Fusion processing method, system and equipment for multi-source heterogeneous data and medium
By constructing a wireless adaptive sensor network and a deep denoising autoencoder combined with a self-attention mechanism and a graph neural network, the problem of fusion of multi-source heterogeneous data and intelligent decision-making in the power Internet of Things was solved, achieving real-time adaptation and efficient data processing, and improving communication reliability and fault early warning accuracy.
Patent Information
- Application Number
- CN202511454955.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-13
- Publication Date
- 2026-01-23
AI Technical Summary
Existing technologies suffer from poor protocol compatibility, insufficient dynamic response capabilities, and limited intelligence levels in processing multi-source heterogeneous data in the context of the Internet of Things (IoT) for the power industry. They are unable to effectively integrate structured and unstructured data, and traditional methods cannot dynamically mine potential correlation value, resulting in response delays and insufficient system efficiency of the communication resource management platform.
A wireless adaptive sensor network is constructed, using a distributed message queue middleware and a deep denoising autoencoder for data buffering and denoising operations. The self-attention mechanism and graph neural network are combined to dynamically calculate data weights, and an elastic weight solidification strategy is adopted to achieve incremental learning and construct a multi-source data graph structure.
It achieves real-time adaptation of multi-protocol data fusion, improves communication reliability and fault early warning accuracy, reduces operation and maintenance costs, maintains millisecond-level response capability in terminal-intensive scenarios, and solves the problem of dynamic fusion and intelligent decision-making of multi-source heterogeneous data.
Smart Images

Figure CN121397489A_ABST