Electromechanical installation engineering automatic debugging method and system based on intelligent control
By introducing adaptive control algorithms and machine learning technology into the intelligent control system, the optimization strategy is dynamically adjusted, and the problem that the system is difficult to adapt to when equipment aging or environmental changes is solved, achieving more efficient debugging and more stable equipment operation.
CN119717635AInactive Publication Date: 2025-03-28GUANGDONG PENGCHENHUI CONSTRUCTION CO LTD
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Patent Information
- Application Number
- CN202411891459.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-20
- Publication Date
- 2025-03-28
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Technical Problem
The existing intelligent control system is difficult to adapt and respond quickly when the equipment is aging or the environment changes, resulting in a decrease in debugging efficiency or failure.
Method used
Adaptive control algorithms and machine learning technology are introduced to dynamically adjust optimization strategies based on the operating status of the equipment, environmental changes and historical data to realize the self-optimization and adjustment of the system.
Benefits of technology
Improves the system's response speed and adaptability, avoids static adjustment problems in traditional control methods, ensures that the equipment is always running in the optimal state, and reduces the possibility of failure and downtime.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention discloses an electromechanical installation engineering automatic debugging method and system based on intelligent control, and the method comprises the steps: enabling an intelligent control system to monitor the energy consumption of equipment in real time, optimizing an operation mode, adjusting the working state according to data, and enabling the equipment to work cooperatively through the intelligent control system; deploying temperature and humidity, pressure, current and voltage sensors for stable wiring, and configuring parameters by adopting an intelligent debugging tool; based on the preliminary data, optimizing the system by applying an adaptive control algorithm, adjusting a strategy, fusing multi-dimensional data, and identifying performance bottleneck and abnormality; the centralized monitoring platform monitors the equipment state in real time and sends out an early warning signal; after multiple rounds of debugging, the energy-saving effect and performance are evaluated, a control algorithm is optimized, and optimization is continuously carried out according to historical data and environmental changes; according to the electromechanical installation engineering automatic debugging method and system based on intelligent control, the adaptive control algorithm and the machine learning technology are introduced, and the strategy is dynamically optimized according to the equipment operation state, the environment change and the historical data.
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