Wei Yan

With the acceleration of urbanization, the issue of entrepreneurship and the development of the ethnic minority migrant population, as an important component of urban multiculturalism, is receiving increasing attention. However, due to differences in language, skills, funding, information accessibility, policy understanding and social adaptability, ethnic minority migrant populations face many challenges in the process of entrepreneurship. This study investigates the language, skills, funding, information acquisition, policy understanding and social adaptability of urban ethnic minority floating population, and establishes an indicator system based on these characteristics. Then, a decision model integrating the knowledge graph and causal mediation reasoning method is established. The model aims to calculate dynamic entrepreneurship assistance policies for this group of urban ethnic minority floating populations based on real-time changes in indicator system data. The results show that the decision model based on knowledge graphs and causal mediation reasoning can significantly improve the pertinence and effectiveness of entrepreneurship assistance policies for ethnic minority migrant populations. In the future, research will continue to deepen, further improve and optimize the model, in order to provide more comprehensive and accurate assistance services for ethnic minority migrant populations, while promoting research and development in related fields.

In modern times, financial institutions are the core carrier of efficient operation of financial markets. With the continuous development of financial models such as IoT (Internet of Things) finance, commercial banks have made many attempts in the integration and innovation of finance and logistics, but they have also increased the types and complexity of risks they face while improving financing efficiency. It has the characteristics of great destructiveness, strong infectivity, and high complexity. The establishment of a perfect emergency security optimization management for early warning of bank liquidity risk is an important part of timely detection and effective management of liquidity risk. In order to enable decision makers to accurately use the effective disposal of similar liquidity risk emergencies as a reference for decision-making, this paper studies the generation method of emergency security plan for bank liquidity risk using big data analytic-based case reasoning. Firstly, analyze the characteristics of various types of bank liquidity emergencies a, and identify the key risk points, and form the accident case database. Secondly, carry out the interval division according to the different numerical distribution characteristics of the indicators, and calculate the repeatability of the involved stages. Finally, calculate the comprehensive similarity to obtain the emergency security plan for reference. At the same time, taking A commercial bank as an example, verify the effectiveness of the method by using the constructed case-based reasoning model to generate emergency security plans intelligently. It provides reference for commercial bank liquidity risk management.

With the downward pressure of China’s economy and the impact of the epidemic, the accumulated market risk has increased the liquidity pressure of the banking industry, and the mismatch between deposit maturity and loan maturity is the main cause for the increase of liquidity risk. The twenty-first century is the era of rapid and in-depth development of data management technology. The explosive growth of massive financial data makes the information data related to the liquidity risk of commercial banks present the characteristics of complexity, diversity, and heterogeneity. The traditional risk early warning model cannot deal with the influence between a large number of influencing factors and the nonlinear factors of commercial bank liquidity risk. Based on this transformation, the circular neural network model is introduced into the field of liquidity risk early warning of commercial banks from the perspective of the mismatch risk of financing maturity of commercial banks, and the driving factors and risk warning signs of liquidity risk of commercial banks are further analyzed from the institutional level, policy level, industry level, and micro commercial bank level. This paper uses network crawler technology, text analysis, and grounded analysis technology to intelligently identify the liquidity risk of commercial banks and establishes an early warning index system based on the influencing factors of commercial banks and internal liquidity risk. Also, it constructs an intelligent early warning model of commercial bank liquidity risk based on deep learning and uses the data of commercial banks from 2000 to 2020 for early warning. The results show that the constructed model has high accuracy, which can provide support for banks and relevant government departments to formulate and resolve bank liquidity risk.

With the change of social structure and management mode, emergency social security incidents have become the focus of social security management. In order to solve the problems of insufficient overall performance and low security in the practical application of cloud management mode in traditional social security emergencies. With the use of improved pick-KX load balancing algorithm and RSA encryption algorithm, this paper optimizes the cloud computing model and applies it to the cloud management mode of emergent social security events. Then the optimized cloud management model is simulated and evaluated by the multi-level fuzzy comprehensive evaluation model. The simulation results reflect that the proposed cloud management model in this paper is able to effectively improve the management effect of social security events.