Electric power pipe gallery operation state monitoring method, system and device and computer equipment

Abstract

The invention relates to an electric power pipe gallery operation state monitoring method, system and device and computer equipment. The method comprises the steps of obtaining multi-source sensor data of an electric power pipe gallery, integrating the multi-source sensor data, determining model parameters of a pre-constructed electric power pipe gallery digital twinning model based on the integrated multi-source sensor data and a preset virtual-real mapping relation, mapping the model parameters to the pre-constructed electric power pipe gallery digital twinning model, and obtaining the electric power pipe gallery digital twinning model. The method comprises the steps of integrating multi-source sensor data to update a digital twin model of the power pipe gallery, performing state monitoring on the power pipe gallery based on the integrated multi-source sensor data, determining a state monitoring result of the power pipe gallery, and visually displaying the state monitoring result of the power pipe gallery based on the updated digital twin model of the power pipe gallery. By adopting the method, the running state monitoring efficiency can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of equipment monitoring, and in particular to a method, device, computer equipment, storage medium, and computer program product for monitoring the operating status of a power pipeline corridor. Background Art

[0002] With the continuous development of the electronic and power fields, the number, volume and complexity of power cables have gradually increased. In order to reduce the overhead laying of cables on the ground or the dispersed underground laying of cables and reduce the occupation of urban space, power pipeline corridors came into being.

[0003] In order to ensure the safety of the integrated power pipeline corridor during operation and improve the risk response capability of the pipeline corridor, a large number of sensors are usually deployed in the existing power pipeline corridor, which can be used to monitor the partial discharge, sheath circulation, temperature and other status data of the power cables.

[0004] However, in traditional solutions, even if an abnormal state of the power corridor is detected, operation and maintenance personnel still need to enter the power corridor for manual inspection and maintenance. This leads to limitations in data analysis and abnormality diagnosis of the power corridor, and the problem of low efficiency in operating status monitoring. Summary of the Invention

[0005] Based on this, it is necessary to provide a power pipeline corridor operation status monitoring method, device, computer equipment, computer readable storage medium and computer program product that can improve the operation monitoring efficiency of the power pipeline corridor in response to the above technical problems.

[0006] In a first aspect, the present application provides a method for monitoring the operating status of a power pipeline corridor. The method comprises:

[0007] Acquire multi-source sensor data from power pipeline corridors;

[0008] Integrate the multi-source sensor data, and determine model parameters of a pre-built power pipeline corridor digital twin model based on the integrated multi-source sensor data and a preset virtual-real mapping relationship;

[0009] Mapping the model parameters to the pre-built power pipeline gallery digital twin model to update the power pipeline gallery digital twin model;

[0010] Based on the integrated multi-source sensor data, the power pipeline corridor is subjected to status monitoring, and a status monitoring result of the power pipeline corridor is determined;

[0011] Based on the updated digital twin model of the power pipeline corridor, the status monitoring results of the power pipeline corridor are visually displayed.

[0012] In a second aspect, the present application also provides a power pipeline corridor operation status monitoring system. The system includes an edge computing module, a sensor module and a cloud platform that are communicatively connected to the edge computing module;

[0013] The sensor module is used to collect initial multi-source sensor data of the power pipeline corridor and send it to the edge computing module;

[0014] The edge computing module is configured to receive and preprocess the initial multi-source sensor data to obtain multi-source sensor data, and transmit the data to the cloud platform, wherein the multi-source sensor data includes ambient gas concentration and device current data, and push an alarm message when the ambient gas concentration is greater than a preset concentration threshold or the rate of change of the ambient gas concentration is greater than a preset concentration change rate threshold; and push an alarm message when the device current data is greater than a preset current threshold or the rate of change of the device current data is greater than a preset current change rate threshold;

[0015] The cloud platform is used to receive and integrate the multi-source sensor data, determine the model parameters of the pre-built power pipeline corridor digital twin model based on the integrated multi-source sensor data and the preset virtual-real mapping relationship, map the model parameters to the pre-built power pipeline corridor digital twin model to update the power pipeline corridor digital twin model, perform status monitoring on the power pipeline corridor based on the integrated multi-source sensor data, determine the status monitoring results of the power pipeline corridor, and visually display the status monitoring results of the power pipeline corridor based on the updated power pipeline corridor digital twin model.

[0016] In a third aspect, the present application also provides a device for monitoring the operation status of a power pipeline corridor. The device comprises:

[0017] Data acquisition module, used to obtain multi-source sensor data of the power pipeline corridor;

[0018] A model parameter determination module is used to integrate the multi-source sensor data and determine the model parameters of the pre-built power pipeline corridor digital twin model based on the integrated multi-source sensor data and a preset virtual-real mapping relationship;

[0019] A model updating module, configured to map the model parameters to the pre-built power pipeline gallery digital twin model to update the power pipeline gallery digital twin model;

[0020] A state monitoring module is used to perform state monitoring on the power pipeline corridor based on the integrated multi-source sensor data and determine a state monitoring result of the power pipeline corridor;

[0021] A visualization module is used to visualize the status monitoring results of the power pipeline corridor based on the updated digital twin model of the power pipeline corridor.

[0022] In a fourth aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned power corridor operation status monitoring method embodiment when executing the computer program.

[0023] In a fifth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the above-mentioned method for monitoring the operating status of a power pipeline corridor.

[0024] In a sixth aspect, the present application further provides a computer program product, comprising a computer program that, when executed by a processor, implements the steps of the above-mentioned method for monitoring the operating status of a power pipeline corridor.

[0025] The above-mentioned power pipeline corridor operation status monitoring method, system, device, computer equipment, storage medium and computer program product are different from traditional solutions. This solution can monitor the pipeline corridor status in real time and comprehensively by acquiring multi-source sensor data of the power pipeline corridor. It also integrates multi-source sensor data by pre-establishing a digital twin model of the power pipeline corridor. Based on the integrated multi-source sensor data and the preset virtual-real mapping relationship, the model parameters of the pre-built digital twin model of the power pipeline corridor are determined, and the model parameters are mapped to the digital twin model of the power pipeline corridor to update the digital twin model of the power pipeline corridor, so that operation and maintenance personnel can monitor the power pipeline corridor status in a virtual environment. The status of the power pipeline corridor is analyzed and simulated, thereby reducing the need for operation and maintenance personnel to enter the power pipeline corridor for on-site inspection. Furthermore, this solution also monitors the status of the power pipeline corridor based on the integrated multi-source sensor data and determines the status monitoring results of the power pipeline corridor. This multimodal status monitoring data can help operation and maintenance personnel determine the status of the power pipeline corridor more accurately, improving the accuracy and efficiency of status monitoring. Finally, based on the digital twin model of the power pipeline corridor, the status monitoring results of the power pipeline corridor are visualized, allowing operation and maintenance personnel to understand the operating status of the power pipeline corridor more intuitively, further improving the status monitoring efficiency of the power pipeline corridor. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 2. This is a diagram showing an application environment of a method for monitoring the operating status of a power pipeline corridor in one embodiment;

[0027] Figure 2 1 is a flow chart of a method for monitoring the operating status of a power pipeline corridor in one embodiment;

[0028] Figure 3 Schematic diagram of a flow chart of steps for monitoring gas concentration and equipment current data in a power pipeline corridor in one embodiment;

[0029] Figure 4 A flowchart illustrating steps for integrating multi-source sensor data in one embodiment;

[0030] Figure 5 Schematic diagram of a flow chart of power pipeline corridor status monitoring steps in one embodiment;

[0031] Figure 6 A schematic diagram of a process for visualizing a digital twin model of a power pipeline corridor in one embodiment;

[0032] Figure 7 A flowchart of a method for monitoring the operating status of a power pipeline corridor in a detailed embodiment;

[0033] Figure 8 This is a structural block diagram of a power pipeline corridor operation status monitoring system in one embodiment;

[0034] Figure 9 This is a structural block diagram of a power pipeline corridor operation status monitoring device in one embodiment;

[0035] Figure 10 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0036] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0037] The power pipeline corridor operation status monitoring method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown, the terminal 102 communicates with the server 104 via a network. The data storage system can store data that the server 104 needs to process. The data storage system can be integrated on the server 104 or placed on the cloud or other network servers.

[0038] Specifically, the operation and maintenance personnel may upload the multi-source sensor data of the power corridor collected by the sensors to the server 104 through the terminal 102. The server 104 integrates the multi-source sensor data and determines the model parameters of the pre-built power corridor digital twin model based on the integrated multi-source sensor data and the preset virtual-real mapping relationship. The server 104 then maps the model parameters to the power corridor digital twin model to update the power corridor digital twin model, and performs status monitoring on the power corridor based on the integrated multi-source sensor data to determine the status monitoring results of the power corridor. Finally, the server 104 visually displays the status monitoring results of the power corridor based on the updated power corridor digital twin model.

[0039] Terminal 102 may include, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices may include smart speakers, smart TVs, smart air conditioners, and smart car devices. Portable wearable devices may include smart watches, smart bracelets, and head-mounted devices. Server 104 may be implemented as a standalone server or a server cluster consisting of multiple servers.

[0040] In one embodiment, Figure 2 As shown, a method for monitoring the operation status of a power pipeline corridor is provided. Figure 1 Taking the server 104 in the example as an example, the following steps are included:

[0041] S100, acquiring multi-source sensor data of the power pipeline corridor.

[0042] A power pipeline corridor, also known as a power cable tunnel or cable trench, is an underground facility used for the centralized laying of power cables, providing a relatively stable and safe environment. Multi-source sensor data refers to data from multiple different types of sensors. For example, in a power pipeline corridor, sensors include but are not limited to temperature, humidity, pressure, and visual sensors. These sensors monitor and collect data from different angles regarding the physical state of the pipeline corridor, generating multi-source sensor data.

[0043] For example, temperature sensors can be deployed in the power corridor to collect temperature data such as ambient temperature data and equipment temperature data. Humidity sensors, gas concentration sensors, vibration sensors, cameras, infrared sensors, etc. can also be deployed to collect the ambient humidity data, ambient gas concentration, equipment vibration data, visual image data, infrared image data, etc. of the power corridor. In addition, current sensors can also be deployed to collect equipment current data of the power corridor.

[0044] Furthermore, the initial multi-source sensor data collected by the multi-source sensors can be automatically uploaded by the corresponding sensors to the corresponding edge computing modules for preliminary processing. The edge computing modules can be composed of embedded computing devices with sufficient computing power to perform data preprocessing tasks such as data cleaning, data format conversion, and data compression. For example, during the data cleaning phase, the edge computing modules can filter out noise and invalid data to provide accurate analysis data for subsequent processing. The multi-source sensor data processed by the edge computing modules can be uploaded by the edge computing modules to server 104 via an appropriate communication network. Server 104 can be a cloud platform or cloud control center.

[0045] S300: Integrate multi-source sensor data and determine model parameters of a pre-built power pipeline corridor digital twin model based on the integrated multi-source sensor data and a preset virtual-real mapping relationship.

[0046] The preset virtual-real mapping relationship is used to represent the mapping relationship between the real world (the power corridor) and the virtual world (the digital twin model of the power corridor). It defines the association between real-world physical quantities (such as multi-source sensor data) and the model parameters of the virtual model. The digital twin model of the power corridor uses digital technology to highly simulate and map the real-world power corridor. The model parameters that need to be determined mainly include operational parameters and environmental parameters. Some model parameters, such as collective parameters (such as the corridor's length, width, and height) and physical parameters (such as the resistance and capacitance of cables and the elastic modulus of the corridor's structural materials), can be determined in advance based on the actual structure of the power corridor. However, operational parameters and environmental parameters need to be determined based on the acquired multi-source sensor data.

[0047] Following the above steps, after the server 104 receives the multi-source sensor data, it is necessary to first integrate the multi-source sensor data. The data integration methods include but are not limited to data Kalman filtering, data classification, format unification, time synchronization, etc. Furthermore, for some model parameters directly related to the multi-source sensor data, they can be directly converted according to the virtual-real mapping relationship. For example, the ambient temperature data in the multi-source sensor data can be directly mapped to the ambient temperature parameters of the power corridor digital twin model. For some model parameters that are not directly related, the model parameters can be mapped through simple calculation processing. For example, by measuring the equipment temperature data, equipment current data and ambient temperature data of the power corridor, the thermal resistance parameters of the equipment can be inverted and calculated using the heat conduction equation.

[0048] S500: Map the model parameters to the pre-built digital twin model of the power pipeline corridor to update the digital twin model of the power pipeline corridor.

[0049] Continuing from the above embodiment, after determining the model parameters, the model parameters need to be mapped to the pre-built digital twin model of the power pipeline corridor to obtain an updated digital twin model of the power pipeline corridor. It should be noted that since the operating status of the power pipeline corridor is changing in real time, the above update process can be real-time. The updated digital twin model of the power pipeline corridor can reflect the actual operating status changes of the power pipeline corridor in real time, and provide reliable data support for subsequent power pipeline corridor operating status monitoring.

[0050] It's important to note that the power corridor digital twin model can be updated in real time. After integrating multi-source sensor data, the server automatically synchronizes the power corridor digital twin model. Each type of multi-source sensor data corresponds one-to-one with a model parameter in the power corridor digital twin model, and the model is updated in real time using a pre-set virtual-to-real mapping relationship. The power corridor digital twin model update process utilizes multi-threaded processing technology and a distributed database to improve data synchronization efficiency.

[0051] S700, based on the integrated multi-source sensor data, performs status monitoring on the power pipeline corridor and determines the status monitoring results of the power pipeline corridor.

[0052] Among them, status monitoring of power pipeline corridors refers to real-time or regular monitoring of various operating parameters and environmental parameters of power pipeline corridors to obtain working status information of the power pipeline corridors and their internal equipment.

[0053] For example, after obtaining the integrated multi-source sensor data, server 104 can analyze the integrated multi-source sensor data using specific data analysis algorithms, machine learning models, artificial intelligence algorithms, etc. to implement status monitoring of the power pipeline corridor. For example, statistical analysis methods can be used to calculate statistical features such as the mean, standard deviation, maximum, and minimum values of the integrated multi-source sensor data. Machine learning algorithms, such as image processing models and data prediction models, can also be used to perform feature extraction and pattern recognition on the integrated multi-source sensor data to determine whether there are any abnormal conditions in the power pipeline corridor.

[0054] Furthermore, based on the data analysis results of the integrated multi-source sensor data, the status of the power corridor can be evaluated in combination with the operating standards and specifications of the power corridor to obtain the status monitoring results of the power corridor. For example, if the equipment temperature data exceeds the normal range, it can be judged that the power corridor may have a poor heat dissipation problem. If the visual image data in the integrated multi-source sensor data identifies the presence of foreign objects, cracks, etc. in the power corridor, it can be judged that the power corridor may have a mechanical failure problem.

[0055] S900, based on the updated digital twin model of the power pipeline corridor, visualizes the status monitoring results of the power pipeline corridor.

[0056] Visual display refers to presenting the status monitoring results of the power pipeline corridor in intuitive graphics, images, charts, animations, etc., to facilitate user understanding and analysis. In this embodiment, the visual display of the status monitoring results of the power pipeline corridor allows operation and maintenance personnel and managers to quickly understand the operating status of the power pipeline corridor.

[0057] For example, the visualization method package can utilize the three-dimensional geometric information of the updated digital twin model to display the overall structure and internal equipment layout of the power corridor in the form of a three-dimensional model, and allow users to observe the power corridor from different angles through interactive operations, and view detailed information of the equipment in the power corridor, so as to quickly obtain the status monitoring results of the power corridor.

[0058] The above-mentioned power pipeline corridor operation status monitoring method is different from traditional solutions. This solution can monitor the pipeline corridor status in real time and comprehensively by acquiring multi-source sensor data from the power pipeline corridor. It also integrates the multi-source sensor data by pre-establishing a digital twin model of the power pipeline corridor. Based on the integrated multi-source sensor data and the preset virtual-real mapping relationship, the model parameters of the pre-built power pipeline corridor digital twin model are determined, and the model parameters are mapped to the power pipeline corridor digital twin model to update the power pipeline corridor digital twin model. This allows operation and maintenance personnel to analyze and simulate the power pipeline corridor status in a virtual environment, thereby reducing the need for operation and maintenance personnel to enter the power pipeline corridor for on-site inspection. Furthermore, this solution also monitors the power pipeline corridor status based on the integrated multi-source sensor data and determines the power pipeline corridor status monitoring results. This multi-modal status monitoring data can help operation and maintenance personnel more accurately determine the status of the power pipeline corridor, improving the accuracy and efficiency of status monitoring. Finally, based on the power pipeline corridor digital twin model, the power pipeline corridor status monitoring results are visualized, allowing operation and maintenance personnel to more intuitively understand the operating status of the power pipeline corridor, further improving the efficiency of power pipeline corridor status monitoring.

[0059] In one embodiment, the multi-source sensor data includes ambient gas concentration and device current data, such as Figure 3 As shown, the method further includes:

[0060] S210 : Pushing an alarm message when the ambient gas concentration is greater than a preset concentration threshold or when the rate of change of the ambient gas concentration is greater than a preset concentration change rate threshold.

[0061] S220: Push an alarm message when the device current data is greater than a preset current threshold, or when the rate of change of the device current data is greater than a preset current change rate threshold.

[0062] Among them, the ambient gas concentration refers to the content of various gases (such as oxygen, carbon monoxide, methane, etc.) in the power pipeline corridor per unit volume space, and the equipment current data refers to the current passing through the equipment (such as cables, transformers, etc.) in the power pipeline corridor during operation, which can reflect the operating status of the equipment.

[0063] For example, gas concentration sensors deployed within a power corridor can collect real-time data on ambient gas concentrations and their rate of change within the corridor. If the monitored ambient gas concentration exceeds a preset concentration threshold, this indicates that the levels of one or more gases within the corridor have exceeded safety limits. For example, this could be due to an increase in the concentration of flammable gases (such as methane) or toxic gases (such as carbon monoxide) within the corridor, posing a risk of fire, explosion, or suffocation. In this case, an alarm message needs to be immediately sent to the operator's terminal so they can take timely countermeasures. If the monitored rate of change of ambient gas concentration exceeds a preset concentration change rate threshold, this indicates a possible gas leak source within the corridor, such as a ruptured pipeline or a malfunction in the corridor's ventilation system. Even if the current gas concentration has not yet reached the dangerous threshold, continued leakage could quickly cause the gas concentration to exceed the standard. Therefore, an alarm message needs to be immediately sent to the operator's terminal so they can take timely countermeasures.

[0064] When the device current data is detected to be greater than the preset current threshold, it means that the device is bearing a current exceeding its rated load and is in an overloaded operating state. There is a risk of damage, and even short circuit, burning and other faults. For example, long-term overload operation of the cable will damage the insulation layer and cause leakage accidents. In this case, it is also necessary to push an alarm message to remind the operation and maintenance personnel to check and handle it. When the rate of change of the device current data is detected to be greater than the preset current change rate threshold, it means that the device current data has undergone a sudden change. This may be an early signal of a fault inside the device, or it may indicate an unstable operating state of the power system. For example, problems such as poor contact inside the device and winding short circuit will cause sudden changes in the device current data. At this time, it is also necessary to push an alarm message to the operation and maintenance personnel so that timely countermeasures can be taken.

[0065] In this embodiment, through real-time monitoring and threshold judgment of ambient gas concentrations and equipment current data and their change rates, potential safety hazards and equipment failures in the power corridor can be discovered in a timely manner, and alarm information can be pushed when abnormal ambient gas concentrations or equipment current data appear, so that relevant personnel can respond quickly and take corresponding measures to improve the stability and reliability of the power corridor.

[0066] In one embodiment, the multi-source sensor data includes visual image data, infrared image data, ambient temperature data, ambient humidity data, ambient gas concentration, device temperature data, device current data, and device vibration data. Figure 4 As shown, integrating multi-source sensor data includes:

[0067] S310 , performing Kalman filtering on the device temperature data and the ambient temperature data to obtain processed device temperature data and processed ambient temperature data.

[0068] S320, performing an image superposition operation on the visual image data and the infrared image data to obtain image data of the power pipeline corridor.

[0069] S330 , performing feature extraction and correlation analysis on the processed device temperature data, device vibration data, and device current data to obtain device operation data.

[0070] The integrated multi-source sensor data includes environmental data, power corridor image data, and equipment operation data. Environmental data includes processed ambient temperature, humidity, and gas concentration data. Visual image data can be captured by cameras installed in the power corridor, and infrared image data can be captured by infrared sensors such as thermal imagers installed in the corridor, which can reflect the temperature distribution of objects.

[0071] For example, after obtaining multi-source sensor data, Kalman filtering can be performed on the device temperature data and ambient temperature data. During the collection process of the device temperature data and ambient temperature data, the sensor is easily interfered by noise, resulting in measurement errors. Since the Kalman filtering algorithm can effectively suppress noise interference, the device temperature data and ambient temperature data are Kalman filtered. The processed device temperature data and ambient temperature data can be closer to the actual temperature value, and the two can have better data consistency.

[0072] For the processing of visual image data and infrared image data, computer vision algorithms and infrared image registration technology can be applied to enable the visual image data and infrared image data to be spatially aligned, and the corresponding points in the two image data can be accurately matched in spatial position. After completing the image registration, the visual image data and infrared image data are superimposed, for example, by using fusion methods such as pixel addition and weighted fusion, so that the superimposed image data can simultaneously contain the appearance information and temperature information of the object.

[0073] Furthermore, the processed equipment temperature data, equipment vibration data, and equipment current data can be subjected to multimodal fusion to more comprehensively reflect the operating status of the equipment in the power corridor. Specifically, the multimodal fusion process can include feature extraction and association rule analysis. For example, key features that can effectively characterize the equipment status are extracted from the processed equipment temperature data, equipment vibration data, and equipment current data, and then association analysis is performed on the features corresponding to the data of different modes. For example, through a specific association rule mining algorithm, potential association rules between the processed equipment temperature data, equipment vibration data, and equipment current data are found. The association rule can be that when a certain frequency component of the equipment vibration data increases, the equipment current data also increases accordingly. The final equipment operation data includes not only the processed equipment temperature data, equipment vibration data, and equipment current data, but also the association rules between these multimodal data. When the power corridor is subsequently monitored based on the equipment operation data, these association rules can be used to effectively determine whether the equipment working mode of the power corridor is abnormal, thereby improving the efficiency of status monitoring.

[0074] In this embodiment, the temperature data is processed by Kalman filtering, which can effectively remove noise interference and improve the accuracy of the equipment temperature data and the ambient temperature data. The image overlay operation fuses the visual image data and the infrared image data, so that the fused image data contains both the appearance information and the temperature information of the equipment. Feature extraction and association analysis are performed on the operation data of multiple equipment, which can explore the potential relationship between multimodal data. The fused equipment operation data can reflect the operating status of the equipment more deeply. By processing and integrating different types of sensor data, integrated multi-source sensor data including environmental data, image data of the power corridor and equipment operation data is formed, providing a rich and accurate data source for subsequent status monitoring of the power corridor.

[0075] In one embodiment, Figure 5 As shown, S700 includes:

[0076] S710 , using the equipment operation data as input, calling the trained operation status prediction model to obtain the operation status prediction result of the power pipeline corridor.

[0077] S720, using the image data of the power pipeline corridor as input, calling the trained image processing model to obtain the image processing result.

[0078] S730: Perform anomaly detection on the image processing results, environmental data, and equipment operation data to determine anomaly detection results of the power pipeline corridor.

[0079] S740: Determine the status monitoring result of the power pipeline corridor based on the operation status prediction result and the abnormality detection result.

[0080] Among them, the trained operation status prediction model can be a model constructed and trained based on a deep learning model. It is trained based on historical equipment operation data and can learn the mapping relationship between equipment operation data and the operation status of the power pipeline corridor, so that the future operation status of the power pipeline corridor can be predicted based on the current equipment operation data. In addition, the operation status prediction model can include a long short-term memory network. This is because the long short-term memory network can effectively capture the changing trends and periodic fluctuations of the data and can process the equipment operation data more accurately and efficiently. The trained image processing model is trained based on the historical image data of the power pipeline corridor and can be used to identify specific targets in the image data. In this embodiment, the trained image processing model can identify specific targets such as cracks, leaks, foreign objects, etc. in the power pipeline corridor.

[0081] For example, equipment operation data is provided as input to a trained operation status prediction model. The trained operation status prediction model will analyze and calculate the input data based on the patterns and rules learned during training, and output the predicted operation status of the power corridor. For example, the trained operation status prediction model may predict the current, vibration, and temperature conditions of the equipment in the future based on the change trends of the current equipment data, equipment vibration data, equipment temperature data, etc. The image data of the power corridor is input into the trained image processing model. The trained image processing model performs feature processing, structural feature analysis, target recognition, and other processing on the image data. It can identify whether there are abnormal conditions such as leaks, cracks, foreign objects, etc. in the power corridor, and output the image processing results.

[0082] Furthermore, a comprehensive analysis is performed on the image processing results, environmental data and equipment operation data. Based on the image processing results, it is possible to effectively analyze whether there are foreign objects in the power corridor and whether the equipment in the power corridor has any external faults. Based on the environmental data, it is possible to determine whether the ambient temperature data, ambient humidity data, and ambient gas concentration of the power corridor are within the normal range. Based on the equipment operation data, it is possible to determine whether the equipment parameters of the power corridor are within the normal range and whether the equipment is in normal working mode. Based on the above abnormal analysis process, the abnormal detection results of the power corridor can be determined.

[0083] Finally, combining anomaly detection results with status prediction results not only allows real-time monitoring of the power corridor's operating status, but also effectively determines whether the power corridor will experience abnormal operation in the future, allowing for early warnings and reducing the risk of equipment downtime. For example, if the operating status prediction results indicate that the power corridor's equipment may experience abnormal operating conditions in the future, and the anomaly detection results also reveal some abnormal signs of the current power corridor, an early warning can be issued, promptly notifying operations and maintenance personnel to conduct repairs. If both the operating status prediction and anomaly detection results indicate that the power corridor is operating normally, it can be assumed that the power corridor does not require maintenance for the time being.

[0084] In this embodiment, the operation status prediction model can be used to provide early warning before equipment failure in the power corridor occurs, so that operation and maintenance personnel have enough time to take preventive and treatment measures. By comprehensively utilizing image data, environmental data, and equipment operation data for status monitoring, the power corridor can be evaluated from multiple angles, and the operation status of the corridor can be monitored more comprehensively, thereby improving the accuracy and reliability of status monitoring.

[0085] In one embodiment, Figure 6 As shown, S900 includes:

[0086] S910 calls the preset visualization tool to visualize the updated digital twin model of the power pipeline corridor.

[0087] S920: Based on the status monitoring results of the power pipeline corridor, determine the abnormal area, abnormality type, abnormality cause and abnormality handling suggestions on the updated power pipeline corridor digital twin model.

[0088] S930 highlights abnormal areas and visualizes the abnormal type, cause, and handling suggestions of the power pipeline corridor in a preset text format.

[0089] Among them, the methods of visualizing the updated digital twin model of the power corridor include but are not limited to three-dimensional models, charts and heat maps. Specifically, the preset visualization tool is called to use a three-dimensional model to display the spatial structure, internal equipment layout, environmental status, equipment operating status, etc. of the power corridor. Users can observe the status of the power corridor from different angles, or use bar charts, line charts, pie charts, etc. to display the changing trend of specific data in the digital twin model of the power corridor, such as displaying the changing trend of ambient temperature data and equipment current data in the power corridor over time. Heat maps can also be used to present the digital twin model of the power corridor, using different colors and color depths to display the distribution of parameters such as temperature, humidity, and equipment load in the power corridor. It should be noted that the three visualization forms mentioned above can also be used simultaneously to visualize the updated digital twin model of the power corridor from multiple dimensions.

[0090] Furthermore, based on the status monitoring results of the power corridor, the updated digital twin model of the power corridor can be analyzed to identify areas with anomalies, determine the type of anomaly (such as equipment failure, environmental anomaly, etc.), the cause of the anomaly (such as long-term equipment wear and tear, external environmental impact, etc.), and provide recommended solutions for the anomaly (such as replacing equipment components or improving ventilation conditions). For example, if the temperature of a cable section in the power corridor is detected to be too high, the area where the cable is located will be determined to be an abnormal area, the anomaly type will be equipment overheating, and the cause may be cable aging or excessive load. The recommended solution is to inspect the cable and consider replacing or adjusting the load.

[0091] In the visualized digital twin model of the power pipeline corridor, identified abnormal areas can be highlighted, for example, with a red marker or flashing icon, allowing users to quickly locate the abnormal location. Furthermore, the abnormality type, cause, and handling suggestions are clearly displayed in a preset text format, allowing users to intuitively understand the details of the abnormal situation. For example, an abnormal area is highlighted in red, with a text box next to it displaying "Abnormality Type: Equipment Overheating; Cause: Cable Aging, Excessive Load; Abnormal Handling Suggestion: Check Cable, Consider Replacing or Adjusting the Load." Furthermore, users can query log records and historical data to further analyze the abnormality type, cause, and other factors, as well as develop subsequent abnormality handling strategies.

[0092] In this embodiment, the updated digital twin model is visualized through preset three-dimensional models, charts, and heat maps, so that the operating status and related data of the power pipeline corridor can be presented in an intuitive and easy-to-understand form, and the abnormal area is highlighted so that the operation and maintenance personnel can quickly locate the problem of the power pipeline corridor, saving time in troubleshooting the abnormality. The abnormality type, abnormality cause and abnormality handling suggestions are clearly displayed in text form, which helps the operation and maintenance personnel make accurate decisions and take appropriate measures to solve the abnormal problems, thereby improving the operation and maintenance efficiency.

[0093] In one embodiment, the method also includes: responding to a user's touch operation, the touch operation carries an operation instruction. When the operation instruction is an alarm instruction, based on the severity of the abnormal area selected by the user, the alarm information is pushed, as well as the visual alarm status and the processing status of the alarm information. When the operation instruction is an event recording instruction, the event selected by the user is recorded. When the operation instruction is a parameter adjustment instruction, the status monitoring parameters of the power pipeline corridor are adjusted.

[0094] The power pipeline corridor digital twin model also provides user interaction capabilities. Users can interact with the visual display interface by touching the screen of a device (such as a mobile phone or tablet). Such touch operations include but are not limited to clicking, sliding, and long pressing. Users can use touch operations to convey specific operational instructions to the server, instructing the server to perform corresponding functions, such as alarms, event logging, and parameter adjustments.

[0095] For example, after the server recognizes the user's touch operation, it can first parse the operation instructions carried by the touch operation. If the operation instruction is an alarm instruction, the server first needs to determine the abnormal area selected by the user, then evaluate the severity of the abnormal area, and grade the alarm according to the severity. For example, when the severity is relatively low, the alarm information only needs to be pushed to ordinary operation and maintenance personnel with lower job levels. When the severity is relatively high, the alarm information is pushed to senior operation and maintenance personnel with higher job levels. At the same time, the server can also set up a notification center on the visual interface to display the alarm status and the operation and maintenance personnel's processing status of the alarm information in real time, so that the user can intuitively understand the progress of the alarm information processing. If the operation instruction is an event recording instruction, the server will record the relevant information of the event selected by the user, including the time, location, type, specific circumstances, etc. of the event. These event records can be used as the basis for subsequent analysis and tracing. If the operation instruction is a parameter adjustment instruction, the server can adjust the status monitoring parameters of the power pipeline corridor according to the user's settings, such as adjusting the acquisition frequency, temperature threshold, humidity threshold, etc. of the multi-source sensor.

[0096] In this embodiment, through the corresponding user's touch operation on the visual interface, the user can flexibly perform a series of operations such as alarm, event recording, parameter adjustment, etc. on the digital twin model of the power corridor and the status monitoring results of the power corridor according to their own needs, thereby improving the status monitoring efficiency and user interactivity of the power corridor, and also improving the work efficiency of operation and maintenance personnel.

[0097] In order to make a clearer explanation of the power pipeline corridor operation status monitoring method provided by this application, the following Figure 7 and one The detailed embodiment includes the following steps:

[0098] S701, obtain multi-source sensor data of the power pipeline corridor, push alarm information when the ambient gas concentration is greater than the preset concentration threshold, or the rate of change of the ambient gas concentration is greater than the preset concentration change rate threshold, push alarm information when the equipment current data is greater than the preset current threshold, or the rate of change of the equipment current data is greater than the preset current change rate threshold.

[0099] S702, perform Kalman filtering on the equipment temperature data and the ambient temperature data to obtain processed equipment temperature data and processed ambient temperature data, perform image overlay operation on the visual image data and the infrared image data to obtain image data of the power corridor, perform feature extraction and correlation analysis on the processed equipment temperature data, equipment vibration data and equipment current data to obtain equipment operation data.

[0100] S703, based on the integrated multi-source sensor data and the preset virtual-real mapping relationship, determine the model parameters of the pre-built power pipeline corridor digital twin model, map the model parameters to the power pipeline corridor digital twin model, and update the power pipeline corridor digital twin model.

[0101] S704, using the equipment operation data as input, calling the trained operation status prediction model to obtain the operation status prediction result of the power corridor, using the image data of the power corridor as input, calling the trained image processing model to obtain the image processing result, performing anomaly detection on the image processing result, environmental data and equipment operation data, and determining the anomaly detection result of the power corridor.

[0102] S705: Based on the operation status prediction results and the abnormality detection results, determine the status monitoring results of the power pipeline corridor, and according to the status monitoring results of the power pipeline corridor, determine the abnormal area, abnormality type, abnormality cause and abnormality handling suggestions on the updated power pipeline corridor digital twin model.

[0103] S706, calling the preset visualization tool to visualize the updated digital twin model of the power pipeline corridor, highlighting the abnormal area, and visualizing the abnormal type, abnormal cause and abnormal handling suggestions of the power pipeline corridor in the form of preset text.

[0104] In one embodiment, Figure 8 As shown, a power pipeline corridor operation status monitoring system 800 is provided, which includes an edge computing module 810, and a sensor module 820 and a cloud platform 830 that are communicatively connected to the edge computing module 810.

[0105] The sensor module 820 is used to collect initial multi-source sensor data of the power pipeline corridor and send it to the edge computing module 810.

[0106] The edge computing module 810 is used to receive and preprocess the initial multi-source sensor data to obtain multi-source sensor data and send it to the cloud platform 830. The multi-source sensor data includes ambient gas concentration and device current data. When the ambient gas concentration is greater than a preset concentration threshold or the rate of change of the ambient gas concentration is greater than a preset concentration change rate threshold, an alarm message is pushed. When the device current data is greater than a preset current threshold or the rate of change of the device current data is greater than a preset current change rate threshold, an alarm message is pushed.

[0107] Cloud platform 830 is used to receive and integrate multi-source sensor data, determine the model parameters of the pre-built power pipeline corridor digital twin model based on the integrated multi-source sensor data and the preset virtual-real mapping relationship, map the model parameters to the pre-built power pipeline corridor digital twin model to update the power pipeline corridor digital twin model, perform status monitoring of the power pipeline corridor based on the integrated multi-source sensor data, determine the status monitoring results of the power pipeline corridor, and visualize the status monitoring results of the power pipeline corridor based on the updated power pipeline corridor digital twin model.

[0108] Since the implementation solution provided by this system is similar to the implementation solution described in the above method, the specific limitations of the power corridor operation status monitoring system 800 provided in this embodiment can be found in the above limitations on the power corridor operation status monitoring method, and will not be repeated here.

[0109] It should be noted that the edge computing module 810 can be composed of an embedded computing device. In addition to performing tasks such as data cleaning, data format conversion, and data compression, the edge computing module 810 also needs to synchronize timestamps on multi-source sensor data to maintain event consistency in subsequent modeling and condition monitoring processes. If the communication network between the edge computing module 810 and the cloud platform 830 is terminated, the multi-source sensor data can be temporarily stored in the edge computing module 810 and uploaded after the network is restored, reducing the risk of data loss. When uploading multi-source sensor data from the edge computing module 810 to the cloud platform 830, it is necessary to rely on an appropriate communication network. In semi-open or wired environments, WiFi and fiber optics can provide high-speed transmission. For long-distance, low-data-volume sensors, such as gas monitoring equipment, LoRa (Low Power Wide Area Network) is used. In scenarios requiring large data transmission or low latency, 5G networks can be used for real-time video monitoring and remote control. In order to maximize the continuity of data transmission, a redundant network solution can be designed. The communication method between the edge computing module 810 and the cloud platform 830 automatically switches between 5G and LoRa networks, and multi-path transmission is used to enhance data transmission reliability. In addition, TLS / SSL encryption protocol can be used during the transmission of multi-source sensor data to improve the security of multi-source sensor data.

[0110] On the cloud platform 830 side, cloud platform 830 can receive and integrate multi-source sensor data through the MQTT (Message Queuing Telemetry Transport) protocol. The integration process can be that cloud platform 830 uses the distributed data processing framework Apache Kafka to perform streaming analysis and fusion on multi-source sensor data. Different fusion algorithms can be set for different types of multi-source sensor data. In addition, cloud platform 830 can also synchronize the integrated multi-source sensor data with the pre-built power pipeline corridor digital twin model in real time. The power pipeline corridor digital twin model can be dynamically updated in real time. Parameters such as temperature, current, and gas concentration in the multi-source sensor data will be mapped to the power pipeline corridor digital twin model in real time, allowing the real-time status of the physical power pipeline corridor to be efficiently presented in the digital space, so that operation and maintenance personnel can accurately monitor the operating status of the power pipeline corridor through the virtual platform.

[0111] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0112] Based on the same inventive concept, the embodiments of the present application also provide a power pipeline corridor operation status monitoring device for implementing the above-mentioned power pipeline corridor operation status monitoring method. The implementation solution provided by this device is similar to the implementation solution described in the above-mentioned method. Therefore, the specific limitations of one or more power pipeline corridor operation status monitoring device embodiments provided below can be referred to the limitations of the power pipeline corridor operation status monitoring method above, and will not be repeated here.

[0113] In one embodiment, Figure 9 As shown, a power pipeline corridor operation status monitoring device 900 is provided, including: a data acquisition module 910, a model parameter determination module 920, a model update module 930, a status monitoring module 940 and a visualization module 950, wherein:

[0114] The data acquisition module 910 is used to acquire multi-source sensor data of the power pipeline corridor.

[0115] The model parameter determination module 920 is used to integrate multi-source sensor data and determine the model parameters of the pre-built power pipeline corridor digital twin model based on the integrated multi-source sensor data and the preset virtual-real mapping relationship.

[0116] The model updating module 930 is used to map the model parameters to the pre-built power pipeline corridor digital twin model to update the power pipeline corridor digital twin model.

[0117] The status monitoring module 940 is used to perform status monitoring on the power pipeline corridor based on the integrated multi-source sensor data and determine the status monitoring results of the power pipeline corridor.

[0118] The visualization module 950 is used to visualize the status monitoring results of the power pipeline corridor based on the updated digital twin model of the power pipeline corridor.

[0119] In one embodiment, the multi-source sensor data includes ambient gas concentration and equipment current data. The power pipeline operation status monitoring device 900 is also used to push alarm information when the ambient gas concentration is greater than a preset concentration threshold or the rate of change of the ambient gas concentration is greater than a preset concentration change rate threshold; and push alarm information when the equipment current data is greater than a preset current threshold or the rate of change of the equipment current data is greater than a preset current change rate threshold.

[0120] In one embodiment, the multi-source sensor data includes visual image data, infrared image data, ambient temperature data, ambient humidity data, ambient gas concentration, equipment temperature data, equipment current data and equipment vibration data. The model parameter determination module 920 is also used to perform Kalman filtering on the equipment temperature data and the ambient temperature data to obtain processed equipment temperature data and processed ambient temperature data, perform image overlay operations on the visual image data and the infrared image data to obtain image data of the power corridor, perform feature extraction and correlation analysis on the processed equipment temperature data, equipment vibration data and equipment current data to obtain equipment operation data. The integrated multi-source sensor data includes environmental data, image data of the power corridor and equipment operation data, and the environmental data includes processed ambient temperature data, ambient humidity data and ambient gas concentration.

[0121] In one embodiment, the status monitoring module 940 is also used to take the equipment operation data as input, call the trained operation status prediction model, obtain the operation status prediction result of the power corridor, take the image data of the power corridor as input, call the trained image processing model, obtain the image processing result, perform anomaly detection on the image processing result, environmental data and equipment operation data, determine the anomaly detection result of the power corridor, and determine the status monitoring result of the power corridor based on the operation status prediction result and the anomaly detection result, wherein the trained operation status prediction model is trained based on historical equipment operation data, and the trained image processing model is trained based on historical image data of the power corridor.

[0122] In one embodiment, the visualization module 950 is also used to call a preset visualization tool to visualize the updated digital twin model of the power corridor, determine the abnormal areas, abnormal types, abnormal causes and abnormal handling suggestions on the updated digital twin model of the power corridor based on the status monitoring results of the power corridor, highlight the abnormal areas, and visualize the abnormal types, abnormal causes and abnormal handling suggestions of the power corridor in a preset text form.

[0123] In one embodiment, the power corridor operation status monitoring device 900 is also used to respond to the user's touch operation, which carries an operation instruction. When the operation instruction is an alarm instruction, the device pushes alarm information based on the severity of the abnormal area selected by the user, as well as the visualized alarm status and the processing status of the alarm information. When the operation instruction is an event recording instruction, the event selected by the user is recorded. When the operation instruction is a parameter adjustment instruction, the status monitoring parameters of the power corridor are adjusted.

[0124] Each module in the above-mentioned power pipeline corridor operation status monitoring device can be implemented in whole or in part through software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of the processor of the computer device in hardware form, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of each of the above modules.

[0125] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 10 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store data such as multi-source sensor data of the power pipeline corridor. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for monitoring the operating status of a power pipeline corridor is implemented.

[0126] Those skilled in the art will understand that Figure 10 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0127] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the power pipeline corridor operation status monitoring embodiment when executing the computer program.

[0128] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the power pipeline corridor operation status monitoring embodiment are implemented.

[0129] In one embodiment, a computer program product is provided, including a computer program, which, when executed by a processor, implements the steps in the power pipeline corridor operation status monitoring embodiment.

[0130] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions.

[0131] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), data processing logic devices based on quantum computing, and the like.

[0132] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0133] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for monitoring the operating status of a power pipeline corridor, characterized in that: The method comprises: Acquire multi-source sensor data from power pipeline corridors; Integrate the multi-source sensor data, and determine model parameters of a pre-built power pipeline corridor digital twin model based on the integrated multi-source sensor data and a preset virtual-real mapping relationship; Mapping the model parameters to the pre-built power pipeline gallery digital twin model to update the power pipeline gallery digital twin model; Based on the integrated multi-source sensor data, the power pipeline corridor is subjected to status monitoring, and a status monitoring result of the power pipeline corridor is determined; Based on the updated digital twin model of the power pipeline corridor, the status monitoring results of the power pipeline corridor are visually displayed.

2. The method according to claim 1, characterized in that The multi-source sensor data includes ambient gas concentration and device current data, and the method further includes: Pushing an alarm message when the ambient gas concentration is greater than a preset concentration threshold, or when the rate of change of the ambient gas concentration is greater than a preset concentration change rate threshold; When the device current data is greater than a preset current threshold, or the change rate of the device current data is greater than a preset current change rate threshold, an alarm message is pushed.

3. The method according to claim 1, characterized in that The multi-source sensor data includes visual image data, infrared image data, ambient temperature data, ambient humidity data, ambient gas concentration, device temperature data, device current data, and device vibration data; The integrating the multi-source sensor data includes: Performing Kalman filtering on the device temperature data and the ambient temperature data to obtain processed device temperature data and processed ambient temperature data; Performing an image superposition operation on the visual image data and the infrared image data to obtain image data of the power pipeline corridor; performing feature extraction and correlation analysis on the processed device temperature data, the device vibration data, and the device current data to obtain device operation data; The integrated multi-source sensor data includes environmental data, image data of the power pipeline corridor and equipment operation data, and the environmental data includes processed ambient temperature data, ambient humidity data and ambient gas concentration.

4. The method according to claim 3, characterized in that The step of performing status monitoring on the power pipeline corridor based on the integrated multi-source sensor data and determining a status monitoring result of the power pipeline corridor includes: Taking the equipment operation data as input, calling the trained operation status prediction model to obtain the operation status prediction result of the power pipeline corridor; Taking the image data of the power pipeline corridor as input, calling the trained image processing model to obtain an image processing result; Performing anomaly detection on the image processing result, the environmental data, and the equipment operation data to determine an anomaly detection result of the power pipeline corridor; Determining a status monitoring result of the power pipeline corridor based on the operation status prediction result and the abnormality detection result; Among them, the trained operation status prediction model is trained based on historical equipment operation data, and the trained image processing model is trained based on historical image data of the power pipeline corridor.

5. The method according to claim 3, characterized in that The updated digital twin model of the power pipeline corridor is used to visually display the status monitoring results of the power pipeline corridor, including: Calling a preset visualization tool to visualize the updated power pipeline corridor digital twin model; Determine, based on the status monitoring results of the power pipeline corridor, abnormal areas, abnormal types, abnormal causes, and abnormal handling suggestions on the updated digital twin model of the power pipeline corridor; The abnormal area is highlighted, and the abnormal type, abnormal cause and abnormal handling suggestions of the power pipeline corridor are visualized in a preset text form.

6. The method according to claim 5, characterized in that After visually displaying the status monitoring results of the power pipeline corridor based on the updated digital twin model of the power pipeline corridor, the method further includes: Responding to a touch operation of a user, wherein the touch operation carries an operation instruction; In the case where the operation instruction is an alarm instruction, based on the severity of the abnormal area selected by the user, an alarm message is pushed, as well as a visual alarm status and a processing status for the alarm message; In the case where the operation instruction is an event recording instruction, recording the event selected by the user; When the operation instruction is a parameter adjustment instruction, the status monitoring parameters of the power pipeline corridor are adjusted.

7. A power pipeline corridor operation status monitoring system, characterized in that: The system includes an edge computing module, and a sensor module and a cloud platform communicatively connected to the edge computing module; The sensor module is used to collect initial multi-source sensor data of the power pipeline corridor and send it to the edge computing module; The edge computing module is configured to receive and preprocess the initial multi-source sensor data to obtain multi-source sensor data, and transmit the data to the cloud platform, wherein the multi-source sensor data includes ambient gas concentration and device current data, and push an alarm message when the ambient gas concentration is greater than a preset concentration threshold or the rate of change of the ambient gas concentration is greater than a preset concentration change rate threshold; and push an alarm message when the device current data is greater than a preset current threshold or the rate of change of the device current data is greater than a preset current change rate threshold; The cloud platform is used to receive and integrate the multi-source sensor data, determine the model parameters of the pre-built power pipeline corridor digital twin model based on the integrated multi-source sensor data and the preset virtual-real mapping relationship, map the model parameters to the pre-built power pipeline corridor digital twin model to update the power pipeline corridor digital twin model, perform status monitoring on the power pipeline corridor based on the integrated multi-source sensor data, determine the status monitoring results of the power pipeline corridor, and visually display the status monitoring results of the power pipeline corridor based on the updated power pipeline corridor digital twin model.

8. A power pipeline corridor operation status monitoring device, characterized in that: The device comprises: Data acquisition module, used to obtain multi-source sensor data of the power pipeline corridor; A model parameter determination module is used to integrate the multi-source sensor data and determine the model parameters of the pre-built power pipeline corridor digital twin model based on the integrated multi-source sensor data and a preset virtual-real mapping relationship; A model updating module, configured to map the model parameters to the pre-built power pipeline gallery digital twin model to update the power pipeline gallery digital twin model; A state monitoring module is used to perform state monitoring on the power pipeline corridor based on the integrated multi-source sensor data and determine a state monitoring result of the power pipeline corridor; A visualization module is used to visualize the status monitoring results of the power pipeline corridor based on the updated digital twin model of the power pipeline corridor.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

Images