Big data compression method and compound disk playing method based on sequential logic

Abstract

The invention discloses a big data compression method and compound disk playing method based on sequential logic. The method comprises the following steps of: acquiring original data of all entities according to time points in sequence; sequentially querying all the entities, comparing the attribute data of adjacent time points of the same entity, if there is no difference, discarding the data, and if there is difference, extracting attribute values with difference; encoding all the extracted difference data at the same time point to form a character string; and sequentially storing character strings formed by encoding the difference data extracted at the different time points in a Redis cache library in a key value form to realize data compression, extracting original data at the time point where a playing bar is located for rendering on the basis, extracting data at the time point where the current playing bar is located in a cache library for local update rendering. Therefore, the resources of the client are saved, and the rendering speed is improved.

Description

technical field [0001] The invention belongs to the technical field of time series data processing, and in particular relates to a time series logic-based big data compression method and a replaying method. Background technique [0002] Traditional time-series logic data replay is a snapshot or linear recording of the recorded time-series process. It is a frame-by-frame scene superimposition. It can only be viewed back and forth. It is impossible to choose the viewing scene and angle according to one's own interests, and it is also impossible to use the recorded content. Carrying out scientific and comprehensive evaluation and analysis, a large amount of information is discarded and wasted, as described in Document 1 "Song Xiangbin, Ma Xin's drill review and application based on big data [J] "Electronic Technology and Software Engineering" 2019: 170-171" . [0003] Existing time-series data replay systems mostly use the method of directly reading time-series data for displa...

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Problems solved by technology

Some existing time-series data compression methods are relatively simple in processing data structures, and mainly serve to save database storage space, such as document 3 "Ding Liang, Li Zaixue, Cai Fudong, Lv Changfeng, Chen Lei, Gan Fagang A time-series data compression method, device and equipment [P]: China, as stated in CN201911421733.1", cannot meet the requirements of multi-object data structures, and some data encoding methods are relatively complex, such as document 4 "Wang Jianmin, Huang Xiangdong, Qiao Jialin, Wang Chen, Long Mingsheng An adaptive encoding length The method and device for storing time-series data [P]: China, CN201711319331.1", the decoding cannot meet the fast and efficient requirements of data playback

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