A flink-based regional deployment control method and device

By using Flink CDC and SQL to synchronize deployment and capture data in real time in MySQL and Kafka, and combining it with Elasticsearch and Doris storage, the problem of low development efficiency and complex data processing in existing regional deployment methods is solved, and efficient acquisition of multi-dimensional early warning data and tracking of deployment object trajectories are achieved.

CN117216167BActive Publication Date: 2026-06-02WUHAN FIBERHOME DIGITAL TECH CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
WUHAN FIBERHOME DIGITAL TECH CO LTD
Filing Date
2023-09-27
Publication Date
2026-06-02

AI Technical Summary

Technical Problem

Existing real-time computing frameworks suffer from long development cycles, low development efficiency, high functional coupling, and poor code reusability in regional deployment methods, making it difficult to quickly obtain multi-dimensional early warning information of deployed objects and track their activity trajectories.

Method used

Flink CDC is used to create a synchronous virtual table to monitor changes in deployment information in MySQL and capture data in Kafka in real time. Flink SQL is used to compare and filter the data, and output to Elasticsearch database and Kafka. Doris is used to store multi-dimensional early warning data.

Benefits of technology

It enables rapid, multi-dimensional acquisition of early warning data and tracking of the trajectory of controlled objects, improving the timeliness and consistency of data and supporting efficient regional control methods and equipment.

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Abstract

The application discloses a regional control method and equipment based on Flink, and the method comprises the following steps: a first synchronous virtual table is created through Flink cdc, the control information in Mysql issued by a business end is acquired, and the control information in the first synchronous virtual table is changed synchronously; a second synchronous virtual table is created through Flink cdc, and the snapshot data information in a first Kafka used for real-time transmission of the snapshot data information is read; the control information in the first synchronous virtual table is compared with the snapshot data information in the second synchronous virtual table, and required early warning data is obtained; the early warning data is synchronously output to a first database, used for detailed result query of the business end, the early warning data is synchronously written into a second Kafka, the second Kafka data is imported into Doris, an Aggregate model table and a Duplicate model table are respectively created in Doris, and the Aggregate model table and the Duplicate model table are used for storing statistical information of a target in different acquisition devices and detailed record data of the target at different times.
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