基于动态热度评估与3级划分的Redis缓存优化方法OA

To address issues such as low hit ratio,discontinuous evaluation,and unstable migration of high-frequency time series data from the electron cyclotron resonance heating(ECRH)system in cache,a remote dictionary server cache optimization method based on dynamic heat evaluation and three-level partition(DHE-TP-Redis)was proposed.The data value was quantified through a dynamic heat calculation model,and resources were intelligently allocated via a three-level data classification strategy.Basic heat retention was introduced to ensure the continuity of data heat,while stable operation of the cache under high concurrency was achieved through gradual elimination and module collaboration.The results demonstrated that this method exhibited outstanding performance under three access patterns and continuous migration scenarios.In the burst concentration mode,the hit ratio of DHE-TP-Redis method increased by 0.8 and 26.0 percentage points respectively,compared with the least recently used(LRU)method and least frequently used(LFU)method.In the continuous migration scenario,its migration frequency was reduced by 5.7 times/h compared with LRU method and 6.7 times/h compared with LFU method,and the fluctuation of service response time was reduced by 4.4 percentage points compared with LRU method and 5.1 percentage points compared with LFU method.This method provided a stable solution for high-frequency time series data caching.