In recent years, with the continuous expansion and development of cities, urban population has become more and more dense. At present, there are few researches on community emergency evacuation management system in China. Therefore, the establishment of community emergency evacuation management system based on mobile big data can not only meet the efficient, fast, smooth, and orderly evacuation and settlement needs of the masses but also minimize emergencies. All kinds of losses and influences caused by accidents have very important practical significance. In this paper, an emergency evacuation management system is developed and designed on smart community platform based on mobile big data technology. In the process of development, it is found that the service integration method of mobile big data and application model is the best combination of this system. The construction process of community emergency evacuation management system framework is introduced in detail. Through the research and design of the system, and then test and analysis of the experiment, the sixth grade teachers and students of a primary school group experiment, there are 50 people in each group. The experimental group is familiar with the system operation in advance, and the knowledge of emergency evacuation management is previewed in advance. It is concluded that the class with emergency evacuation management system has much faster response time and decision-making time to emergencies than the class without emergency evacuation management system, and the injury situation of the class is much better. The study shows that the emergency evacuation management system plays an important role in dealing with emergencies, improving the escape rate and reducing the number of injured. I believe it will be further promoted in the future.

As the digital economy promotes economic growth, the Internet of Things can solve the problem of productivity, and the blockchain can solve the problem of production relations, which realizes the industrial transformation of blockchain smart contract technology and becomes a new driving force for the industry. It is becoming more and more difficult for companies to control their own raw material supply, production, and sales by relying only on their own strength, and this is the key to influencing a company to become bigger and stronger. This problem will be efficiently solved by the implementation of “Internet of Things + Blockchain” technology. As a result, research into a new sort of smart supply chain management based on “Internet of Things + Blockchain” is required. From the perspective of building a smart supply chain, this paper makes full use of literature data methods, theoretical analysis methods, case analysis methods, logic analysis methods, and other methods. To study the effectiveness of IoT and blockchain technology through case study of changes in the order quantity in the procurement link of the supply chain. By understanding the current status of related research at home and abroad, as well as the Internet of Things technology and blockchain technology, this paper analyzes China. The main problems existing in the supply chain management of enterprises are the combination of the Internet of Things technology and the blockchain with the enterprise supply chain to create a smart supply chain platform, and the feasibility and functional efficiency of the smart supply chain platform. The related check was found, its deficiencies were found, and remedial measures were taken. The research results show that this intelligent supply chain management platform based on the Internet of Things (IoT) and blockchain can make the operation of the entire supply chain clearly visible. Information and data sharing can be achieved among the various departments of the supply chain to achieve scientific management and precision of the enterprise prediction. And this model can also be used in other industries to achieve industrial upgrading.

With the development of information technology and computer technology, the work of the library is becoming more and more digitized and networked. This article mainly studies the performance evaluation method of library knowledge management based on data mining. This paper uses attribute-oriented induction algorithm to mine generalized features. First, scan the entire data set to obtain different values of all attributes. Statistics, classification, and analysis of historical records help to understand the usage of books and periodicals and perform predictive analysis. This article uses statistical weighted weight calculation formula to calculate the library knowledge management ability evaluation index weight. The evaluation of the knowledge management ability of the library mainly adopts the questionnaire survey method, and the knowledge management status of the library is deeply understood in the form of interviews on the spot, and the obtained evaluation data and materials of the knowledge management ability of the library are organized and statistics. In order to prevent the model from remembering the patterns of the training set too deeply, to make the model more general, and to adapt to unknown data well, we use the test set to rest the model. The part of the data set that has not been used in the process of modeling and testing correction can be used to estimate the effect of the model, or to compare the effect of the model. For expert value, the value of the office is 1.02, which is approximately equal to 1, which means that the value and cost of the office are basically equal in the knowledge service of the library. The results show that the combination of knowledge management and university library management will help to form a systematic theoretical framework and behavioral model framework in the research of knowledge management models and strategies in university libraries.