Research on Human Resource Management Optimization and Decision Support System Construction Based on Electric Power Enterprise Strategy

At the present stage, the development of electric power enterprises needs to update the concept of human resource management by fully combining it with their actual situation and constructing a systematic human resource management model so as to occupy a favorable position in the market competition. This paper brings the classification rule mining algorithm based on the ant colony algorithm into human resource planning decision-making, adds an adaptive mechanism to the ant colony algorithm, updates the algorithm strategy, and improves the quality of algorithmic decision rules. Establish a fuzzy comprehensive judgment model for performance appraisal and salary decision-making in electric power enterprises and design a comprehensive system of dynamic decision support for human resource management in electric power enterprises. Analyze the effect of α, β and ρ values on the performance of the adaptive ant colony algorithm and determine the utility of the classification rule mining algorithm based on the adaptive ant colony algorithm for human resources planning and decision-making. Combine the evaluation level and scoring factors of employees in electric power enterprises to obtain the employee performance appraisal rankings and determine their remuneration packages. The practical benefits of the HRM dynamic decision support system designed in this paper are examined by a system reliability test. The reliability of the human resource management decision-making system designed in this paper is maintained at 0.93~0.97, and the decision-making reliability of traditional system 1 and traditional system 2 is maintained at 0.90~0.92 and 0.90~0.91, respectively. The decision-making system for human resource management in electric power enterprises constructed in this paper is more advantageous for practical operation.