Urban pipe network monitoring method and system based on artificial intelligence and geographic information
Through methods based on artificial intelligence and geographic information, cluster sensors, calculate data similarity and impact range, and predict pipeline data without sensors, solving the problem of limited monitoring coverage, achieving comprehensive monitoring and outlier judgment, and improving data accuracy and troubleshooting efficiency.
CN119646556AInactive Publication Date: 2025-03-18GUANGDONG PULAN GEOGRAPHIC INFORMATION SERVICE CO LTD
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Patent Information
- Application Number
- CN202510170537.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-02-17
- Publication Date
- 2025-03-18
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Technical Problem
In the existing urban pipeline monitoring, the monitoring coverage is limited, and it is impossible to obtain information on all locations of pipelines in a timely manner, and the pipeline cannot be monitored in all aspects.
Method used
Using a method based on artificial intelligence and geographic information, we use clustering sensors, calculating the similarity and impact range of sensor data, predict pipeline data without sensors, and set up a pipeline monitoring model for outlier judgment.
Benefits of technology
It realizes that when the monitoring coverage is limited, all-round monitoring is carried out by predicting pipeline data without sensors, improving the accuracy and reliability of pipeline data and enhancing troubleshooting efficiency.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention relates to the field of urban pipe network monitoring, in particular to an urban pipe network monitoring method and system based on artificial intelligence and geographic information, and the method comprises the steps: carrying out the clustering of sensors according to the position information of the sensors, obtaining a clustering cluster, calculating the similarity of pipeline data collected by each sensor in the clustering cluster, and calculating the similarity of pipeline data collected by each sensor; calculating the influence range of the pipeline data of each sensor according to the similarity; predicting pipeline data of pipeline positions without sensors according to the collected pipeline data and the influence range; a pipeline monitoring model is set, the input of the model is pipeline data, and the output of the model is whether the pipeline data is abnormal. Under the condition that the monitoring coverage is limited, the effect of monitoring the pipelines in the area in an all-around mode is achieved.
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