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通过分析云储存系统的数据处理及存储的原理,提出基于粒群优化算法的云存储数据检索方法主要通过对云存储数据的关键词进行相似度对比,利用粒群优化算法的全局最优及局部最优算法,对查询数据进行匹配,直至寻找到最优查询结果。为了验证设计方法的可行性及性能,在Matlab软件中实现优化模型并构建实验场景,模拟云存储过程及数据检索过程,对此数据检索优化方法进行测试验证。仿真结果表明,在模型稳定性方面,粒群优化算法随着粒子位置的迭代,模型逐渐收敛且能够查询出最优解;在模型应用方面,查询响应延时较随机查询模型减少了34.7%,且准确率达到99.6%。总之,设计的基于粒群优化算法的云存储数据检索方法具有较高的检索精度及稳定性。
By analyzing the principle of data processing and storage in cloud storage system, this paper proposes a cloud storage data retrieval method based on Particle Swarm Optimization (PSO) algorithm. By comparing the similarity of key words in cloud storage data and using the global optimization and local Optimal algorithm to match the query data, until you find the best query results. In order to verify the feasibility and performance of the design method, the optimization model and the experiment scene are constructed in Matlab software to simulate the cloud storage process and data retrieval process, and the data retrieval optimization method is tested and verified. The simulation results show that in the aspect of model stability, the particle swarm optimization algorithm converges gradually with the particle position iteration and can find out the optimal solution. In terms of model application, the query response delay is reduced by 34.7% compared with the random query model, And the accuracy rate reached 99.6%. In conclusion, the proposed cloud storage data retrieval method based on PSO has high retrieval accuracy and stability.