Abstract:Mass manufacturing industry is a platform and carrier that most comprehensively involves various new technologies such as digitalization, networking and intelligence in the new round of industrial transformation. Due to its characteristics of high industrial chain correlation, strong competitiveness and remarkable cluster development, mass manufacturing industry is a representative industry of advanced manufacturing technology and information technology. The industrial chain, supply chain, sales chain, and energy chain involved in mass manufacturing industry are constantly cross integrated, gradually forming a complex digital ecosystem composed of network structure and multiple value chains. Enterprises in mass manufacturing industry have an important ecological relationship of mutual growth, symbiosis, cooperation and competition. Ensuring the good evolution of digital ecosystem is an important way to promote the healthy and sustainable development of mass manufacturing industry, but it is extremely challenging due to data interaction barriers, information asymmetry, value chain islands, vicious competition and value fiction. Therefore, aiming at the bottlenecks of network structured value chain synergy in mass manufacturing industry, this paper focused on the network topology structure of multiple value chains to solve intra-chain or cross-chain enterprise trust and collaboration, multi-source information integration, business process optimization, resource scheduling integration and other problems. Two engines were developed in this paper: digital fusion engine with the functions of generation, aggregation, storage, management, analysis, and usage; and collaborative processing engine with the functions of requirement analysis, business modeling, process optimization, and execution management. Integrating digital flows such as supply and demand flow, business flow, technology flow, relationship flow and work flow, the evolutionary mechanism of digital ecological value chain in value flow, population status, system evolution and so on was investigated. The digital ecological theory of network structured value chain was studied and the digital ecological model based on ecological value chain was established to overcome the key challenges such as the difficulty of trustworthiness assurance, value management and control, and cross-chain traceability in cross-chain interaction under the mesh structure, thus, the robustness and resilience of the value chain are ensured. On this basis, for the application scenario of data-oriented intelligent service and network structured ecological value chain, the value chain operation technology based on block chain, the value chain optimization technology based on data driven techniques, and the new service technology based on artificial intelligence were studied. The service platforms with big data processing, analysis and mining of digital ecological multi-value chain were developed to achieve resource optimization, supply and demand optimization, collaborative optimization, value optimization and ecological optimization. Furthermore, the credible operation, data optimization, horizontal and horizontal collaboration, intelligent service and other needs of the network structured ecological value chain were realized, so as to effectively improve the output efficiency, service level and capability of the ecological value chain eventually. Aiming at providing reliable theoretical and technical support for the healthy and sustainable development of mass manufacturing industry in China, this work focused on the major national scientific and technological needs of mass manufacturing industry, such as digital ecological model construction and network structured value chain collaboration, and carry out relevant R & D and engineering demonstration. Finally, future research directions of network structured value chain digital ecology theory were prospected.