Promoting the deep integration of the innovation, industrial, capital, and talent chains, and improving industrial innovation capabilities are key for China’s strategic emerging industries to move toward the middle and high ends of the global industrial chain and achieve high-quality development. This study systematically analyzes the essential requirements and implications of high-quality development of strategic emerging industries, clarifies their evolution and trends worldwide, and summarizes the development status, problems, and challenges of China’s strategic emerging industries. Additionally, it proposes a high-quality development path that is innovation-driven, enterprise-oriented, open, and integrated, and the development principles of system integration, coordinated development, independence, open innovation, enterprise dominance, market mechanism, forward-looking layout, and high-end guidance. Moreover, key development directions and the development strategies of systematic improvement, integrated innovation, market-oriented promotion, and guided development are proposed.
As an important part of strategic emerging industries, the new-generation information technology industry plays a major leading role in the overall and long-term development of the economy and society. The new-generation information technology industry is also the cornerstone for cultivating new quality productivity, and is an important starting point for opening up new fields and tracks for development and for shaping new momentum and advantages. Based on the implications of the new-generation information technology industry, this study sorts out the global development trend of the new-generation information technology industry, summarizes the current status and development trend of the industry in China, and analyzes the opportunities and challenges faced by the industry. Moreover, focusing on the requirements of high-quality development of the new-generation information technology industry, the study proposes the following countermeasures and suggestions: (1) improving the integrated strategic system and capabilities while adhering to a system engineering thinking; (2) strengthening the research of key core technologies to strengthen the basic capacity of the industry; (3) promoting the integrated and clustered development of industries, highlighting enterprises as the main body; (4) constructing an open innovation ecosystem based on scenario-driven innovation; and (5) promoting the coordinated development of production, education, research, application, and funds to cultivate a high-level talent team.
New materials are the foundation of emerging industries and future industries, which is an important field to seize the strategic commanding heights in science and economic development and an important driving force for China to promote its new industrialization. This study analyzes the application trend of new materials in the fields of information, energy, biotechnology, and deep-space and deep-sea exploration, and finds that the combination of new materials or deep integration with other disciplines or fields is becoming an important feature of new materials development. Moreover, it analyzes the development status of the new material industry in China from the aspects of industrial scale, technological innovation capability, enterprises, and industry clusters, and summarizes the problems regarding the development of the industry. For instance, key raw materials of the industry still rely on imports, the core equipment cannot be independently produced, the self-sufficiency rate of high-end products is insufficient, some key products lack application iteration, and the standards and evaluation systems require improvement. Nine and seven key areas are proposed for the development of the emerging industries and future industries, respectively. Furthermore, we propose the following suggestions to promote the high-quaility development of the new material industry of China: strengthening the foundation for new material industry development, improving the industry chain of new materials, creating a sound environment for industrial development, and improving the supporting policies for industrial development.
The virtual reality (VR) technology brings a new multi-sensory interactive experience to users with a high degree of immersion, generates emerging service formats, empowers the upgrading of traditional industries, and provides high value-added, high-level, and diversified services to society. This study outlines the VR industry chain and the development trends of key technologies, and analyzes the prospects, methods, and scenario classification of the integration of VR and the service industry. The study reveals the development trends and major challenges of China’s VR industry in terms of industrial chain supply, application implementation, industry standards, and professional talent cultivation. Furthermore, we propose the following recommendations: (1) strengthening investment in basic research and development, focusing on breaking through core technical difficulties; (2) cultivating leading pilot projects to create demonstration platforms; (3) building a supporting service system to create a sound industrial development environment; and (4) increasing efforts in talent training and international cooperation to enhance China’s competitive advantages in the global arrangement of the VR industry, thereby accelerating the intelligent, interactive, and customized development of the service industry.
Vehicle-energy-road-cloud collaboration seamlessly integrates vehicles, energy, transportation, and the cloud, which is conducive to accelerating the formation of a new intelligent, low-carbon, safe, and efficient ecosystem of travel and life. The collaboration aims to promote the high-quality development of the automobile industry. However, in the new technological situation, further forming a sound ecology that features “intelligent car + interactive energy + digital road + collaborative cloud” and exploiting its value advantages are important tasks for the vehicle-energy-road-cloud collaboration in China. This study analyzes the development trend of China’s automobile industry in the context of new energy transformation, the development of intelligent connected vehicles, artificial intelligence, carbon neutrality, and other new technological situations. It introduces the system composition, current development status, interrelationships, and overall framework of the vehicle-energy-road-cloud industry from five aspects: vehicle, energy, road, cloud, and industry integration. This study also summarizes the opportunities and challenges faced by the integrated development of China’s vehicle-energy-road-cloud industry. Based on this analysis, this study proposes countermeasures and suggestions for the vehicle-energy-road-cloud collaboration, covering five aspects: adhering to an application-oriented principle, promoting positive innovation, demonstrating city-level applications, leveraging diversified investments, and promoting the efficient development of circular industries. These suggestions aim to provide references for the high-quality collaboration of China’s vehicle, energy, road, and cloud industries, thereby strengthening the automobile, transportation, and manufacturing sectors of China.
The green environmental protection industry has the dual attributes of driving economic growth and coping with environmental problems. The deep integration of “four chains” (i.e., innovation, industrial, capital, and talent chains) is crucial for the high-quality development of the green environmental protection industry and the achievement of the carbon peaking and carbon neutrality goals. Considering the development status of the green environmental protection industry, this study analyzes the characteristics of four-chain integration at different stages of enterprise development. In the early stage of establishment, the capital chain is key to enterprises and an industrial chain is gradually formed driven by the innovation and talent chains. During business expansion of enterprises, the industrial chain is extended, driven by the capital chain and supported by the innovation and talent chains. At the mature stage, enterprises can carry out strategic layout based on the industrial chain while maintaining the innovation chain. However, the four-chain integration in the green environmental protection industry of China still face several problems, including weak connection between the innovation and industrial chains, weak support of the capital chain to other chains, insufficient integration between the talent chain and the innovation and capital chains. To address this, we propose the following suggestions: (1) developing a low-carbon innovation strategy roadmap to clarify the development paths for four-chain integration; (2) improving the compatibility between the innovation and industrial chains; (3) optimizing the capital chain to improve its integration with other chains; (4) improving the talent chain; and (5) clarifying the driving chains according to the development stages of enterprises to ensure the effective integration of the “four chains”.
The carbon peaking and carbon neutralization goals as well as the carbon trading system reforms of the European Union (EU) necessitate the synergy of energy conservation, pollution control, and carbon reduction in China to achieve low- and zero-carbon transformation of its manufacturing industry. This study focuses on the impact of the EU Carbon Border Adjustment Mechanism on China’s manufacturing industry, clarifies the concept of a near-zero-carbon manufacturing system, and elaborates on its core content from the major dimensions of key technologies, measurement basis, and market driving force. It also proposes the technical development directions of the near-zero-carbon manufacturing system from the aspects of product manufacturing and power supply and suggests the establishment of a source-grid-load carbon measurement system to clarify carbon emission responsibilities. Moreover, the future development path for China’s carbon market is explored after reviewing the carbon markets both in China and abroad. The practical solutions proposed by the study is expected to provide a basic reference for promoting the high-quality development and low-carbon transformation of China’s manufacturing industry.
Solid waste recycling saves resources and energy, reduces carbon emissions, and plays a vital role in forming a green and low-carbon mode of production and living and achieving the carbon peaking and carbon neutrality goals. This study analyzes the development status of the solid waste recycling industry in China and abroad and explores the major challenges faced by the industry of China in terms of process of industrialization, capital investment, and technological innovation. Combined with typical case analyses, we propose the following suggestions to promote the high-quality development of China’s solid waste recycling industry: (1) promoting the process of industrialization to integrate the industrial, innovation, talent, and capital chains; (2) reinforcing incentive policies to consolidate the financial foundation; (3) establishing scientific innovation hubs by strengthening project demonstrations; (4) gathering professional talents to build a high-level cooperation platform.
Collective intelligence is an important component of the new generation of artificial intelligence (AI). It plays a decisive role in stimulating and converging innovative forces as well as coupling and integrating large-scale intelligent systems. It is of great significance for promoting deep integration of AI and traditional industries and enabling the sustainable development of the national economy. This study summarizes the overall technical framework of collective intelligence and its major research areas, including: multi-agent systems and optimal decision-making, unmanned swarm systems, open source collective intelligence software, and federated learning. Moreover, it analyzes how these core technologies can be applied in industrial scenarios, in order to establish intelligent processing loops of perception-cognition-decision-action, to support platform economy with distributed intelligence, and to reshape industrial development and digital economy ecosystems. Based on the subjects and application modes of the technical framework, this study analyzes the core industries related to collective intelligence, particularly the software service industry, the smart city industrial cluster, and the intelligent agriculture and port industries based on unmanned swarm systems, by highlighting their significant requirements and empowerment approaches for collective intelligence technologies. Furthermore, this study presents suggestions on how to utilize collective intelligence technologies to foster development of rated industries. It is suggested that we should continuously promote the establishment of open source communities of collective intelligence, enhance the intellectual core of the AI technological innovation ecosystem, and accelerate the domestic substitute of unmanned swarm systems through integrated system research.
Collaborative intelligence formed via information and behavioral interactions of multiple autonomous systems is an inevitable trend of future intelligent systems. It is a focus of planning of the next-generation artificial intelligence in China and is crucial for supporting national security and strengthening the manufacturing industry. Research aimed at overcoming bottlenecks regarding collaborative multiple autonomous systems will significantly aid the advancement of intelligent industries and accelerate industrial transformation and upgrading in China. Focusing on the challenge that collaborative multiple autonomous systems cannot adapt to complex tasks, this study thoroughly analyzes the research status and major bottlenecks of collaborative multiple autonomous systems from the aspects of fundamental research and engineering. Using multi-robot collaborative intelligent manufacturing as an example, we provide an in-depth analysis of relevant theoretic and technical problems. Our research indicates that collaborative multiple autonomous systems will inevitably evolve toward human - machine teaming. To master this opportunity, it is critical to proactively lay the groundwork for the theoretical exploration and technological breakthroughs of human-machine teaming and to conduct exemplary applications.
As intelligent technologies and unmanned systems develop rapidly, the concept of cross-domain cooperative technology of intelligent unmanned swarm systems has emerged, received widespread attention, and gradually become the high ground in the competition of unmanned system technologies among countries worldwide. Based on the development demand for the cross-domain cooperative technology of intelligent unmanned swarm systems in China, this study summarizes the research status of the cross-domain cooperative technology in typical unmanned scenarios such as sea – air, air – ground, and sea – ground/sea – ground – air, and thoroughly analyzes the current status, technological demand, and key research directions of the technology. Additionally, countermeasures and suggestions are proposed to promote the steady and rapid development of the cross-domain cooperative technology from the perspectives of overall concept, system architecture, theoretical innovation, and technological breakthroughs, with the aim of facilitating the sustained development of unmanned systems in China.
Device-cloud collaborative intelligent computing, an emergent result of the development in big data, cloud computing, and edge computing, offers significant improvements in data utilization while protecting user privacy. This approach synergizes the real-time response capabilities of intelligent computing with service robustness. The study explores the application value of this computing paradigm, highlighting technical challenges such as optimizing on-device learning efficiency, mitigating overfitting with limited samples at the device, customizing on-device models, learning false associations under distributional discrepancies, and balancing communication overhead with computational efficiency. We systematically review the progress in mainstream methods within device-cloud collaborative intelligent computing, encompassing efficient computation hardware as the application cornerstone, device-centric collaborative computing, cloud-centric collaborative computing, bidirectional device-cloud collaborative computing, and trustworthy device-cloud collaborative computing. The study also summarizes applications in vertical domains such as recommendation systems, autonomous driving, security systems, and educational models. Looking toward the future of device-cloud collaborative intelligent computing, it underscores the need for focused research on cloud resource application strategies in device model personalization, multi-objective optimization algorithms for device-cloud collaboration, and optimized collaborative strategies between devices and the cloud.
Embodied intelligence stands as a strategic technology in the ongoing scientific and technological revolution, forming a frontier in global competition. The mobile manipulator robot system, with its exceptional mobility, planning, and execution capabilities, has become the preferred hardware carrier for embodied intelligence. Moreover, the mobile manipulator robot system, rooted in embodied intelligence, emerges as a pivotal platform capable of cross-domain functionality. Positioned at the forefront of a new era in information technology and artificial intelligence, this system is integral for future development. Addressing the strategic demand for embodied-intelligence-based mobile manipulator robot systems, this study presents an overview of the current developmental landscape. It delves into the challenges faced by this field, proposing key common technologies such as multimodal perception, world cognition, intelligent autonomous decision-making, and joint planning for movement and manipulation. Furthermore, the study offers recommendations for advancing the field, encompassing national policy support, breakthroughs in common technologies, interdisciplinary collaboration, talent cultivation, and construction of comprehensive verification platforms. These suggestions aim to facilitate the rapid progress of mobile manipulator robots in China amid the wave of embodied intelligence development.
Space robots can adapt to the extreme environment of space, break through the limits of human space exploration, and greatly improve the safety and economy of space operation and control. Moreover, space robots are the core equipment to improve the level of space science and technology, providing important support and a strong guarantee for promoting space industry development. This study elaborates on the great values of developing space robot technology for the autonomous repair and maintenance of spacecraft, which include promoting the construction of strengthening China’s space powerindustry, promoting the development of national defense science and technology, and leading transformative scientific and technological innovation. The progress and development trends of domestic and foreign space robotics technologies in China and abroad are analyzed from the policy, technology, and market perspectives. In addition, technical challenges and problems faced by China are dissected toward the autonomous repair and maintenance of spacecraft. The development system and breakthrough path of China’s space robot technologies for autonomous repair and maintenance of spacecraft are demonstrated based on major national strategic needs, research foundation, and development directions. Furthermore, the following suggestions are proposed: (1) accelerating the implementation of major special projects for on-orbit services (Scientific and Technological Innovation 2030), (2) increasing support for basic research on intelligent operation and control of space robots, (3) accelerating the construction of a government-enterprise-university collaborative innovation mechanism, and (4) strengthening international cooperation to attract foreign science and technology talents to China.
Autonomous driving is an important research direction in computer vision which has broad application prospects. Pure vision perception schemes have significant research value in autonomous driving scenarios. Different from traditional cameras, spike vision sensor offers imaging speeds over a thousand times faster than traditional cameras, possess advantages such as high temporal resolution, high dynamic range, low data redundancy, and low power consumption. This study focuses on autonomous driving scenarios, introducing the imaging principles, perception capabilities, and advantages of the spike camera. Besides, focusing on visual tasks related to autonomous driving, this study elaborates on the principles and methods of spike-based image/video reconstruction, discusses the approach to image enhancement based on sensor fusion with spike cameras,and provides a detailed description of the algorithms and technical routes for motion optical flow estimation, object recognition, detection, segmentation, and tracking, and deep estimation of three-dimensional scenes based on spike cameras. It also summarizes the development of the spike camera data and systems. At last, it analyzes the challenges, potential solutions, and future directions for spike vision research. Spike cameras and their algorithms and systems hold great potentials in the field of autonomous driving and represent one of the future research directions in computer vision.
Recently, the autonomous driving industry in China has been gradually shifting its focus from individual-vehicle intelligence to vehicle-infrastructure cooperation. This shift has brought significant opportunities for the intelligent transportation industry. Although research on vehicle-infrastructure cooperative sensing is still in its early stage in China, it shows a strong dedication to technological innovation, indicating significant potentials for future growth. This study examines the development status of vehicle-infrastructure cooperative sensing and thoroughly explores the characteristics and status of core technologies that support vehicle-infrastructure cooperative sensing. It discusses ongoing advancements in this field, investigates future technology trends, and proposes a range of recommendations for further development. Research indicates that vehicle-infrastructure cooperative sensing is evolving toward the integration of multi-source data. Presently, its development directions mainly focus on the optimization of pure visual cooperative sensing, upgrades in LiDAR point cloud processing, advancements in multi-sensor spatiotemporal information matching and data fusion, as well as the establishment of a standards system for vehicle-infrastructure cooperative sensing technologies. To further boost the rapid growth of vehicle-infrastructure cooperation in China, increasing investment in the research and development of relevant technologies is advised. Enhancing partnerships among different industry sectors, establishing unified standards for processing perception data, and expediting the broad application of these technologies are also key recommendations. These strategies aim to position China advantageously in the global market of autonomous driving, contributing to the sustainable development of the industry.
Environmental judicial adjudication is an essential component of the eco-environment governance system. The artificial intelligence large language model (AI-LLM), developed based on generative artificial intelligence, has offered significant opportunities for the environmental judicial adjudication to develop toward a higher level of smart adjudication. This study aims to promote the integration of AI-LLM technology with environmental judicial adjudication and promote the intelligent development of the environmental judicial adjudication. It explores the role and practical applications of AI-LLM in environmental judicial adjudication and summarizes prominent problems such as poor data quality, bias caused by algorithmic opacity, and limited capabilities for deep application. Using eco-environmental protection cases as an example, this study establishes a smart environmental judicial adjudication system based on AI-LLM and elaborates on the architecture design of the system and the technical elements involved. Furthermore, it proposes the following suggestions to promote the intelligent development of environment judicial adjudication: (1) emphasizing top-level design and establishing a high-end think tank for environmental justice; (2) building an environmental justice data center to improve the judicial data standards system; (3) establishing an algorithmic governance mechanism to promote the fairness and justice in environmental judicial adjudication; and (4) improving the multiple accountability mechanism for environmental justice to strengthen the judicial supervision and management system.
In the era of big data, the widespread adoption of Internet applications and information services has resulted in the extensive collection of individuals' sensitive biological information, increasing the risk of privacy breaches. Event cameras, as novel bio-inspired sensors, exhibit characteristics such as low latency, high dynamics, and texture independence. They offer a fresh technological approach to addressing privacy protection issues on the data side, making them suitable for private applications like home monitoring. This study thoroughly analyzes the research background of using event cameras for privacy protection, focusing on the privacy leakage issues of the big data era and the advantages of event cameras in privacy protection. It systematically reviews traditional methods for protecting sensitive bioinformation privacy, including face-template-based privacy protection, de-identification-based privacy protection, and privacy protection based on point cloud chaotic encryption. Additionally, it has examined the research progress in privacy-preserving event perception methods, including pedestrian re-identification, gesture recognition, and facial analysis. Further, the study also summarizes advancements in event-based image reconstruction and restoration, including intensity image reconstruction, image restoration, and video reconstruction, based on six algorithms. The results demonstrated that existing reconstruction algorithms have limited capability in recovering texture information, and the feasibility of the privacy protection technology based on event cameras is confirmed. For the future scaled-up application of event cameras, development recommendations are proposed, including reducing hardware costs, improving algorithm networks, and driving initiatives from a market perspective, aiming to provide a foundational reference for the deepened application of privacy protection using event cameras.
China has considerable heavy oil reserves, with 60% being deep heavy oil. However, the mainstream thermal recovery technologies, such as cyclic steam stimulation, have a recovery rate of less than 20%. The development potential of heavy oil resources is enormous, and actively exploring new development methods to improve the recovery rate is an inevitable choice for the high-quality development of the petroleum industry. This study focuses on the construction of a chemical compound flooding technology system for heavy oil and its field application, providing an effective solution for the development of green and low-cost sequential technologies for deep heavy oil. Based on the analysis of the components of heavy oil, this study elaborates on the structural viscous mechanism and the recovery improving mechanisms (i.e., chemical viscosity reduction, starting pressure gradient reduction, and oil displacement efficiency improvement mechanisms), which enriches the theoretical understanding. In response to urgent need of engineering applications, this study breaks through the green chemical flooding system for heavy oil from two aspects: the design and synthesis of water-soluble viscosity reducers and the development of self-assembling plugging agents. The developed chemical compound flooding technology for heavy oil has been successfully applied in three demonstration projects, achieving good results in increasing oil production and controlling water cut. Furthermore, this study outlines the key points for the subsequent development of molecular oil recovery theory and technology, as well as percolation theory and numerical simulation technology, providing inspiration and reference for research on green and efficient development technologies for deep heavy oil and the promotion of chemical compound flooding technology for heavy oil.
Intelligent manufacturing is a systematic engineering and technological innovation that propels the transformation and upgrading of China’s manufacturing industry, enhancing industrial competitiveness. It is a pivotal element in strengthening the manufacturing sector of China. The comprehensive and profound advancement of intelligent manufacturing necessitates adherence to ideological innovation, requiring the formulation of a scientifically grounded development strategy from its philosophical roots. This paper, based on the phenomenological reductionism and ontological perspective method, starts from the perspective of the philosophy of technology, integrates systemic dialectical logical reasoning, investigates and identifies the “hard problems” of human–machine collaborative technology in intelligent manufacturing, and insightfully explores its philosophical essence from the standpoint of the philosophy of scientific epistemology. The research findings indicate that explicit knowledge and tacit knowledge are commonly present in manufacturing technology activities. However, the long-standing technological development has overlooked the significance of tacit knowledge and has made an unreasonable assumption about the existence of “rational people” in human subjects, neglecting their crucial role in manufacturing systems. Therefore, this paper, through ontological reflection, proposes a Cartesian intelligent manufacturing technology development path based on the Internet of Things, Internet of Contents & Knowledge, and Internet of Bodies. Building upon this foundation, it establishes a Heideggerian intelligent manufacturing system architecture grounded in behavior-oriented, deictic representations, and embodied embedding. Through this traceable causal analysis, it constructs a three-stage progressive development knowledge paradigm for resolving the “hard problems” of human – machine collaborative technology. This paradigm is characterized by data-driven, functional representation, and embodied integration. To efficiently promote the development of the new-generation intelligent manufacturing, a knowledge engineering classification assessment system can be established. The application of theoretical cognitive models can drive the resolution of technological challenges and the formulation of industry support policies. Establishing a multi-faceted application-oriented intelligent manufacturing public service platform strengthens knowledge circulation and integrated application.
Space assets are crucial resources for a nation, and effectively managing the various risks associated with them will help maximize their benefits, enhance their contribution to national modernization and security, and improve the strength of the space industry. First, this study clarifies the definition and scope of space assets. In a narrow sense, space assets encompass all spacecraft in orbit. Broadly speaking, space assets also comprise related physical and intangible assets. Second, potential risk sources are identified from multiple dimensions and categorized into technical risks (such as collision disintegration, explosive disintegration, and load failure), military risks (including the use of spacecraft as weapons or weapon platforms), and management risks (referring mainly to mismatches in management systems, leading to loss of intangible assets). Subsequently, this study explores a quantitative assessment method for the technical risks, establishes a model for quantifying space asset values, and briefly analyzes possible risk control methods. Furthermore, a preliminary classification model for risk events is proposed along with several considerations including establishment of systems and mechanisms, formulation of response plans, responsibility allocation, and implementation and supervision of disposal actions.
Pinglu Canal is the backbone project of the New Western Land-Sea Corridor of China. The canal project generated a total of 3.39 × 108 m3 of earthwork that covers approximately 23 types of rocks and soil and is characterized by large amount, diverse composition, and scattered distribution. Currently, the earthwork is utilized mainly through landfill and reclamation (over 50%); however, basic problems exist, including a low high-quality utilization rate, lagging research on demand for earthwork-reused products, lack of innovative technologies for earthwork reuse, a low level of digitalization, and lack of carbon emission evaluation. To address these problems, this study proposes innovative solutions from the perspectives of resource utilization, digitization, and carbon reduction. First, it is necessary to explore the potential application demands for the canal project itself and surrounding areas and propose corresponding utilization paths according to different types of rock and soil, thus to achieve multi-scenario, multi-path utilization. Second, geological information models and information databases should be established for earthwork in excavation areas to help develop a digital excavation-transportation-storage-utilization technology for earthwork. Moreover, it is recommended to conduct a lifecycle assessment to clarify the carbon emissions of multi-path utilization technologies, achieve a dynamic evaluation of carbon emissions by combining with the information from the earthwork databases, and develop modular mobile-type disposal equipment and in-situ utilization technologies to achieve the reduction of cost and carbon emissions. The organic combination of resource utilization, digitization, and carbon reduction is expected to provide a favorable support for the green construction of the Pinglu Canal Project.