Configurable timing data acquisition, data scheduling, encrypted transmission and visualization method

Abstract

The invention discloses a configurable timing data acquisition, data scheduling, encrypted transmission and visualization method. The method comprises the following steps: a core data acquisition engine acquires data, cleans the data and enables the data to fall into a target data source; a visual command center which reads the data source settled by the core data acquisition engine, and performs intelligent modeling and intelligent analysis according to the data to generate an intelligent report; a core scheduling engine which also reads the data source precipitated by the core data acquisition engine, the rule engine analyzes the configured rule, generates a scheduling task and finally issues the scheduling task to each acquisition end / user end, in the process, secondary data precipitation is carried out through MySql, high-performance caching of hotspot data is realized through Redis, and time sequence data storage is carried out through InfluxDB. The method is suitable for resource allocation optimization in multiple scenes, a perfect and powerful visual command center is constructed, and powerful foundation stone and strength are provided for resource expectation, adjustment, efficiency and the like.

Description

Technical field: [0001] The invention belongs to the fields of data transmission and data scheduling, and more specifically relates to a method for configurable timing data collection, data scheduling, encrypted transmission, and visualization. Background technique: [0002] In the context of big data, data, as the most important part, guides the production and operation of enterprises, how to use and analyze data has become an indispensable part, and data security, data sharing, data application and data analysis are in the The context of big data is a crucial link. [0003] However, in the traditional software industry, data is generally stored in a variety of heterogeneous third-party data services, which only meet the needs of its own applications. This creates the following disadvantages: due to the internal blockage of data services, data islands are formed, resulting in the inability to share and analyze data; due to the diversity of heterogeneous data services, data...

AI Technical Summary

Problems solved by technology

[0003] However, in the traditional software industry, data is generally stored in a variety of heterogeneous third-party data services, which only meet the needs of their own applications.

This creates the following disadvantages: due to the internal blockage of data services, data islands are formed, which makes data sharing and analysis impossible; due to the diversity of heterogeneous data services, data types and data rules do not match in the docking of different data services. Problem; in actual production, we need to extract, transform and load the data in it to form data marts, business libraries and data warehouses to provide data analysis and use, while the original data service model does not support this one operation

Images