Tea garden abnormal data correction method and system based on deep learning and storage medium

Abstract

The invention discloses a tea garden abnormal data correction method and system based on deep learning, and a storage medium, and the method comprises the steps: collecting the environment data of a target tea garden, carrying out the preprocessing of the environment data, taking the preprocessed environment data of the target tea garden as the input data, and inputting the input data into a CNN-SVM (Convolutional Neural Network-Support Vector Machine), carrying out anomaly detection on the data, and meanwhile, inputting the data into a long-term and short-term memory neural network LSTM to predict the environmental data; and when the detection data of the CNNSVM model is abnormal data, extracting time characteristics of the abnormal data detected by the CNNSVM model, selecting data with the same time characteristics from the data predicted by the LSTM model for correction, and then outputting the data to a tea garden data set. According to the method, abnormal data can be corrected, the specific fault location of the sensor can be judged according to the abnormal data, and high correction accuracy, specificity and generalization ability are achieved.

Description

technical field [0001] The invention relates to a method for correcting abnormal data of tea gardens based on deep learning, which belongs to the field of data identification of the Internet of Things in tea gardens. Background technique [0002] The application of Internet of Things technology has generated and accumulated a large amount of data in the field of agricultural production, providing a rich source of data for agricultural intelligent management and decision-making. [0003] However, due to the influence of factors such as the complex agricultural production environment and the cost of agricultural production, the data also contains a large number of abnormal data, which affects the usability of the data. [0004] Therefore, the correction of abnormal data is the first problem to be solved in the process of agricultural data processing. However, most researchers are currently studying abnormal data detection, and there is no mature technical method to correct ab...

AI Technical Summary

Problems solved by technology

[0006] In the past, sensor abnormal data focused on the detection of abnormal data, and rarely considered the correction of sensor abnormal data.

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