Network quality detection method, apparatus, device, storage medium, and program product
By employing a network quality detection method that integrates multi-dimensional data and multiple models, and utilizing long short-term memory networks and gradient boosting decision tree models to process different types of device indicators, this approach solves the problem of inaccurate network quality detection in existing technologies, enabling real-time and accurate detection and self-repair of intelligent devices.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA MOBILEHANGZHOUINFORMATION TECH CO LTD
- Filing Date
- 2026-03-06
- Publication Date
- 2026-07-17
AI Technical Summary
Existing network quality detection methods are unable to accurately reflect changes in the network quality of smart devices, resulting in poor detection results, especially in complex network scenarios where accuracy is low.
By employing a multi-dimensional data and multi-model fusion approach, the aggregated and non-aggregated indicators of the device under test are obtained and processed using a long short-term memory network model and a gradient boosting decision tree model, respectively. Dynamic weight allocation and online learning are then performed to generate the final quality defect identification result.
It improves the accuracy and robustness of network quality detection, can reflect the real network quality changes of smart devices in real time, reduces the need for manual intervention, and realizes the self-healing and automated repair of smart devices.
Smart Images
Figure CN122420152A_ABST