模型训练方法、信息确定方法和设备
By acquiring communication data between sample devices and target objects, and training models based on device categories and features, the problem of low accuracy in determining IoT device categories is solved, achieving more efficient device identification.
CN116186533BActive Publication Date: 2026-07-17SANGFOR TECH INC
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SANGFOR TECH INC
- Filing Date
- 2022-12-19
- Publication Date
- 2026-07-17
AI Technical Summary
Technical Problem
The communication data of IoT devices contains a large amount of interference data, resulting in low accuracy in determining device categories using machine learning methods.
Method used
The system acquires communication data between sample devices and target objects, trains an initial classification model based on device category and device characteristics, constructs a target classification model, excludes unrepresentative communication data, reduces data volume, and improves data processing speed.
Benefits of technology
It improves the accuracy of IoT device category identification, more accurately identifies device categories through target classification models, and reduces data processing time and redundant interference.
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Figure CN116186533B_ABST
Abstract
本申请实施例公开了一种模型训练方法，该方法包括：获取样本设备的样本通信数据；其中，样本通信数据为与样本设备通信的多个对象中的目标对象对应的通信数据；样本通信数据表征样本设备与目标对象之间的数据交互；基于样本通信数据确定样本设备的设备特征；其中，设备特征表征样本设备与目标对象之间的数据交互情况；基于每一样本设备的设备类别和每一样本设备的设备特征，对初始分类模型进行模型训练得到目标分类模型。本申请实施例还公开了一种模型训练设备、信息确定方法和设备。
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