Interface data monitoring method and device, computer device, and storage medium

By performing feature extraction and normalization on interface data, an alarm level prediction model was trained, which solved the problem of low alarm accuracy in interface data monitoring and enabled dynamic anomaly identification and accurate alarms for interface data.

CN115454774BActive Publication Date: 2026-06-02PING AN TECH (SHENZHEN) CO LTD
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Patent Information

Application Number
CN202211155921.6
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2022-09-22
Publication Date
2026-06-02
Estimated Expiration
2042-09-22

AI Technical Summary

Technical Problem

Existing technologies fail to dynamically analyze and identify abnormal requests in interface data monitoring, resulting in low accuracy of interface data alarms and an inability to distinguish between auxiliary function interfaces that have a minor impact on the system and critical interfaces that require urgent handling.

Method used

By determining the target historical data characteristics of the preset interface, normalizing them, training an initial alarm level prediction model, constructing an alarm level prediction model using the nearest neighbor algorithm, and calculating the alarm level of the interface data to be monitored based on Euclidean distance and weight coefficients.

Benefits of technology

It improves the accuracy of interface data monitoring, can dynamically identify abnormal requests, reduce false alarms, and improve system stability and availability.

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Abstract

The application relates to an artificial intelligence technology and provides an interface data monitoring method and device, computer equipment and a storage medium, which comprises the following steps: determining target historical data features corresponding to a preset interface; determining a target historical interface data set corresponding to the target historical data features; determining a historical alarm level corresponding to each target historical interface data in the target historical interface data set to obtain a historical alarm level set; combining data in the target historical interface data set and the historical alarm level set into training data, training an initial alarm level prediction model to obtain an alarm level prediction model; obtaining initial to-be-monitored interface data, preprocessing the initial to-be-monitored interface data to obtain target to-be-monitored interface data; and calling the alarm level prediction model to process the target to-be-monitored interface data to obtain a target alarm level corresponding to the target to-be-monitored interface data. The application can improve the accuracy of interface data alarm and promote the rapid development of smart cities.
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