Abnormal water detection method based on immune negative selection

Abstract

The invention relates to an abnormal water detection method based on immune negative selection, which comprises the following steps: 1) carrying out real-time monitoring with zebra fish as an object of biological monitoring; 2) carrying out real-time detection and tracking of the zebra fish through a multi-target tracking algorithm based on particle filtering, thereby obtaining a zebra fish tracking video sequence; and 3) analyzing and detecting water data, which comprises the following steps: 3.1) producing a large amount of novel data through high-frequency variation of a self-set and simultaneously carrying out low-frequency variation of the self-set; 3.2) producing a maturity detector through a detector generating algorithm based on negative selection; 3.3) judging whether the detector set is mature through combination of expectation coverage and overlapping coefficient calculation (W); and 3.4) carrying out online detection of real-time water data with the maturity detector and finally carrying out online detection of the abnormal water. The invention does not need a large amount of abnormal data, thereby reducing the detection cost and improving the real-time detection.

Description

technical field [0001] The invention relates to the fields of biological water quality monitoring, computer vision, artificial immunity, water quality safety and the like, and proposes an intelligent detection method for abnormal water quality. Background technique [0002] Water quality anomaly detection is based on the normal data index of the monitored water quality, and determines whether an abnormal water quality has occurred by checking the deviation degree between the current data index of water quality and the normal data index. It is essentially a classification problem, which divides data into normal data or abnormal data. The purpose of anomaly detection is to determine whether the system is in a normal working state. The working state of the system can be described by a feature set, and the anomaly detection problem is defined as follows: [0003] Definition 1 state space: state space X is represented by eigenvector x, x={x 1 ,...,x n},x i ∈ [0, 1]]. x i I...

AI Technical Summary

Problems solved by technology

[0008] In order to overcome the shortcomings of existing water quality abnormality detection methods that require a large amount of abnormal data, high detection cost, and poor real-time performance, the present invention provides a method based on immune negative selection that does not require a large amount of abnormal data, reduces detection costs, and improves real-time detection. Abnormal water quality detection method

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