《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》

Table 1.1.1 Top 10 engineering research fronts in engineering management

《Table 1.1.2》

Table 1.1.2 Annual number of core papers published for the Top 10 engineering research fronts in engineering management

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》

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”

《Table 1.2.2》

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”

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》

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》

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》

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”

《Table 1.2.4》

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”

《Table 1.2.5》

Table 1.2.5 Countries with the greatest output of core papers on “research on blockchain-based data security management”

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》

Table 1.2.6 Institutions with the greatest output of core papers on “research on blockchain-based data security management”

《Figure 1.2.3》

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》

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》

Table 1.2.7 Countries with the greatest output of citing papers on “research on blockchain-based data security management”

《Table 1.2.8》

Table 1.2.8 Institutions with the greatest output of citing papers on “research on blockchain-based data security management”

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》

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”

《Table 1.2.10》

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”

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》

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》

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》

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”

《Table 1.2.12》

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”

(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》

Table 2.1.1 Top 10engineering development fronts in engineering management

《Table 2.1.2》

Table 2.1.2 Annual number of core patents published for the Top 10 engineering development fronts in engineering management