The use of new technologies to empower the social co-governance of food safety is a consensus in the theoretical and practical fields of food safety governance modernization. Based on the innovative features of the block chain such as non-tampering, consensus mechanism, and a smart contract, this paper proposes a new method to facilitate the achievement of the effect of social co-governance of food safety. Firstly, it provides a profound analysis of the causes of food safety problems and the drawbacks of traditional centralized supervision, and proposes a solution with decentralized features and the design process of smart contracts to realize the chaining and integration of finite credible data in the process of food circulation; secondly, it constructs a mathematical model through finite credible data on the chain and obtains the probabilistic consensus results of food safety through rigorous derivation; Finally, the scheme, model and algorithm are effectively practiced through the experimental environment of mature block chain platform and provincial food supervision platform. The example shows that the market failure problem in food safety can be greatly alleviated by the non-tampering feature of block chain; the consensus results formed by the credible data uploading, model construction and data derivation through smart contracts can greatly alleviate the problem of involution of government administrative supervision; and the social co-governance capacity of food safety can be improved through information sharing with the active synergy of multiple subjects of co-governance.

Information technology has penetrated into all aspects of politics, economy, and culture of the whole society. The information revolution has changed the way of communication all over the world, promoted the giant development of human society, and also drawn unprecedented attention to network security issues. Studies, focusing on network security, have experienced four main stages: idealized design for ensuring security, auxiliary examination and passive defense, active analysis and strategy formulation, and overall perception and trend prediction. Under the background of the new strategic command for the digital control that all countries are scrambled for, the discussion of network security situational awareness presents new characteristics both in the academic study and industrialization. In this regard, a thorough investigation has been made in the present paper into the literature of network security situational awareness. Firstly, the research status both at home and abroad is introduced, and then, the logical analysis framework is put forward concerning the network security situational awareness from the perspective of the data value chain. The whole process is composed of five successive stages: factor acquisition, model representation, measurement establishment, solution analysis, and situation prediction. Subsequently, the role of each stage and the mainstream methods are elaborated, and the application results on the experimental objects and the horizontal comparison between the methods are explained. In an attempt to provide a panoramic recognition of network security situational awareness, and auxiliary ideas for the industrialization of network security, this paper aims to provide some references for the scientific research and engineering personnel in this field.

The reasonable sharing and effective integration of data is the premise of improving the comprehensive governance ability of society through data empowerment. It can be determined that the fine-grained splitting of microservice framework can promote the reasonable sharing and effective integration of data. However, how to determine the granularity of microservice splitting is a multiparameter and multiobjective decision-making problem, which is also a key basic problem to be solved urgently both in academic research and application. From the perspective of application, this paper puts forward the criteria and basic framework that can guide the microservice splitting, and for the first time, based on the perspective of maintainability, it gives the decision criteria and methods of microservice splitting granularity. After the theoretical research, this paper also takes the provincial microservice governance of food and drug regulation as an example: the example shows that the framework and methods proposed can effectively improve data sharing and system expandability. Through microservice governance, collaborative social governance featuring “one network to achieve management objectives, one network to realize comprehensive business processing, one network for comprehensive view of all information” can be achieved. It is an exemplary mode worth popularization.

The mismatch between the supply and demand of online-listed rental housing (ORH) is an important factor restricting the operational efficiency of online rental service platforms. However, extant literature pays little attention to this problem. This study proposes an ORH multiattribute supply and demand matching decision model based on the perceived utility of matching both sides of this market. The model considers the multiattribute information of ORH, such as area, transportation, rent, room, and interior decoration, and quantifies their perceived utility values based on the theory of disappointment. Thereafter, we construct the matching decision model and verify it for feasibility by applying it to Shanghai’s ORH supply and demand information—our empirical case. The results show that this method can be applied to online rental housing platforms and meet the supply and demand matching requirements to the greatest extent. The constructed model takes into account the perceptions of both supply and demand parties, may promote the effective matching of ORH supply and demand, and bears theoretical implications for the improvement of rental housing matching in ORH platforms.

This study examines the discrepancy between big data talent training and industry demand. The study analyzed 85 training programs and over 10,000 job postings from two job boards in China (51job and Zhaopin). Using content analysis, social network analysis, and the BERTopic-TOPSIS model, it mined implicit information from training programs and labeled key competencies in job descriptions. A key finding was a significant supply-demand misalignment: while “data application ability” was a stated goal in 52% of programs, only 11% of graduation requirements specified concrete, measurable skills to achieve it. The study identified three primary employment pathways for big data management and application majors: data management, data analysis, and data platform development. Institutions such as Peking University and Hefei University of Technology were identified as best practices. The study then delineated a cultivation path for the major by integrating the characteristics of these employment pathways, and optimised general knowledge and compulsory courses, core courses, graduation requirements, and the cultivation objectives of the major.

In order to solve the problems of asymmetry between supply and demand of data science talents and slow construction of discipline system, this study analyses 85 texts of curriculum system of big data management and application majors collected through social network analysis, and at the same time, constructs TOPSIS-LDA model of job qualities based on more than 10,000 job competence demands of the big data industry in 2 mainstream recruitment websites, and draws the focus of attention and logical links of talent cultivation and talent demand side. The focus of attention and logical links of talent demand side were obtained. Finally, based on BERT-LSTM-CRF depth model, entity extraction was carried out on data science classic books and thesis texts, and the knowledge map was constructed for the big data management and application speciality in combination with the logical links of supply and demand, which provided the theoretical and practical basis for the construction of the speciality.