Low-code industrial control method based on AOE network and event driving

Abstract

The invention provides a low-code industrial control method based on an AOE network and event driving. The method comprises the following steps: 1) designing a control strategy based on event driving; 2) constructing an AOE network; 3) defining basic information, variables, events and actions of the AOE network by using an AOE network configuration file, thereby describing the AOE network constructed in the step 2) and the control strategy designed in the step 1); 4) compiling a measuring point configuration file and a channel configuration file; and 5) analyzing and executing the AOE network. According to the method, a control strategy is expressed in a low-code mode, the development process is simplified, waste of unnecessary computing resources and communication resources can be reduced through event driving, and digital control and multi-thread control can be achieved more easily; and a complete AOE and event-driven control strategy execution framework is designed, and the method can adapt to an environment with high uncertainty.

Description

technical field [0001] The present invention relates to event-driven technology, to the field of industrial control, and in particular to an AOE network-based and event-driven low-code industrial control method. Background technique [0002] Traditional industrial control requires code programming for the designed control strategy. There are three main problems in this way: First, it puts forward very high requirements on the programming ability of the technicians who design the control algorithm. A large amount of code may be required to implement the strategy, and the code needs to be repeatedly debugged and optimized to ensure its correctness and relatively high execution efficiency; second, the code iteration cycle is long and correctness verification is difficult. Usually control strategy testing is divided into steps such as local testing and online testing. The more links between the distance from the final goal, the higher the error and probability; third, there is ...

AI Technical Summary

Problems solved by technology

There are three main problems in this method: first, it puts forward very high requirements on the programming ability of the technicians who design the control algorithm

Implementing the strategy may require writing a large amount of code, and the code needs to be repeatedly debugged and optimized to ensure its correctness and relatively high execution efficiency; second, the code iteration cycle is long and correctness verification is difficult

Usually control strategy testing is divided into steps such as local testing and online testing. The more links between the final goal and the higher the error and probability; The whole set of control method, the development cost is very high

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