Research on Visual Management System of State Grid Power Planning Data Based on Genetic Algorithm

Most of the traditional methods are collected by hand, which not only requires a lot of manpower, but also greatly reduces the efficiency of data collection of mining power, and can not avoid mistakes in the collection process. In order to solve the problem of traditional data acquisition, this paper studies the visualization management system of state grid power planning data based on GA (genetic algorithm). The improved GA is used to solve the multi-objective distribution planning problem, and a concrete repair scheme is proposed for the infeasible solution generated by genetic operation. The improved GA is used to solve the multi-objective distribution planning problem, and the adjustment operator is added to the algorithm to satisfy the relationship between the upgrading projects, and the mutation probability is dynamically changed by adding the adjustment operator to prevent GA from falling into "premature" phenomenon. The research results show that the improved GA obtains the optimal solution in every experiment, and the operation speed is very fast, and the average calculation time is only 0.081s. It shows that the improved GA can find the optimal solution with greater probability for the small-scale distribution planning problem, and it has a good optimization effect. State grid power planning data visualization management system can effectively improve the work efficiency, management level and decision-making level of state grid power planning and construction through the visualization of power planning data, and greatly reduce the intensity of work review.