Data processing method and device, electronic equipment and storage medium

Abstract

The invention discloses a data processing method and device, electronic equipment and a storage medium. The method further comprises the steps of obtaining first clustering information of first sampledata based on a classification model, and determining a selection threshold value according to the first clustering information; acquiring second clustering information of first monitoring data of atarget based on the classification model, and selecting second sample data from the first monitoring data according to the second clustering information and the selection threshold; performing activelearning of the classification model based on the second sample data, and optimizing model parameters of the classification model; classifying the second monitoring data of the target by using the optimized classification model to obtain a classification label of the second monitoring data; and mapping the classification label of the second monitoring data into semantic information, updating the ontology by utilizing the semantic information, and obtaining an event decision for executing a predetermined operation on the target based on the updated ontology.

Description

technical field [0001] The present invention relates to the field of information technology, in particular to a data processing method and device, electronic equipment and a storage medium. Background technique [0002] The learning model can obtain model parameters through the learning of sample data; thus use the trained model to make various decisions; for example, it can be applied to agricultural systems to provide planting-related decisions. [0003] In the process of training the model, the amount of sample data and the comprehensiveness of the values ​​directly determine the decision-making quality of the model. Obtaining data for all cases is nearly impossible. For example, taking the learning model applied to the agricultural system as an example, various conditions such as weather and crops will change dynamically, and it is difficult to collect complete sample data, so the accuracy of the learning model's decision-making cannot meet the expected results. Conte...

AI Technical Summary

Problems solved by technology

For example, taking the learning model applied to the agricultural system as an example, various conditions such as weather and crops will change dynamically, and it is difficult to collect all sample data, so the accuracy of the learning model to obtain decision-making cannot achieve the expected effect

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