and guarantee the intangible assets of the parties. In terms of value creation, product quality big data is enriched in the intervention of end users and all-round acquisition of use information. Through integration with AI, the blockchain system enables the value of the silent information mined and fed to the front-end production link so as to provide support for product design and production decision-making and form a loop of continuous value creation. In summary, the blockchain has opened up the upstream and downstream of the industrial chain, enabled engineering product quality information to be strictly protected and ensured high- efficient and low-cost flow. Consequently, the quality value creation participated by users can run through the whole process of value creation, from resources development and design to production and obtaining production and consumption value.
Participants of the Field Group
DING Lieyun, HE Jishan, HU Wenrui, XIANG Qiao
DING Lieyun, HE Jishan, HU Wenrui, XIANG Qiao, CHEN Xiaohong, CHAI Hongfeng, CHEN Qingquan, FU Zhihuan,
LIU Renhuai, LU Youmei, LUAN Enjie, LING Wen, SUN Yongfu, SHAO Anlin, WANG Jiming, WANG Liheng, WANG Longde,
WANG Yingluo, WANG Yupu, WANG Zhongtuo, XUE Lan, XU Qingrui, XU Shoubo, YANG Shanlin, YIN Ruiyu,
YUAN Qingtang, ZHU Gaofeng, ZHENG Jingchen, ZHAO Xiaozhe, Mirosław Skibniewski, Peter E. D. Love, BI Jun,
CAI Li, CHEN Jin, DING Jinliang, DU Wenli, FANG Dongping, GAO Ziyou, HU Xiangpei, HUA Zhongsheng, HUANG Jikun,
HUANG Wei, JIANG Zhibin, KAN Jian, LI Heng, LI Yongkui, LI Zheng, LIU Xiaojun, LUO Hanbin, REN Hong, TANG Jiafu,
TANG Lixin, TANG Pingbo, WANG Hongwei, WANG Huimin, WANG Mengjun, WANG Xianjia, WANG Yaowu, WEI Yiming,
WU Desheng, WU Jianjun, WU Qidi, XU Lida, YANG Hai, YANG Jianbo, YE Qiang, ZENG Saixing, ZHOU Jianping, CHENG Zhe,
FENG Bo, LI Guo, LI Xiaodong, LI Yulong, LIN Han, LIU Bingsheng, LIU Dehai, LUO Xiaochun, LV Xin, MA Ling, OU Yangmin,
PEI Jun, SI Shubin, WANG Zongrun, WU Jie, XIAO Hui, YANG Hongming, YANG Yang, YU Shiwei, YUAN Jingfeng,
ZHANG Yuejun, ZHEN Lv, ZHOU Peng, ZHU Wenbin
ZHONG Botao, WANG Hongwei, LUO Hanbin, NIE Shuqin, CHANG Junqian, ZHENG Wenjiang, MU Zhirui, ZHANG Linan,
LI Yong, DONG Huiwen, PAN Xing, SHENG Da, XIANG Ran, GUO Jiadong
Members of Frontier Report Writing Group
CHENG Hong, PENG Zhinan, HUANG Rui, LI Guo, HUA Lianlian, ZHOU Peng, WU Zezhou, FANG Chao, ZHENG Xiaolong,
GUO Kaiming, LV Xin, JIANG Zehao, AN Haizhong, GAO Xiangyun, LI Huajiao, FANG Wei, HUANG Shupei, AN Feng
XU Shunqing, MA Ling, ZHONG Botao, WU Haitao, WANG Fan, YU Shiwei, NIU Baozhuang, DONG Jian, HUANG Sihan,
XING Lining, LI Huimin, XU Xianhao, ZOU Bipan