Top Ten Technology Trends in China’s Industrial Internet Development in 2023

First, effective management of the surging edge resources by enterprises. The ratio of resources such as devices, computing power, and data on the edge will rise rapidly. For example, concerning data, over 50% of digital industrial data will be generated on the edge in the future for safety and efficiency reasons, along with a large number of application services deployed on the edge, requiring enterprises to manage and utilize these resources effectively. Second, empowering enterprises to achieve collaborative scheduling of cloud-edge-end resources. As integrated operating systems for cloud-edge-end mature, enterprises will tend to manage and schedule cloud-edge-end resources through unified platform systems. In industrial scenarios, industrial operating systems and industrial brains will become unified platforms for collaborative scheduling, and edge computing integrated machines will become new carriers for deploying digital applications.

First, high-precision computer vision technology will develop in depth. The popularization of hyperspectral machine vision perception technology, optimization of visual algorithms and computing power deployment, and the combination with technologies such as knowledge graphs will drive computer vision towards high precision. In 2023, many new scene implementation opportunities will arise in smart healthcare, aerospace, and high-precision product quality inspection.

Second, the standardization packaging of computer vision technology. Leading vendors provide small and medium enterprises with easy-to-use and integrable solutions such as open APIs and packaged SDKs, lowering the threshold for large-scale technical development and use, and fostering new technological commercialization models. In this process, standardization is essential to establish a good iterative ecosystem, promote algorithm and sample sharing, and provide algorithms with data for research and experimentation; at the same time, difficult samples can be referred to more professional algorithm teams for resolution.

In 2023, although the deep application of extended reality interaction technology is still in its early stages, its entry point value for the digital transformation and technological application of industrial enterprises will be further highlighted. First, the entry point for industrial production scenarios realizes comprehensive visualization, simulation, and optimization of the industrial production process, improving efficiency and quality in design, manufacturing, inspection, and maintenance. Second, the entry point for industrial training and education provides high-quality training and education experiences for employees and partners based on realistic simulated scenarios. Third, the entry point for product service usage allows customers to preview and customize products in a virtual environment, driving product sales.

First, industrial knowledge graph technology rapidly penetrates various links in the industrial production chain. It helps enterprises integrate and utilize specialized knowledge in various aspects such as production data, equipment data, and quality data, providing decision support for production optimization and quality control. Second, artificial intelligence accelerates the implementation of industrial knowledge graphs. Knowledge graphs can provide cognitive and understanding capabilities for AI, while AI is also accelerating the construction of knowledge graphs for enterprises, including acquiring specialized knowledge in various fields such as literature, patent information, and technical standards, while automating the processing of specialized knowledge in equipment information, process parameters, and quality data. Third, the application of industrial knowledge graph technology will pay more attention to data quality and data security. With the optimization of regulations and policies regarding data security in the country, industrial enterprises will pay more attention to data quality and security issues and propose more effective solutions.

2023 is the year when general AI large models enter a “phenomenal” growth phase and large-scale applications. For industrial enterprises, the integration of industrial mechanisms with general large models via knowledge injection will become a key trend in the next 1-2 years. Through knowledge injection, industrial large models with industrial mechanisms will gain strong vertical industry implementation capabilities, helping industrial enterprises achieve richer business benefits.

First, handling more types of industrial data, including text, images, videos, sounds, and sensor data. Second, processing cross-industry and specialized data, such as mechanical, electrical, chemical, and material data. Third, executing various industrial scenarios and tasks, such as fault diagnosis, quality inspection, process optimization, production scheduling, and product design. Fourth, providing robust and interpretable results, offering reasoning processes, evidence support, and confidence assessments for industrial cognition and decision conclusions.

Green and low-carbon are new dimensions for the transformation and development of the manufacturing industry. In 2023, the “dual carbon” technology stack will support the gradual implementation of green manufacturing models in industries such as power generation, steel, chemicals, and building materials, with carbon footprint and decarbonization at the core technological position, bringing many trend changes: First, carbon emission quantification technology will construct the intrinsic logical relationship between industrial production and carbon emissions through process mechanisms and high-quality data, and with the continuous improvement of carbon emission accounting capabilities, enterprises will find effective ways to measure carbon assets. Second, the spatiotemporal perspective of carbon emissions, focusing on carbon emission accounting throughout the product lifecycle (temporal characteristics) and carbon neutrality across the entire supply chain in manufacturing (spatial characteristics) will be the development direction. Third, the green transformation of industrial energy, based on effective measurement and pricing of carbon assets and the gradual implementation of carbon markets, will genuinely relate the green transformation of industrial energy to the operational indicators of enterprises, thereby promoting enterprises to actively advance the energy greening process. Fourth, the energy internet, enabling enterprises to manage and dispatch various clean energy and energy networks using virtual power plants and integrated energy systems, making it possible to optimize overall ROI from building-level to park-level.

In 2023, breakthroughs in artificial intelligence have led the industry to focus on the industrial application of large models, and industrial big data has become a key support for industrial enterprises in building AI-usable data systems and developing industrial large models. For enterprises at the forefront of digital transformation, the potential enormous value of industrial big data will attract them to continue increasing IT investments in the coming years, bringing some trend changes:

First, the practice of data lifecycle management is accelerating; the high complexity of industrial big data is a challenge for traditional data technologies applied in industry, while AI technologies are very adept at handling complex but structured data, so the concept of lifecycle data management will be increasingly implemented by more enterprises. Second, the advanced application of big data technology is accelerating its implementation, such as integrated lake-house models, batch-stream integration in data processing, and advanced analysis techniques in data analysis, including algorithm models, intelligent tagging, knowledge graphs, and visualization.

In 2023, crowd intelligence technology will be more widely discussed in the industry and begin to integrate into the technological challenges of digital transformation in manufacturing. Supported by various technology stacks such as large language models, edge computing, IoT, and knowledge graphs, the foundation for the development of crowd intelligence technology has matured, and the technology will gradually emerge from laboratories. In terms of technological breakthroughs, the focus of crowd intelligence technology exploration will be on distributed collaborative computing among multiple intelligent devices or systems at edge nodes. Specifically, utilizing computing clusters on the edge to enhance the real-time performance, flexibility, and robustness of distributed crowd intelligence, while reducing reliance on central nodes and cloud computing, such as industrial robot clusters using edge computing for real-time collaborative control, fault detection, and self-repair tasks; device sensor clusters can use edge computing to perform real-time data fusion, compression, and analysis tasks. These are all scene trends for the implementation of crowd intelligence.

Based on the foundation of industrial digital twins, enterprises can effectively build industrial simulation systems, thereby scaling up the testing of various digital technologies within the system and promoting the large-scale application of technologies. It is expected that in 2023, industrial digital twin technology will continue to develop deeply, significantly enhancing the usability of industrial digital twin systems in complex operational environments, thus supporting the large-scale implementation of digital technologies. First, the construction technology of digital twins will expand from simulating specific scenarios to simulating complex systems, achieving digital modeling of complex systems such as the entire production process, supply chain networks, and the entire lifecycle of products, supported by industrial big data. Second, digital twin interaction technology will emphasize the timely feedback of optimization results from digital space to the physical world, obtaining the expected economic benefits. This will promote more real-time bidirectional mapping of technology products between digital space and the physical world, significantly improving the intelligent collaborative level of physical objects. Third, digital twins will support business innovation by improving monitoring and optimizing factory operational cost structures, and predicting analysis and scheduling management based on industrial simulation environments, enabling adversarial product development and differentiated design.