A boiler combustion state intelligent early warning method and system for a digital and intelligent power plant
By constructing a static structural model and a full-dimensional dynamic data spatiotemporal mapping model, the problems of lack of static structural parameters and insufficient fusion of multi-source data in existing boiler combustion status early warning technologies are solved, realizing full-dimensional perception and forward-looking early warning of combustion status, and improving the accuracy and adaptability of early warning.
Patent Information
- Application Number
- CN202610362861.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-03-24
- Publication Date
- 2026-07-17
AI Technical Summary
Existing boiler combustion status early warning technologies lack consideration of static structural parameters, have insufficient multi-source data fusion, single feature extraction dimensions, crude risk assessment methods, and static early warning modes, making it difficult to meet the precise, comprehensive, dynamic, and forward-looking requirements of intelligent power plants for combustion status.
By constructing a static structural model and a full-dimensional dynamic data spatiotemporal mapping model, and through spatiotemporal alignment and feature extraction of multi-source data, combined with historical fault records and load prediction information, the risk of abnormal combustion status is dynamically analyzed, thereby achieving full-dimensional perception and forward-looking early warning of combustion status.
It achieves spatiotemporal full-dimensional perception of combustion status, improves the consistency between feature representation and actual working conditions, reduces false alarm rate and false alarm rate, adapts to dynamic early warning under load fluctuation scenarios, and improves the accuracy and foresight of early warning.
Smart Images
Figure CN122416679A_ABST