Method and device for improving performance of data processing model, storage medium and electronic device

A device for improving performance of a data processing model, comprising:

• a reading unit configured to determine a test data stream for the data processing model to read all pieces of data in the test data stream one by one, and determine a piece of data read currently as target data;
• a first acquiring unit configured to acquire outlier detection parameters pre-generated in a detection module;
• a detecting unit configured to perform detection of concept drift on the data processing model based on the target data and the outlier detection parameters;
• a triggering unit configured to trigger, in response to detecting that concept drift occurs in the data processing model, the detection module to update each of the outlier detection parameters, and retrain the data processing model;
• a resuming unit configured to, after the data processing model is already trained, determine a piece of data to be read next as the target data, acquire the updated outlier detection parameters in the detection module, and resume the detection of concept drift on the data processing model based on the target data and the updated outlier detection parameters, until all the pieces of data in the test data stream are read;
• and
• a processing unit configured to process data by using the trained data processing model after the detection of concept drift, and output a data processing result;
• wherein the a detecting unit is further configured to perform detection of concept drift on the data processing model based on the target data and the outlier detection parameters by:
• storing the target data into a received dataset, and acquiring the number of pieces of data in the received dataset;
• determining the number of outliers in the received dataset based on the upper boundary and the lower boundary among the outlier detection parameters;
• and
• performing the detection of concept drift on the data processing model based on the minimum outlier ratio among the outlier detection parameters, the number of pieces of data in the received dataset and the number of outliers in the received dataset.