Time series data processing method, device and system, server and readable storage medium

Abstract

The invention discloses a time series data processing method, device and system, a server and a readable storage medium. The method comprises the following steps: receiving a plurality of pieces of to-be-processed data sent by a plurality of pieces of equipment, wherein the plurality of pieces of to-be-processed data respectively comprise time series data acquired by the plurality of pieces of equipment and identification codes of the plurality of pieces of equipment; according to grouping results of the multiple pieces of equipment, respectively importing multiple pieces of to-be-processed data into corresponding task queues respectively for sequential processing, and correspondingly obtaining multiple pieces of result data; and sequentially storing the result data into a first cache queue. According to the time series data processing method provided by the invention, based on the grouping result of the equipment, high-performance time series processing of large-batch data can be realized in parallel, and the advantages of multi-core operation are exerted. Meanwhile, a single machine can process data of tens of thousands of devices, a complex computer group is prevented from beingbuilt, and the deployment cost is remarkably reduced.

Description

technical field [0001] The present invention relates to the field of data processing, in particular to a time series data processing method, device, system, server and readable storage medium. Background technique [0002] With the development of Internet of Things technology, the processing volume and processing difficulty of Internet of Things data are increasing. At present, the data processing methods commonly used in the field of Internet of Things are mainly divided into the following two categories: [0003] The first category: Big data cluster computing methods based on typical flow / batch processing frameworks such as Hadoop, Spark, and Storm, which are mainly suitable for high-speed processing of massive data, have the advantages of high parallelism and high throughput, and can support thousands of machines Data processing for IoT devices. But at the same time, the big data cluster processing framework has high requirements on the resources required for its own op...

AI Technical Summary

Problems solved by technology

However, when the data cannot be guaranteed to be processed in chronological order, for example, the data at the 4th moment is processed before the data at the 3rd moment, an early warning prompt is only triggered when the data at the 3rd moment (20°C) is processed, resulting in transportation Delay in detecting failure of cabin temperature control system

[0006] Serialization can ensure the orderly processing of data, but it is bound to fail to take advantage of multi-core computing, resulting in a large waste of resources

In addition, the existing dedicated time series databases generally only focus on storing time series data and providing data query services, and cannot directly complete the aforementioned logical processing

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