Contingency response decision of network public opinion emergencies based on intuitionistic fuzzy entropy and preference information of decision makers

Research on optimization of mining methods for broken ore bodies based on interval-valued pythagorean fuzzy sets and TOPSIS-GRA

Identifying the optimal mining methods plays a pivotal role in ensuring both economic efficiency and environmental sustainability. This study aims to propose a model that combines interval-valued Pythagorean fuzzy sets (IVPFS) and TOPSIS-GRA to select the optimal mining method for broken ore bodies. First, a multi-factor comprehensive evaluation system, including economic, safety, and technical aspects, was established. IVPFS was introduced to express the fuzzy information of the decision-making process within the evaluation system. Additionally, an objective method combining the principle of fuzzy entropy measurement with EWM was proposed to determine the weights of fuzzy information. This method distinguished the importance of decision-makers and indicators. Then, an integration of distance and similarity (TOPSIS-GRA) was employed for ranking alternative solutions to select the optimal one. This model was applied to the decision-making problem of mining methods for the broken and difficult-to-mine ore bodies in the Tanyaokou mining area. Initial fuzzy evaluation information was obtained by having decision-makers score the mining methods. Results showed that the comprehensive scores of four alternatives are 0.5172, 0.4683, 0.5192, and 0.5465, respectively. The optimal method was the point-pillar upward horizontal layered filling mining method. Finally, the sensitivity analysis confirmed the stability of the model. The comparative results under different fuzzy environments (PFS and TFS) demonstrated the strong capability of IVPFS in handling fuzzy information for optimizing mining methods.

Intuitionistic fuzzy-based entropy weight method-TOPSIS for multi-attribute group decision-making in drilling fluid waste treatment technology selection

Drilling fluid waste is produced by oil and gas industry operations and can potentially cause serious environmental pollution and energy consumption if not properly treated. Currently, there are several treatment methods available for drilling fluid waste such as bioremediation, thermal treatment, solidification/stabilization treatment, electrochemical remediation, physiochemical treatment, and supercritical fluid treatment. However, selecting an adequate method to treat drilling fluid waste is a critical consideration. The objective of this work is to analyze the problem of drilling fluid waste pollution and treatment methods, establish a drilling fluid waste treatment decision index system that takes into account various factors, and apply the intuitionistic fuzzy-based entropy weight method (EWM)-technique for order of preference by similarity to ideal solution (TOPSIS) method to make a multi-attribute group decision on drilling fluid waste treatment methods. The method is then applied to the WBQ004-1-H1 drilling project as an example for comprehensive analysis. The final decision results show that A3 (0.566) > A1 (0.537) > A6 (0.526) > A5 (0.485) > A4 (0.478) > A2 (0.447), so the solidification/stabilization treatment is the most suitable method for this project, providing new insights into selecting drilling fluid waste treatment methods in actual projects.

Emergency Decision-making Method of Unconventional Emergencies in Higher Education Based on Intensive Learning

In order to improve the emergency decision-making and management ability of unconventional emergencies in Colleges and universities, an emergency decision-making method for unconventional emergencies in Colleges and Universities based on reinforcement learning is proposed. The function of the emergency decision-making model of unconventional emergencies in Colleges and universities is constructed, and the optimal control of the emergency decision-making process of unconventional emergencies in Colleges and universities is realized by using the queuing theory model. Based on the sub-sequence decomposition method, the reinforcement learning function is optimized. Combined with big data scheduling and the empirical mode decomposition method, the effective probability density function of emergency decision-making for unconventional emergencies in higher education is calculated, and the optimal solution vector analysis of emergency management and scheduling decision-making for unconventional emergencies in Colleges and universities is realized according to parameter estimation and quantitative optimization results. The test results show that this method has a good ability to optimize and schedule the emergency decision-making of unconventional emergencies in higher education, and has strong convergence. This method improves the emergency response-ability, and the time cost is short.