Acoustic imaging monitoring: Deploy a cloud-top online acoustic imager to collect abnormal sound generated by pipeline leakage and accurately locate the leakage source.

Refrigerated infrared monitoring: deploy a cloud-top refrigerated infrared thermal imager to capture the "smoke" formed after carbon monoxide leakage. The characteristic absorption wavelength of carbon monoxide is about 4.6μm, while the traditional refrigeration infrared mainly captures the medium-long wave of 8-14μm, so it is necessary to change the lens of medium-short wave to capture the carbon monoxide leakage.

Use a combination of wired and wireless. The data collected by the front-end equipment is transmitted to the nearby data aggregation nodes through industrial Ethernet cables. For areas with difficult wiring, wireless communication technologies such as LoRa and 5G are used for data transmission to ensure that the data is uploaded to the back-end processing platform stably and in real time.

The software platform integrating AI algorithm comprehensively analyzes and processes the multi-source data from the front end. Through the deep learning model, the characteristics of visible light image, acoustic signal and infrared temperature are integrated to improve the accuracy and reliability of leakage judgment. At the same time, the electronic fence and personnel positioning functions are integrated. The electronic fence is set based on the geographical information of the pipeline area. When the leakage is detected and the leakage position is in the electronic fence, an alarm will be given immediately. The personnel positioning system uses Bluetooth, UWB and other technologies to track the position of personnel entering the dangerous area in real time. In case of leakage, it can quickly inform relevant personnel to evacuate and provide personnel distribution information for rescue.

The local alarm is installed in a conspicuous position near the pipeline. When the back-end platform judges that there is a leak, it will immediately trigger the local alarm and remind the field personnel by sound and light alarm. At the same time, the alarm information will be pushed to the mobile APP of relevant managers, so as to take timely countermeasures.

1. AI model of image recognition: train visible light image recognition model to recognize the characteristics of powder fog, dust accumulation and flying caused by coal powder leakage, as well as abnormal conditions such as damage and discoloration of pipeline surface. By labeling a large number of sample data, the model can learn the difference between normal and abnormal States and improve the recognition accuracy.

2. Personnel positioning and AI management of electronic fence: the intelligent management of electronic fence is realized by combining geographic information system (GIS) and AI algorithm. When a person enters a dangerous area (electronic fence), the system automatically identifies and records the position information of the person; In case of leakage, the best evacuation path is planned by AI algorithm, and evacuation instructions are pushed to relevant personnel.

The visible light, acoustic and infrared monitoring data are fused in time and space dimensions. By establishing a unified data timestamp and spatial coordinate system, different types of data can be correlated and verified, and the accuracy and reliability of leakage monitoring are improved. For example, when the suspected pulverized coal leakage area is found by visible light monitoring, the leakage source and nature are further confirmed by acoustic imaging and infrared monitoring data.

1. According to the characteristics of different monitoring data, optimize the structure and parameters of AI algorithm. For example, in image recognition, the appropriate convolutional neural network architecture (such as ResNet, YOLO, etc.) is selected and adjusted according to the characteristics of pipeline monitoring scenes to improve the recognition ability of micro-leakage characteristics.

2. Continuously update and optimize the AI model. Collect new leakage case data regularly, and retrain and optimize the model to adapt to changing working conditions and emerging leakage modes.

Due to the complex industrial site environment, there are interference sources such as electromagnetic interference and mechanical vibration. Front-end monitoring equipment should have good anti-interference performance, such as shielding cables, filtering circuits and other measures to reduce electromagnetic interference; The influence of mechanical vibration on the equipment is reduced by damping devices and stable supports, so as to ensure the accuracy and stability of monitoring data.

Third, the application effect display