This project belongs to the field of information technology. With the advancement of technologies such as "big data" and "Industrial 4.0," the scale and complexity of data in various industries are growing rapidly. This project focuses on the thorny issues involved in large-scale heterogeneous data collection, management, and services during enterprise informatization construction. It relies on national science and technology support programs, among others, to form innovative results through large-scale, multi-departmental collaborative research and development and comprehensive integration demonstration, covering distributed data collection, standardized management, intelligent analysis, personalized recommendations, etc., breaking through the technical bottlenecks faced throughout the entire lifecycle of data from its source, management to application. Specifically, the main innovative achievements of the project are as follows: 1. Large-scale heterogeneous data management technology. For multidimensional structured data, a storage method based on hierarchical joint proxy encoding is proposed. Construct bitmap connection indexes to reduce the cost of connections and aggregations; for unstructured data, proposed a minimal path storage method for graph structure data and index technology for graph structure data; in response to the shortcomings of inflexible representation forms and low retrieval efficiency in metadata management processes, proposed distributed metadata management and retrieval technologies; at the physical level of data management, implemented virtualized elastic management of underlying data resources and optimized the infrastructure of data management. 2. Distributed collection technology for large-scale heterogeneous data. In terms of data collection in wireless sensor networks, considering the characteristics of dispersed collection points and strict collection times, proposed high-coverage wireless sensor network protocols, highly reliable fault detection mechanisms, and high-stability wireless sensor network information transmission technologies; in terms of data collection in the internet space, Research and developed distributed data collection technology based on map-reduce, solving technical difficulties such as node overload and data synchronization during the collection process. 3. Service-oriented intelligent data analysis technology. To improve the efficiency and quality of intelligent data analysis, encapsulated the analysis model into web services, and proposed a utility-based evaluation technique for selecting analysis services; around the problem of limited number and capacity of analysis services, proposed intelligent collaboration technology for multiple analysis services in an open network environment; aiming at big data environments, proposed a balance technique between analysis accuracy and cost, significantly reducing the time overhead of data analysis under an acceptable approximation value of user-acceptable analysis results. 4. Personalized recommendation technology integrating social network information. For large-scale enterprise social networking platforms, proposed a method for discovering hidden user groups based on node position analysis in topological potential fields; In response to the issue that traditional scoring strategies cannot accurately reflect demand preferences, an integrated user-item social information ranking learning recommendation technology has been proposed. At the same time, a random walk strategy is used to improve the quality of personalized recommendations in a social network environment. The project has obtained 20 authorized invention patents (including 1 PCT patent), 6 utility model patents, applied for 2 invention patents, obtained 6 software copyright registrations, formulated 6 technical standards, published more than 100 papers in academic journals and conferences such as 'IEEE Transactions on Knowledge and Data Engineering', 'SCIENCE CHINA Information Sciences', 'Journal of Software', etc., with over 80 SCI/EI indexed papers and more than 900 citations. Among them, two papers have successively won the Excellent Paper Award at international conferences, delivered invited reports at international conferences 4 times, and published a monograph. The project achievements have been recognized by the Ministry of Education's scientific and technological achievement appraisal and evaluated by renowned experts both domestically and internationally as reaching domestic leading and international advanced levels. The project achievements have been widely applied in more than 30 large enterprises in Shanghai, Beijing, Jiangsu, and other places. In the past three years, the project has cumulatively directly added 932.168 million yuan to industrial output value, increased profits and taxes by 224.848 million yuan, and generated new tax revenue of 87.701 million yuan. It has promoted rapid transformation of scientific management informatization in many enterprises, optimized business processes, improved efficiency and quality of data services, and produced significant social and economic benefits.