Electric power optical cable fault positioning method and system using electronic signboard

Abstract

The invention provides a power optical cable fault positioning method and system using an electronic signboard. The method comprises the following steps: constructing a temperature distribution cloud picture based on temperature data and geographical distribution data; reconstructing a three-dimensional temperature field of the power optical cable in the power pipe gallery based on the temperature distribution cloud picture, and identifying whether an optical cable overheating fault area exceeding a preset reference exists in the three-dimensional temperature field; based on the image data and the geographical distribution data, identifying whether an optical cable external damage fault area exists; and based on the position information pre-stored in the electronic signboard in the target fault area and the azimuth distance information between the target fault area and the electronic signboard, generating power optical cable fault positioning information, the target fault area being an optical cable overheating fault area or an optical cable external damage fault area. According to the technical scheme provided by the invention, unmanned intelligent inspection of the electric power pipe gallery and precise positioning of double-type faults are realized, and the fault response efficiency and the operation and maintenance safety are improved.

Description

Technical Field

[0001] This application relates to the field of power optical cable fault location technology, and in particular to a method and system for power optical cable fault location using electronic identification tags. Background Technology

[0002] As the core carrier of power optical cables, urban power utility tunnels require real-time monitoring and location of two types of frequent faults: localized overheating of the optical cable caused by abnormal current or environmental factors, and physical damage to the optical cable caused by external forces such as construction damage. To ensure rapid repair, fault location accuracy must be within meters, and the location information must be strongly correlated with the inherent spatial markings of the tunnel to avoid coordinate system transformation errors. Furthermore, the complex environment of power utility tunnels makes traditional manual inspections inefficient, necessitating automated, high-precision fault location technology.

[0003] The existing solution involves deploying monitoring nodes along the power utility tunnel that integrate BeiDou positioning modules, environmental sensors, and radar ranging units, powered by solar energy. When the fiber optic cable sags, breaks, or experiences abnormal temperature, the monitoring nodes use radar to detect deformation or trigger an alarm based on a temperature threshold. Simultaneously, they invoke the BeiDou positioning module to obtain the coordinates of the fault point and then transmit the fault type, environmental data, and coordinates back to the backend server via BeiDou short messages. Maintenance personnel then navigate to the fault area using the coordinate information and an electronic map.

[0004] However, existing solutions rely on absolute geographic coordinates for positioning, but the BeiDou module's signal is easily shielded in the enclosed environment of the utility tunnel, resulting in positioning drift errors and making it difficult to meet the meter-level accuracy requirements. Furthermore, its coordinate information is not directly related to the physical markers inside the utility tunnel, requiring maintenance personnel to manually check surrounding signs to identify the specific faulty fiber optic cable after arriving near the coordinates. In addition, the system cannot distinguish vertical positions within the multi-layered structure of the utility tunnel, which can easily lead to misjudgments. Summary of the Invention

[0005] This application provides a method and system for locating power optical cable faults using electronic identification tags, in order to solve the problems of low fault location accuracy caused by large satellite positioning drift errors in enclosed spaces, the separation of fault coordinates from physical markers, and the lack of vertical positioning in multi-layer pipe corridors in the prior art.

[0006] In a first aspect, this application provides a method for locating faults in power optical cables using electronic identification tags, including:

[0007] Acquire temperature data, image data, and geographical distribution data of the power utility tunnel's outer wall;

[0008] Based on the temperature data and the geographical distribution data, a temperature distribution cloud map is constructed;

[0009] Based on the temperature distribution cloud map, a three-dimensional temperature field of the power optical cable inside the power tunnel is reconstructed, and the presence of an overheating fault area of ​​the optical cable exceeding a preset benchmark is identified from the three-dimensional temperature field.

[0010] Based on the image data and the geographical distribution data, identify areas where there are external damage faults in optical cables;

[0011] Based on the pre-stored location information in the electronic signage within the target fault area, and the directional distance information between the target fault area and the electronic signage, power optical cable fault location information is generated. The target fault area is either an optical cable overheating fault area or an optical cable external damage fault area.

[0012] Optionally, the step of reconstructing the three-dimensional temperature field of the power optical cable inside the power duct based on the temperature distribution cloud map, and identifying whether there are optical cable overheating fault areas exceeding a preset benchmark from the three-dimensional temperature field, includes:

[0013] The temperature distribution cloud map is mapped to the optical cable surface nodes of the spatial topology to generate the optical cable surface temperature field.

[0014] Based on the thermal conductivity characteristics, the temperature distribution inside the power optical cable is deduced layer by layer from the surface temperature field of the optical cable to construct the three-dimensional temperature field of the power optical cable.

[0015] In the three-dimensional temperature field, all spatial units are traversed, and spatial units whose temperature values ​​continuously exceed the preset benchmark in multiple consecutive sampling points are marked as optical cable overheating fault units.

[0016] All the aforementioned optical cable overheating fault units are aggregated into an optical cable overheating fault area.

[0017] Optionally, the step of deducing the internal temperature distribution of the power optical cable layer by layer from the surface temperature field of the optical cable based on its thermal conductivity characteristics to construct the three-dimensional temperature field of the power optical cable includes:

[0018] Based on the material thermophysical properties and structural layering parameters of power optical cables, the layering deduction rules of power optical cables are determined.

[0019] Based on the surface temperature field of the optical cable, and according to the layered deduction rules and thermal conduction characteristics, the temperature distribution of each spatial unit inside the power optical cable is deduced layer by layer to obtain the temperature value of each spatial unit.

[0020] By integrating the temperature distribution of the surface temperature field of the optical cable and the temperature values ​​of each spatial unit, a three-dimensional temperature field of the power optical cable is constructed.

[0021] Optionally, identifying the presence of an area with external fiber optic cable damage based on the image data and the geographic distribution data includes:

[0022] Based on the image data, abnormal morphological feature regions on the surface of the power optical cable are detected;

[0023] Map the image coordinates of the abnormal morphological feature region to the corresponding spatial coordinates in the geographic distribution data to determine the set of abnormal spatial locations;

[0024] Spatial coordinates that meet the preset spatial distribution density and preset spatial boundary continuity in the set of abnormal spatial locations are aggregated to generate a spatial boundary range;

[0025] The area within the spatial boundary is designated as the external damage fault area of ​​the optical cable.

[0026] Optionally, the step of mapping the image coordinates of the abnormal morphological feature region to the corresponding spatial coordinates in the geographic distribution data to determine the set of abnormal spatial locations includes:

[0027] Based on the aforementioned geographic distribution data, a mapping function between the image coordinate system and the spatial coordinate system is established;

[0028] Extract the image coordinates of the abnormal morphological feature regions in the image coordinate system;

[0029] The image coordinates are converted into spatial coordinates in a spatial coordinate system using the mapping function.

[0030] Based on the spatial topology of the power optical cables inside the power tunnel, the spatial coordinates are associated with the corresponding location points on the surface of the power optical cables to determine the set of abnormal spatial locations.

[0031] Optionally, constructing a temperature distribution cloud map based on the temperature data and the geographical distribution data includes:

[0032] Based on the geographical distribution data, multiple spatial location points of the power utility tunnel are extracted to obtain a set of spatial location points of the power utility tunnel;

[0033] The temperature data is matched with the geographic coordinates corresponding to the spatial location points of the power utility tunnel, and the matched temperature data is merged into the corresponding spatial location points to form a location temperature point set.

[0034] Based on the set of location temperature points and combined with the geometric features of the power utility tunnel, a temperature distribution cloud map is constructed.

[0035] Optionally, generating power fiber optic cable fault location information based on pre-stored location information in electronic markers within the target fault area and directional distance information between the target fault area and the electronic markers includes:

[0036] Based on the spatial boundary range of the target fault area, the electronic signs within the target fault area are filtered out from the set of electronic signs using a spatial filtering function;

[0037] A wake-up command is sent to the electronic signage via an Internet of Things (IoT) communication network deployed within the power utility tunnel, triggering the electronic signage to transmit pre-stored location information back.

[0038] When there are multiple electronic signs, for each electronic sign, the azimuth angle and straight-line distance of the target fault area relative to the electronic sign are calculated to obtain azimuth distance information;

[0039] By combining the directional and distance information of multiple electronic signs, the location information of the power optical cable fault location point is determined through a triangulation algorithm;

[0040] The pre-stored location information, the azimuth distance information, and the location information of the power optical cable fault location point are integrated into a structured data object to form power optical cable fault location information.

[0041] Secondly, this application provides a power optical cable fault location system utilizing electronic identification tags, comprising:

[0042] The acquisition module is used to acquire temperature data, image data, and geographical distribution data of the power utility tunnel's outer wall.

[0043] The construction module is used to construct a temperature distribution cloud map based on the temperature data and the geographical distribution data;

[0044] The reconstruction module is used to reconstruct the three-dimensional temperature field of the power optical cable inside the power tunnel based on the temperature distribution cloud map, and to identify whether there is an optical cable overheating fault area that exceeds the preset benchmark from the three-dimensional temperature field.

[0045] The identification module is used to identify, based on the image data and the geographical distribution data, whether there is an area of ​​external damage to the optical cable;

[0046] The generation module is used to generate power optical cable fault location information based on the pre-stored location information in the electronic signage within the target fault area and the directional distance information between the target fault area and the electronic signage. The target fault area is either an optical cable overheating fault area or an optical cable external damage fault area.

[0047] Thirdly, this application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are to be invoked and executed by the processing component to implement a method for locating power optical cable faults using electronic tags as described in any of the first aspects.

[0048] Fourthly, this application provides a computer storage medium storing a computer program, which, when executed by a computer, implements a method for locating power optical cable faults using electronic identification tags as described in any of the first aspects.

[0049] This application provides a method for locating power optical cable faults using electronic identification tags. The method includes: acquiring temperature data, image data, and geographical distribution data of the outer wall of the power utility tunnel; constructing a temperature distribution cloud map based on the temperature data and geographical distribution data; reconstructing a three-dimensional temperature field of the power optical cable inside the power utility tunnel based on the temperature distribution cloud map, and identifying whether there is an optical cable overheating fault area exceeding a preset benchmark from the three-dimensional temperature field; identifying whether there is an optical cable external damage fault area based on the image data and geographical distribution data; and generating power optical cable fault location information based on the pre-stored location information in the electronic identification tags within the target fault area and the directional distance information between the target fault area and the electronic identification tags, wherein the target fault area is either an optical cable overheating fault area or an optical cable external damage fault area.

[0050] This application has the following advantages: By simultaneously collecting external wall temperature, image, and geographic distribution data, it provides a multimodal data foundation for dual-type fault identification, avoiding missed detections by a single sensor; by fusing temperature data and geographic information to generate a spatialized heat map, it enables the visual location of abnormal external wall temperature in the utility tunnel, providing input for internal temperature field reconstruction; by inferring the three-dimensional temperature distribution inside the optical cable based on the external wall temperature cloud map, it overcomes the limitations of surface monitoring and accurately identifies concealed overheating fault units; by combining image data and geographic coordinates for spatial matching analysis, it achieves automated identification and spatial labeling of physical damage; and by utilizing the pre-stored location and azimuth distance data of the electronic signs adjacent to the fault area, it generates meter-level positioning information strongly correlated with the physical signs of the utility tunnel, eliminating coordinate system transformation errors.

[0051] Furthermore, the temperature distribution cloud map is mapped onto the surface nodes of the optical cable to generate a surface temperature field. Based on the thermophysical properties and layered structural parameters of the optical cable material, the temperature values ​​of each internal spatial unit are deduced layer by layer from the surface temperature field using a heat conduction model to construct a three-dimensional temperature field. The three-dimensional temperature field is then traversed to mark continuously overheated units, which are aggregated into overheating fault regions. Existing technologies rely on the limitations of surface temperature threshold detection. By using heat conduction physical modeling to achieve penetrating reconstruction of the internal temperature field of the optical cable, the depth and accuracy of overheating fault identification are improved. Simultaneously, the continuous overheating criterion effectively avoids interference from instantaneous temperature fluctuations, ensuring the reliability of fault determination.

[0052] These or other aspects of this application will become more apparent from the description of the following embodiments. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0054] Figure 1 A flowchart illustrating a method for locating power optical cable faults using electronic identification tags, provided as an embodiment of this application;

[0055] Figure 2 A schematic diagram of a power optical cable fault location system using electronic identification tags is provided in an embodiment of this application;

[0056] Figure 3 This is a schematic diagram of the structure of a computing device provided in an embodiment of this application. Detailed Implementation

[0057] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0058] In some of the processes described in the specification, claims, and accompanying drawings of this application, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 11, 12, etc., are merely used to distinguish different operations and do not themselves represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to different types.

[0059] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0060] To address the low accuracy of power fiber optic cable fault location in existing technologies, caused by large satellite positioning drift errors in enclosed spaces, the disconnect between fault coordinates and physical markers, and the lack of vertical positioning in multi-layered utility tunnels, this application provides a power fiber optic cable fault location method using electronic markers. This method employs the following concept: First, a spatialized temperature cloud map is constructed by simultaneously collecting external wall temperature, images, and geographic distribution data, overcoming the limitations of surface monitoring. Then, based on a thermal conduction physical model, the three-dimensional temperature distribution inside the fiber optic cable is inversely deduced from the external wall temperature field, and fault units are identified through continuous over-temperature criteria. Simultaneously, spatial matching of geographic information and images is used to achieve automated labeling of external damage. Finally, an innovative approach is taken by introducing pre-installed electronic markers in the utility tunnel as spatial anchor points. Based on their pre-stored locations and the relative azimuth distance of the fault area, meter-level positioning information strongly correlated with the physical markers is generated, forming a closed-loop process from fault identification to spatial anchoring.

[0061] Figure 1 A flowchart illustrating a method for locating faults in power optical cables using electronic identification tags, as provided in this application embodiment, is shown below. Figure 1 As shown, the method includes:

[0062] S11. Obtain temperature data, image data, and geographical distribution data of the power utility tunnel's outer wall.

[0063] In this context, "power utility tunnel" refers to an underground tunnel or trench structure used for the centralized laying of power optical cables, including a concrete enclosure, support system, and ancillary facilities. "Outer wall" refers to the inner surface of the main structure of the power utility tunnel, directly contacting the internal environment. "Temperature data" refers to a time-series dataset reflecting the temperature values ​​at various points on the outer wall of the power utility tunnel, collected by infrared sensors. "Image data" refers to a collection of optical images capturing the surface condition of the power optical cables, acquired by dual-mode visible light and infrared cameras. "Geographical distribution data" refers to vector geographic information describing the three-dimensional spatial coordinates, topological connections, and structural dimensions of the power utility tunnel.

[0064] In this embodiment, the temperature data of the outer wall is first collected by an infrared temperature sensor array deployed on the outer wall of the power utility tunnel. At the same time, the panoramic camera on the top of the tunnel is used to obtain image data of the optical cables and supports inside the power utility tunnel. The three-dimensional geographic distribution data of the power utility tunnel is exported through a pre-set geographic information system. Finally, the three types of data are transmitted synchronously to the central processing unit.

[0065] S12. Construct a temperature distribution cloud map based on temperature data and geographical distribution data.

[0066] Among them, the temperature distribution cloud map refers to a visualized thermal map of the outer wall of the utility tunnel generated by fusing spatial location point sets and temperature data.

[0067] In this embodiment of the application, firstly, the spatial location point set of the power utility tunnel is extracted based on the geographical distribution data of the tunnel. Secondly, the temperature data is spatially matched with the corresponding geographical coordinates. Subsequently, the location point set and the temperature data are fused using the Kriging spatial interpolation algorithm to generate a discrete temperature point set. Finally, a temperature distribution cloud map covering the entire outer surface of the tunnel is constructed by combining the geometric structural features of the tunnel.

[0068] S13. Reconstruct the three-dimensional temperature field of the power optical cable inside the power tunnel based on the temperature distribution cloud map, and identify whether there are optical cable overheating fault areas exceeding the preset benchmark from the three-dimensional temperature field.

[0069] Among them, power optical cable refers to the insulated conductor bundle and its protective layer structure laid in a conduit to carry power transmission. Three-dimensional temperature field refers to a three-dimensional matrix model reflecting the temperature distribution of all spatial units from the surface to the core of the power optical cable. Preset benchmark refers to the temperature safety threshold set according to the heat resistance limit of the optical cable material. Optical cable overheating fault zone refers to a three-dimensional overheated area formed by the aggregation of adjacent spatial units that continuously exceed the preset benchmark. Optical cable external damage fault zone refers to a continuous area on the optical cable surface with physical damage characteristics determined after spatial clustering.

[0070] In this embodiment, the temperature distribution cloud map is first mapped to the surface spatial topology nodes of the power optical cable to generate the surface temperature field of the optical cable. Then, the heat conduction inference rules are determined based on the thermophysical property parameters and layered structure parameters of the optical cable material. Subsequently, the temperature values ​​of each spatial unit are inferred layer by layer from the surface temperature field inward according to Fourier's heat conduction law. Finally, all unit temperature values ​​are integrated to construct a three-dimensional temperature field of the power optical cable. The spatial units that continuously exceed the preset benchmark are marked as overheating fault units in the three-dimensional temperature field, and adjacent fault units are aggregated to form an overheating fault region of the optical cable.

[0071] S14. Based on image data and geographic distribution data, identify areas where there is external damage to optical cables.

[0072] In this embodiment, firstly, a target detection algorithm is used to identify abnormal morphological feature areas on the surface of the optical cable. Secondly, a mapping function between the image and the spatial coordinate system is established based on the geographic distribution data. Then, the image coordinates of the abnormal area are converted into spatial coordinates to generate a set of abnormal locations. Finally, a clustering algorithm is used to aggregate the spatial coordinates according to the spatial distribution density and boundary continuity to output the spatial boundary range of the external damage fault area of ​​the optical cable.

[0073] S15. Based on the pre-stored location information in the electronic signage within the target fault area, and the directional distance information between the target fault area and the electronic signage, generate power optical cable fault location information. The target fault area is either an optical cable overheating fault area or an optical cable external damage fault area.

[0074] The target fault area can refer to the three-dimensional spatial range of the overheating or external damage fault that needs to be located. An electronic tag is a label device pre-embedded in the inner wall of the utility tunnel, storing location codes and supporting wireless wake-up. Location information refers to the absolute geographic coordinates and associated optical cable number data pre-stored in the electronic tag. Orientation and distance information refers to the polar coordinate angle and straight-line distance data of the fault area's center point relative to the electronic tag. Power optical cable fault location information refers to a structured message integrating fault location coordinates, associated tag data, and spatial relationships.

[0075] In this embodiment, electronic signs within the target fault area are first selected based on the spatial boundary range. Then, a wake-up command is sent via the Internet of Things to trigger the signs to transmit pre-stored location information. Subsequently, the azimuth angle and straight-line distance of the fault area relative to each of the multiple signs are calculated. Finally, multiple sets of azimuth distance data are merged and a triangulation algorithm is used to generate power fiber optic cable fault location points. All information is integrated to form structured fault location information.

[0076] Here is a specific example: In the operation and maintenance of a 5km-long underground power tunnel in a city, containing three layers of fiber optic cable enclosures, an infrared temperature sensor array deployed on the outer wall of the tunnel collects temperature data at various points on the outer wall at a sampling frequency of 1 minute / time. Simultaneously, dual-mode visible light and infrared cameras installed every 50 meters on the top of the tunnel capture 4K resolution images of the fiber optic cable surface, acquiring image data. Three-dimensional geographic distribution data, including the latitude, longitude, elevation, layered structure of the enclosures, and spatial coordinates of the fiber optic cable supports for each section of the tunnel, is exported from the tunnel's Geographic Information System (GIS). All three types of data are synchronously transmitted to the central processing unit via industrial Ethernet.

[0077] Based on GIS geographic distribution data, 500 key spatial location points, including bends and cabin interfaces, were extracted from the outer wall of the utility tunnel to form a spatial location point set. Each point has three-dimensional coordinates, such as 116.3°E, 39.9°N, -5m. Temperature data collected by infrared sensors, such as a temperature of 28°C at a certain point, were matched with the geographic coordinates of the spatial location point set to form a location temperature point set. Using the Kriging spatial interpolation algorithm, combined with the geometric structure of the rectangular cabin of the utility tunnel (3m wide and 4m high), the discrete point set was interpolated into a continuous temperature distribution cloud map. The temperature gradient in the cloud map is marked with red, yellow, and blue, with areas above 35°C designated as red high-temperature zones.

[0078] The temperature distribution cloud map is mapped onto the spatial topology nodes of the optical cable surface. The optical cable surface is divided into nodes at 10cm intervals to generate the surface temperature field, such as a node temperature of 32℃. Based on the material parameters of the optical cable, the thermal conductivity of the polyethylene sheath is 0.3W / (m·K), the thermal conductivity of the steel armor layer is 45W / (m·K), and the layered structure has a sheath thickness of 2mm, a steel armor thickness of 1mm, and a fiber core diameter of 0.1mm, the layered extrapolation rules are determined. According to Fourier's law of heat conduction, the temperature field is extrapolated layer by layer from the surface inwards, and the internal spatial units (1cm) are calculated. 3 Temperature values ​​of the cubes, such as 33°C inside the sheath and 35°C in the steel armor layer, are integrated to form a three-dimensional temperature field. A preset reference temperature of 60°C is set as the upper limit of the optical cable insulation layer's tolerance. The three-dimensional temperature field is traversed, and spatial units with temperatures exceeding 60°C within three consecutive acquisition cycles (3 minutes) are marked, such as units with temperatures of 62°C, 63°C, and 65°C. Adjacent overheating fault units are aggregated to form a cylindrical overheating fault region with a length of 0.5m and a diameter of 0.1m, corresponding to GIS coordinates of 116.302°E, 39.901°N, -5.2m.

[0079] The image data captured by the camera is processed, and the YOLOv5 algorithm is used to identify abnormal morphologies on the surface of the optical cable, such as sheath damage and deformation, marking an area of ​​0.02m². 2 Irregularly damaged areas were identified. A mapping function between pixel coordinates in the image coordinate system and three-dimensional coordinates in the spatial coordinate system was established based on GIS data, achieved through camera intrinsic parameter matrices and perspective transformation. The pixel coordinates of the damaged areas in the image, such as 800600, were extracted and converted to spatial coordinates of 116.305°E, 39.902°N, -4.8m. The converted abnormal spatial coordinates were clustered, and areas with a spatial distribution density ≥ 5 points / m were selected. 2 The set of coordinates with an adjacent point spacing of ≤0.2m generates a closed rectangular area with a spatial boundary of 0.3m in length and 0.2m in width. This boundary area is the external fault area of ​​the optical cable, corresponding to section A of the optical cable in the second-level cabin of the utility tunnel.

[0080] Within the spatial boundary of the target fault area (overheating / external damage), three pre-embedded electronic markers (E1, E2, and E3) were selected using a spatial filtering function. A wake-up command was sent to these three markers via a LoRa IoT connection within the utility tunnel, triggering them to transmit their pre-stored location information. The azimuth angle (e.g., 30° from E1 to the center point) and the straight-line distance (e.g., 0.8m from E1 to the center point) of each marker were calculated to obtain azimuth and distance information. Combining these three sets of azimuth and distance information, a least-squares optimized triangulation algorithm was used to determine the fault location point coordinates as 116.302°E, 39.901°N, -5.2m. The pre-stored location, azimuth distance, and positioning point coordinates are integrated into the structured data to form the positioning information "Fault type: overheating; Positioning point: 116.302°E, 39.901°N, -5.2m; Distance from adjacent signboard E1: 0.8m, Azimuth: 30°; Corresponding to optical cable No. 2 in section B of the utility tunnel", which is then pushed to the operation and maintenance terminal.

[0081] By executing S11 to S15, this embodiment of the application achieves penetrating identification of dual-type faults through multi-source data collaborative processing, breaking through the limitations of closed pipe gallery environment on internal status monitoring; it deduces the deep temperature distribution of optical cable based on physical heat conduction model, solving the problem that surface detection cannot detect hidden overheating; it uses electronic tag spatial anchoring mechanism to directly associate fault coordinates with physical tags, eliminating positioning drift error, and finally forming an automated, high-precision closed loop for power optical cable fault location.

[0082] In one possible embodiment, S13, reconstructing the three-dimensional temperature field of the power optical cable inside the power duct based on the temperature distribution cloud map, and identifying whether there are optical cable overheating fault areas exceeding a preset benchmark from the three-dimensional temperature field, including:

[0083] Step 131: Map the temperature distribution cloud map to the optical cable surface nodes of the spatial topology to generate the optical cable surface temperature field.

[0084] The spatial topology refers to the three-dimensional geometric model of the optical cable, including the spatial coordinates of the surface nodes and their connections, used to describe the physical shape and spatial distribution characteristics of the cable. Surface nodes are discrete measurement points distributed on the outer surface of the cable, used to collect temperature data and form the basic units of the cable's surface temperature field. The surface temperature field is the set of temperature distributions at each node on the cable surface, which, through interpolation algorithms, forms a continuous temperature cloud map reflecting the thermal state of the cable surface.

[0085] In this embodiment, firstly, the surface geometry and node distribution of the power optical cable are determined based on its spatial topology, which includes the spatial coordinates and connection relationships of the nodes on the cable surface. Secondly, real-time temperature data of each node on the cable surface is acquired using infrared thermometry or distributed fiber optic sensing technology. Subsequently, a spatial interpolation algorithm is used to map the discrete temperature data to the nodes on the cable surface, forming a continuous temperature distribution cloud map. Finally, a temperature field on the cable surface is generated based on the mapping results, reflecting the thermal state at various locations on the cable surface.

[0086] Step 132: Based on the thermal conduction characteristics, the temperature distribution inside the power optical cable is deduced layer by layer from the surface temperature field of the optical cable to construct the three-dimensional temperature field of the power optical cable.

[0087] Among them, thermal conductivity refers to the thermal diffusion capability of optical cable materials, including parameters such as thermal conductivity and specific heat capacity, which are used to calculate the process of temperature transfer from the surface to the interior.

[0088] In this embodiment, firstly, a heat conduction equation is established based on the thermal conductivity characteristics of the optical cable material to describe the temperature transfer from the surface to the interior. Secondly, using the finite element method or the finite difference method, with the surface temperature field of the optical cable as the boundary condition, the temperature distribution of each layer inside the optical cable is calculated layer by layer. Subsequently, combined with the physical structural parameters of the optical cable, the three-dimensional temperature field inside the power optical cable is deduced. Finally, this three-dimensional temperature field encompasses the temperature gradient change from the surface to the core of the optical cable.

[0089] Step 133: Traverse all spatial units in the three-dimensional temperature field and mark the spatial units whose temperature values ​​continuously exceed the preset benchmark in multiple consecutive sampling points as optical cable overheating fault units.

[0090] Among them, a spatial unit refers to a tiny volumetric region in a three-dimensional temperature field, representing a computational unit inside the optical cable, used to analyze local temperature changes. An optical cable overheating fault unit refers to a spatial unit whose temperature continuously exceeds limits during continuous monitoring, representing a potential local overheating fault point.

[0091] In this embodiment, firstly, a number of spatial units are divided in the three-dimensional temperature field, each representing a tiny volume region inside the optical cable. Secondly, all spatial units are traversed, and their temperature values ​​at multiple consecutive sampling points are recorded. Subsequently, spatial units whose temperature values ​​consistently exceed a preset benchmark are marked as optical cable overheating fault units. Finally, this marking process ensures that potential fault areas are accurately identified.

[0092] Step 134: Aggregate all optical cable overheating fault units into an optical cable overheating fault area.

[0093] In this embodiment, firstly, all marked optical cable overheating fault units are extracted, and their spatial distribution characteristics are analyzed. Secondly, clustering algorithms or region growing algorithms are used to aggregate adjacent overheating fault units into continuous regions. Subsequently, the boundaries and extent of the optical cable overheating fault region are generated based on the aggregation results. Finally, this fault region can be used for subsequent early warning or maintenance decisions.

[0094] Here is a specific example: First, an infrared sensor array installed on the outer wall of the power cable tunnel collects real-time temperature data at various locations on the surface of the optical cable. Simultaneously, a camera deployed at the top of the tunnel captures real-time images of the cable, and the tunnel's three-dimensional geographic coordinates are retrieved from a geographic information system. Next, the temperature data collected by the infrared sensors is matched with the tunnel's geographic coordinates, and a Kriging spatial interpolation algorithm is used to transform discrete temperature points into a continuous temperature distribution cloud map, which completely covers the entire area of ​​the tunnel's outer wall. Then, based on the spatial topology of the optical cable, the temperature cloud map of the tunnel's outer wall is mapped to the nodes on the cable surface, forming the cable surface temperature field. Finally, a heat conduction equation is established based on the thermal conductivity and specific heat capacity parameters of the cable material, and the three-dimensional temperature distribution field inside the cable is calculated using finite element analysis. Subsequently, the three-dimensional temperature field was divided into cubic spatial units with a side length of 10 cm. Units with a temperature exceeding 90 degrees Celsius within five consecutive acquisition cycles were detected and marked as overheating fault units. Simultaneously, image recognition algorithms were used to analyze fiber optic cable images captured by cameras to detect surface deformation or damage areas. Through coordinate transformation, abnormal areas in the images were mapped to three-dimensional coordinates in the geographic information system, and clustering algorithms were used to generate external fault areas. Then, for the identified fault areas, pre-embedded electronic markers within the area were automatically activated, and their stored absolute position information was read. Based on the spatial relationship between the marker coordinates and the fault area boundary, the azimuth and straight-line distance of the fault center point relative to each marker were calculated. Finally, the measurement data of at least three markers were integrated, and the least squares method was used to optimize the triangulation calculation, outputting the latitude and longitude coordinates of the fault point. A structured location report containing the tunnel section number, fiber optic cable identification code, fault type, and relative entrance / exit distance was automatically generated, allowing maintenance personnel to quickly navigate to the fault point for handling.

[0095] In another exemplary scenario, in the operation and maintenance of the aforementioned urban underground power tunnel, the specific process for identifying optical cable overheating fault areas is as follows: A temperature distribution cloud map is mapped onto the optical cable surface nodes of the spatial topology to generate a surface temperature field for the optical cable. The spatial topology of a section of optical cable within the tunnel is a cylindrical three-dimensional geometric model with a diameter of 0.1m. Nodes are evenly distributed on the surface at 10cm intervals. Each node contains three-dimensional coordinates (e.g., the coordinates of a node 100m from the tunnel entrance are 116.301°E, 39.901°N, -5.2m), and adjacent nodes are connected as a continuous surface through topological relationships. Based on this structure, the constructed temperature distribution cloud map (where the corresponding area of ​​the optical cable shows a temperature distribution of 32℃-35℃) is mapped to these surface nodes: the 32℃ area in the cloud map corresponds to node A (116.301°E, 39.901°N, -5.2m), the 33℃ area corresponds to node B (116.3015°E, 39.901°N, -5.2m), and the 35℃ area corresponds to node C (116.302°E, 39.901°N, -5.2m). Then, a linear interpolation algorithm is used to fill the temperature gaps between nodes, forming a continuous temperature distribution covering the entire surface of the optical cable. Finally, a surface temperature field of the optical cable is generated, clearly reflecting the surface thermal state where the temperature gradually increases from node A to node C.

[0096] Next, based on the thermal conductivity characteristics, the internal temperature distribution was deduced layer by layer from the surface temperature field to construct a three-dimensional temperature field. The thermophysical properties of the optical cable are as follows: the thermal conductivity of the polyethylene sheath is 0.3 W / (m·K), and its specific heat capacity is 1.8 kJ / (kg·K); the thermal conductivity of the steel armor layer is 45 W / (m·K), and its specific heat capacity is 0.46 kJ / (kg·K); the structural layering parameters are: sheath thickness 2 mm, steel armor layer thickness 1 mm, and fiber core diameter 0.1 mm. Based on these parameters, a heat conduction equation was established (…). Where α is the thermal diffusivity. The Laplace operator is used, T is time, and a layered extrapolation rule is defined: calculations are performed layer by layer from the surface to the interior, following the order of sheath, steel armor layer to fiber core, with each layer divided into calculation units of 0.5mm thickness. Using 35℃ at node C in the surface temperature field as the boundary condition, the finite difference method is used to calculate: the temperature inside the sheath (2mm from the surface) is 36℃ (due to the sheath's weak thermal conductivity, heat accumulation causes a slight temperature rise); the temperature of the steel armor layer (2-3mm from the surface) is 40℃ (the steel armor has strong thermal conductivity, and rapid heat conduction causes a significant temperature rise); the temperature of the fiber core (within 3mm of the surface) is 42℃. The distribution of the surface temperature field is integrated with the temperature values ​​of each internal calculation unit to form a three-dimensional temperature field encompassing the temperature of all spatial units from the optical cable surface to the fiber core, fully presenting the temperature gradient of each layer.

[0097] Then, all spatial elements are traversed in the three-dimensional temperature field, and elements that continuously exceed the preset reference are marked as overheating fault elements. This three-dimensional temperature field is set at 1 cm intervals. 3 The cube was divided into spatial units, each corresponding to a specific location. For example, a unit within the steel armor layer had coordinates of 116.302°E, 39.901°N, -5.2m, and 2.5mm from the surface. The preset reference temperature was 60℃ (the upper limit of the optical cable insulation layer's tolerance), and the continuous acquisition cycle was set to 3 times (1 minute per cycle). During the traversal, it was found that the temperatures of 5 spatial units in a certain area within the steel armor layer were 62℃, 63℃, and 65℃ respectively during the 3 acquisitions, all exceeding 60℃ and continuously exceeding the limit. These 5 units were marked as optical cable overheating fault units.

[0098] Finally, all overheating fault units were aggregated into an overheating fault region. These five marked overheating fault units were extracted, and their spatial distribution was analyzed: the distance between each unit was less than 0.1m, indicating a continuous adjacent state. Using a region growing algorithm, starting from one of the units, adjacent fault units were gradually merged, ultimately forming a cylindrical region 0.5m long and 0.1m in diameter. Its GIS coordinates ranged from 116.3018°E to 116.3023°E, 39.901°N to 39.9011°N, and -5.2m. This region is the optical cable overheating fault region and can be directly used for subsequent fault location and early warning.

[0099] By executing steps 131 to 134, this embodiment of the application combines spatial topology and temperature sensing technology to achieve accurate modeling of the surface and internal temperature field of the power optical cable, and infers the internal temperature distribution based on thermal conductivity. By traversing and analyzing the three-dimensional temperature field, fault units that are continuously overheating can be accurately identified and aggregated into fault regions, thereby improving the detection accuracy and early warning capability of power optical cable overheating faults and ensuring the safe operation of the power communication system.

[0100] In one possible embodiment, step 132, based on the thermal conductivity characteristics, deduces the internal temperature distribution of the power optical cable layer by layer from the surface temperature field of the optical cable to construct a three-dimensional temperature field of the power optical cable, including:

[0101] Step a1: Based on the material thermophysical property parameters and structural layering parameters of the power optical cable, determine the layering derivation rules of the power optical cable.

[0102] Among them, the material thermophysical properties parameters refer to physical quantities describing the thermal characteristics of power optical cable materials, including thermal conductivity reflecting the rate of heat transfer, specific heat capacity reflecting the heat absorption capacity, and density reflecting mass distribution. These three factors jointly determine the temperature transfer law in the material. Structural layering parameters refer to the geometric and physical characteristics of each layer of material within the power optical cable, including the thickness of each layer, material type, and interlayer contact method, used to define the computational domain boundary for temperature derivation. Layered derivation rules refer to the temperature calculation logic established based on the heat conduction equation and layered structure, specifying how to derive the temperatures of each internal layer step by step from the surface temperature, including the transfer algorithm and interlayer coupling conditions.

[0103] In this embodiment, firstly, the thermophysical properties of the power optical cable material are obtained, including thermal conductivity, specific heat capacity, and density. These parameters reflect the material's thermal conductivity and energy storage characteristics. Secondly, the structural layering parameters of the power optical cable are extracted, including the thickness, arrangement order, and contact relationship of each layer, to describe the internal structure of the cable. Subsequently, based on the heat conduction equation and the layered structural characteristics, a layered deduction rule from the surface to the core is established. This rule specifies the calculation method and boundary conditions for temperature transfer from the outside to the inside. Finally, the layered deduction rule will serve as the basis for subsequent temperature field calculations.

[0104] Step a2: Based on the surface temperature field of the optical cable, and according to the layered deduction rules and heat conduction characteristics, the temperature distribution of each spatial unit inside the power optical cable is deduced layer by layer to obtain the temperature value of each spatial unit.

[0105] Among them, the temperature value refers to the quantitative result of the thermal state of the spatial unit obtained by calculation or measurement, which is used to describe the temperature level of a certain point inside the optical cable.

[0106] In this embodiment, firstly, the surface temperature field of the optical cable is used as the initial input, which includes the temperature distribution data of each node on the optical cable surface. Secondly, according to the layered deduction rules, the surface temperature is used as the first layer boundary condition. Combining the thermal conductivity and specific heat capacity in the thermal conductivity characteristics, the temperature values ​​of adjacent internal spatial units are calculated layer by layer using the finite difference method. Subsequently, this process is repeated until the temperature distribution within all layers of the optical cable is deduced. Finally, the temperature value of each spatial unit is obtained, forming a discrete temperature dataset inside the optical cable.

[0107] Step a3: Integrate the temperature distribution of the optical cable surface temperature field and the temperature values ​​of each spatial unit to construct the three-dimensional temperature field of the power optical cable.

[0108] In this embodiment, firstly, the continuous temperature distribution data of the optical cable surface temperature field is integrated with the discrete temperature values ​​of each spatial unit obtained in step a2. Secondly, a three-dimensional interpolation algorithm is used to fill the temperature gradient between spatial units, ensuring the continuity of the temperature field as a whole. Subsequently, the temperature data is mapped to the corresponding three-dimensional coordinate points according to the geometric model of the optical cable, constructing a complete three-dimensional temperature field for the power optical cable. Finally, this three-dimensional temperature field can intuitively display the temperature change of the optical cable from the surface to the core.

[0109] Here is a specific example: First, an infrared sensor array installed on the outer wall of the power cable tunnel collects temperature data at various locations on the surface of the optical cable. Simultaneously, a camera on the top of the tunnel captures real-time images of the cable, and the tunnel's three-dimensional geographic coordinates are retrieved from a geographic information system. Next, the temperature data collected by the infrared sensors is matched with the tunnel's geographic coordinates, and a Kriging spatial interpolation algorithm is used to transform discrete temperature points into a continuous temperature distribution cloud map, which completely covers the entire area of ​​the tunnel's outer wall. Then, based on the spatial topology of the optical cable, the temperature cloud map of the tunnel's outer wall is mapped to nodes on the cable surface, forming a surface temperature field. A heat conduction equation is then established based on the thermal conductivity and specific heat capacity parameters of the cable material, and the three-dimensional temperature distribution field inside the cable is calculated using finite element analysis. Subsequently, cubic spatial units with sides of 10 cm are divided within the three-dimensional temperature field. This size is determined based on 1 / 20th of the cable diameter to ensure calculation accuracy. Units with temperatures exceeding 90 degrees Celsius within five consecutive acquisition cycles are identified and marked as overheating fault units. The 90-degree Celsius threshold is derived from the maximum allowable operating temperature standard of the power cable's insulation material. Simultaneously, image recognition algorithms are used to analyze fiber optic cable images captured by cameras to detect surface deformation or damage areas. Anomaly areas in the images are mapped to three-dimensional coordinates in a geographic information system through coordinate transformation, and clustering algorithms are used to generate external fault areas. Then, for the identified fault areas, pre-embedded electronic markers within the area are automatically activated, their stored absolute position information is read, and based on the spatial relationship between the marker coordinates and the fault area boundary, the azimuth angle θ = arctan((y2-y1) / (x2-x1)) and the straight-line distance of the fault center point relative to each marker are calculated. Where (x1, y1) are the x and y coordinates of the sign, and (x2, y2) are the x and y coordinates of the center point of the fault area. Finally, the measurement data of at least three signs are integrated, and the least squares method is used to optimize the triangulation calculation. The latitude and longitude coordinates of the fault point are output, and a structured positioning report containing the tunnel section number, optical cable identification code, fault type, and relative entrance / exit distance is automatically generated, allowing maintenance personnel to quickly navigate to the fault point for handling.

[0110] In another exemplary scenario, within the aforementioned urban underground power tunnel operation and maintenance scenario, the specific process for constructing the three-dimensional temperature field in step 132 is as follows:

[0111] The hierarchical deduction rules were determined. Specifically, the thermophysical properties of the power optical cable are as follows: thermal conductivity of the polyethylene sheath is 0.3 W / (m·K), specific heat capacity is 1.8 kJ / (kg·K), and density is 950 kg / m³. 3 The steel armor layer has a thermal conductivity of 45 W / (m·K), a specific heat capacity of 0.46 kJ / (kg·K), and a density of 7850 kg / m³. 3 The fiber core has a thermal conductivity of 0.2 W / (m·K), a specific heat capacity of 1.0 kJ / (kg·K), and a density of 2200 kg / m³. 3 The structural layering parameters are as follows: from the outside to the inside, the layers are a polyethylene sheath (2mm thick), a steel armor layer (1mm thick), and a fiber core (0.1mm diameter), with tight contact between the layers (no air gaps). Based on these parameters and the heat conduction equation ( (α is the thermal diffusivity). A layered deduction rule is established: calculations are performed layer by layer from the outside in, following the order of sheath, steel armor layer, and fiber core; each layer is divided into calculation units with a thickness of 0.5 mm, and heat transfer between adjacent units is calculated according to Fourier's law; the temperature at the interlayer contact point is continuous (without temperature abrupt changes), serving as the boundary condition. Next, the temperature of the internal spatial units is deduced layer by layer. The surface temperature field of the optical cable is used as the initial input, where the surface temperature of node C (116.302°E, 39.901°N, -5.2m) is 35°C. According to the layered deduction rule, this temperature is used as the boundary condition for the outer layer of the sheath.

[0112] The internal temperature distribution is as follows: Sheath layer (0-2mm): Divided into four spatial units of 0.5mm, the temperature is calculated using the finite difference method. The unit temperature is 35.2℃ at 0.5mm from the surface, 35.5℃ at 1.0mm, 35.8℃ at 1.5mm, and 36℃ at 2.0mm (inner side of the sheath). Steel armor layer (2-3mm): Similarly, divided into two spatial units of 0.5mm, based on the 36℃ temperature inside the sheath and the high thermal conductivity of the steel armor, the calculated temperature is 40℃ at 2.5mm and 42℃ at 3.0mm (inner side of the steel armor). Fiber core (within 3mm): Due to its 0.1mm diameter, it is divided into one spatial unit. Based on the 42℃ temperature inside the steel armor, the calculated fiber core temperature is 43℃. The final temperature values ​​for each spatial unit form a discrete internal temperature dataset.

[0113] Finally, a three-dimensional temperature field is constructed. The continuous distribution of the surface temperature field of the optical cable (e.g., node A 32℃, node B 33℃, node C 35℃) is integrated with the internal spatial unit temperatures (inner sheath 36℃, steel armor 40℃, fiber core 43℃). An inverse distance weighted three-dimensional interpolation algorithm is used to fill in the temperature gradient between each spatial unit (e.g., the temperature in the sheath layer smoothly transitions from 35.2℃ to 35.5℃ between 0.5mm and 1.0mm), ensuring the continuity of the temperature field. Combined with the cylindrical geometric model of the optical cable (0.1m in diameter), all temperature data are mapped to corresponding three-dimensional coordinate points (e.g., a point on the outer sheath at 116.302°E, 39.901°N, -5.2m, with a temperature of 35℃ 0.05m from the center; a point on the steel armor layer at the same coordinates, with a temperature of 40℃ 0.048m from the center). This finally constructs a complete three-dimensional temperature field, which can intuitively display the gradual temperature increase from the surface of the optical cable to the fiber core.

[0114] By executing steps a1 to a3, this embodiment of the application establishes scientific layering deduction rules by combining material thermophysical properties and structural layering parameters, achieving accurate calculation from the surface temperature field of the optical cable to the internal three-dimensional temperature field. This method can fully reflect the internal thermal distribution state of the optical cable, providing a reliable data foundation for overheating fault detection and improving the precision level of thermal management of power optical cables.

[0115] In one possible embodiment, S14, based on image data and geographic distribution data, identifies whether there is an area of ​​external damage to the optical cable, including:

[0116] Step 141: Based on image data, detect abnormal morphological feature areas on the surface of the power optical cable.

[0117] Among them, abnormal morphological feature areas refer to areas of damage, deformation or external force damage on the surface of power optical cables detected by image analysis. Their morphological features include irregular contours, abnormal textures or local structural distortions.

[0118] In this embodiment, firstly, image data of the surface of the power fiber optic cable is acquired using a camera deployed at the top of the utility tunnel. Secondly, computer vision algorithms are used to process the image data to identify whether there are abnormal morphological features such as damage, deformation, or compression on the surface of the fiber optic cable. Subsequently, the contour, area, and location information of the abnormal areas are extracted to form preliminary marking results of the abnormal morphological feature areas.

[0119] Step 142: Map the image coordinates of the abnormal morphological feature areas to the corresponding spatial coordinates in the geographic distribution data to determine the set of abnormal spatial locations.

[0120] Image coordinates refer to the row and column positions of pixels in a two-dimensional image captured by a camera, used to describe the specific distribution of anomaly areas within the image plane. Spatial coordinates refer to three-dimensional geographic location data defined by a geographic information system, including longitude, latitude, and elevation, used to map the actual location of anomaly areas in physical space. The anomaly spatial location set refers to the set of coordinate points of all anomaly morphological feature areas mapped to three-dimensional space, reflecting the discrete distribution of anomalies on the fiber optic cable surface in the real environment.

[0121] In this embodiment, firstly, three-dimensional geographic distribution data of the utility tunnel provided by a geographic information system is acquired, including the mapping relationship between optical cable surface nodes and geographic coordinates. Secondly, based on the camera calibration parameters and spatial perspective model, the image coordinates of the abnormal morphological feature areas are converted into three-dimensional spatial coordinates in the geographic distribution data. Subsequently, the spatial coordinates of all abnormal points are integrated to generate an abnormal spatial location set, providing a location basis for subsequent aggregation.

[0122] Step 143: Aggregate the spatial coordinates in the abnormal spatial location set that meet the preset spatial distribution density and preset spatial boundary continuity to generate the spatial boundary range.

[0123] Among them, the preset spatial distribution density refers to the minimum threshold number of abnormal spatial coordinate points per unit volume, used to filter sparse noise points and focus on dense abnormal areas. The preset spatial boundary continuity refers to the maximum allowable distance threshold between abnormal spatial coordinate points, ensuring that the aggregated boundary has physical coherence. The spatial boundary range refers to the closed geometric region generated by the aggregation algorithm, covering all abnormal points that meet the density and continuity conditions, used to define the physical range of external fiber optic cable damage.

[0124] In this embodiment, firstly, based on the set of abnormal spatial locations, a density clustering algorithm is used to analyze the distribution characteristics of spatial coordinates and filter points that meet a preset spatial distribution density. Secondly, a boundary tracing algorithm is used to detect coordinate points that satisfy a preset spatial boundary continuity, ensuring the topological connectivity between adjacent points. Subsequently, coordinate points that meet the density and continuity conditions are aggregated into a continuous spatial boundary range.

[0125] Step 144: Define the area within the spatial boundary as the external fault zone of the optical cable.

[0126] In this embodiment, firstly, the generated spatial boundary range is used as the geometric boundary of the optical cable external damage fault area. Secondly, based on the physical structural parameters of the optical cable, the region within the boundary range is morphologically optimized to form a complete closed region. Finally, this region is output as the final definition of the optical cable external damage fault area for maintenance personnel to locate and handle.

[0127] Here's a specific example: First, high-resolution images of the fiber optic cable surface are captured by an infrared camera mounted on top of the power utility tunnel. A convolutional neural network is then used to identify areas of surface damage and deformation in the images. Second, the 3D coordinate model of the tunnel stored in a geographic information system is used to convert the pixel coordinates of abnormal areas in the images into geospatial coordinates. Next, a clustering algorithm is used to filter the coordinate set where the density of abnormal points per unit length exceeds a set threshold. A boundary tracing algorithm then connects adjacent points with a spacing less than a preset value, generating a closed boundary polygon. Finally, the physical area covered by this polygon is marked as the external damage fault area of ​​the fiber optic cable, and this is simultaneously output to the operation and maintenance management system to trigger an early warning.

[0128] Another example is the identification process for areas with external damage to optical cables in the operation and maintenance of underground power tunnels in this city.

[0129] First, abnormal morphological feature areas were detected based on image data. 4K visible light + infrared dual-mode cameras, installed every 50 meters at a height of -5m above ground level on the top of the utility tunnel, captured clear images of the A-section optical cable in the second-level compartment. Using the YOLOv5 computer vision algorithm, the images revealed a damaged area on the cable surface caused by external pressure: this area had an irregular, roughly triangular outline and an area of ​​approximately 0.02m². 2 The edges exhibit obvious texture breaks and structural distortions. The algorithm simultaneously extracts the pixel coordinates of the contour vertices of this region in the image coordinate system, which are (780,590), (820,610), and (800,630), respectively, forming a preliminary labeling result of the abnormal morphological feature region.

[0130] Secondly, the image coordinates are mapped to spatial coordinates to determine the set of abnormal spatial locations. Three-dimensional geographic distribution data of this section of optical cable is retrieved from the GIS (Geographic Information System) of the utility tunnel, including the latitude, longitude, and elevation of the cable surface nodes and their mapping relationship with the image coordinate system. Based on the camera's intrinsic parameter matrix (focal length 10mm, pixel size 1.5μm) and perspective transformation model, a mapping function between the image coordinate system (pixel rows and columns) and the spatial coordinate system (latitude, longitude, and elevation) is established: Spatial longitude = image column coordinate × 0.00001° + 116.3°, spatial latitude = image row coordinate × 0.00001° + 39.9°, and the elevation is fixed at -4.8m (the height of the second-level cabin). Substituting the pixel coordinates (780, 590), (820, 610), and (800, 630) of the abnormal region into the mapping function, the corresponding spatial coordinates are obtained as follows: (116.3078°E, 39.9059°N, -4.8m), (116.3082°E, 39.9061°N, -4.8m), and (116.3080°E, 39.9063°N, -4.8m). Combining this with the spatial topology of the optical cable segment (segment A is distributed along 116.3075°E-116.3085°E), these spatial coordinates are associated with specific locations on the surface of segment A, forming a set of abnormal spatial locations.

[0131] Then, the spatial coordinates are aggregated to generate the spatial boundary range. A preset spatial distribution density of ≥5 points / m is set. 2 The spatial boundary continuity is assumed to be ≤0.2m between adjacent points. Density cluster analysis of the coordinates in the set of anomalous spatial locations revealed that the distribution density of the three coordinate points mentioned above is 6.25 points / m. 2 (Area area 0.048m) 2 The three points meet the density threshold; and the distances between adjacent points are 0.15m, 0.18m, and 0.12m, respectively, all meeting the continuity threshold. A boundary tracing algorithm is used to connect these three coordinate points, generating a closed spatial boundary: longitude range 116.3078°E-116.3082°E, latitude range 39.9059°N-39.9063°N, elevation -4.8m, forming a rectangular area 0.3m long and 0.2m wide.

[0132] Finally, the external damage fault area of ​​the optical cable was determined. The aforementioned spatial boundary was used as the geometric boundary of the external damage fault area. Morphological optimization was performed based on the 0.1m diameter of section A optical cable to ensure the boundary completely covered the abnormal area and conformed to the physical shape of the optical cable. This area was ultimately determined as the external damage fault area, corresponding to section A optical cable in the second-level compartment of the utility tunnel, located 1500m from the tunnel entrance. This location can be directly used for subsequent fault location and maintenance scheduling.

[0133] By executing steps 141 to 144, this embodiment of the application achieves automated detection and precise location of abnormal morphologies on the surface of power optical cables through the fusion of image recognition and geospatial mapping technologies. Combining density clustering and boundary continuity analysis, discrete abnormal points are aggregated into continuous fault regions, effectively eliminating noise interference and improving the completeness of external fault identification. This method significantly reduces the cost of manual inspections, provides operable fault region definitions for power facility maintenance, and enhances the reliability of power grid operation.

[0134] In one possible embodiment, step 142, mapping the image coordinates of the abnormal morphological feature region to the corresponding spatial coordinates in the geographic distribution data to determine the set of abnormal spatial locations, includes:

[0135] Step b1: Based on geographic distribution data, establish a mapping function between the image coordinate system and the spatial coordinate system.

[0136] The image coordinate system refers to a two-dimensional coordinate system within the camera's imaging plane, using pixel row and column values ​​to represent the position of targets in the image and describing the distribution of abnormal areas within the frame. The spatial coordinate system refers to a three-dimensional reference system defined by a geographic information system (GIS), accurately describing the actual physical location of objects using longitude, latitude, and elevation data. The mapping function refers to a mathematical transformation rule established based on the camera's geometric model, which maps pixel positions in the image coordinate system to three-dimensional geographical locations in the spatial coordinate system through a parameter matrix.

[0137] In this embodiment, firstly, geographical distribution data of the power utility tunnel is acquired, including the three-dimensional spatial coordinates of the tunnel's internal structure and fiber optic cable layout. Secondly, a mapping function between the image coordinate system and the spatial coordinate system is established using camera calibration parameters. This function, based on a spatial perspective transformation model, converts two-dimensional pixel positions to three-dimensional geographical locations by solving the camera's intrinsic and extrinsic parameter matrices. Subsequently, the mapping function parameters are optimized using the control point coordinates from the utility tunnel's geographic information system to ensure conversion accuracy. Finally, the mapping function serves as the mathematical basis for subsequent image coordinate to spatial coordinate conversion.

[0138] Step b2: Extract the image coordinates of the abnormal morphological feature regions in the image coordinate system.

[0139] In this embodiment, firstly, image data of the surface of the power fiber optic cable collected by a camera at the top of the utility tunnel is retrieved. Secondly, computer vision algorithms are used to identify abnormal morphological feature regions such as damage and deformation in the image. Subsequently, the contour vertex coordinates of the abnormal regions are extracted to generate a sequence of polygon vertices in the image coordinate system. Finally, the image coordinate set of all pixels in the abnormal regions is output, providing input data for coordinate transformation.

[0140] Step b3: Convert the image coordinates into spatial coordinates in the spatial coordinate system using a mapping function.

[0141] In this embodiment, the mapping function generated in step b1 is first loaded. This function contains the conversion relationship between the horizontal and vertical coordinates of the image coordinate system and the latitude, longitude, and elevation of the spatial coordinate system. Next, the image coordinates extracted in step b2 are input into the mapping function, and the spatial coordinates corresponding to each pixel are calculated through matrix multiplication. Subsequently, based on the continuity of the spatial coordinates, interpolation processing is performed on the discrete points to form a complete sequence of spatial coordinates for the abnormal region. Finally, the set of coordinates of the abnormal region in three-dimensional space is output.

[0142] Step b4: Based on the spatial topology of the power optical cables inside the power tunnel, associate the spatial coordinates with the corresponding location points on the surface of the power optical cables to determine the set of abnormal spatial locations.

[0143] The location point refers to the specific location on the surface of the power optical cable determined through coordinate mapping and topological association, including the optical cable number, section identifier, and three-dimensional geographic coordinates.

[0144] In this embodiment, firstly, spatial topology data of optical cables within the power utility tunnel is obtained based on a geographic information system, including the cable routing, branch nodes, and surface node geographic labels. Secondly, the spatial coordinates obtained in step b3 are matched with the coordinates of the optical cable surface nodes using nearest neighbor matching, and associated with the corresponding location points of specific optical cables. Subsequently, the optical cable segment number to which the coordinates belong is verified based on the connection relationships in the topology. Finally, all abnormal location points are integrated to generate an abnormal spatial location set, which is marked as a candidate area for external damage faults.

[0145] Here's a specific example: First, high-resolution images of the fiber optic cable surface are captured by a wide-angle camera mounted on top of the power utility tunnel. A convolutional neural network is then used to identify scratched areas on the cable sheath and extract their contour vertex image coordinates. Next, the 3D model data of the tunnel from the geographic information system is accessed. Based on the camera's focal length and installation angle parameters, an affine transformation matrix from the image coordinate system to the spatial coordinate system is calculated, converting the image coordinates of the abnormal areas into geospatial coordinates. Then, based on the spatial topology of the fiber optic cables within the tunnel, the spatial coordinates are matched with the nearest fiber optic cable surface nodes, associating them with mid-segment locations on the cables. Finally, all abnormal location points are integrated to generate a set of external damage fault areas, which is then pushed to the maintenance platform to trigger an alarm.

[0146] Another example involves establishing a mapping function between the image coordinate system and the spatial coordinate system. The 3D geographic distribution data for this section is retrieved from the utility tunnel's geographic information system, including the coordinates of control points on the tunnel's inner wall, such as (116.307°E, 39.905°N, -4.8m) and (116.309°E, 39.907°N, -4.8m), as well as the direction coordinates of the optical cable in section A. The 4K camera on the top of the utility tunnel has been calibrated, with the following intrinsic parameters: focal length f = 10mm, pixel size 1.5μm, principal point coordinates (2048, 1536); and extrinsic parameters: installation position (116.308°E, 39.906°N, -4.5m), pitch angle -30°, and azimuth angle 0°. Based on these parameters, a spatial perspective transformation model is established, and the mapping function is obtained by solving the intrinsic and extrinsic parameter matrices.

[0147] Spatial longitude = (image column coordinates - 2048) × 1.5μm × cos(-30°) / 10mm × (1 / 111319m / °) + 116.308°; Spatial latitude = (image row coordinates - 1536) × 1.5μm × sin(-30°) / 10mm × (1 / 111319m / °) + 39.906°; Elevation is fixed at -4.8m (the height of the optical cable in section A). Parameter optimization is performed by combining the image coordinates and spatial coordinates of three known control points within the utility tunnel to control the conversion error within ±0.05m.

[0148] Next, the image coordinates of the abnormal area were extracted. Using an image of the surface of section A of the optical cable (resolution 4096×3072) captured by a camera, the YOLOv5 algorithm was used to identify the abnormal area of ​​sheath damage. This area has a triangular outline. The algorithm extracted the image coordinates of its edge pixels, with the core vertex coordinates being (780, 590), (820, 610), and (800, 630). Simultaneously, the coordinates of 10 edge points within the area were collected (e.g., (790, 600), (810, 620), etc.), forming an image coordinate set containing 13 points.

[0149] Then, the coordinates are converted to spatial coordinates. A mapping function is loaded, and the 13 image coordinates are sequentially substituted into the calculation. For example, (780, 590) is converted to (116.3078°E, 39.9059°N, -4.8m), (820, 610) is converted to (116.3082°E, 39.9061°N, -4.8m), and (800, 630) is converted to (116.3080°E, 39.9063°N, -4.8m). The coordinates of the remaining 10 points are distributed around the above three points after conversion. Linear interpolation is used to supplement the intermediate points for the discrete coordinates, forming a continuous spatial coordinate sequence containing 50 points, completely covering the spatial range of the anomaly area.

[0150] Finally, the associated topology was used to determine the set of anomalous spatial locations. Spatial topology data for fiber optic cable segment A was obtained from the Geographic Information System (GIS): the cable is distributed along 116.3075°E-116.3085°E and 39.9058°N-39.9065°N, with a node every 0.1m on the surface, such as nodes A1 (116.3078°E, 39.9059°N, -4.8m) and A2 (116.3080°E, 39.9061°N, -4.8m). Using a nearest neighbor matching algorithm, the spatial coordinates obtained in step b3 were compared with the nodes on the cable surface. It was found that all coordinates fell on the cable segment between nodes A1 and A2, and the vertical distance from the cable surface was ≤0.05m (less than the cable diameter of 0.1m), confirming that it belonged to fiber optic cable segment A. These coordinates are integrated to generate a set of abnormal spatial locations, which are marked as candidate areas for external damage faults, and the associated information is "second floor of the utility tunnel - section A optical cable - 1500m from the entrance".

[0151] By executing steps b1 to b4, this embodiment of the application establishes a precise mapping relationship between images and spatial coordinates, enabling rapid location of abnormal areas on the surface of power optical cables. By combining the optical cable topology inside the utility tunnel, the image detection results are associated with specific physical locations, improving the identification efficiency and location accuracy of external damage faults, providing maintenance personnel with intuitive and reliable fault area information, and shortening on-site troubleshooting time.

[0152] In one possible embodiment, S12, based on temperature data and geographical distribution data, constructing a temperature distribution cloud map includes:

[0153] Step 121: Based on the geographical distribution data, extract multiple spatial location points of the power utility tunnel to obtain the spatial location point set of the power utility tunnel.

[0154] Among them, spatial location points refer to the set of three-dimensional coordinate points extracted from the geographical distribution data of power utility tunnels, used to identify key locations of the tunnel structure, including turning points, branch nodes, and equipment installation points. Geographic coordinates refer to longitude, latitude, and elevation data defined based on the geodetic surveying system, used to accurately describe the actual location of spatial location points in the physical world. The location temperature point set refers to a composite dataset that integrates geographic coordinates and temperature data, with each point containing spatial location information and its corresponding real-time temperature value.

[0155] In this embodiment, firstly, three-dimensional geographic distribution data of the power utility tunnel is extracted from a geographic information system. This data includes key node information of the tunnel structure. Secondly, based on the topological connections of the tunnel, spatial location points representing the tunnel's direction, branches, and equipment locations are selected. Subsequently, all spatial location points are integrated to form a spatial location point set for the power utility tunnel, providing a spatial reference for subsequent temperature data matching.

[0156] Step 122: Match the temperature data with the geographic coordinates corresponding to the spatial location points of the power utility tunnel, and merge the matched temperature data into the corresponding spatial location points to form a location temperature point set.

[0157] In the power utility tunnel monitoring scenario, geographic coordinates refer to global location parameters defined based on the Earth ellipsoid model, usually expressed in longitude and latitude, used to describe the absolute geographical location of power utility tunnel nodes on the Earth's surface. Spatial coordinates in the spatial coordinate system refer to three-dimensional rectangular coordinates under the local engineering coordinate system. Their origin is usually set as a specific reference point of the utility tunnel, and the coordinate axis direction is aligned with the direction of the utility tunnel. They directly represent the relative position and distance relationship of the internal structural points of the utility tunnel in meters.

[0158] In this embodiment, firstly, discrete temperature data collected by a distributed optical fiber temperature measurement system is acquired. This data includes the temperature value of each measurement point and the corresponding optical fiber length information. Secondly, based on the geographic coordinate mapping relationship between optical fiber length and spatial location point set, the temperature data is matched with spatial location points one by one. Subsequently, the temperature values ​​are bound to the corresponding three-dimensional geographic coordinate points through a coordinate transformation model, and a location temperature point set containing both location and temperature attributes is generated.

[0159] Step 123: Based on the location temperature point set and combined with the geometric structural features of the power utility tunnel, construct a temperature distribution cloud map.

[0160] Among them, geometric structural features refer to the physical morphological attributes of the power utility tunnel, including the cross-sectional dimensions, axial curvature, layer height, and internal compartment layout of the tunnel, which are used to constrain the spatial modeling range of the temperature cloud map.

[0161] In this embodiment, firstly, based on the spatial distribution of location temperature points, and combined with the geometric structural features of the power utility tunnel, including the tunnel's cross-sectional shape, curvature, and layer height, the temperature values ​​of unsampled areas are calculated using a Kriging spatial interpolation algorithm, based on the distance between adjacent location temperature points and the temperature gradient relationship. Subsequently, the interpolation results are superimposed on the tunnel's geometric model to generate a temperature distribution cloud map covering the entire tunnel structure, visually demonstrating the continuous spatial changes in temperature.

[0162] Here is a specific example: First, the 3D model of the power utility tunnel stored in the Geographic Information System (GIS) is accessed to extract the coordinates of key nodes on the tunnel's top wall, side walls, and cable supports, forming a spatial location point set. Second, temperature data along the optical cable laying path is collected using a distributed fiber optic temperature monitoring host. Based on the mapping table between fiber length and spatial location points, the temperature values ​​are matched to each coordinate point, generating a location temperature point set. Next, based on the rectangular cross-sectional characteristics and axial bending parameters of the tunnel, a spatial interpolation algorithm is used to complete the temperature gradient between location points, constructing a temperature distribution cloud map covering the entire length of the tunnel. High-temperature areas are then visually identified using color rendering and pushed to the operation and maintenance platform to assist in decision-making.

[0163] By executing steps 121 to 123, this embodiment of the application achieves precise matching of temperature information and geographic coordinates by associating the spatial location of the utility tunnel with distributed temperature data, and generates a high-fidelity temperature distribution cloud map by combining geometric structural features. This method can comprehensively visualize the internal temperature field of the utility tunnel, improve the location efficiency of abnormal temperature rise areas, provide a spatial analysis basis for overheating fault early warning, and optimize the accuracy of operation and maintenance resource scheduling.

[0164] In one possible embodiment, S15, based on the pre-stored location information in the electronic marker within the target fault area and the directional distance information between the target fault area and the electronic marker, generates power fiber optic cable fault location information, including:

[0165] Step 151: Based on the spatial boundary range of the target fault area, use a spatial filtering function to filter out the electronic signs within the target fault area from the set of electronic signs.

[0166] The spatial filtering function refers to a screening algorithm based on geometric relationships, used to extract tags from the set of electronic tags that are completely located within the boundary of the target fault area. The set of electronic tags refers to a cluster of electronic devices embedded in the power utility tunnel and storing their own location data; each tag contains a unique number and three-dimensional geographic coordinates. The formula for the spatial filtering function is: Among them, E set For the collection of all electronic signs within the utility tunnel, Filter(E) set () indicates selecting electronic signs from the set of electronic signs that are located within the spatial boundary of the target fault area. i Let x be the i-th electronic sign object in the set. min x max For the target fault area along the length of the utility tunnel, y min y max For the target fault area in the horizontal width direction, z min , z max The target fault area is located in the vertical direction.

[0167] In this embodiment, the spatial boundary range data of the target fault area is first obtained, which defines the geometric boundary coordinates of the fault area. Secondly, a spatial filtering function is constructed based on the spatial boundary range. By calculating the inclusion relationship between the coordinates of the electronic sign and the boundary polygon, electronic signs completely located within the target fault area are selected. Subsequently, the set of electronic signs meeting the criteria is output as the data source for subsequent positioning.

[0168] Step 152: Send a wake-up command to the electronic signboard through the Internet of Things communication network deployed in the power utility tunnel, triggering the electronic signboard to transmit the pre-stored location information.

[0169] The Internet of Things (IoT) communication network refers to a wireless communication network covering power utility tunnels, used to transmit instructions and data back to electronic signs under low power consumption conditions. A wake-up command is a command that triggers the electronic sign to switch from sleep mode to working mode via a specific radio frequency signal, used to reduce standby power consumption.

[0170] In this embodiment, a low-power wake-up command is first sent to the selected electronic signs via an IoT communication network deployed within the power utility tunnel. Secondly, after being woken up, the electronic signs activate their positioning modules and read pre-stored absolute position coordinate data. Subsequently, the position coordinates are transmitted back to the central processing system via the IoT communication network, completing the location information acquisition.

[0171] Step 153: When there are multiple electronic signs, calculate the azimuth angle and straight-line distance of the target fault area relative to the electronic sign for each electronic sign to obtain the azimuth distance information.

[0172] The azimuth angle refers to the horizontal angle between the center point of the target fault area and true north, with the electronic signboard as the origin of the coordinate system. It describes the target's orientation in a two-dimensional plane. The straight-line distance refers to the three-dimensional Euclidean distance between the center point of the target fault area and the electronic signboard, calculated by taking the square root of the sum of the squares of the coordinate differences.

[0173] In this embodiment, the location coordinates of each electronic sign and the coordinates of the center point of the target fault area are first analyzed. Next, a local coordinate system is established with the electronic sign's location as the origin. The azimuth angle of the center point of the target fault area relative to the origin is calculated using vector operations. The azimuth angle is determined by rotating the horizontal direction counterclockwise to the target vector. Subsequently, the straight-line distance between the two points is calculated using the Euclidean distance formula, and the resulting information, including both the azimuth angle and the distance, is integrated to generate azimuth-distance information.

[0174] Step 154: Combining the directional distance information of multiple electronic signs, the location information of the power optical cable fault location point is determined by the triangulation algorithm.

[0175] Among them, the triangulation algorithm refers to a mathematical method that uses the azimuth or distance data of at least two observation points to calculate the position coordinates of a target point through the principle of geometric intersection. The fault location point of a power fiber optic cable refers to the core location coordinates of the fault output by the positioning algorithm, including longitude, latitude, and elevation information.

[0176] In this embodiment, the azimuth and distance information of multiple electronic markers is first integrated, with each information including the azimuth angle and straight-line distance of the same fault area relative to different origins. Secondly, a triangulation algorithm is used to determine candidate coordinates of the fault location point by solving for the intersection of multiple sets of azimuth angles. Subsequently, a least squares optimization algorithm is used to eliminate measurement errors and output the precise location coordinates of the power fiber optic cable fault location point.

[0177] Step 155: Integrate the pre-stored location information, azimuth distance information, and location information of the power optical cable fault location point into a structured data object to form power optical cable fault location information.

[0178] Structured data objects refer to multi-source data sets integrated according to predefined fields, used to standardize the description of fault location results.

[0179] In this embodiment, the pre-stored location of the electronic tag, the azimuth distance information generated in step 153, and the coordinates of the fault location point determined in step 154 ​​are first associated. Secondly, they are integrated into a single structured data object according to a preset data structure, including the fault type, location point latitude and longitude, associated tag number, and spatial relationship. Finally, this structured data object is output as power fiber optic cable fault location information and pushed to the operation and maintenance platform.

[0180] Here's a specific example: First, based on the spatial boundary coordinates of the overheating fault area, a spatial inclusion algorithm is used to select three electronic markers within that area. Next, a wake-up command is sent to the markers via the narrowband IoT of the power utility tunnel, triggering them to transmit their pre-stored latitude and longitude coordinates. Then, using each marker's location as the origin, the azimuth and straight-line distance to the center point of the fault area are calculated. Subsequently, the three sets of azimuth and distance data are integrated, and the fault point coordinates are solved using triangulation equations, with a least-squares optimization algorithm used to correct positioning errors. Finally, the marker locations, azimuth and distance data, and fault point coordinates are integrated into a structured data object, generating a positioning report containing the tunnel section code and fiber optic cable number, which is automatically pushed to the operation and maintenance system.

[0181] By executing steps 151 to 155, this embodiment of the application improves the accuracy and efficiency of power fiber optic cable fault location by integrating the pre-stored location of electronic identification signs with a triangulation algorithm. A spatial filtering mechanism accurately selects valid identification signs, IoT communication enables low-power data backhaul, collaborative calculation of multi-source azimuth distance information eliminates single measurement errors, and structured data output provides a complete spatial relationship chain for operation and maintenance decisions, ultimately achieving rapid repair and optimized resource scheduling of fault points.

[0182] Figure 2 A schematic diagram of a power optical cable fault location system using electronic identification tags is provided as an embodiment of this application. Figure 2 As shown, the system includes:

[0183] The acquisition module 21 is used to acquire temperature data, image data, and geographical distribution data of the outer wall of the power utility tunnel.

[0184] Module 22 is used to construct a temperature distribution cloud map based on temperature data and geographic distribution data.

[0185] The reconstruction module 23 is used to reconstruct the three-dimensional temperature field of the power optical cable inside the power tunnel based on the temperature distribution cloud map, and to identify whether there is an optical cable overheating fault area that exceeds the preset benchmark from the three-dimensional temperature field.

[0186] The identification module 24 is used to identify the presence of areas with external damage to optical cables based on image data and geographical distribution data.

[0187] The generation module 25 is used to generate power optical cable fault location information based on the pre-stored location information in the electronic signage within the target fault area and the directional distance information between the target fault area and the electronic signage. The target fault area is either an optical cable overheating fault area or an optical cable external damage fault area.

[0188] Figure 2 The aforementioned power optical cable fault location system using electronic identification tags can perform... Figure 1 The implementation principle and technical effects of the power optical cable fault location method using electronic identification tags described in the illustrated embodiment will not be repeated here. The specific operation methods of each module and unit in the power optical cable fault location system using electronic identification tags in the above embodiments have been described in detail in the embodiments related to this method, and will not be elaborated upon here.

[0189] In one possible design, Figure 2 The power fiber optic cable fault location system using electronic identification tags in the illustrated embodiment can be implemented as a computing device, such as... Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32.

[0190] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are invoked and executed by the processing component 32.

[0191] The processing component 32 is used to: acquire temperature data, image data, and geographical distribution data of the power utility tunnel's outer wall; construct a temperature distribution cloud map based on the temperature data and geographical distribution data; reconstruct the three-dimensional temperature field of the power optical cables inside the power utility tunnel based on the temperature distribution cloud map, and identify whether there are overheating fault areas of optical cables exceeding a preset benchmark from the three-dimensional temperature field; identify whether there are external damage fault areas of optical cables based on the image data and geographical distribution data; and generate power optical cable fault location information based on the pre-stored location information in the electronic signage within the target fault area and the directional distance information between the target fault area and the electronic signage, whereby the target fault area is either an overheating fault area or an external damage fault area.

[0192] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above-described method. Alternatively, the processing component may be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described method.

[0193] Storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented from any type of volatile or non-volatile storage device or a combination thereof, such as Random Access Memory (RAM), Static Random-Access Memory (SRAM), Electrically Erasable Programmable Read Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Read Only Memory (PROM), Read Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0194] Of course, computing devices may also include other components, such as input / output interfaces, display components, communication components, etc.

[0195] Input / output interfaces provide interfaces between processing components and peripheral interface modules, which can be output devices, input devices, etc.

[0196] The communication components are configured to facilitate wired or wireless communication between computing devices and other devices.

[0197] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform. In this case, the computing device can refer to a cloud server, and the aforementioned processing components, storage components, etc., can be basic server resources rented or purchased from the cloud computing platform.

[0198] This application also provides a computer storage medium storing a computer program, which, when executed by a computer, can perform the above-described functions. Figure 1 The illustrated embodiment is a method for locating faults in power optical cables using electronic identification tags.

[0199] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0200] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0201] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0202] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for locating faults in power optical cables using electronic identification tags, characterized in that, include: Acquire temperature data, image data, and geographical distribution data of the power utility tunnel's outer wall; Based on the temperature data and the geographical distribution data, a temperature distribution cloud map is constructed; Based on the temperature distribution cloud map, a three-dimensional temperature field of the power optical cable inside the power tunnel is reconstructed, and the presence of an overheating fault area of ​​the optical cable exceeding a preset benchmark is identified from the three-dimensional temperature field. Based on the image data and the geographical distribution data, identify areas where there are external damage faults in optical cables; Based on the pre-stored location information in the electronic signage within the target fault area, and the directional distance information between the target fault area and the electronic signage, power optical cable fault location information is generated. The target fault area is either an optical cable overheating fault area or an optical cable external damage fault area.

2. The method according to claim 1, characterized in that, The process of reconstructing the three-dimensional temperature field of the power cable inside the power utility tunnel based on the temperature distribution cloud map, and identifying whether there are overheating fault areas of the cable exceeding a preset benchmark from the three-dimensional temperature field, includes: The temperature distribution cloud map is mapped to the optical cable surface nodes of the spatial topology to generate the optical cable surface temperature field. Based on the thermal conductivity characteristics, the temperature distribution inside the power optical cable is deduced layer by layer from the surface temperature field of the optical cable to construct the three-dimensional temperature field of the power optical cable. In the three-dimensional temperature field, all spatial units are traversed, and spatial units whose temperature values ​​continuously exceed the preset benchmark in multiple consecutive sampling points are marked as optical cable overheating fault units. All the aforementioned optical cable overheating fault units are aggregated into an optical cable overheating fault area.

3. The method according to claim 2, characterized in that, The process of constructing a three-dimensional temperature field for the power optical cable by deducing the internal temperature distribution of the cable layer by layer from the surface temperature field based on its thermal conductivity characteristics includes: Based on the material thermophysical properties and structural layering parameters of power optical cables, the layering deduction rules of power optical cables are determined. Based on the surface temperature field of the optical cable, and according to the layered deduction rules and thermal conduction characteristics, the temperature distribution of each spatial unit inside the power optical cable is deduced layer by layer to obtain the temperature value of each spatial unit. By integrating the temperature distribution of the surface temperature field of the optical cable and the temperature values ​​of each spatial unit, a three-dimensional temperature field of the power optical cable is constructed.

4. The method according to claim 1, characterized in that, The step of identifying whether there is an area of ​​external damage to optical cables based on the image data and the geographical distribution data includes: Based on the image data, abnormal morphological feature regions on the surface of the power optical cable are detected; Map the image coordinates of the abnormal morphological feature region to the corresponding spatial coordinates in the geographic distribution data to determine the set of abnormal spatial locations; Spatial coordinates that meet the preset spatial distribution density and preset spatial boundary continuity in the set of abnormal spatial locations are aggregated to generate a spatial boundary range; The area within the spatial boundary is designated as the external damage fault area of ​​the optical cable.

5. The method according to claim 4, characterized in that, The step of mapping the image coordinates of the abnormal morphological feature region to the corresponding spatial coordinates in the geographic distribution data to determine the set of abnormal spatial locations includes: Based on the aforementioned geographic distribution data, a mapping function between the image coordinate system and the spatial coordinate system is established; Extract the image coordinates of the abnormal morphological feature regions in the image coordinate system; The image coordinates are converted into spatial coordinates in a spatial coordinate system using the mapping function. Based on the spatial topology of the power optical cables inside the power tunnel, the spatial coordinates are associated with the corresponding location points on the surface of the power optical cables to determine the set of abnormal spatial locations.

6. The method according to claim 1, characterized in that, The construction of a temperature distribution cloud map based on the temperature data and the geographical distribution data includes: Based on the geographical distribution data, multiple spatial location points of the power utility tunnel are extracted to obtain a set of spatial location points of the power utility tunnel; The temperature data is matched with the geographic coordinates corresponding to the spatial location points of the power utility tunnel, and the matched temperature data is merged into the corresponding spatial location points to form a location temperature point set. Based on the set of location temperature points and combined with the geometric features of the power utility tunnel, a temperature distribution cloud map is constructed.

7. The method according to claim 1, characterized in that, The generation of power fiber optic cable fault location information based on the pre-stored location information in the electronic markers within the target fault area and the directional distance information between the target fault area and the electronic markers includes: Based on the spatial boundary range of the target fault area, the electronic signs within the target fault area are filtered out from the set of electronic signs using a spatial filtering function; A wake-up command is sent to the electronic signage via an Internet of Things (IoT) communication network deployed within the power utility tunnel, triggering the electronic signage to transmit pre-stored location information back. When there are multiple electronic signs, for each electronic sign, the azimuth angle and straight-line distance of the target fault area relative to the electronic sign are calculated to obtain azimuth distance information; By combining the directional and distance information of multiple electronic signs, the location information of the power optical cable fault location point is determined through a triangulation algorithm; The pre-stored location information, the azimuth distance information, and the location information of the power optical cable fault location point are integrated into a structured data object to form power optical cable fault location information.

8. A power optical cable fault location system using electronic identification tags, characterized in that, include: The acquisition module is used to acquire temperature data, image data, and geographical distribution data of the power utility tunnel's outer wall. The construction module is used to construct a temperature distribution cloud map based on the temperature data and the geographical distribution data; The reconstruction module is used to reconstruct the three-dimensional temperature field of the power optical cable inside the power tunnel based on the temperature distribution cloud map, and to identify whether there is an optical cable overheating fault area that exceeds the preset benchmark from the three-dimensional temperature field. The identification module is used to identify, based on the image data and the geographical distribution data, whether there is an area of ​​external damage to the optical cable; The generation module is used to generate power optical cable fault location information based on the pre-stored location information in the electronic signage within the target fault area and the directional distance information between the target fault area and the electronic signage. The target fault area is either an optical cable overheating fault area or an optical cable external damage fault area.

9. A computing device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement a method for locating power optical cable faults using electronic tags as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that, The device contains a computer program that, when executed by a computer, implements a method for locating power optical cable faults using electronic identification tags as described in any one of claims 1 to 7.

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