Control method and device for measuring beetle of longhorn beetle at one time and readable storage medium

Abstract

The invention relates to a control method and device for measuring beetles of longhorn beetles at a time and a readable storage medium, and the method comprises the steps: a starting step: receiving time sequence measurement data, and starting an iterative search step after receiving at least two pieces of measurement data; an iterative search step: substituting the latest measurement data into a left beard function of a beard of longhorn beard algorithm to obtain a first error, and substituting the previous measurement data into a right beard function of the beard of longhorn beard algorithm to obtain a second error; and according to the first error and the second error, obtaining a control parameter based on a beetle antennae algorithm, and exiting the iterative search step until an iterative push-out condition is met. Compared with the prior art, only one sensor is needed to provide time sequence measurement data, and the longhorn beetle algorithm can be used for searching to obtain the control parameters, so that the measurement times of the sensor can be reduced, and the data utilization rate and the algorithm efficiency are further improved.

Description

technical field [0001] The present invention relates, in particular, to a single measurement method, device and readable storage medium for controlling the beard of the beetle. Background technique [0002] For existing data processing analysis and parameter optimization methods, most algorithms that can perform global optimization or global optimization require high computing power, making it difficult to run in real time. At present, most scenarios such as industrial automation, agricultural automation, chemical industry In production, pharmaceuticals, etc., there are applications that require very strict real-time performance. In these applications, low-cost and weak computing power terminal devices are usually used as computing devices. These devices cannot provide enough computing power to run most of them in real time. Existing global optimization algorithms, but most of the existing real-time data analysis and parameter optimization methods are difficult to perform gl...

AI Technical Summary

Problems solved by technology

[0002] For existing data processing analysis and parameter optimization methods, most algorithms that can perform global optimization or global optimization require high computing power, making it difficult to run in real time. Currently, most scenarios such as industrial automation, agricultural automation, chemical engineering, etc. In production, pharmaceuticals, etc., there are applications that have very demanding real-time requirements. In these applications, computing devices are usually low-cost terminal devices with weak computing power. These devices cannot provide enough computing power to run most real-time applications. There are existing global optimization algorithms, but most of the existing real-time data analysis and parameter optimization methods are difficult to perform global optimization

At present, methods that can perform real-time data analysis and parameter optimization, such as Extremum Seeking, SPSA, and beetle beetle search optimization algorithm, among them, the beetle beard search optimization algorithm with better performance needs to use two measurement data in each iterative update, because In some special scenarios, especially when the amount of acquired data is small or the sampling frequency is low, it is easy to cause data loss

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