| The rapid development of the weather forecast puts forwards higher requirements to the meteorological data statistical work. The fast and accurate data statistics is an important and indispensable information foundation for the development of weather forecast as well as the basis for leader to make decision. Affected by the recording mode, there exist amounts of historical meteorological data. It is a an important part of the meteorological data statistical work that how to statistic those data to provide accurate decision to operational weather forecasts.Based on the project of Jiading weather bureau, the evolutional optimization and Database technology are used for the storage and retrieval of meteorological data to solve the problem that the response time are too long in the different meteorological elements retrieval for the massive meteorological data. In order to improve the efficiency of the retrieval and statistical analysis of meteorological data, a fast meteorological data retrieval system is developed with database to store the data.The experimental analysis shows that a good multi-table join SQL statement can greatly improve the retrieval efficiency of system and the good execution strategy depends on the query optimization algorithm. In the paper, according to the specific characteristics of multi-table join queries optimization, the particle swarm optimization algorithm is introduced to solve the multi-table join queries optimization problem in database. Around the PSO algorithm and the multi-table join query optimization problem in the meteorological data retrieval, some deeper exploration research are made and some research results are obtained. Concretely speaking, the main research work and innovation are as follows:1) Analyze the static inertia weight problems of the standard particle swarm algorithm in detailed.Through the application of the PSO for standard testing benchmarks, we found, the method of setting inertia weight can’t be adaptive to different problems. The value of inertia weight decreases from generation to generation, which can induce the decreasing of population diversity. And the decreasing of weights values is restricted by the maximum evolutionary generation, which has an influence on the convergence speed and search performance. As a result, it may fall into the local optimum early.2) A Self-guided Particle Swarm Optimization Algorithm with Independent Dynamic Inertia Weights Setting on Each Particle is proposed. Its core idea is to set the inertia weight and accelerator learning factor dynamically and self-guided by considering the deviation between the objective value of each particle and that of the best particle in swarm and the difference of the objective value of each particle’s best position in the two continuous generations, which prevent the premature as well as improving the speed and accurateness effectively. The performance of our method compared with the standard PSO algorithm based on9standard testing benchmark functions demonstrate the convergence accurateness is improved by more than20%.3) According to the multi-join meteorological data query optimization problem, the improved particle swarm optimization algorithm are used in the Meteorological Information Comprehensive Search System of Jiading weather bureau. The operation results show that it can improve the Query Efficiency to use the improved particle swarm optimization algorithm to sovle the multi-table join query optimization problem. Our method is feasible and provides an alternative way for multi-table join query optimization problem.The above work not only enrichs the research of particle swarm optimization Algorithm but also expands its application fields. More importantly, our work provides a referential optimization algorithm for the multi-table join query optimization of meteorology database, and also gives a study case for the combination of computer secience and meteorology, which can promote the applications of the optimization algorithm in meteorological research. |
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