Method for improving rule engine response speed

Abstract

The invention relates to a method for improving a rule engine response speed. The method comprises the following steps: establishing a fact set which is a set of a plurality of facts; establishing a rule set which is a set of a plurality of rules comprising condition parts and action parts; adding a priority attribute to each rule in the rule set; and matching the facts in the fact set with the rules in the rule set on the basis of a matching algorithm according to a priority sequence of the rules, executing the action parts of the rules if one rule is matched successfully, continuously performing matching after execution is finished, and repeating the process until all the facts in the fact set already match with the rules in the rule set. The method improves the rule engine response speed, and then improves rule engine real-time performance.

Description

technical field [0001] The invention relates to the rule engine technology in the field of artificial intelligence, in particular to a method for improving the response speed of the rule engine. Background technique [0002] Developed from the inference engine, the rule engine is a component embedded in the application, which separates business decisions from application code and writes business decisions using predefined semantic modules. Since the rule engine is a software component, only developers can use and control it through the program interface. Usually, the five steps for developers to use the rule engine in the program are: [0003] 1) Create a rule engine object; [0004] 2) Load the rule set into the engine or replace the rule set; [0005] 3) Submit the fact set to be processed by the rule set to the engine; [0006] 4) Command the engine to execute; [0007] 5) Export the engine execution result and withdraw the processed data from the engine. [0008] Ru...

AI Technical Summary

Problems solved by technology

[0017] The above research methods have improved the response speed of the rule engine to varying degrees, but most of these research methods are suitable for rule engines running on PCs or servers, but not suitable for rule engines on lightweight devices. The computing power and storage capacity of the equipment are limited, and the direct application of the above research methods will have certain limitations

[0018] Since the traditional rule engine completely matches the rule set and the fact set, and then executes the actions corresponding to all the rules that match successfully, such an execution order will prevent the earlier matching rules from being executed in time, and the earlier the matching success The longer the waiting time for execution, the serial execution sequence will affect the real-time performance of the application system

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