Industrial carbon emission data anomaly detection method

The invention relates to an abnormal detection method for industrial carbon emission data, and the method comprises the following steps: carrying out the preprocessing of the industrial carbon emission data, and obtaining a time sequence T of carbon emission; performing feature extraction on the time sequence T by adopting a matrix contour technology to obtain a feature sequence of the time sequence T; establishing a prediction model according to the feature sequence, and inputting a test set into the prediction model to obtain a prediction feature sequence of the test set; and comparing the feature sequence with a prediction feature sequence, calculating a prediction error value, and taking data of which the error value is higher than a threshold value as an abnormal data point. According to the invention, the method achieves the abnormal detection of the industrial carbon emission data through setting the threshold value, and is higher in detection precision. 本发明涉及一种工业碳排放数据的异常检测方法，包括以下内容对工业碳排放数据进行预处理，获得碳排放的