A pollution source abnormal data identification method based on a deep learning algorithm

Abstract

The invention discloses a pollution source abnormal data identification method based on a deep learning algorithm. The method specifically comprises the following steps that S1, a system processing module controls a data acquisition module to acquire detection data of an external pollution source, and transmits the acquired data to a data classification module for classification, and relates to the technical field of data processing. The invention discloses a pollution source abnormal data identification method based on a deep learning algorithm. the feature learning algorithm can take the original data as the input; an unsupervised feature learning process is adopted in the learning process; the accuracy of abnormal identification of the pollution source data can be greatly enhanced; Compared with the prior art, the abnormal data identification time is shortened, rich information of the data is depicted through a deep learning model algorithm, the classification performance is improved, the purpose that no manual extraction exists in the whole feature learning and data abnormity process is achieved, and the problem that label abnormal data are difficult to obtain is well solved.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to a method for identifying abnormal data of pollution sources based on a deep learning algorithm. Background technique [0002] Pollution sources usually refer to places, equipment and devices that discharge or release harmful substances to the environment or have harmful effects on the environment. Any substances or Energy, collectively referred to as pollutants, can be divided into two categories according to the pollution generation process: 1. Primary pollutants: pollutants released by pollution sources that directly endanger human health or cause environmental quality degradation; 2. Secondary pollutants: discharged substances A series of physical, chemical and biochemical reactions produced under certain environmental conditions lead to a decline in environmental quality. According to the source of pollutants, they can be divided into natural pollution sources and man...

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Problems solved by technology

[0005] Aiming at the deficiencies of the prior art, the present invention provides a method for identifying abnormal data of pollution sources based on a deep learning algorithm, which solves the problem that the existing identification accuracy of abnormal data of pollution sources is low, and the recognition speed is slow, which cannot be described by deep learning model algorithms. The rich information of data and the improvement of classification performance cannot achieve the purpose of no manual extraction in the whole process of feature learning and data anomaly detection, and cannot solve the problem of difficulty in obtaining labeled abnormal data, which brings people to the monitoring of pollution source data. great inconvenience

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