Weak current cable visual operation and maintenance method based on GIS fusion

Abstract

The application discloses a weak current cable visual operation and maintenance method based on GIS fusion, and belongs to the technical field of weak current cable intelligent management, and comprises the following steps: establishing a weak current cable GIS basic database; normalizing and cleaning multi-source heterogeneous data in the weak current cable GIS basic database and performing spatial registration to construct a weak current cable GIS fusion model; and rendering cable trends on a GIS map according to laying modes based on the weak current cable GIS fusion model; the application realizes penetrating management from a macro geographic perspective to a micro device state through GIS fusion, greatly shortens fault positioning time, and two-dimensional drawings can intuitively show the depth, trend and spatial relationship with surrounding facilities of the cable, avoids misoperation at complex intersections, and greatly improves the visualization degree. Dynamic early warning and risk prediction are performed by making full use of historical data and real-time monitoring data, the intelligentization of the operation and maintenance mode is greatly improved, and the emergency response efficiency is improved through visual repair path planning.

Description

Technical Field

[0001] This invention belongs to the field of intelligent management technology for low-voltage cables, specifically involving a visual operation and maintenance method for low-voltage cables based on GIS (Geographic Information System) integration. Background Technology

[0002] With the rapid development of smart cities, the scale of low-voltage cables (such as communication optical cables, network cables, and coaxial cables) in urban underground spaces is growing rapidly, forming a complex network. Currently, the operation and maintenance management of low-voltage cables mainly relies on manual paper-based ledgers and decentralized monitoring systems. The problems are: two-dimensional drawings cannot intuitively show the cable's burial depth, route, and spatial relationship with surrounding facilities, especially at complex intersections, where misoperation is highly likely, resulting in low visualization. Furthermore, historical data and real-time monitoring data are not fully utilized for dynamic early warning and risk prediction, leading to a passive operation and maintenance model and insufficient intelligent operation and maintenance.

[0003] Patent publication number CN106228496A discloses a cable maintenance management system, including a terminal platform system and a management platform system. The management platform system performs cable fault analysis on the cable information and includes a GIS server. The GIS server communicates with PC clients, terminal detection devices, and mobile terminal devices. The GIS server can summarize and integrate the cable location information detected by the terminal detection devices and display it on the PC client. The GIS server performs cable map modeling on the cable information detected by the terminal detection devices and displays it on the PC client and mobile terminal devices. This patent document only discloses the GIS server performing map modeling and display; it does not cover the normalization of multi-source heterogeneous data of low-voltage cables, GIS fusion modeling, spatial binding of link status, topology tracing of fault impact range, work order closed loop, and model self-updating. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a visual operation and maintenance method for low-voltage cables based on GIS fusion, thereby solving the technical problems mentioned in the background.

[0005] The objective of this invention is achieved as follows: a GIS-based visualization operation and maintenance method for low-voltage cables, comprising the following steps: establishing a basic GIS database for low-voltage cables; normalizing, cleaning, and spatially registering the multi-source heterogeneous data in the basic GIS database for low-voltage cables; associating and mapping cable body parameters, link topology data, and GIS geographic elements through spatial indexing to construct a GIS fusion model for low-voltage cables; based on the GIS fusion model for low-voltage cables, rendering the cable routing layer by layer on a GIS map according to the laying method, and generating interactive 3D or 2D cross-sectional visualizations of manholes, junction boxes, terminals, and cable tray nodes. The system binds the link status, port occupancy, and signal indicators of each cable to a corresponding visual object; it acquires real-time operational monitoring data of low-voltage cables, spatially matches the operational monitoring data with the corresponding cable segments in the GIS fusion model, and when the monitoring data meets preset alarm conditions, it spatially highlights the abnormal cable segments on the GIS map and generates alarm events with geographic coordinates; based on the geographic coordinates of the alarm events and the cable link topology, it automatically calculates the fault impact range and the list of affected services, generates maintenance work orders, and pushes the work orders to mobile terminals, while simultaneously rendering the fault location point and the optimal repair path on the GIS map.

[0006] Leveraging the spatial indexing capabilities of GIS, the physical attributes (such as length and material) and logical attributes (such as link topology and service ports) of low-voltage cables are strongly correlated with geospatial coordinates, forming a digital twin foundation. Based on this, real-time data-driven status monitoring triggers alarms, and graph theory algorithms are used to perform topology diffusion analysis of fault impacts and spatial scheduling of repair resources. During implementation, multi-source heterogeneous data (CAD drawings, ledgers, test data) is accessed, a unified coordinate system is established, and the data is cleaned. Using manholes and pole lines as anchor points, cable segments are mapped onto the GIS map, establishing a triple mapping model of geospatial, cable entities, and link logic. Cables are rendered layer by layer on the GIS platform according to their laying method. Interactive 2D / 3D cross-sectional views are generated for key nodes (junction boxes, terminals), and business indicators such as optical power and port occupancy are bound to specific objects. Sensor data is accessed, and real-time values ​​are matched with corresponding line segments in the GIS model. Once data exceeds limits, abnormal line segments are highlighted on the map, generating alarm events with latitude and longitude coordinates. The upstream and downstream topologies of the fault point are traced, and a list of affected services is compiled. By combining road network data and manhole accessibility, the optimal repair route is calculated and work orders are dispatched. Through GIS integration, penetrating management from a macro-geographical perspective to the micro-equipment status is achieved, significantly shortening fault location time. Two-dimensional maps can intuitively display the cable burial depth, route, and spatial relationship with surrounding facilities, avoiding misoperation at complex intersections and greatly improving visualization. Full utilization of historical and real-time monitoring data for dynamic early warning and risk prediction greatly enhances the intelligence of the operation and maintenance model, and improved emergency response efficiency through visualized repair route planning.

[0007] Furthermore, it also includes: collecting geospatial data, cable body parameters, link topology data, and ancillary facility data of low-voltage cables, wherein the geospatial data includes cable routing, laying method, burial depth or overhead height, inflection point coordinates, and manhole / pole / cable tray location information; responding to on-site verification data, repair data, and inspection trajectories transmitted back by maintenance personnel via mobile terminals, updating the low-voltage cable GIS basic database and GIS fusion model, and synchronously refreshing the cable status and topology relationship on the GIS map.

[0008] At the data input end, automated data collection (RFID, GPS, OTDR) reduces manual data entry costs. At the output end, the real-world coordinates and verification results transmitted from mobile terminals trigger incremental updates to the database, ensuring consistency between the GIS model and the real physical world (continuous synchronization with Digital Twin). Basic parameters are entered manually on-site or in batches via CAD, and physical parameters such as fiber optic attenuation are tested and entered using OTDR equipment. This solves the pain points of traditional pipeline data being "built but not managed, and becoming distorted over time."

[0009] Furthermore, the low-voltage cables include at least one of communication optical cables, network cables, coaxial cables, security cables, and building low-voltage cables; the data acquisition is achieved through at least one of the following methods: manual inspection and data entry, CAD drawing import, OTDR testing, optical power monitoring, RFID tag reading, and GPS / BeiDou positioning. By utilizing various acquisition methods with different physical characteristics (electromagnetic induction RFID, geometric optical OTDR, and radio GPS), accurate capture of data throughout the entire lifecycle of different types of cables can be achieved.

[0010] Furthermore, the on-site verification data transmitted back by the mobile terminal includes: RFID tag scanning results, GPS positioning coordinates, on-site images, voice notes, and post-repair optical power test values. The system compares the on-site GPS positioning coordinates with the coordinates of the corresponding inspection point in the GIS fusion model. If they match, it automatically extracts and displays the associated cable information for that point from the database. Based on Location-Based Services (LBS) and spatial query algorithms, the mobile terminal performs a spatial geometric comparison between its real-time GPS coordinates and the preset "electronic fence" or "inspection point coordinates" in the GIS model. After confirming the personnel's arrival, it automatically triggers the "on-demand loading" of relevant spatial data. During implementation... Location comparison: When maintenance personnel arrive at the site, they use their mobile phone's GPS to obtain coordinates.

[0011] Conditional trigger: The system determines whether the current coordinates are within the preset error range.

[0012] Information display: After confirmation, the system automatically retrieves information such as the pipeline 3D model, historical fault records, and surrounding hazards from the database and pushes it to the terminal screen.

[0013] It achieves intelligent assistance where "information follows the personnel on-site." This avoids the inefficiency of manually reviewing drawings and ensures that maintenance personnel see the most accurate and up-to-date local 3D scene.

[0014] Furthermore, the normalization cleaning and spatial registration specifically include: uniformly converting geospatial data under different coordinate systems to the same coordinate reference; establishing data layers according to the type, laying method, and ownership unit of low-voltage cables; and using manholes, poles, and cable tray nodes as anchor points to associate the spatial geometry of cable segments with ledger attributes, establishing a triple mapping relationship of geospatial, cable entity, and link logic.

[0015] By establishing spatial indexes (such as R-trees), a one-to-one or one-to-many relationship is created between the ledger of non-spatial data and spatial geometry, enabling seamless switching between spatial and attribute queries. This triple mapping mechanism ensures the topological correctness of the data, providing a reliable logical foundation for subsequent spatial analyses (such as burst analysis and shortest path analysis).

[0016] Furthermore, for underground low-voltage cables, auxiliary fitting points are added between adjacent anchor points based on the road alignment, transforming straight connections into flush connections along the road alignment. A cross-sectional view of the jacking section and a view of available manhole resources are provided on the GIS map. For overhead low-voltage cables, safety distance boundaries between them and power lines, trees, and structures are marked. The GIS geometric editing and spatial analysis engine is utilized. For underground pipelines, linear referencing technology is used to ensure the pipeline alignment conforms to the road centerline; for overhead pipelines, buffer analysis is used to calculate the spatial distance between the cable and obstacles to determine if there are conflicts.

[0017] During implementation, Underground rendering: Extract road centerline data and insert encrypted nodes at corners to allow pipelines to be laid along the road.

[0018] Cross-sectional analysis: Click on the underground pipeline, and the system will capture a cross-section of the road, displaying the cable burial depth and its relationship with the upstream and downstream pipelines.

[0019] Overhead analysis: A 5-meter buffer zone is generated with the high-voltage line as the source object to determine whether the low-voltage cable falls within the buffer zone.

[0020] This greatly enhances the visualization experience in complex scenarios. Underground integration transforms pipelines from rigid straight lines into realistic routes, while overhead safety distance markings provide intuitive red-line warnings for safe production.

[0021] Furthermore, the operational monitoring data includes at least one of the following: optical power, bit error rate, link connectivity, junction box temperature and humidity, well water level, external force vibration signal, and video image; the preset alarm conditions are determined by a dynamic threshold obtained by combining historical monitoring data with meteorological data, specifically: the maximum fluctuation value within the historical monitoring period is recorded as the baseline threshold; the baseline threshold is corrected based on meteorological data and geographic location image of the cable location to obtain a corrected threshold; when the real-time monitoring data exceeds the corrected threshold, a spatialized highlight alarm is triggered.

[0022] By establishing a correlation model between historical meteorological data (temperature, humidity, rainfall) and cable physical characteristics (such as fiber optic attenuation and connector oxidation), the static alarm threshold is dynamically adjusted to eliminate environmental noise interference. During implementation, historical data from the past year is collected to set an initial baseline threshold (e.g., optical attenuation of 0.2 dB / km). When heavy rain is detected, the system automatically queries the historical rainfall attenuation model for the area and relaxes the threshold to 0.5 dB / km. Highlighting is only triggered when the threshold is exceeded, avoiding a large number of false alarms during severe weather. This solves the problem of traditional fixed-threshold alarms easily producing the "crying wolf" effect, improving alarm accuracy and reducing unnecessary manual intervention.

[0023] Furthermore, the calculation method for the scope of the fault impact is as follows: starting from the fault point, tracing bidirectionally along the cable link topology in the GIS fusion model, extracting all business nodes sharing the cable segment or related upstream and downstream cable segments, and generating a list of affected businesses; the optimal repair path is calculated by combining road network data, manhole accessibility, and real-time traffic information. The GIS is used as a graph database, and graph traversal algorithms (such as breadth-first search, BFS) are used to achieve topology tracing; combined with Dijkstra's algorithm or A* algorithm, path planning is performed by comprehensively considering weight factors such as distance, congestion, and manhole accessibility. This achieves a leap from "single-point maintenance" to "network-level impact assessment." The optimal path planning directly guides the driving routes of vehicles on site, reducing physical arrival time.

[0024] Furthermore, this includes: rendering the risk level of faulty cable segments based on fault type and cable health assessment model, and displaying the risk distribution on a GIS map using color gradients; the health assessment model is based on multimodal sensor data and uses a temporal convolutional network (TCN) to predict cable degradation trends. The TCN processes the time-series data from the sensors, extracts features, and predicts future trends. The predicted risk probability values ​​are mapped to a color chart (e.g., blue, green, yellow, red), generating a heat map on the GIS map to achieve visualized risk classification. This transforms "post-incident repair" into "pre-incident prevention." Managers can visually see which areas of pipelines are most vulnerable through the heat map, allowing for proactive resource allocation and intervention.

[0025] Furthermore, this includes: based on historical maintenance work orders, fault records, and cable ledger data, multi-dimensional statistics are performed by time period, cable type, geographical region, and fault cause. High-incidence fault areas are displayed on a GIS map in the form of heatmaps, and inspection frequency and routes are dynamically adjusted accordingly. The inspection routes are generated by segmenting cable links according to cable segment attribute information, assigning differentiated inspection frequencies based on the health and risk levels of different segments, and generating a sequence of nearest inspection points based on the current location of the mobile terminal. High-fault-density hotspots are identified by performing kernel density estimation (KDE) or simple aggregation on historical fault data. Based on risk assessment results, the allocation weight of inspection resources is dynamically adjusted, and inspection routes are optimized using the TSP (Traveling Salesman Problem) algorithm. This achieves lean management of maintenance resources, investing limited engineer resources in the areas most in need of maintenance and reducing overall operating costs.

[0026] The beneficial effects of this invention are as follows: Real-time data-driven status monitoring triggers alarms, and graph theory algorithms are used to achieve topological diffusion analysis of fault impacts and spatial scheduling of repair resources. During implementation, multi-source heterogeneous data (CAD drawings, ledgers, test data) is accessed, a unified coordinate system is established, and the data is cleaned. Using manholes and pole lines as anchor points, cable segments are mapped onto a GIS map, establishing a triple mapping model of geographic space, cable entities, and link logic. Cables are rendered layer by layer on the GIS platform according to their laying method. Interactive 2D / 3D cross-sectional views are generated for key nodes (junction boxes, terminals), and business indicators such as optical power and port occupancy are bound to specific objects. Sensor data is accessed, and real-time values ​​are matched with corresponding line segments in the GIS model. Once data exceeds limits, abnormal line segments are highlighted on the map, generating alarm events with latitude and longitude coordinates. The upstream and downstream topologies of the fault point are traced, and a list of affected services is compiled. Combining road network data and manhole accessibility, the optimal repair path is calculated, and work orders are dispatched. By integrating GIS, comprehensive management from a macro-geographical perspective to the micro-equipment status is achieved, significantly shortening fault location time. Two-dimensional drawings can intuitively display the burial depth, route, and spatial relationship with surrounding facilities of cables, avoiding misoperation at complex intersections and greatly improving visualization. Full utilization of historical and real-time monitoring data for dynamic early warning and risk prediction greatly enhances the intelligence of operation and maintenance models, and visualized emergency repair path planning improves emergency response efficiency. Attached Figure Description

[0027] Figure 1 This is a schematic diagram of the method flow of the present invention; Figure 2 This is a schematic diagram of the normalized cleaning and spatial registration process of the present invention; Figure 3 This is a schematic diagram of the preset alarm condition dynamic threshold determination process of the present invention. Detailed Implementation

[0028] The present invention will now be described in further detail with reference to the accompanying drawings. It should be noted that this is only for the purpose of more clearly illustrating and explaining the present invention.

[0029] This embodiment discloses a GIS-based visualized operation and maintenance method for low-voltage cables. GIS is an abbreviation for Geographic Information System, often translated as "Geographic Information System" or "Geographic Information System" in Chinese. This embodiment uses geospatial location as the framework to uniformly model and visualize the physical laying information of low-voltage cables (routing, burial depth, overhead height, manhole / pole / cable tray coordinates, etc.) and the cable's business attributes (link topology, port occupancy, optical power, signal indicators, etc.) in a spatial dimension. One of the biggest differences between low-voltage cables and traditional power system cables is that low-voltage cables are often distributed along roads, buildings, and parks, with a large geographical span, dense nodes, and diverse ownership. Using only CAD drawings or pure ledger management cannot solve spatial problems such as "which water pipe this optical cable crosses 1.2 meters underground at a certain intersection," "how many empty pipe holes are left in this manhole," and "which business customers within a 500-meter radius of the fault point are affected." By adopting GIS fusion, instead of simply "drawing a line on the map", multi-source heterogeneous cable data (geospatial, ontological parameters, link topology, real-time monitoring) are associated and mapped through spatial indexing to construct a fusion model of geospatial, cable entity, and link logic triple mapping.

[0030] The GIS-integrated visualization-based operation and maintenance method for low-voltage cables includes the following steps: like Figure 1 As shown, a basic GIS database for low-voltage cables is established. The multi-source heterogeneous data in the database is normalized, cleaned, and spatially registered. Cable body parameters, link topology data, and GIS geographic elements are associated and mapped through spatial indexing to construct a GIS fusion model for low-voltage cables. Based on this GIS fusion model, cable routes are rendered layer by layer on the GIS map according to laying method. Interactive 3D or 2D cross-sectional visualization objects are generated for manholes, junction boxes, terminals, and cable tray nodes. The link status, port occupancy, and signal indicators of each cable are bound to the corresponding visualization objects. Real-time monitoring data of low-voltage cables is acquired, and spatial matching is performed between the monitoring data and the corresponding cable segments in the GIS fusion model. When the monitoring data meets preset alarm conditions, abnormal cable segments are spatially highlighted on the GIS map, and alarm events with geographic coordinates are generated. Based on the geographic coordinates of the alarm events and the cable link topology, the fault impact range and affected business list are automatically calculated, maintenance work orders are generated, and the work orders are pushed to mobile terminals. Simultaneously, the fault location point and optimal repair path are rendered on the GIS map.

[0031] First, the system integrates multi-source heterogeneous data. Geospatial data is imported via GPS / BeiDou positioning devices or existing municipal maps; cable parameters are obtained through manual input or analysis of CAD drawings; and link topology data is obtained through OTDR (Optical Time Domain Reflectometer) testing. During data processing, the system uniformly converts all data to the CGCS2000 National Geodetic Coordinate System. Subsequently, different GIS data layers are established according to cable type (e.g., optical cable, electrical cable) and ownership unit. The core step lies in establishing a "triple mapping": using manholes and poles as physical anchor points, the actual physical cable segments are strongly correlated with the logical link IDs in the database, forming a GIS fusion model with geospatial attributes.

[0032] The system utilizes WebGL technology to achieve 3D visualization in a browser. For underground cables, considering that actual construction often involves laying cables along roads, the system uses an algorithm to insert auxiliary fitting points between adjacent corner points, ensuring that the cable route conforms to the road centerline rather than a rigid straight connection. Users can click on a junction box node to view a 2D cross-sectional view of the internal fiber optic core occupancy, or view a cross-sectional view of the jacking pipe section to intuitively understand the pipe hole occupancy rate. For overhead cables, the system uses spatial analysis algorithms to calculate the vertical and horizontal distances between the cable and overhead high-voltage lines and nearby trees. If these distances are less than safety specifications, a red warning boundary is automatically generated on the map.

[0033] The system receives real-time data from optical power sensors deployed in junction boxes, water level sensors in wells, and surrounding video surveillance. A major innovation of this solution lies in the dynamic correction of alarm thresholds. For example, the historical loss baseline threshold for a certain optical cable section is 0.3dB / km. However, when meteorological data shows that the area is experiencing heavy rainfall or fog, the system automatically retrieves the historical rainfall attenuation model for that geographical location and temporarily relaxes the threshold to 0.5dB / km. If the monitored optical power attenuation value exceeds 0.5dB / km at this time, the system immediately marks that section of cable as a flashing red highlight on the GIS map and generates a precise latitude and longitude alarm event, thereby effectively avoiding a large number of false alarms caused by severe weather.

[0034] Upon receiving an alarm event, the system backend automatically performs a topology traversal. Starting from the fault point, it traces upstream and downstream bidirectionally, extracting all affected business ports and customer nodes, and generating a list of affected businesses for dispatchers' reference. Simultaneously, the system calls the Gaode Map API, combining the accessibility of manholes in the GIS fusion model (e.g., manholes inaccessible to vehicles in some residential areas) with real-time traffic big data, and uses the A* pathfinding algorithm to calculate the optimal repair route with the shortest time, directly pushing it to the maintenance personnel's navigation terminal.

[0035] Upon arrival at the site, maintenance personnel open a mobile app. The app automatically obtains the current GPS location and compares it with the planned inspection point in the GIS model. After confirming arrival, the app automatically downloads the 3D pipeline model and historical fault records for that location. Maintenance personnel use an RFID handheld device to scan the manhole cover tag, input the repaired optical power test value, and upload photos of the site. After receiving this data, the server automatically updates the link status in the GIS fusion model to "normal" and removes the alarm highlight from the map.

[0036] Leveraging the spatial indexing capabilities of GIS, the physical attributes (such as length and material) and logical attributes (such as link topology and service ports) of low-voltage cables are strongly correlated with geospatial coordinates, forming a digital twin foundation. Based on this, real-time data-driven status monitoring triggers alarms, and graph theory algorithms are used to perform topology diffusion analysis of fault impacts and spatial scheduling of repair resources. During implementation, multi-source heterogeneous data (CAD drawings, ledgers, test data) is accessed, a unified coordinate system is established, and the data is cleaned. Using manholes and pole lines as anchor points, cable segments are mapped onto the GIS map, establishing a triple mapping model of geospatial, cable entities, and link logic. Cables are rendered layer by layer on the GIS platform according to their laying method. Interactive 2D / 3D cross-sectional views are generated for key nodes (junction boxes, terminals), and business indicators such as optical power and port occupancy are bound to specific objects. Sensor data is accessed, and real-time values ​​are matched with corresponding line segments in the GIS model. Once data exceeds limits, abnormal line segments are highlighted on the map, generating alarm events with latitude and longitude coordinates. The upstream and downstream topologies of the fault point are traced, and a list of affected services is compiled. By combining road network data and manhole accessibility, the optimal repair route is calculated and work orders are dispatched. Through GIS integration, penetrating management from a macro-geographical perspective to the micro-equipment status is achieved, significantly shortening fault location time. Two-dimensional maps can intuitively display the cable burial depth, route, and spatial relationship with surrounding facilities, avoiding misoperation at complex intersections and greatly improving visualization. Full utilization of historical and real-time monitoring data for dynamic early warning and risk prediction greatly enhances the intelligence of the operation and maintenance model, and improved emergency response efficiency through visualized repair route planning.

[0037] Collect geospatial data, cable body parameters, link topology data, and ancillary facility data of low-voltage cables. The geospatial data includes cable routing, laying method, burial depth or overhead height, inflection point coordinates, and manhole / pole / cable tray location information. Respond to on-site verification data, repair data, and inspection trajectories transmitted by maintenance personnel via mobile terminals, update the low-voltage cable GIS basic database and GIS fusion model, and simultaneously refresh the cable status and topology on the GIS map.

[0038] At the data input end, automated data collection (RFID, GPS, OTDR) reduces manual data entry costs. At the output end, the real-world coordinates and verification results transmitted from mobile terminals trigger incremental updates to the database, ensuring consistency between the GIS model and the real physical world (continuous synchronization with Digital Twin). Basic parameters are entered manually on-site or in batches via CAD, and physical parameters such as fiber optic attenuation are tested and entered using OTDR equipment. This solves the pain points of traditional pipeline data being "built but not managed, and becoming distorted over time."

[0039] The low-voltage cables include at least one of communication optical cables, network cables, coaxial cables, security cables, and building low-voltage cables. Data acquisition is achieved through at least one of the following methods: manual inspection and data entry, CAD drawing import, OTDR testing, optical power monitoring, RFID tag reading, and GPS / BeiDou positioning. Utilizing various acquisition methods with different physical characteristics (electromagnetic induction RFID, geometric optical OTDR, and radio GPS), accurate capture of data throughout the entire lifecycle of different types of cables is achieved.

[0040] The on-site verification data transmitted back by the mobile terminal includes: RFID tag scanning results, GPS positioning coordinates, on-site images, voice notes, and post-repair optical power test values. The system compares the on-site GPS positioning coordinates with the coordinates of the corresponding inspection point in the GIS fusion model. If they match, it automatically extracts and displays the associated cable information for that point from the database. Based on Location-Based Services (LBS) and spatial query algorithms, the mobile terminal performs a spatial geometric comparison between its real-time GPS coordinates and the preset "electronic fence" or "inspection point coordinates" in the GIS model. Once personnel are confirmed to be in place, it automatically triggers the "on-demand loading" of relevant spatial data. During implementation... Location comparison: When maintenance personnel arrive at the site, they use their mobile phone's GPS to obtain coordinates.

[0041] Conditional trigger: The system determines whether the current coordinates are within the preset error range.

[0042] Information display: After confirmation, the system automatically retrieves information such as the pipeline 3D model, historical fault records, and surrounding hazards from the database and pushes it to the terminal screen.

[0043] It achieves intelligent assistance where "information follows the personnel on-site." This avoids the inefficiency of manually reviewing drawings and ensures that maintenance personnel see the most accurate and up-to-date local 3D scene.

[0044] like Figure 2As shown, the normalization cleaning and spatial registration specifically includes: uniformly converting geospatial data under different coordinate systems to the same coordinate reference; establishing data layers according to the type, laying method, and ownership unit of low-voltage cables; and using manholes, poles, and cable tray nodes as anchor points to associate the spatial geometry of cable segments with ledger attributes, establishing a triple mapping relationship of geospatial, cable entity, and link logic.

[0045] By establishing spatial indexes (such as R-trees), a one-to-one or one-to-many relationship is created between the ledger of non-spatial data and spatial geometry, enabling seamless switching between spatial and attribute queries. This triple mapping mechanism ensures the topological correctness of the data, providing a reliable logical foundation for subsequent spatial analyses (such as burst analysis and shortest path analysis).

[0046] For underground low-voltage cables, auxiliary fitting points are added between adjacent anchor points based on the road alignment to transform straight connections into flush connections along the road alignment. A cross-sectional view of the jacking section and a view of available manhole resources are provided on the GIS map. For overhead low-voltage cables, safety distance boundaries between them and power lines, trees, and structures are marked. The GIS geometric editing and spatial analysis engine is utilized. For underground pipelines, linear referencing technology is used to ensure the pipeline alignment conforms to the road centerline. For overhead pipelines, buffer analysis is used to calculate the spatial distance between the cable and obstacles to determine if there are any conflicts.

[0047] During implementation, Underground rendering: Extract road centerline data and insert encrypted nodes at corners to allow pipelines to be laid along the road.

[0048] Cross-sectional analysis: Click on the underground pipeline, and the system will capture a cross-section of the road, displaying the cable burial depth and its relationship with the upstream and downstream pipelines.

[0049] Overhead analysis: A 5-meter buffer zone is generated with the high-voltage line as the source object to determine whether the low-voltage cable falls within the buffer zone.

[0050] This greatly enhances the visualization experience in complex scenarios. Underground integration transforms pipelines from rigid straight lines into realistic routes, while overhead safety distance markings provide intuitive red-line warnings for safe production.

[0051] like Figure 3As shown, the operational monitoring data includes at least one of the following: optical power, bit error rate, link connectivity, junction box temperature and humidity, well water level, external force vibration signal, and video image; the preset alarm conditions are determined by a dynamic threshold obtained by combining historical monitoring data with meteorological data, specifically: the maximum fluctuation value within the historical monitoring period is recorded as the baseline threshold; the baseline threshold is corrected based on meteorological data and geographic location image of the cable location to obtain the corrected threshold; when the real-time monitoring data exceeds the corrected threshold, a spatialized highlight alarm is triggered.

[0052] By establishing a correlation model between historical meteorological data (temperature, humidity, rainfall) and cable physical characteristics (such as fiber optic attenuation and connector oxidation), the static alarm threshold is dynamically adjusted to eliminate environmental noise interference. During implementation, historical data from the past year is collected to set an initial baseline threshold (e.g., optical attenuation of 0.2 dB / km). When heavy rain is detected, the system automatically queries the historical rainfall attenuation model for the area and relaxes the threshold to 0.5 dB / km. Highlighting is only triggered when the threshold is exceeded, avoiding a large number of false alarms during severe weather. This solves the problem of traditional fixed-threshold alarms easily producing the "crying wolf" effect, improving alarm accuracy and reducing unnecessary manual intervention.

[0053] The calculation method for the scope of the fault impact is as follows: starting from the fault point, tracing bidirectionally along the cable link topology in the GIS fusion model, extracting all business nodes sharing the cable segment or related upstream and downstream cable segments, and generating a list of affected businesses; the optimal repair path is calculated by combining road network data, manhole accessibility, and real-time traffic information. The GIS is used as a graph database, and graph traversal algorithms (such as breadth-first search, BFS) are used to achieve topology tracing; combined with Dijkstra's algorithm or A* algorithm, path planning is performed by comprehensively considering weight factors such as distance, congestion, and manhole accessibility. This achieves a leap from "single-point maintenance" to "network-level impact assessment." The optimal path planning directly guides the driving routes of vehicles on site, reducing physical arrival time.

[0054] Based on the fault type and cable health assessment model, the risk level of faulty cable segments is rendered, and the risk distribution is displayed on a GIS map using color gradients. The health assessment model is based on multimodal sensor data and uses a temporal convolutional network (TCN) to predict cable degradation trends. The TCN processes the time-series data from the sensors, extracts features, and predicts future trends. The predicted risk probability values ​​are mapped to a color chart (e.g., blue, green, yellow, red), generating a heat map on the GIS map for visualized risk grading. This transforms "post-incident repair" into "pre-incident prevention." Managers can visually see which areas of pipelines are most vulnerable through the heat map, allowing for proactive resource allocation and intervention.

[0055] Based on historical maintenance work orders, fault records, and cable ledger data, multi-dimensional statistics are performed by time period, cable type, geographical region, and fault cause. High-incidence fault areas are displayed as heatmaps on a GIS map, and inspection frequency and routes are dynamically adjusted accordingly. The inspection routes are generated by segmenting cable links according to cable segment attribute information, assigning differentiated inspection frequencies based on the health and risk levels of different segments, and generating a sequence of nearest inspection points based on the current location of the mobile terminal. High-fault-density hotspots are identified by kernel density estimation (KDE) or simple aggregation of historical fault data. Based on risk assessment results, the allocation weight of inspection resources is dynamically adjusted, and inspection routes are optimized using the TSP (Traveling Salesman Problem) algorithm. This achieves lean management of maintenance resources, allocating limited engineer resources to the areas most in need of maintenance and reducing overall operating costs.

[0056] The system management team can use big data dashboards to generate monthly fault heatmaps based on historical work orders, visually demonstrating the extremely high fiber optic cable failure rate in a certain old residential area. Based on this, the system automatically adjusts the inspection strategy for that area, shortening the three-month inspection cycle for ordinary lines to one month. It also uses machine learning models to predict the remaining lifespan of cables in the area, proactively scheduling pre-maintenance and shifting from "reactive repair" to "proactive prevention." Through deep integration of GIS geographic information and low-voltage cable business logic, it achieves end-to-end management from data modeling, panoramic visualization, intelligent alarms to closed-loop repair. Dynamic threshold correction, bidirectional topology tracing, and optimal path planning significantly improve the operation and maintenance efficiency and intelligence level of urban low-voltage cables.

[0057] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A visual operation and maintenance method for low-voltage cables based on GIS fusion, characterized in that, Includes the following steps: Establish a GIS basic database for low-voltage cables; normalize, clean, and spatially register the multi-source heterogeneous data in the GIS basic database for low-voltage cables; associate and map the cable body parameters, link topology data, and GIS geographic elements through spatial indexing to construct a GIS fusion model for low-voltage cables. Based on the aforementioned low-voltage cable GIS fusion model, the cable routing is rendered in layers on the GIS map according to the laying method, and interactive three-dimensional or two-dimensional profile visualization objects are generated for manholes, junction boxes, terminals, and cable tray nodes. The link status, port occupancy, and signal indicators of each cable are bound to the corresponding visualization objects. Real-time acquisition of operation monitoring data of low-voltage cables, spatial matching of the operation monitoring data with the corresponding cable segments in the GIS fusion model, and spatial highlighting of abnormal cable segments on the GIS map when the monitoring data meets the preset alarm conditions, and generation of alarm events with geographic coordinates. Based on the geographical coordinates and cable link topology of the alarm event, the system automatically calculates the scope of the fault impact and the list of affected services, generates maintenance work orders and pushes them to mobile terminals, and simultaneously renders the fault location point and the optimal repair route on the GIS map.

2. The method for visualized operation and maintenance of low-voltage cables based on GIS fusion according to claim 1, characterized in that, Also includes: Collect geospatial data, cable body parameters, link topology data and ancillary facility data of low-voltage cables. The geospatial data includes cable route, laying method, burial depth or overhead height, inflection point coordinates and manhole / pole / cable tray location information. In response to the on-site verification data, repair data and inspection trajectory transmitted back by maintenance personnel via mobile terminals, the system updates the GIS basic database and GIS fusion model of low-voltage cables, and simultaneously refreshes the cable status and topology on the GIS map.

3. The method for visualized operation and maintenance of low-voltage cables based on GIS fusion according to claim 2, characterized in that, The low-voltage cables include at least one of communication optical cables, network cables, coaxial cables, security cables, and building low-voltage cables; the data collection is achieved through at least one of manual inspection and data entry, CAD drawing import, OTDR testing, optical power monitoring, RFID tag reading, and GPS / BeiDou positioning.

4. The method for visualized operation and maintenance of low-voltage cables based on GIS fusion according to claim 3, characterized in that, The on-site verification data transmitted back by the mobile terminal includes: RFID tag scanning results, GPS positioning coordinates, on-site images, voice notes, and optical power test values ​​after repair. The system compares the on-site GPS positioning coordinates with the coordinates of the corresponding inspection point in the GIS fusion model. If they match, the system automatically extracts the associated cable information of that point from the database and displays it.

5. The method for visualized operation and maintenance of low-voltage cables based on GIS fusion according to claim 1, characterized in that, The normalization cleaning and spatial registration specifically include: Transform geospatial data from different coordinate systems to the same coordinate datum. Establish data layers according to the type of low-voltage cable, laying method, and ownership unit; Using manholes, poles, and cable tray nodes as anchor points, the spatial geometry of cable segments is linked to the ledger attributes to establish a triple mapping relationship between geographic space, cable entity, and link logic.

6. The method for visual operation and maintenance of low-voltage cables based on GIS fusion according to claim 1, characterized in that, For underground low-voltage cables, auxiliary fitting points are added between adjacent anchor points based on the road direction to transform straight connections into fitting connections along the road direction, and a cross-sectional view of the pipe jacking section and a view of the spare resources of the well are provided on the GIS map; for overhead low-voltage cables, the safety distance boundaries between them and power lines, trees, and structures are marked.

7. The method for visualized operation and maintenance of low-voltage cables based on GIS fusion according to claim 1, characterized in that, The operational monitoring data includes at least one of the following: optical power, bit error rate, link connectivity, junction box temperature and humidity, well water level, external force vibration signal, and video images; The preset alarm conditions are determined by a dynamic threshold obtained by combining historical monitoring data with meteorological data, specifically: The maximum fluctuation value within the historical monitoring period is recorded as the benchmark threshold. The baseline threshold is corrected based on meteorological data and geographic location images of the cable's location to obtain the corrected threshold. When real-time monitoring data exceeds the correction threshold, a spatial highlighting alarm is triggered.

8. The method for visualized operation and maintenance of low-voltage cables based on GIS fusion according to claim 1, characterized in that, The calculation method for the scope of the fault impact is as follows: starting from the fault point, trace bidirectionally along the cable link topology in the GIS fusion model, extract all business nodes that share the cable segment or upstream and downstream related cable segments, and generate a list of affected businesses; the optimal repair path is calculated by combining road network data, manhole accessibility and real-time traffic information.

9. The method for visual operation and maintenance of low-voltage cables based on GIS fusion according to claim 7, characterized in that, Also includes: Based on the fault type and cable health assessment model, the risk level of the faulty cable segment is rendered, and the risk distribution is displayed on the GIS map with color gradient. The health assessment model is based on multimodal sensor data and uses a temporal convolutional network to predict the degradation trend of cables.

10. The method for visualized operation and maintenance of low-voltage cables based on GIS fusion according to claim 1, characterized in that, Also includes: Based on historical maintenance work orders, fault records, and cable ledger data, multi-dimensional statistics are performed by time period, cable type, geographical area, and fault cause. High-incidence areas of faults are displayed on the GIS map in the form of heat maps, and the inspection frequency and inspection routes are dynamically adjusted accordingly. The inspection path is generated as follows: the cable link is segmented according to the attribute information of the cable segment, and different inspection frequencies are assigned to different segments based on their health and risk levels. The sequence of the nearest inspection points is generated by combining the current location of the mobile terminal.

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