Electric power pipe gallery detection method, computer equipment, readable storage medium and program product

Abstract

The invention relates to an electric power pipe gallery detection method, computer equipment, a readable storage medium and a program product, and is applied to the technical field of big data, the method comprises the following steps: determining a key node of a target electric power pipe gallery, and collecting pipe gallery node data of the key node, the key nodes are nodes playing a key role in operation of the target electric power pipe gallery; according to the pipe gallery node data, node features of the key nodes are recognized, node feature information is obtained, and the node features are used for representing at least one of environment features, node structure features and node operation features of the environment where the key nodes are located; and detecting the potential risk of the target electric power pipe gallery according to the node feature information to obtain a pipe gallery risk detection result. By adopting the method, the detection accuracy of the electric power pipe gallery can be improved.

Description

Technical Field

[0001] The present application relates to the field of big data technology, and in particular to a power pipeline corridor detection method, computer equipment, computer-readable storage medium, and computer program product. Background Art

[0002] Power pipeline corridors are tunnels or conduits designed specifically to house cables and other power infrastructure, primarily used for power transmission and distribution. These corridors are typically located underground in cities, but can also be located in other areas, such as industrial facilities. Their primary purpose is to protect cables from the outside environment while facilitating maintenance and management of the power system. Deeply burying transmission cables in power pipeline corridors not only enhances a city's image and ensures the rational use of land resources, but also ensures stable power transmission. To ensure the normal operation of the power system, a method for inspecting power pipeline corridors is urgently needed.

[0003] Currently, power pipeline corridor inspections rely on manual inspections. However, this approach is labor-intensive and inefficient. Furthermore, this inspection method is limited by the professional capabilities of the staff and may fail to accurately detect problems in the power pipeline corridor, resulting in low inspection accuracy. Summary of the invention

[0004] Based on this, it is necessary to provide a power pipeline corridor detection method, device, computer equipment, computer-readable storage medium and computer program product that can improve the detection accuracy of power pipeline corridors in response to the above technical problems.

[0005] In a first aspect, the present application provides a method for detecting a power pipeline corridor, comprising:

[0006] Determine the key nodes of the target power pipeline corridor and collect pipeline corridor node data of the key nodes, wherein the key nodes are nodes that play a key role in the operation of the target power pipeline corridor;

[0007] Identify node features of the key nodes based on the pipeline corridor node data to obtain node feature information, wherein the node features are used to characterize at least one of environmental features, node structural features, and node operation features of an environment where the key nodes are located;

[0008] Based on the node characteristic information, the potential risks of the target power pipeline corridor are detected to obtain a pipeline corridor risk detection result.

[0009] In a second aspect, the present application also provides a power pipeline corridor detection device, comprising:

[0010] The acquisition module is used to determine the key nodes of the target power cable tunnel and acquire the cable tunnel node data of the key nodes, where the key nodes are the nodes that play a key role in the operation of the target power cable tunnel;

[0011] The identification module is used to identify the node features of the key nodes according to the cable tunnel node data to obtain node feature information, where the node features are used to characterize at least one of the environmental features, node structure features, and node operation features of the environment where the key nodes are located;

[0012] The detection module is used to detect the potential risks of the target power cable tunnel according to the node feature information to obtain the cable tunnel risk detection result.

[0013] In a third aspect, the present application also provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0014] Determine the key nodes of the target power cable tunnel and acquire the cable tunnel node data of the key nodes, where the key nodes are the nodes that play a key role in the operation of the target power cable tunnel;

[0015] Identify the node features of the key nodes according to the cable tunnel node data to obtain node feature information, where the node features are used to characterize at least one of the environmental features, node structure features, and node operation features of the environment where the key nodes are located;

[0016] Detect the potential risks of the target power cable tunnel according to the node feature information to obtain the cable tunnel risk detection result.

[0017] In a fourth aspect, the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0018] Determine the key nodes of the target power cable tunnel and acquire the cable tunnel node data of the key nodes, where the key nodes are the nodes that play a key role in the operation of the target power cable tunnel;

[0019] Identify the node features of the key nodes according to the cable tunnel node data to obtain node feature information, where the node features are used to characterize at least one of the environmental features, node structure features, and node operation features of the environment where the key nodes are located;

[0020] Detect the potential risks of the target power cable tunnel according to the node feature information to obtain the cable tunnel risk detection result.

[0021] In a fifth aspect, the present application further provides a computer program product, including a computer program which, when executed by a processor, implements the following steps:

[0022] Determine the key nodes of the target power cable tunnel, and collect the cable tunnel node data of the key nodes, where the key nodes are the nodes that play a key role in the operation of the target power cable tunnel;

[0023] Identify the node characteristics of the key nodes according to the cable tunnel node data to obtain node characteristic information, where the node characteristics are used to characterize at least one of the environmental characteristics, node structure characteristics, and node operation characteristics of the environment where the key nodes are located;

[0024] Detect the potential risks of the target power cable tunnel according to the node characteristic information to obtain a cable tunnel risk detection result.

[0025] The above-mentioned power cable tunnel detection method, device, computer device, computer-readable storage medium, and computer program product determine the key nodes of the target power cable tunnel and collect the cable tunnel node data of the key nodes, where the key nodes are the nodes that play a key role in the operation of the target power cable tunnel; identify the node characteristics of the key nodes according to the cable tunnel node data to obtain node characteristic information, where the node characteristics are used to characterize at least one of the environmental characteristics, node structure characteristics, and node operation characteristics of the environment where the key nodes are located; detect the potential risks of the target power cable tunnel according to the node characteristic information to obtain a cable tunnel risk detection result. In this way, based on the cable tunnel node data of the key nodes that play a key role in the operation of the target power cable tunnel, the characteristics of the key nodes are identified to obtain node characteristic information, and then based on the node characteristic information, the potential risks of the target power cable tunnel are detected. Since the detection process is a series of identification and detection processes based on objective data and focuses on the key nodes that play a key role in the operation of the target power cable tunnel, it is ensured that the potential risks in the target power cable tunnel can be accurately detected. Therefore, the detection accuracy of the power cable tunnel is improved. Description of the Drawings

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for the description of the embodiments of the present application or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other related drawings can be obtained without creative efforts.

[0027] Figure 1 It is an application environment diagram of the power cable tunnel detection method in an embodiment;

[0028] Figure 2 is a schematic flow chart of a power pipe gallery detection method in an embodiment;

[0029] Figure 3 is a schematic flow chart of the step of identifying the node features of key nodes according to the pipe gallery node data in an embodiment to obtain node feature information;

[0030] Figure 4 is a schematic flow chart of the process of detecting a power pipe gallery in an embodiment;

[0031] Figure 5 is a structural block diagram of a power pipe gallery detection device in an embodiment;

[0032] Figure 6 is an internal structure diagram of a computer device in an embodiment. Detailed implementation manners

[0033] In order to make the objectives, technical solutions and advantages of the present application clearer and more understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0034] It should be noted that the information and data involved in the present application (including but not limited to data for analysis, stored data, displayed data, etc.) are all information and data authorized by users or fully authorized by all parties, and the acquisition, transmission, storage, use and processing of relevant data all comply with the relevant regulations of national laws and regulations. For the content pushed to users (such as node feature information, pipe gallery node data, pipe gallery risk detection results, preset inspection routes, updated inspection routes, etc.), users can refuse or can conveniently refuse content push, etc. In the embodiments of the present application, some industry-existing solutions such as certain software, components, models, etc. may be mentioned, and they should be considered exemplary. The purpose is only to illustrate the feasibility in the implementation of the technical solution of the present application, but it does not mean that the applicant has already or necessarily used this solution.

[0035] The power pipe gallery risk early warning processing method provided by the embodiments of the present application can be applied to, for example Figure 1In the application environment shown. Among them, the data acquisition component 102, the target power cable tunnel 104, and the terminal 106 communicate with the server 108 respectively. The data acquisition component 102 is deployed in the target power cable tunnel 104, and the data acquisition component 102 is used to collect the tunnel node data of the key nodes of the target power cable tunnel 104. The data storage system can store the data that the server 108 needs to process. The data storage system can be integrated on the server 108, or can be placed on the cloud or other network servers. The server 108 determines the key nodes of the target power cable tunnel 104 and obtains the tunnel node data of the key nodes collected by the data acquisition component 102, where the key nodes are the nodes that play a key role in the operation of the target power cable tunnel; according to the tunnel node data, identify the node characteristics of the key nodes to obtain node characteristic information, where the node characteristics are used to characterize at least one of the environmental characteristics, node structure characteristics, and node operation characteristics of the environment where the key nodes are located; according to the node characteristic information, detect the potential risks of the target power cable tunnel 104 to obtain the tunnel risk detection result. The server 108 can push at least one of the node characteristic information, the tunnel node data, the tunnel risk detection result, the preset inspection route, and the updated inspection route to the terminal 106. Among them, the terminal 106 can be but is not limited to various personal computers, laptop computers, smart phones, tablet computers, Internet of Things devices, and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, projection devices, etc. The portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The head-mounted devices can be virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, etc. The server 108 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services.

[0036] The power pipe gallery risk early warning processing method provided by the embodiment of the present application can also be applied to the following application environments. Among them: The data acquisition component 102 and the terminal 106 are respectively in communication with the target power pipe gallery 104. The data acquisition component 102 is deployed in the target power pipe gallery 104, and the data acquisition component 102 is used for the pipe gallery node data of the key nodes of the target power pipe gallery 104. Determine the key nodes of the target power pipe gallery 104 through the target power pipe gallery 104, and obtain the pipe gallery node data of the key nodes collected by the data acquisition component 102, where the key nodes are the nodes that play a key role in the operation of the target power pipe gallery; according to the pipe gallery node data, identify the node characteristics of the key nodes to obtain node characteristic information, where the node characteristics are used to characterize at least one of the environmental characteristics, node structure characteristics, and node operation characteristics of the environment where the key nodes are located; according to the node characteristic information, detect the potential risks of the target power pipe gallery 104 to obtain the pipe gallery risk detection result. The target power pipe gallery 104 can push at least one of the node characteristic information, the pipe gallery node data, the pipe gallery risk detection result, the preset inspection route, and the updated inspection route to the terminal 106.

[0037] In an exemplary embodiment, as Figure 2 shown, a power pipe gallery detection method is provided. Taking the method applied to Figure 1 the server 108 in it as an example and described in an abbreviated form of the main body, it includes the following steps 202 to 206. Among them:

[0038] Step 202, determine the key nodes of the target power pipe gallery and collect the pipe gallery node data of the key nodes, where the key nodes are the nodes that play a key role in the operation of the target power pipe gallery.

[0039] Among them, the target power pipe gallery in step 202 is the power pipe gallery selected for risk detection. The pipe gallery node data includes at least one of node environment data, node structure data, and node operation data. The node environment data includes at least one of environmental temperature data, environmental humidity data, environmental water level data, and gas concentration data. The node operation data includes at least one of cable current data, cable voltage data, and cable temperature. The node structure data includes at least one of node vibration data, node image data, and node stress data.

[0040] As an embodiment, determining the key nodes of the target power pipe gallery includes: obtaining the pipe gallery structure information of the target power pipe gallery, and splitting the target power pipe gallery into multiple pipe gallery nodes according to the pipe gallery structure information; for each pipe gallery node, evaluating the importance of the pipe gallery node to the target power pipe gallery according to the structural components corresponding to the pipe gallery node in the target power pipe gallery, to obtain node importance information; determining the nodes with the importance represented by the node importance information higher than the preset importance threshold among the multiple pipe gallery nodes as the key nodes.

[0041] Further, evaluating the importance of the pipe gallery node to the target power pipe gallery according to the structural components corresponding to the pipe gallery node in the target power pipe gallery to obtain node importance information includes: identifying the structural role of the pipe gallery node to the target power pipe gallery according to the structural components corresponding to the pipe gallery node in the target power pipe gallery, to obtain the node structural role, determining the node function of the pipe gallery node, and evaluating the importance of the pipe gallery node to the target power pipe gallery according to the node structural role and the node function, to obtain node importance information, wherein the more the node structural role of the pipe gallery node represents the load-bearing role and the supporting role, the higher the importance represented by the node importance information; the stronger the relevance between the node function and the operation of the target power pipe gallery, the higher the importance represented by the node importance information.

[0042] Thus, considering the influence of the pipe gallery nodes with greater influence on the operation and structural support and load-bearing of the target power pipe gallery on the stable operation of the target power pipe gallery, therefore, by collecting the pipe gallery node data of the key nodes, the accurate detection of the potential risks of the target power pipe gallery can be realized.

[0043] As an embodiment, collecting the pipe gallery node data of the key nodes includes: obtaining the pipe gallery node data collected by the data collection component.

[0044] Wherein, the data collection component includes at least one of a sensor and a camera component. The sensor can be a lidar sensor or other sensors. The camera component can be a dual-spectrum pan-tilt for collecting visible light image data and thermal imaging data; the sensor includes at least one of a temperature sensor, a humidity sensor, a water level sensor, and a gas concentration sensor. The temperature sensor is used to collect at least one of the ambient temperature data of the environment where the key node is located and the cable temperature of the key node. The camera component is used to collect the node image data and node thermal imaging data of the key node.

[0045] Further, the data collection component can be mounted on a robot, and the robot is used to drive the data collection component to move.

[0046] Thus, in the case where the number of key nodes is large, if one wants to collect the data of the utility tunnel nodes of all key nodes, it is necessary to deploy data acquisition components on all key nodes, which is likely to result in a relatively high cost. By mounting the data acquisition components on a robot, the cost can be saved.

[0047] Step 204: Identify the node features of the key nodes according to the utility tunnel node data to obtain node feature information, where the node features are used to represent at least one of the environmental features, node structure features, and node operation features of the environment where the key nodes are located.

[0048] Exemplarily, step 204 includes: identifying the node features of the key nodes according to the utility tunnel node data by using a preset node feature identification method to obtain node feature information, where the preset node feature identification method includes at least one of a node environmental feature identification method, a node structure feature identification method, and a node operation feature identification method.

[0049] As an embodiment, identifying the node features of the key nodes according to the utility tunnel node data by using a preset node feature identification method to obtain node feature information includes at least one of the following: identifying the environmental features of the environment where the key nodes are located by using a node environmental feature identification method according to the utility tunnel node data to obtain environmental feature information; identifying the structural features of the key nodes by using a node structure feature identification method according to the utility tunnel node data to obtain node structure feature information; identifying the operation features of the key nodes by using a node operation feature identification method according to the utility tunnel node data to obtain node operation feature information.

[0050] As an embodiment, identifying the environmental features of the environment where the key nodes are located by using a node environmental feature identification method according to the utility tunnel node data to obtain environmental feature information includes: the utility tunnel node data includes node environmental data, and the node feature information includes node environmental feature information; predicting the change trend of the environmental data of the environment where the key nodes are located according to the node environmental data to obtain predicted environmental data; identifying the environmental features of the environment where the key nodes are located according to the predicted environmental data to obtain node environmental feature information.

[0051] Further, predicting the change trend of the environmental data of the environment where the key nodes are located according to the node environmental data to obtain predicted environmental data includes: obtaining the historical environmental data of the environment where the key nodes are located, and predicting the change trend of the environmental data of the environment where the key nodes are located according to the historical environmental data and the node environmental data to obtain predicted environmental data.

[0052] As an embodiment, according to the predicted environmental data, the environmental characteristics of the environment where the key node is located are identified to obtain node environmental characteristic information, including: if the predicted environmental data are not all within the corresponding environmental data range, the environmental anomaly identification result is determined as the node environmental characteristic information; if the predicted environmental data are all within the corresponding environmental data range, the environmental normal identification result is determined as the node environmental characteristic information.

[0053] Among them, the predicted environmental data includes at least one of predicted environmental temperature data, predicted environmental humidity data, predicted environmental water level data, and predicted gas concentration data, and the environmental data range corresponding to the key node includes at least one of an environmental temperature data range, an environmental humidity data range, an environmental water level data range, and a gas concentration data range.

[0054] As an embodiment, according to the utility tunnel node data, the node operation characteristics are identified by using the node operation characteristic identification method to obtain node operation characteristic information, including: the utility tunnel node data includes node operation data, and the node characteristic information includes node operation characteristic information; according to the node operation data, the power-on state of the cable of the key node is identified to obtain cable power-on identification information; according to the cable power-on identification information, the operation state of the key node is identified to obtain node operation identification information; according to the node operation identification information, the operation characteristics of the key node are identified to obtain node operation characteristic information.

[0055] Further, according to the node operation data, the power-on state of the cable of the key node is identified to obtain cable power-on identification information, including: according to the node operation data, the cable power-on current, cable power-on voltage, and cable power-on power of the key node are respectively determined, and the cable power-on current, cable power-on voltage, and cable power-on power are determined as the cable power-on identification information.

[0056] As an embodiment, according to the cable power-on identification information, the operation state of the key node is identified to obtain node operation identification information, including: determining the corresponding operation power range and current constraint conditions of the key node; if the cable power-on power in the cable power-on identification information is not within the operation power range, or if the cable power-on current in the cable power-on identification information does not meet the current constraint conditions, the node abnormal operation information is determined as the node operation characteristic information; if the cable power-on power in the cable power-on identification information is within the operation power range and the cable power-on current in the cable power-on identification information meets the current constraint conditions, the node normal operation information is determined as the node operation characteristic information.

[0057] Among them, the current constraint conditions include at least one of a current mutation constraint condition and a current balance constraint condition.

[0058] It can be understood that when the power of the energized cable is relatively high, equipment overload, short circuit or insulation aging may occur at key nodes, resulting in heating or even equipment damage; when the power of the energized cable is relatively low, power supply failures, poor contacts or abnormal operations may occur at key nodes; when the current of the energized cable suddenly changes, short circuits, insulation breakdowns or instantaneous load impacts may occur at key nodes; when the current of the energized cable is unbalanced, the service life of the motor may be reduced at key nodes.

[0059] In this way, considering the impact of abnormal power and current of the energized cable on key nodes, the accuracy of identifying the operating characteristics of key nodes is improved.

[0060] Step 206: Detect the potential risks of the target power cable tunnel based on the node characteristic information to obtain the cable tunnel risk detection result.

[0061] Among them, the cable tunnel risk detection result in step 206 can characterize at least one of whether there are potential risks in the target power cable tunnel and the types of potential risks. The types of potential risks include at least one of structural risk types, environmental risk types and operating risk types. Structural risk types include at least one of surface defect risk types and internal structural stability risk types. Environmental risk types include at least one of gas concentration risk types, temperature risk types, humidity risk types and high water level risk types. Gas concentration risk types include at least one of high concentration risk types of hazardous gases and low concentration risk types of oxygen. Hazardous gases include at least one of combustible gases and toxic gases.

[0062] Exemplarily, step 206 includes: determining the defect characteristics of the key node characterized by the node characteristic information; identifying the potential risks of the target power cable tunnel based on the relationship between the key node and the target power cable tunnel and the defect characteristics to obtain the cable tunnel risk detection result.

[0063] Furthermore, determining the defect characteristics of the key node characterized by the node characteristic information includes: if the node structure characteristic information characterizes that the structure of the key node is unstable, determining the structural stability defect as the defect characteristic; if the node structure characteristic information characterizes that there are surface defects in the key node, determining the node surface defect as the defect characteristic; if the node environmental characteristic information characterizes that there are abnormalities in the environment where the key node is located, determining the environmental abnormality defect as the defect characteristic; if the node operating characteristic information characterizes that the key node is not operating normally, determining the node operating defect as the defect characteristic.

[0064] As an embodiment, according to the relationship between the key node and the target power pipe gallery and the defect characteristics, the potential risks of the target power pipe gallery are identified to obtain the pipe gallery risk detection result, including: if the relationship between the key node and the target power pipe gallery indicates that the target power pipe gallery is not affected by the key node, the key node risk detection result is generated according to the defect characteristics, and the key node risk detection result is determined as the pipe gallery risk detection result; if the relationship between the key node and the target power pipe gallery indicates that the target power pipe gallery is affected by the key node, the pipe gallery risk detection result is generated according to the defect characteristics.

[0065] Optionally, the above method further includes: constructing a digital twin model of the target power pipe gallery according to the pipe gallery node data, and performing annotation on the digital twin model according to the pipe gallery risk detection result to obtain the power pipe gallery detection model.

[0066] In the above power pipe gallery detection method, based on the pipe gallery node data of the key node that plays a key role in the operation of the target power pipe gallery, the characteristics of the key node are identified to obtain the node characteristic information. Then, based on the node characteristic information, the potential risks of the target power pipe gallery are detected. Since the detection process is a series of identification and detection processes based on objective data, and focuses on the key nodes that play a key role in the operation of the target power pipe gallery, it is ensured that the potential risks in the target power pipe gallery can be accurately detected. Therefore, the detection accuracy of the power pipe gallery is improved.

[0067] In an exemplary embodiment, as Figure 3 shown, a method for accurately identifying the structural characteristics of the node characteristics of the key node is provided. The pipe gallery node data includes node vibration data, node image data, and node stress data, and the node characteristic information includes node structural characteristic information. Identifying the node characteristics of the key node according to the pipe gallery node data to obtain the node characteristic information includes steps 302 to 306. Among them:

[0068] Step 302, evaluating the internal structural stability of the key node according to the node stress data to obtain the structural stability evaluation result.

[0069] Among them, the structural stability evaluation result in step 302 includes a node stability evaluation result or a node instability evaluation result.

[0070] As an embodiment, step 302 includes: identifying the stress characteristics of the key node according to the node stress data to obtain stress characteristic information, where the stress characteristic information is used to characterize at least one of the stress magnitude and stress distribution of the key node; evaluating the internal structural stability of the key node according to the stress characteristic information to obtain the structural stability evaluation result.

[0071] It can be understood that when the stress distribution of the key node is uneven, it is easy to have a situation where the local stress on the structure of the key node is too large, resulting in local collapse or damage of the key node, leading to damage to the key node and further affecting the normal operation of the target power pipe gallery. When the stress of the key node is large, it is easy to have a situation where the structural material of the key node reaches the yield limit or strength limit, and then the structure of the key node collapses or is damaged, resulting in damage to the key node and further affecting the normal operation of the target power pipe gallery.

[0072] In this way, based on the node stress data, the stress characteristics used to characterize at least one of the stress magnitude and stress distribution of the key node are identified, and then the internal structural stability of the key node is evaluated, which can ensure that the influence on the internal structural stability of the key node caused by these two factors of stress distribution and stress magnitude is evaluated, improving the accuracy of the stability evaluation of the key node.

[0073] Furthermore, according to the stress characteristic information, the internal structural stability of the key node is evaluated to obtain a structural stability evaluation result, including: if the stress characteristic information characterizes that the stress distribution of the key node is uneven, or the stress characteristic information characterizes that the stress of the key node is greater than the corresponding stress threshold of the key node, then it is determined that the structural stability evaluation result is a node instability evaluation result; if the stress characteristic information characterizes that the stress distribution of the key node is uniform, and the stress characteristic information characterizes that the stress of the key node is not greater than the corresponding stress threshold of the key node, then it is determined that the structural stability evaluation result is a node stability evaluation result.

[0074] Among them, the stress threshold is determined by at least one of the structure, size, shape, material, and load of the key node.

[0075] Optionally, the above method further includes: if the stress characteristic information characterizes that the stress distribution on each plane of the key node is uniform and the directions are the same, then it is determined that the stress characteristic information characterizes that the stress distribution of the key node is uniform; if the stress characteristic information characterizes that the stress distribution on each plane of the key node is not all uniform and the directions are the same, then it is determined that the stress characteristic information characterizes that the stress distribution of the key node is uneven.

[0076] Step 304, based on the node vibration data and node image data, identify the surface defects of the key node to obtain a defect identification result.

[0077] Among them, the surface defects in step 304 include at least one of surface deformation, surface discoloration, and surface damage. Surface deformation includes at least one of surface collapse and surface settlement. Surface damage includes at least one of surface cracks and surface holes. The vibration data (including node vibration data and fault vibration data) involved throughout the text includes at least one of the resonance frequency and vibration amplitude.

[0078] As an embodiment, the above method further includes: obtaining fault vibration data corresponding to a key node, where the fault vibration data includes vibration data when there are surface defects corresponding to the key node; identifying surface defects of the key node according to the matching condition between the node vibration data and the fault vibration data to obtain a first identification result; identifying surface defects of the key node according to the node image data to obtain a second identification result; and identifying surface defects of the key node according to the first identification result and the second identification result to obtain a defect identification result.

[0079] Further, obtaining fault vibration data corresponding to a key node includes: obtaining first historical vibration data of the key node, and selecting, from the first historical vibration data, fault vibration data corresponding to a node state indicating at least one of surface deformation and surface damage of the key node; and / or obtaining second historical vibration data of other nodes belonging to the same node as the key node, and selecting, from the second historical vibration data, fault vibration data corresponding to a node state indicating at least one of surface deformation and surface damage of the node; and / or obtaining a node model obtained by simulating and modeling the key node, and performing simulation of at least one of surface deformation and surface damage on the node model to obtain fault vibration data.

[0080] In this way, the diversity of the acquisition method of the fault vibration data is ensured, and further the data richness of the fault vibration data is ensured.

[0081] As an embodiment, identifying surface defects of the key node according to the matching condition between the node vibration data and the fault vibration data to obtain a first identification result includes: if the fault vibration data includes target vibration data matching the node vibration data, generating a first identification result according to the surface defect corresponding to the target vibration data, where the surface defect of the key node represented by the first identification result is consistent with the surface defect corresponding to the target vibration data; if the fault vibration data does not include target vibration data matching the node vibration data, determining the non-structural defect identification result as the first identification result.

[0082] It can be understood that since surface deformation and surface damage involve changes in the surface structure of the key node, then, it will have a certain impact on the internal vibration of the key node. Therefore, through the dimension of vibration data, surface defects in these two aspects of surface deformation and surface damage in the key node can be identified. Therefore, through the comparison of vibration data, structural defects of the key node can be identified.

[0083] As an embodiment, based on the node image data, surface defects of key nodes are identified to obtain a second identification result, including: the second identification result includes a structure identification result and a discoloration identification result; at least one of surface deformation and surface damage of the key nodes in the node image data is identified to obtain the structure identification result, and the surface discoloration of the key nodes in the node image data is identified to obtain the discoloration identification result.

[0084] It can be understood that since the surface of the key node will not discolor during normal use of the key node or when the usage time is not long, and when the surface of the key node discolors, it may be due to abnormal use or long-term use of the key node, which may lead to potential operation hazards of the key node, resulting in abnormal operation of the key node, and further affecting the normal operation of the target power pipe gallery. Therefore, by identifying the surface discoloration of the key node, this potential hazard can be predicted in advance.

[0085] As an embodiment, based on the first identification result and the second identification result, surface defects of the key nodes are identified to obtain a defect identification result, including: the defect identification result includes a target structure identification result and a target discoloration identification result; the first identification result and the structure identification result are fused to obtain the target structure identification result; the discoloration identification result is determined as the target discoloration identification result.

[0086] Further, fusing the first identification result and the structure identification result to obtain the target structure identification result includes: determining a first confidence level corresponding to the first identification result, and determining a second confidence level corresponding to the structure identification result. According to the first confidence level and the second confidence level, the first identification result and the structure identification result are fused to obtain the target structure identification result, where the higher the first confidence level, the closer the target structure identification result is to the first identification result, and the higher the second confidence level, the closer the target structure identification result is to the structure identification result.

[0087] Among them, the more the target vibration data matches the node vibration data, the higher the first confidence level.

[0088] As an embodiment, determining the second confidence level corresponding to the structure identification result includes: determining the image acquisition information corresponding to the node image data, where the image acquisition information includes at least one of image acquisition clarity, image occlusion degree, and image integrity. The image occlusion degree is used to characterize the occlusion degree of the key node, and the image integrity is used to characterize the exposure integrity of the key node; according to the image acquisition information, the second confidence level corresponding to the structure identification result is determined, where the higher the image acquisition clarity, the higher the second confidence level, the lower the image occlusion degree, the higher the second confidence level, and the higher the image integrity, the higher the second confidence level.

[0089] Thus, considering that both methods (the above-mentioned vibration recognition method and image recognition method) can identify the surface structure of key nodes, by quantifying the confidence levels of the two methods, the fusion of the structure recognition results obtained by the two methods can be realized, improving the accuracy of the structure recognition of key nodes.

[0090] Step 306: Identify the structural features of the key nodes based on the structural stability evaluation results and defect recognition results to obtain node structural feature information.

[0091] As an embodiment, step 306 includes: combining the structural stability evaluation results and defect recognition results to obtain node structural feature information. For example, the node structural feature information includes (node stable evaluation result / node unstable evaluation result) + target structure recognition result + target color change recognition result.

[0092] In this embodiment, first, based on the node stress data, the internal structural stability of the key nodes is evaluated. Secondly, based on the node vibration data and node image data together, the surface defects of the key nodes are identified. Thus, based on the structural stability evaluation results and defect recognition results, the structural features of the key nodes are identified, enabling the node structural feature information to characterize the internal structural stability status and surface defect status of the key nodes, improving the accuracy of node structure recognition.

[0093] It can be understood that with the popularization and application of power grids, the power pipe galleries are also becoming more and more complex. Therefore, when conducting power pipe gallery inspections, there may be misjudgments in power pipe gallery inspections.

[0094] In an exemplary embodiment, as Figure 4 shown, a method for accurately inspecting a power pipe gallery is provided, including steps 402 to 406. Among them:

[0095] Step 402: Obtain a preset inspection route and control a robot equipped with data acquisition components to inspect the key nodes of the target power pipe gallery according to the preset inspection route to obtain pipe gallery node data.

[0096] Among them, the preset inspection route in step 402 is determined by the node positions corresponding to the respective key nodes of the target power pipe gallery. The preset inspection route is the route for inspecting each key node of the target power pipe gallery at least once. For example, the key nodes include A, B, and C, and the preset inspection route is A→B→C→B→A. At this time, the key nodes A and B are inspected 2 times, and the key node C is inspected 1 time.

[0097] After executing steps 204 to 206, after step 206, the above method further includes:

[0098] Step 404: Update the preset inspection route according to the inspection results of the pipe gallery risks to obtain an updated inspection route.

[0099] Exemplarily, step 404 includes: for each key node, if the inspection results of the pipe gallery risks indicate that there are defects in the key node that cause potential risks to the target power pipe gallery, increase the inspection frequency of the key node; if the inspection results of the pipe gallery risks indicate that there are no defects that cause potential risks to the target power pipe gallery, reduce the inspection frequency of the key node; update the preset inspection route according to the inspection frequencies corresponding to the respective key nodes to obtain an updated inspection route.

[0100] In this way, the updated inspection route is associated with the inspection results of the pipe gallery risks, so that in the case where the inspection results of the pipe gallery risks indicate that there are defects in the key node that cause potential risks to the target power pipe gallery, the inspection frequency of the key node is increased, so as to further confirm whether there are such defects in the key node, thereby avoiding unnecessary manual maintenance caused by misdetection.

[0101] Step 406: Control the robot to inspect the key nodes of the target power pipe gallery according to the updated inspection route to obtain updated node data, update the pipe gallery node data according to the updated node data, and return to step 204.

[0102] Optionally, the above method further includes: updating the power pipe gallery detection model according to the updated node data.

[0103] In this embodiment, first, routine inspections are performed. Based on the pipe gallery node data obtained from the inspections, preliminary feature recognition of the key nodes is carried out, so as to conduct a preliminary risk investigation of the target power pipe gallery. After obtaining the inspection results of the pipe gallery risks, the inspection route is updated, and then a further risk investigation of the target power pipe gallery is carried out. Therefore, the detection accuracy of the target power pipe gallery is further improved.

[0104] As a detailed embodiment, key nodes of the target power cable tunnel are determined, a preset inspection route is obtained, and a robot carrying data acquisition components is controlled to inspect the key nodes of the target power cable tunnel according to the preset inspection route to obtain tunnel node data, where the key nodes are nodes that play a key role in the operation of the target power cable tunnel; according to the node stress data, the internal structure stability of the key nodes is evaluated to obtain a structure stability evaluation result; the fault vibration data corresponding to the key nodes is obtained, where the fault vibration data includes vibration data when there are surface defects corresponding to the key nodes; according to the matching condition between the node vibration data and the fault vibration data, the surface defects of the key nodes are identified to obtain a first identification result; according to the node image data, the surface defects of the key nodes are identified to obtain a second identification result; according to the first identification result and the second identification result, the surface defects of the key nodes are identified to obtain a defect identification result.

[0105] Further, according to the node environment data, the change trend of the environment data of the environment where the key nodes are located is predicted to obtain predicted environment data; according to the predicted environment data, the environmental characteristics of the environment where the key nodes are located are identified to obtain node environmental characteristic information; according to the node operation data, the power-on state of the cables of the key nodes is identified to obtain cable power-on identification information; according to the cable power-on identification information, the operation state of the key nodes is identified to obtain node operation identification information; according to the node operation identification information, the operation characteristics of the key nodes are identified to obtain node operation characteristic information; according to the structure stability evaluation result and the defect identification result, the structural characteristics of the key nodes are identified to obtain node structural characteristic information; the defect characteristics of the key nodes characterized by the node characteristic information are determined; according to the relationship between the key nodes and the target power cable tunnel and the defect characteristics, the potential risks of the target power cable tunnel are identified to obtain a tunnel risk detection result; according to the tunnel risk detection result, the preset inspection route is updated to obtain an updated inspection route; the robot is controlled to inspect the key nodes of the target power cable tunnel according to the updated inspection route to obtain updated node data, the tunnel node data is updated according to the updated node data, and the process returns to the step of identifying the node characteristic information of the key nodes according to the tunnel node data.

[0106] In this way, based on the tunnel node data of the key nodes that play a key role in the operation of the target power cable tunnel, the characteristic identification of the key nodes is carried out to obtain node characteristic information, and then based on the node characteristic information, the potential risks of the target power cable tunnel are detected. Since the detection process is a series of identification and detection processes based on objective data and focuses on the key nodes that play a key role in the operation of the target power cable tunnel, it is ensured that the potential risks in the target power cable tunnel can be accurately detected. Therefore, the detection accuracy of the power cable tunnel is improved.

[0107] Further, first, based on the node stress data, the internal structural stability of the key nodes is evaluated. Secondly, based on the node vibration data and the node image data together, the surface defects of the key nodes are identified. Thus, based on the structural stability evaluation results and the defect identification results, the structural characteristics of the key nodes are identified, enabling the node structure characteristic information to represent the internal structural stability status and the surface defect status of the key nodes, improving the identification accuracy of the node structure; and, first, routine inspections are carried out. Based on the pipe gallery node data obtained from the inspections, preliminary feature identification of the key nodes is performed, thereby conducting a preliminary risk investigation of the target power pipe gallery. After obtaining the pipe gallery risk detection results, the inspection route is updated, and then a further risk investigation of the target power pipe gallery is carried out. Therefore, the detection accuracy of the target power pipe gallery is further improved.

[0108] It should be understood that although the steps in the flowcharts involved in the above embodiments are shown in sequence according to the arrows, these steps do not necessarily have to be executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps does not have a strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages. These steps or stages do not necessarily have to be executed at the same time, but can be executed at different times. The execution order of these steps or stages does not necessarily have to be sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0109] Based on the same inventive concept, an embodiment of the present application further provides a power pipe gallery detection device for implementing the above-mentioned power pipe gallery detection method. The solution provided by this device to solve the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the power pipe gallery detection device provided below can refer to the limitations on the power pipe gallery detection method in the above text, and will not be repeated here.

[0110] In an exemplary embodiment, as Figure 5 shown, a power pipe gallery detection device 500 is provided, including: a collection module 502, an identification module 504, and a detection module 506, where:

[0111] The collection module 502 is configured to determine the key nodes of the target power pipe gallery and collect the pipe gallery node data of the key nodes, where the key nodes are the nodes that play a key role in the operation of the target power pipe gallery;

[0112] An identification module 504, configured to identify node features of key nodes according to the data of the utility tunnel nodes, so as to obtain node feature information, where the node features are used to characterize at least one of the environmental features, node structure features, and node operation features of the environment where the key nodes are located;

[0113] A detection module 506, configured to detect potential risks of the target power utility tunnel according to the node feature information, so as to obtain a utility tunnel risk detection result.

[0114] In one embodiment, the data of the utility tunnel nodes includes node vibration data, node image data, and node stress data, and the node feature information includes node structure feature information; the identification module 504 is further configured to evaluate the internal structure stability of the key nodes according to the node stress data, so as to obtain a structure stability evaluation result; identify surface defects of the key nodes according to the node vibration data and the node image data, so as to obtain a defect identification result; identify the structure features of the key nodes according to the structure stability evaluation result and the defect identification result, so as to obtain node structure feature information.

[0115] In one embodiment, the identification module 504 is further configured to obtain fault vibration data corresponding to the key nodes, where the fault vibration data includes vibration data when there are surface defects corresponding to the key nodes; identify surface defects of the key nodes according to the matching condition between the node vibration data and the fault vibration data, so as to obtain a first identification result; identify surface defects of the key nodes according to the node image data, so as to obtain a second identification result; identify surface defects of the key nodes according to the first identification result and the second identification result, so as to obtain a defect identification result.

[0116] In one embodiment, the data of the utility tunnel nodes includes node environment data, and the node feature information includes node environment feature information; the identification module 504 is further configured to predict the change trend of the environmental data of the environment where the key nodes are located according to the node environment data, so as to obtain predicted environmental data; identify the environmental features of the environment where the key nodes are located according to the predicted environmental data, so as to obtain node environment feature information.

[0117] In one embodiment, the data of the utility tunnel nodes includes node operation data, and the node feature information includes node operation feature information; the identification module 504 is further configured to identify the cable power-on state of the key nodes according to the node operation data, so as to obtain cable power-on identification information; identify the operation state of the key nodes according to the cable power-on identification information, so as to obtain node operation identification information; identify the operation features of the key nodes according to the node operation identification information, so as to obtain node operation feature information.

[0118] In one embodiment, the detection module 506 is further configured to determine the defect features of the key nodes characterized by the node feature information; and identify the potential risks of the target power pipe gallery according to the relationship between the key nodes and the target power pipe gallery and the defect features, so as to obtain the pipe gallery risk detection result.

[0119] In one embodiment, the acquisition module 502 is further configured to obtain a preset inspection route, and control a robot carrying a data acquisition component to inspect the key nodes of the target power pipe gallery according to the preset inspection route, so as to obtain pipe gallery node data; after detecting the potential risks of the target power pipe gallery according to the node feature information to obtain the pipe gallery risk detection result, the above device further includes: an update module, configured to update the preset inspection route according to the pipe gallery risk detection result to obtain an updated inspection route; control the robot to inspect the key nodes of the target power pipe gallery according to the updated inspection route to obtain updated node data, update the pipe gallery node data according to the updated node data, and return to the step of identifying the node feature information of the key nodes according to the pipe gallery node data.

[0120] Each module in the above power pipe gallery detection device can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0121] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as Figure 6As shown in the figure. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. Among them, 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 and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. 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 external terminals in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, near field communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a method for detecting a power pipe gallery. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0122] Those skilled in the art can understand that Figure 6 the structure shown in the figure is only a block diagram of some structures 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 those shown in the figure, or combine some components, or have different component arrangements.

[0123] In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0124] In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by the processor, the steps in the above method embodiments are implemented.

[0125] In an embodiment, a computer program product is provided, including a computer program. When the computer program is executed by the processor, the steps in the above method embodiments are implemented.

[0126] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing 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 embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory 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), magnetoresistive 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 be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, data processing logics based on quantum computing, artificial intelligence (AI) processors, etc., without limitation.

[0127] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, 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, it should be considered as the scope recorded in the present application.

[0128] The above embodiments only represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation to the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A method for detecting a power pipe gallery, characterized in that, The method includes: Determine the key nodes of the target power pipe gallery and collect the pipe gallery node data of the key nodes, where the key nodes are the nodes that play a key role in the operation of the target power pipe gallery; According to the pipe gallery node data, identify the node characteristics of the key nodes to obtain node characteristic information, where the node characteristics are used to characterize at least one of the environmental characteristics, node structure characteristics, and node operation characteristics of the environment where the key nodes are located; According to the node characteristic information, detect the potential risks of the target power pipe gallery to obtain the pipe gallery risk detection result.

2. The method according to claim 1, characterized in that, The pipe gallery node data includes node vibration data, node image data, and node stress data, and the node characteristic information includes node structure characteristic information; The step of identifying the node characteristics of the key nodes according to the pipe gallery node data to obtain node characteristic information includes: Evaluate the internal structure stability of the key nodes according to the node stress data to obtain a structure stability evaluation result; Identify the surface defects of the key nodes according to the node vibration data and node image data to obtain a defect identification result; Identify the structure characteristics of the key nodes according to the structure stability evaluation result and the defect identification result to obtain node structure characteristic information.

3. The method according to claim 2, wherein The step of identifying the surface defects of the key nodes according to the node vibration data and node image data to obtain a defect identification result includes: Obtain the fault vibration data corresponding to the key nodes, where the fault vibration data includes the vibration data when the key nodes have corresponding surface defects; Identify the surface defects of the key nodes according to the matching condition between the node vibration data and the fault vibration data to obtain a first identification result; Identify the surface defects of the key nodes according to the node image data to obtain a second identification result; Identify the surface defects of the key nodes according to the first identification result and the second identification result to obtain a defect identification result.

4. The method according to claim 1, characterized in that The pipe gallery node data includes node environment data, and the node characteristic information includes node environment characteristic information; the step of identifying the node characteristics of the key nodes according to the pipe gallery node data to obtain node characteristic information includes: Predict the change trend of the environmental data of the environment where the key nodes are located according to the node environment data to obtain predicted environmental data; Identify the environmental characteristics of the environment where the key nodes are located according to the predicted environmental data to obtain node environment characteristic information.

5. The method according to claim 1, wherein The pipe gallery node data includes node operation data, and the node characteristic information includes node operation characteristic information; The step of identifying the node characteristics of the key nodes according to the pipe gallery node data to obtain node characteristic information includes: Identify the cable power-on state of the key nodes according to the node operation data to obtain cable power-on identification information; Identify the operation state of the key nodes according to the cable power-on identification information to obtain node operation identification information; Identify the operating characteristics of the key nodes based on the node operation identification information, and obtain the node operation characteristic information.

6. The method according to claim 1, wherein Based on the node characteristic information, identify the potential risks of the target power pipe gallery to obtain the pipe gallery risk detection result, including: Determine the defect characteristics of the key nodes characterized by the node characteristic information; Based on the relationship between the key nodes and the target power pipe gallery and the defect characteristics, identify the potential risks of the target power pipe gallery to obtain the pipe gallery risk detection result.

7. The method according to any one of claims 1 to 6, characterized in that, Collect the pipe gallery node data of the key nodes, including: Obtain a preset inspection route, and control the robot equipped with data collection components to inspect the key nodes of the target power pipe gallery according to the preset inspection route to obtain the pipe gallery node data; After detecting the potential risks of the target power pipe gallery based on the node characteristic information to obtain the pipe gallery risk detection result, the method further includes: Update the preset inspection route according to the pipe gallery risk detection result to obtain an updated inspection route; Control the robot to inspect the key nodes of the target power pipe gallery according to the updated inspection route to obtain updated node data, update the pipe gallery node data according to the updated node data, and return to the step of identifying the node characteristics of the key nodes based on the pipe gallery node data to obtain the node characteristic information.

8. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 7 are implemented.

Images