Cable tunnel digitized inspection and management method and system based on three-dimensional point cloud

Abstract

The application relates to a cable tunnel digital inspection and management method and system based on a three-dimensional point cloud, which comprises the following steps: acquiring original three-dimensional point cloud data of a cable tunnel, asset identification data of a monitoring device in the tunnel, real-time monitoring data flow and video monitoring data; processing the original three-dimensional point cloud data to obtain tunnel point cloud, establishing a unified tunnel coordinate system and constructing a three-dimensional model of a cable tunnel structure; determining the spatial position and attitude parameters of the monitoring device and connecting a parameterized device model; establishing a space-time mapping relationship and updating the state data of the parameterized device model; performing inspection in the scene; when the monitoring data meets the alarm condition, the related information of the alarm device is determined synchronously and linkage display is performed, and an inspection and management result is output; the scheme can realize efficient and accurate inspection and management of the cable tunnel and improve the safety and reliability of the cable tunnel.

Description

Technical Field

[0001] This application relates to the fields of power and information technology, and in particular to a digital inspection and control system for cable tunnels based on three-dimensional point clouds. Background Technology

[0002] Cable tunnel inspection and management play a crucial role in the safe and stable operation of power systems. With the continuous expansion of cable tunnel construction, traditional inspection and management methods are no longer sufficient to meet practical needs. Currently, cable tunnel inspection mainly relies on regular manual patrols, where inspectors check the appearance of cables and monitor the operating status of equipment to identify potential problems. Simultaneously, some tunnels are equipped with monitoring devices such as temperature and humidity sensors. These devices can collect real-time environmental and cable operation data within the tunnel and transmit the data to a monitoring center for analysis and processing.

[0003] However, traditional inspection and control methods have many problems. Manual inspection is inefficient, unable to cover the entire cable tunnel, and easily affected by the experience and subjective factors of the inspectors. Furthermore, manual inspection cannot obtain comprehensive information about the tunnel in real time, making it difficult to detect potential safety hazards in a timely manner. To solve these problems, some digital inspection and control technologies have emerged. These technologies, by introducing sensor networks, video surveillance systems, and other equipment, achieve real-time monitoring and data collection of cable tunnels. Simultaneously, data analysis and processing techniques are used to analyze and mine the collected data to discover potential safety hazards and faults.

[0004] While digital inspection and control technologies have improved the efficiency and accuracy of cable tunnel inspection and control to some extent, current technologies still have some shortcomings. Existing digital inspection and control technologies lack a unified coordinate system and spatiotemporal mapping relationship, making it difficult to effectively integrate and analyze data from different monitoring devices, thus affecting the effectiveness of inspection and control. Summary of the Invention

[0005] The main purpose of this application is to provide a digital inspection and control method and system for cable tunnels based on three-dimensional point clouds, which can establish a unified tunnel coordinate system and spatiotemporal mapping relationship to achieve efficient and accurate inspection and control of cable tunnels.

[0006] To achieve the above objectives, embodiments of the present invention provide a method for digital inspection and control of cable tunnels based on three-dimensional point clouds, the method comprising the following steps: Acquire raw 3D point cloud data of the cable tunnel, asset identification data of monitoring equipment inside the tunnel, real-time monitoring data stream, and video surveillance data; The original 3D point cloud data is denoised, registered, and stitched together to obtain the tunnel point cloud. A unified tunnel coordinate system is established based on the tunnel point cloud, and a three-dimensional model of the cable tunnel structure is constructed under the tunnel coordinate system. Based on the three-dimensional model of the cable tunnel structure, the spatial position and attitude parameters of the monitoring equipment are determined, and the corresponding parameterized equipment model is attached to the target position. Based on the spatial location and attitude parameters of the monitoring device, the device identifier, real-time monitoring data stream channel, video viewpoint parameters and historical status index are associated to establish a spatiotemporal mapping relationship; Based on the spatiotemporal mapping relationship, the parameterized device model corresponding to the real-time monitoring data stream is parsed, and the status data of the parameterized device model is updated according to the timestamp, while the corresponding historical status index is written. The inspection is performed in the scene consisting of the three-dimensional model of the cable tunnel structure and the parameterized equipment model; When the monitoring data corresponding to the real-time monitoring data stream meets the preset alarm conditions, the spatial location of the alarm device, the corresponding video monitoring screen and historical status data are determined synchronously based on the spatiotemporal mapping relationship, and then displayed in a linked manner to output the inspection and control results.

[0007] In summary, by adopting the technical solution of this application, a comprehensive understanding of the actual situation of the cable tunnel can be achieved by acquiring the original 3D point cloud data of the cable tunnel, asset identification data of the monitoring equipment in the tunnel, real-time monitoring data streams, and video surveillance data. Denoising, registration, and stitching processing of the original 3D point cloud data to obtain the tunnel point cloud improves the quality of the point cloud data. A unified tunnel coordinate system is established based on the tunnel point cloud, and a 3D model of the cable tunnel structure is constructed under this coordinate system, providing an accurate spatial reference for subsequent inspection and control. The spatial position and attitude parameters of the monitoring equipment are determined according to the 3D model of the cable tunnel structure, and the corresponding parameterized equipment model is attached to the target position, achieving precise association between the monitoring equipment and the 3D model. Establishing a spatiotemporal mapping relationship effectively associates equipment identification, real-time monitoring data stream channels, video perspective parameters, and historical status indexes, facilitating data integration and analysis. Updating the status data of the parameterized equipment model according to the timestamp and writing it into the corresponding historical status index enables real-time tracking of equipment status and recording of historical data. Performing inspections in a scenario composed of the 3D model of the cable tunnel structure and the parameterized equipment model simulates a real inspection process, improving inspection efficiency. When the monitoring data corresponding to the real-time monitoring data stream meets the preset alarm conditions, the spatial location of the alarm device, the corresponding video monitoring screen and historical status data are determined synchronously based on the spatiotemporal mapping relationship, and the data is displayed in a linked manner to output the inspection and control results. This can promptly detect and handle potential safety hazards and improve the inspection and control level of cable tunnels. Attached Figure Description

[0008] Figure 1 This is a schematic diagram of a scenario for digital inspection and control of cable tunnels based on three-dimensional point clouds in an embodiment of this application; Figure 2 A flowchart of a digital inspection and control method for cable tunnels based on 3D point clouds is provided for embodiments of this application; Figure 3 A schematic diagram illustrating the process of establishing the tunnel coordinate system provided in the embodiments of this application; Figure 4 This is a schematic diagram illustrating the process of generating a 3D model as provided in the embodiments of this application. Figure 5 This is a schematic diagram of the device connection process provided in the embodiments of this application; Figure 6 A schematic diagram illustrating the process of establishing the spatiotemporal mapping relationship provided in the embodiments of this application; Figure 7 A schematic diagram illustrating the state update process provided in this application embodiment; Figure 8 A schematic diagram of the virtual inspection process provided in the embodiments of this application; Figure 9 This is a schematic diagram illustrating the inspection linkage process in an embodiment of this application; Figure 10 This is a schematic diagram of the structure of the digital inspection and control system for cable tunnels in this application embodiment. Detailed Implementation

[0009] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0010] This application provides a method and system for digital inspection and control of cable tunnels based on three-dimensional point clouds, which will be described in detail below.

[0011] This embodiment provides a method and system for digital inspection and management of cable tunnels, such as... Figure 1As shown, the method scenario system includes a data acquisition layer 101, a processing and analysis layer 102, and a display and interaction layer 103. The data acquisition layer 101 is deployed at the cable tunnel site to collect raw 3D point cloud data, equipment asset identification data, real-time monitoring data streams, and video surveillance data. The processing and analysis layer 102 is deployed in a central computer room or edge server and may include a point cloud processing unit 121, a coordinate modeling unit 122, an equipment attachment unit 123, a spatiotemporal mapping unit 124, a status update unit 125, and a patrol linkage control unit 126. It processes the raw 3D point cloud data to construct a 3D tunnel scene, completing equipment positioning and attachment, spatiotemporal mapping, status updates, alarm linkage, and historical backtracking. The display and interaction layer 103 is used to display the digital twin scene, real-time video footage, historical status data, and handling tasks to maintenance personnel.

[0012] Furthermore, the data acquisition layer 101 may include a laser scanning device 111, a sensor group 112, and a video monitoring device 113, wherein the sensor group 112 includes a temperature sensor, a humidity sensor, a partial discharge sensor, a water level sensor, a harmful gas sensor, and a smoke sensor; the display and interaction layer 103 may include a 3D display terminal 131, an operation and maintenance workstation 132, or a mobile inspection terminal 133. Through the above deployment method, a closed-loop application of on-site data collection, platform analysis, and coordinated control can be realized during daily inspections, anomaly warnings, and emergency response in cable tunnels. For example, when a water level sensor detects abnormal water accumulation in a section of a cable tunnel in an urban integrated utility tunnel, the method scene system can locate the alarm position in the 3D scene, retrieve the corresponding camera footage, and issue an on-site verification task to the operation and maintenance terminal.

[0013] Taking a cable tunnel scenario within an urban integrated utility tunnel as an example, the data acquisition layer 101 is deployed at the cable tunnel site. A laser scanning device 111 periodically scans the cable tunnel to acquire raw 3D point cloud data. The laser scanning device 111 accurately acquires the 3D coordinate information of the tunnel walls, cable trays, and cable bodies by emitting a laser beam and measuring the time of reflected light, thereby generating raw 3D point cloud data. For example, in a large cable tunnel, the laser scanning device 111 moves along the tunnel, scanning different locations and transmitting the acquired point cloud data to the processing and analysis layer 102.

[0014] Sensor group 112 includes various sensors such as temperature sensors, humidity sensors, partial discharge sensors, water level sensors, hazardous gas sensors, and smoke sensors. These sensors are distributed at various key locations within the cable tunnel, collecting equipment asset identification data and real-time monitoring data streams. Temperature sensors monitor cable temperature in real time; excessively high cable temperatures may indicate overload or faults. Humidity sensors monitor humidity within the tunnel; excessive humidity may affect cable insulation performance. Partial discharge sensors detect partial discharge in the cable, a crucial indicator of insulation aging. Water level sensors promptly detect water accumulation within the tunnel. Hazardous gas sensors detect the concentration of harmful gases such as carbon monoxide and hydrogen sulfide within the tunnel. Smoke sensors detect smoke generation within the tunnel, preventing fire accidents. The data collected by these sensors is transmitted to the processing and analysis layer 102 via wired or wireless networks.

[0015] Video surveillance devices 113 are installed in various corners and areas within the cable tunnel to collect video surveillance data in real time. These devices provide high-definition video feeds, allowing maintenance personnel to view the tunnel's interior in real time via the interactive display layer 103 and promptly detect any anomalies. For example, in the event of a fire or water leak within the tunnel, the video surveillance devices 113 can capture the scene immediately, providing a basis for emergency response.

[0016] The processing and analysis layer 102 is deployed in the central computer room or on an edge server. After receiving the raw 3D point cloud data, the point cloud processing unit 121 performs denoising, registration, and stitching processes to obtain the tunnel point cloud. Denoising can remove abnormal points caused by noise interference during laser scanning, improving the quality of the point cloud data; registration aligns point cloud data collected from different positions and angles, ensuring they have consistent positions and orientations in the same coordinate system; stitching merges the registered point cloud data to form a complete tunnel point cloud.

[0017] Coordinate modeling unit 122 establishes a unified tunnel coordinate system based on the tunnel point cloud and constructs a 3D model of the cable tunnel structure within this coordinate system. First, the tunnel centerline, multiple cross-sectional contours, and preset reference control points are extracted from the tunnel point cloud. Then, based on this information, the corresponding mileage reference, normal reference, and height reference for each cross-section are determined, and the tunnel coordinate system is constructed using these references. Next, voxel downsampling, noise point removal, and reflection intensity normalization are performed on the tunnel point cloud. Based on the point cloud normal vector, curvature features, and reflection intensity features, the tunnel inner wall, supports, cable trays, and cable bodies are segmented. Finally, surface reconstruction and meshing are performed on the segmented structural point cloud to generate a 3D model of the cable tunnel structure containing region identifiers.

[0018] Equipment attachment unit 123 determines the spatial position and attitude parameters of the monitoring equipment based on the 3D model of the cable tunnel structure, and attaches the corresponding parameterized equipment model to the target position. Using area identifiers and asset identifiers in the 3D model of the cable tunnel structure, it determines the candidate installation area for the monitoring equipment. Combining the mileage range, installation height, and installation side of the candidate installation area, it determines the initial position of the monitoring equipment. It extracts the local high-density point cloud retained within the candidate installation area and performs coarse and fine registration with the preset equipment template to obtain the spatial position and attitude parameters of the monitoring equipment. When the registration confidence level is lower than a preset threshold, it calls the associated video pose data or the spatial constraint relationship of adjacent anchored equipment to correct the spatial position and attitude parameters. Finally, it attaches the corresponding parameterized equipment model to the target position in the 3D model of the cable tunnel structure and writes the corresponding area identifier.

[0019] The spatiotemporal mapping unit 124 establishes a spatiotemporal mapping relationship by associating the device identifier, real-time monitoring data stream channel, video viewpoint parameters, and historical status index based on the spatial location and attitude parameters of the monitoring device. For each monitoring device, a mapping relationship record is established, including the device's unique identifier, device type, spatial location, attitude parameters, target location area identifier, real-time monitoring data stream channel, associated camera identifier, video viewpoint parameters, and historical status index address. A first index is established from the spatial location and target location area identifier to the device's unique identifier, and a second index is established from the device's unique identifier to the data stream, video viewpoint, and historical status index. Alarm thresholds, update timestamps, version identifiers, and valid status flags are written into the mapping relationship record to construct a spatiotemporal mapping relationship library.

[0020] The status update unit 125 parses the parameterized device model corresponding to the real-time monitoring data stream based on the spatiotemporal mapping relationship, updates the status data of the parameterized device model according to the timestamp, and writes it to the corresponding historical status index. It parses the device unique identifier, monitoring value, timestamp, data quality tag, and sampling sequence number in the real-time monitoring data stream. It extracts the parameterized device model, alarm threshold, video viewpoint parameters, and historical status index address corresponding to the device unique identifier from the spatiotemporal mapping relationship library. Based on the monitoring value, alarm threshold, data quality tag, and sampling sequence number, it determines the device status level and drives the parameterized device model to perform the corresponding status update. The updated device status is written to the corresponding historical status index according to the timestamp, and a linked retrieval pointer associated with the video viewpoint parameters and alarm level is generated.

[0021] The patrol linkage control unit 126 performs patrols in a scenario composed of a 3D model of the cable tunnel structure and a parametric equipment model. Based on the tunnel centerline in the 3D model, a main patrol path is generated, and the patrol direction along this path is determined. According to the spatial distribution density of the updated parametric equipment model, historical alarm frequency, and manually defined key areas, multiple patrol observation nodes are set up along the main patrol path. Each observation node is configured with preset video viewpoint parameters, line-of-sight direction, and a set of visible devices. The patrol viewpoint automatically roams according to the observation node, and upon reaching the node, the corresponding area's equipment status information, associated video sources, and historical status summaries are pre-loaded. When the monitoring data corresponding to the real-time monitoring data stream meets preset alarm conditions, the spatial location of the alarm device, the corresponding video monitoring screen, and historical status data are synchronously determined based on the spatiotemporal mapping relationship, and displayed in a linked manner, outputting the patrol control results.

[0022] The interactive display layer 103 is used to show maintenance personnel digital twin scenarios, real-time video feeds, historical status data, and handling tasks. The 3D display terminal 131 can display the structure and equipment layout of the cable tunnel in 3D, allowing maintenance personnel to intuitively view the tunnel's interior. The maintenance workstation 132 provides a centralized management and operation platform for maintenance personnel, allowing them to view real-time monitoring data, historical status data, handle alarms, and assign tasks. The mobile inspection terminal 133 facilitates on-site inspections and operations for maintenance personnel, enabling them to obtain real-time information about the tunnel and receive on-site handling tasks.

[0023] For example, when a water level sensor detects abnormal water accumulation in a section of a cable tunnel within an urban integrated utility tunnel, the sensor transmits real-time monitoring data to the processing and analysis layer 102. The patrol and control unit 126 determines the spatial location of the alarm device based on the spatiotemporal mapping relationship, locates the alarm position in the 3D scene, retrieves video footage from the corresponding camera, and issues an on-site verification task to the maintenance terminal. Maintenance personnel receive the task through the display and interaction layer 103 and then travel to the site with the mobile inspection terminal 133 for verification and processing. This method achieves a closed-loop application of on-site data collection, platform analysis, and coordinated control of cable tunnels, improving the efficiency and accuracy of cable tunnel inspection and control, and ensuring the safe and stable operation of cable tunnels.

[0024] refer to Figure 2 , Figure 2This is a schematic diagram illustrating a process for digital inspection and management of cable tunnels based on 3D point clouds, provided in an embodiment of this application. The execution entity of this method can be computer equipment, a cloud server, etc. Specifically, the digital inspection and management of cable tunnels based on 3D point clouds provided in this embodiment includes:

[0025] S10: Acquire the original 3D point cloud data of the cable tunnel, asset identification data of the monitoring equipment in the tunnel, real-time monitoring data stream, and video surveillance data.

[0026] In this embodiment, the original 3D point cloud data is a set of 3D coordinate data of the surface of objects within the cable tunnel, obtained through 3D laser scanning technology. This data represents the shape and position information of the object surface in the form of points. For example, in a large cable tunnel, a 3D laser scanner scans the tunnel's inner walls, cable trays, and cable bodies, obtaining a large amount of 3D point cloud data. This point cloud data can reflect information such as the tunnel's geometry and cable layout. Asset identification data is information used to uniquely identify monitoring equipment within the tunnel, typically including the equipment's serial number, model, and installation location. Asset identification data allows for accurate identification and management of each monitoring device. Real-time monitoring data streams are data collected in real-time by the monitoring equipment regarding the cable's operating status and tunnel environmental parameters, such as temperature, humidity, current, and voltage. This data reflects the real-time operation of the cable and the environmental conditions within the tunnel. Video surveillance data consists of video footage captured by video surveillance equipment installed within the tunnel, used for real-time monitoring of the tunnel's interior.

[0027] In one embodiment, a high-precision 3D laser scanner can be used to acquire raw 3D point cloud data. This scanner determines the distance to the object's surface by emitting a laser beam and measuring the time of reflected light, thereby acquiring the object's 3D coordinates. During the scanning process, the scanner can move and scan according to a preset path and parameters to ensure complete tunnel point cloud data is acquired. For asset identification data, relevant equipment information can be entered into a database during equipment installation, and each device can be assigned a unique identification number. When acquiring asset identification data, this information can be read from the database via a network interface. For real-time monitoring data streams, the monitoring equipment transmits the collected data to a server via wired or wireless network. The server can use data acquisition software to receive and process this data. For video surveillance data, the video surveillance equipment transmits the acquired video footage to a server via a network. The server can use video management software to store and manage these video footage.

[0028] S20: The original three-dimensional point cloud data is denoised, registered, and stitched together to obtain the tunnel point cloud.

[0029] In this embodiment, denoising refers to removing outliers caused by noise interference from the original 3D point cloud data. This noise may originate from factors such as measurement errors of the laser scanner and environmental interference. Denoising can improve the quality of the point cloud data, making subsequent processing more accurate. Registration aligns point cloud data acquired at different locations and angles, ensuring they have consistent positions and orientations within the same coordinate system. Since 3D laser scanners typically need to scan at different locations to obtain complete tunnel point cloud data, registration of these point cloud data acquired at different locations is necessary. Stitching merges the registered point cloud data to form a complete tunnel point cloud.

[0030] In one embodiment, a statistical filtering algorithm can be used for denoising. This algorithm determines whether a point is an outlier by calculating the statistical information of its neighboring points, such as the mean and standard deviation. If the statistical information of a point differs significantly from that of its neighbors, it is considered an outlier and removed. For registration, the Iterative Closest Point (ICP) algorithm can be used. This algorithm iteratively finds the optimal transformation matrix between two point cloud datasets, minimizing the distance between them in the same coordinate system. During the iteration process, the closest point pairs between the two point cloud datasets are first found, and then the transformation matrix between these point pairs is calculated, continuously updating the position and orientation of the point cloud datasets until the convergence condition is met. For stitching, the registered point cloud datasets can be directly merged. During the merging process, it is necessary to remove duplicate points to reduce the amount of data. Through denoising, registration, and stitching, a complete and accurate tunnel point cloud can be obtained.

[0031] S30: Establish a unified tunnel coordinate system based on the tunnel point cloud, and construct a three-dimensional model of the cable tunnel structure under the tunnel coordinate system.

[0032] In this embodiment, the unified tunnel coordinate system is established to facilitate the unified management and analysis of various objects and data within the cable tunnel. This coordinate system uses the tunnel's centerline as a reference and defines longitudinal, transverse, and vertical coordinate axes. By establishing a unified tunnel coordinate system, data from different sources can be represented and processed within the same coordinate system, improving data consistency and comparability. The three-dimensional model of the cable tunnel structure is a model obtained by three-dimensionally modeling various structures and equipment within the cable tunnel. This model can intuitively display the internal structure and equipment layout of the cable tunnel, providing visual support for inspection and control.

[0033] S40: Determine the spatial position and attitude parameters of the monitoring equipment based on the three-dimensional model of the cable tunnel structure, and attach the corresponding parameterized equipment model to the target position.

[0034] In this embodiment, the spatial position of the monitoring device refers to its specific coordinates within the cable tunnel, and the attitude parameters refer to its orientation and angle information. By determining the spatial position and attitude parameters of the monitoring device, it is possible to accurately associate the monitoring device with the 3D model of the cable tunnel structure. The parameterized device model is a model obtained by abstracting and modeling the monitoring device, and this model contains various parameters and attributes of the monitoring device. Attaching the parameterized device model to the target position means placing the parameterized device model at its corresponding actual position in the 3D model of the cable tunnel structure, thereby achieving a visual association between the monitoring device and the 3D model.

[0035] S50: Based on the spatial location and attitude parameters of the monitoring device, associate the device identifier, real-time monitoring data stream channel, video viewpoint parameters, and historical status index to establish a spatiotemporal mapping relationship.

[0036] In this embodiment, the spatiotemporal mapping relationship is a relationship that associates the spatial location, temporal information, and related data of a monitoring device. By establishing the spatiotemporal mapping relationship, rapid retrieval and analysis of monitoring device data can be achieved. The device identifier is information used to uniquely identify the monitoring device; the real-time monitoring data stream channel refers to the channel through which the monitoring device transmits real-time monitoring data to the server; the video viewing angle parameters refer to the viewing angle and direction information of the video surveillance device; and the historical status index is index information used to record the historical status data of the monitoring device.

[0037] S60: Based on the spatiotemporal mapping relationship, parse the parameterized device model corresponding to the real-time monitoring data stream, update the status data of the parameterized device model according to the timestamp, and write the corresponding historical status index.

[0038] In this embodiment, parsing the parameterized device model corresponding to the real-time monitoring data stream refers to extracting information related to the real-time monitoring data stream from the parameterized device model based on the spatiotemporal mapping relationship. Updating the status data of the parameterized device model according to the timestamp means updating the status data of the parameterized device model according to the time information in the real-time monitoring data stream to reflect the real-time status of the monitoring device. Writing to the corresponding historical status index means recording the updated device status data into the historical status index for subsequent querying and analysis.

[0039] In one embodiment, the device's unique identifier, monitored value, timestamp, data quality marker, and sampling sequence number are parsed from the real-time monitoring data stream. The parameterized device model, alarm threshold, video viewpoint parameters, and historical status index address corresponding to the device's unique identifier are extracted from a spatiotemporal mapping database. The device status level is determined based on the monitored value, alarm threshold, data quality marker, and sampling sequence number, and the parameterized device model is driven to perform the corresponding status update. The updated device status is written to the corresponding historical status index according to the timestamp, and a linked retrieval pointer associated with the video viewpoint parameters and alarm level is generated. By parsing the parameterized device model, updating the status data, and writing to the historical status index, real-time tracking of the monitored device status and recording of historical data can be achieved.

[0040] S70: Perform an inspection in the scene consisting of the three-dimensional model of the cable tunnel structure and the parameterized equipment model.

[0041] In this embodiment, performing inspections in a scenario comprised of a 3D model of the cable tunnel structure and a parametric equipment model refers to conducting inspections in a virtual cable tunnel scenario through computer simulation. This inspection method can improve inspection efficiency and reduce the workload of manual inspections. During the inspection process, real-time viewing of monitoring equipment status data, video surveillance footage, and other information is possible.

[0042] S80: When the monitoring data corresponding to the real-time monitoring data stream meets the preset alarm conditions, the spatial location of the alarm device, the corresponding video monitoring screen and historical status data are determined synchronously based on the spatiotemporal mapping relationship, and the results of the inspection and control are displayed in a coordinated manner.

[0043] In this embodiment, the preset alarm conditions are monitoring data thresholds set based on the actual conditions and safety requirements of the cable tunnel. When the monitoring data corresponding to the real-time monitoring data stream exceeds the preset alarm conditions, it indicates that the monitoring equipment may be malfunctioning. Synchronously determining the spatial location of the alarm device, the corresponding video surveillance footage, and historical status data based on spatiotemporal mapping refers to quickly locating the alarm device's position and obtaining the corresponding video surveillance footage and historical status data based on the spatiotemporal mapping relationship. Linked display refers to displaying the spatial location of the alarm device, the video surveillance footage, and the historical status data on the same interface, facilitating comprehensive analysis and processing by inspection personnel. Outputting inspection and control results refers to generating corresponding processing suggestions and decisions based on the alarm situation, such as arranging for inspection personnel to conduct on-site inspections or initiating emergency response measures.

[0044] In one embodiment, reference Figure 3 Step S20 can specifically include S21-S24, which will be described in detail below: Step S21: Extract the tunnel centerline, multiple cross-sectional profiles, and preset reference control points from the tunnel point cloud along the tunnel extension direction.

[0045] In this embodiment, the tunnel centerline refers to a line located at the geometric center of the cable tunnel, which reflects the tunnel's orientation. Multiple cross-sectional profiles refer to cross-sectional shapes perpendicular to the tunnel centerline taken at different locations within the tunnel. Preset reference control points are pre-defined points with specific location information, used to assist in establishing a coordinate system.

[0046] For example, in a long cable tunnel, a cross-sectional profile is extracted from the tunnel point cloud at regular intervals (e.g., 10 meters) along the tunnel's extension direction. Simultaneously, a specific algorithm is used to fit the tunnel centerline from the tunnel point cloud. Preset reference control points can be selected from locations with distinctive features, such as tunnel entrances and curves.

[0047] In one embodiment, a point cloud clustering and fitting method can be used to extract the tunnel centerline. First, the tunnel point cloud is clustered according to spatial location. Then, each cluster is fitted to obtain approximate line segments, which are then connected to form the tunnel centerline. For extracting the cross-sectional profile, the point cloud data of the cross-section is obtained by intersecting the tunnel point cloud with a plane perpendicular to the tunnel centerline. This point cloud data is then processed to obtain the cross-sectional profile. Preset reference control points can be determined manually or automatically. By extracting the tunnel centerline, multiple cross-sectional profiles, and preset reference control points, basic data can be provided for subsequently establishing a tunnel coordinate system.

[0048] Step S22: Determine the mileage reference, normal reference, and height reference corresponding to each section based on the tunnel centerline and multiple cross-sectional profiles.

[0049] In this embodiment, the mileage reference refers to a distance reference along the tunnel centerline, used to indicate the position of each point on the cross-section in the tunnel's extension direction. The normal reference refers to a direction reference perpendicular to the tunnel centerline, used to determine the lateral direction of the cross-section. The height reference refers to a reference in the vertical direction, used to indicate the height of each point on the cross-section.

[0050] For example, using the tunnel entrance as the starting point of the mileage benchmark, the distance from each cross-section to the starting point is measured along the tunnel centerline, and this distance serves as the mileage benchmark for that cross-section. The normal benchmark can be determined by a direction perpendicular to the tunnel centerline and parallel to the plane containing the cross-section. The height benchmark can be determined by measuring the height of each point on the cross-section relative to the tunnel bottom, with the tunnel bottom as the zero point.

[0051] In one embodiment, the normal reference can be determined by calculating the tangent direction of the tunnel centerline. For the mileage reference, the mileage value of each section can be calculated along the tunnel centerline using an integration method. The height reference can be determined using the vertical coordinates in the point cloud data. By determining the mileage reference, normal reference, and height reference corresponding to each section, accurate direction and position references can be provided for establishing the tunnel coordinate system.

[0052] Step S23: Construct a tunnel coordinate system using the mileage reference as the longitudinal reference, the normal reference as the lateral reference, and the height reference as the vertical reference, and in conjunction with the preset reference control points.

[0053] In this embodiment, the tunnel coordinate system is constructed to unify the management and analysis of various objects and data within the cable tunnel within a single coordinate system. Using the mileage reference as the longitudinal reference, the normal reference as the lateral reference, and the height reference as the vertical reference, the spatial position of each point within the tunnel can be accurately represented.

[0054] For example, in a tunnel coordinate system, the coordinates of a point can be represented as (mileage, normal offset, and height). Preset reference control points can be used to determine the origin and orientation of the coordinate system.

[0055] In one embodiment, the coordinates of a preset reference control point in the tunnel coordinate system are first determined. Then, based on the definitions of mileage reference, normal reference, and height reference, the coordinates of other points are transformed into the tunnel coordinate system. By constructing the tunnel coordinate system, unified management and analysis of various data within the cable tunnel can be achieved.

[0056] Step S24: Perform a unified coordinate transformation on the point cloud data, monitoring equipment location data, and video surveillance pose data based on the tunnel coordinate system to obtain a spatial reference that can be uniformly retrieved.

[0057] In this embodiment, unified coordinate transformation refers to converting point cloud data, monitoring device location data, and video surveillance pose data from different sources into the tunnel coordinate system, so that they have consistent positions and orientations under the same coordinate system. A unified searchable spatial reference refers to the data, after unified coordinate transformation, which can be uniformly searched and analyzed under the tunnel coordinate system.

[0058] For example, point cloud data collected by 3D laser scanners, installation location data of monitoring equipment, and pose data of video surveillance equipment are all converted into the tunnel coordinate system. This allows for direct retrieval of information at a specific location within the tunnel coordinate system, improving data retrieval efficiency.

[0059] In one embodiment, a coordinate transformation matrix can be used to achieve a unified coordinate transformation. Based on the definition of the tunnel coordinate system and the original coordinate system of each data point, the corresponding coordinate transformation matrix is ​​calculated. Then, each data point is multiplied by this matrix to obtain the transformed coordinates. Through unified coordinate transformation, a uniformly searchable spatial reference can be obtained, facilitating subsequent data analysis and processing.

[0060] In one embodiment, reference Figure 4 Step S30 can specifically include S31-S34, which will be described in detail below: Step S31: Perform voxel downsampling, noise point removal, and reflection intensity normalization on the tunnel point cloud according to the preset voxel size.

[0061] In this embodiment, the preset voxel size is a pre-defined size of the cubic unit used to divide the tunnel point cloud. Voxel downsampling refers to dividing the tunnel point cloud according to the preset voxel size, retaining only one representative point within each voxel, thereby reducing the amount of point cloud data. Noise point removal removes abnormal points in the point cloud caused by measurement errors or environmental interference. Reflection intensity normalization adjusts the reflection intensity values ​​of the point cloud to a uniform range.

[0062] For example, the preset voxel size can be set to 0.1 m × 0.1 m × 0.1 m. During voxel downsampling, the tunnel point cloud is divided into small voxels, and a representative point, such as the centroid, is selected within each voxel. Noise points can be removed using methods such as statistical filtering. Reflection intensity normalization maps the reflection intensity values ​​to the range of 0 to 1.

[0063] In one embodiment, a voxel grid filter can be used for voxel downsampling. This filter divides the point cloud into a voxel grid, then calculates the centroid of each point within a voxel, using the centroid as the representative point of that voxel. For noise point removal, a statistical filtering algorithm can be used to determine whether a point is a noise point by calculating the neighborhood statistics. Reflection intensity normalization can be achieved by adjusting the reflection intensity values ​​to a specified range using a linear mapping method. Through voxel downsampling, noise point removal, and reflection intensity normalization, the quality and processing efficiency of point cloud data can be improved.

[0064] Step S32: Segment the tunnel inner wall, supports, cable trays and cable bodies based on point cloud normal vectors, curvature features and reflection intensity features.

[0065] In this embodiment, the point cloud normal vector refers to the direction of the normal to the point cloud surface at a certain point, which reflects the local geometric features of the point cloud surface. Curvature features describe the degree of curvature of the point cloud surface. Reflection intensity features refer to the reflection intensity value of the point cloud; different objects may have different reflection intensities. These features can be used to distinguish between tunnel walls, supports, cable trays, and the cable itself.

[0066] For example, the point cloud normal vectors of the tunnel inner wall are usually relatively regular and have a small curvature; the point cloud of the support may have a specific shape and structure, and its curvature and reflection intensity are different from those of the tunnel inner wall; cable trays and cable bodies also have their own unique characteristics.

[0067] In one embodiment, a feature-based clustering algorithm can be used for segmentation. First, the normal vector, curvature, and reflection intensity features of the point cloud are calculated. Then, the point cloud is clustered based on these features, grouping points with similar features into one class, thereby achieving segmentation of the tunnel wall, supports, cable trays, and cable bodies. Through segmentation, different objects can be separated from the point cloud, providing a foundation for subsequent modeling.

[0068] Step S33: Perform surface reconstruction and meshing on the segmented structural point cloud to generate a 3D model of the cable tunnel structure containing region identifiers.

[0069] In this embodiment, surface reconstruction involves fitting the segmented structural point cloud into a smooth surface to better represent the surface shape of the object. Meshing involves dividing the surface into multiple small triangular or quadrilateral meshes to facilitate computer processing and display. Region identification is identification information used to distinguish different objects and regions.

[0070] For example, for the point cloud of the tunnel inner wall, a smooth tunnel inner wall surface can be obtained through surface reconstruction. Then, this surface is meshed to obtain a tunnel inner wall model composed of multiple small meshes. Each model has a corresponding region label, such as "tunnel inner wall" or "support".

[0071] In one embodiment, the moving least squares method can be used for surface reconstruction. This method obtains a smooth surface by fitting a polynomial surface within the local neighborhood of the point cloud. Meshing can be performed using a triangulation algorithm to divide the surface into multiple triangular meshes. Through surface reconstruction and meshing, and by adding region identifiers, a 3D model of the cable tunnel structure containing region identifiers can be generated.

[0072] Step S34: Retain local high-density point clouds in densely installed equipment areas, and establish the correspondence between the local high-density point clouds, area identifiers, and the three-dimensional model of the cable tunnel structure.

[0073] In this embodiment, densely installed equipment areas refer to areas within cable tunnels where monitoring equipment, cable trays, and other equipment are installed relatively densely. Local high-density point clouds refer to point cloud data preserved in these areas; their density is relatively high, allowing for a more detailed representation of the equipment's structure and location. Establishing a correspondence involves associating the local high-density point clouds, area identifiers, and the 3D model of the cable tunnel structure to facilitate subsequent querying and analysis.

[0074] For example, near a power distribution room in a cable tunnel, where equipment is densely installed, a local high-density point cloud is preserved for this area. A region identifier labeled "Power Distribution Room" is added to this local high-density point cloud, and it is then associated with the corresponding power distribution room region in the 3D model of the cable tunnel structure.

[0075] In one embodiment, the location information and region identifiers of a local high-density point cloud can be recorded and matched with corresponding regions in a 3D model of a cable tunnel structure. When querying information about a certain region, the corresponding local high-density point cloud and region in the 3D model of the cable tunnel structure can be quickly located based on the region identifier. By retaining the local high-density point cloud and establishing the correspondence, a more detailed understanding of the situation in densely installed equipment areas can be obtained.

[0076] In one embodiment, reference Figure 5 Step S40 can specifically include S41-S45, which will be described in detail below: Step S41: Based on the area identifiers and asset identifiers in the three-dimensional model of the cable tunnel structure, determine the candidate installation areas corresponding to the monitoring equipment.

[0077] In this embodiment, the area identifier is the identification information used to distinguish different areas in the three-dimensional model of the cable tunnel structure, and the asset identification data is the information used to uniquely identify the monitoring equipment. Through the area identifier and asset identification data, the areas where the monitoring equipment may be installed can be determined.

[0078] For example, the 3D model of a cable tunnel structure is marked with area identifiers such as "cable tray area" and "power distribution room area," and the asset identification data records the installation requirements of a certain monitoring device, such as needing to be installed near the cable tray. Based on this information, the candidate installation area for the monitoring device can be determined to be the cable tray area.

[0079] In one embodiment, the installation requirements in the asset identification data can be matched with the area identifiers in the 3D model of the cable tunnel structure through a database query, and areas that meet the requirements can be selected as candidate installation areas. By determining the candidate installation areas, the search range for the installation location of the monitoring equipment can be narrowed down.

[0080] Step S42: Determine the initial position of the monitoring equipment by combining the mileage range, installation height and installation side of the candidate installation area.

[0081] In this embodiment, the mileage range refers to the distance range of the candidate installation area along the tunnel extension direction, the installation height refers to the vertical installation height of the monitoring device, and the installation side refers to which side of the tunnel the monitoring device is installed on. Combining this information, the initial position of the monitoring device can be determined.

[0082] For example, the candidate installation area ranges from 100 to 110 meters, the installation height requirement is 2 meters from the bottom of the tunnel, and the installation side is the left side. Based on this information, the initial location of the monitoring equipment can be preliminarily determined to be at mileage 105 meters, on the left side, 2 meters from the bottom of the tunnel.

[0083] In one embodiment, coordinates can be calculated within a candidate installation area based on mileage range, installation height, and installation side to obtain the initial position coordinates of the monitoring device. Determining the initial position provides a basis for subsequent precise registration.

[0084] Step S43: Extract the local high-density point cloud retained within the candidate installation area, and perform coarse and fine registration with the preset equipment template to obtain the spatial position and attitude parameters of the monitoring equipment.

[0085] In this embodiment, the local high-density point cloud is the point cloud data retained within the candidate installation area. Its high density allows for a more detailed representation of the device's structure and location. The preset device template is a pre-designed point cloud model of the monitoring device. Coarse registration roughly determines the position and orientation of the monitoring device, while fine registration further refines the position and orientation.

[0086] For example, a local high-density point cloud is extracted from the candidate installation area and registered with a preset temperature sensor template. First, coarse registration is performed, roughly determining the position and orientation of the temperature sensor by calculating the distances and angles between the point clouds. Then, fine registration is performed, using methods such as the Iterative Closest Point (ICP) algorithm to further optimize the position and orientation of the temperature sensor.

[0087] In one embodiment, coarse registration can use feature-based registration methods, such as Fast Point Feature Histogram (FPFH) features, to determine the approximate position and orientation by matching the features of the point cloud. Fine registration can use the ICP algorithm, which iteratively searches for the optimal transformation matrix to minimize the distance between the local high-density point cloud and the preset device template. Through coarse and fine registration, the accurate spatial position and orientation parameters of the monitoring device can be obtained.

[0088] Step S44: When the registration confidence is lower than a preset threshold, the spatial position and attitude parameters are corrected by calling the associated video pose data or the spatial constraint relationship of adjacent anchoring devices.

[0089] In this embodiment, registration confidence is an indicator of the accuracy of the registration result. The preset threshold is a pre-set value; when the registration confidence is lower than this threshold, it indicates that the registration result may be inaccurate. Associated video pose data refers to the position and orientation information of the video surveillance equipment related to the monitoring equipment. Adjacent anchored equipment refers to equipment near the monitoring equipment whose accurate position and orientation have been determined.

[0090] For example, after registering a monitoring device, the calculated registration confidence score is 0.6, while the preset threshold is 0.8. This indicates that the registration result may be inaccurate and needs correction. Associated video pose data can be used to determine the correctness of the monitoring device's position and orientation based on information from the video footage. Alternatively, spatial constraints between adjacent anchored devices, such as the distance and angle between the monitoring device and adjacent anchored devices, can be utilized to correct the spatial position and orientation parameters of the monitoring device.

[0091] In one embodiment, computer vision algorithms can be used to calculate the position and orientation of the monitoring device based on image features in associated video pose data. For the spatial constraints between adjacent anchored devices, a geometric model can be established, and the parameters of the monitoring device can be corrected by solving the model. This correction can improve the accuracy of the spatial position and orientation parameters of the monitoring device.

[0092] Step S45: Based on the corrected spatial position and attitude parameters, attach the corresponding parameterized device model to the target position in the three-dimensional model of the cable tunnel structure, and write the corresponding area identifier.

[0093] In this embodiment, the parameterized device model is a model obtained by abstracting and modeling the monitoring device, containing various parameters and attributes of the monitoring device. The target location refers to the installation position of the monitoring device in the three-dimensional model of the cable tunnel structure, determined based on the corrected spatial location and attitude parameters. Writing the corresponding area identifier involves writing the area identifier information where the monitoring device is located into the parameterized device model for subsequent management and querying.

[0094] For example, after correction, the spatial position and attitude parameters of a monitoring device are determined to be (x, y, z, α, β, γ). The corresponding parameterized device model is then attached to the target position in the 3D model of the cable tunnel structure according to these parameters. Simultaneously, the area where the target position is located is identified as the "cable tray area" and written into the parameterized device model.

[0095] In one embodiment, the interface of 3D modeling software can be used to place the parametric device model at the target location in the 3D model of the cable tunnel structure based on the corrected spatial position and attitude parameters. Then, the information of the parametric device model is updated in the database, and the corresponding area identifier is written. By attaching the parametric device model and writing the corresponding area identifier, precise association and effective management of the monitoring equipment and the 3D model of the cable tunnel structure can be achieved.

[0096] In one embodiment, reference Figure 6 Step S50 can specifically include S51-S54, which will be described in detail below: Step S51: Based on the parameterized device model after the connection, establish a mapping relationship record for each monitoring device, including the device's unique identifier, device type, spatial location, attitude parameters, target location area identifier, real-time monitoring data stream channel, associated camera identifier, video viewpoint parameters, and historical status index address.

[0097] In this embodiment, the parameterized device model after being attached is a monitoring device model that has been accurately placed in the three-dimensional model of the cable tunnel structure. The unique device identifier is used to distinguish different monitoring devices, and the device type indicates the type of monitoring device, such as a temperature sensor or humidity sensor. Spatial location and attitude parameters determine the specific location and orientation of the monitoring device within the tunnel, and the target location area identifier indicates the area where the monitoring device is located. The real-time monitoring data stream channel is the channel through which the monitoring device transmits data to the server. The associated camera identifier is the identifier of the video surveillance device associated with the monitoring device, the video viewing angle parameter indicates the viewing angle and direction of the associated camera, and the historical status index address is the index address used to store the historical status data of the monitoring device.

[0098] For example, for a connected temperature sensor, the established mapping relationship record can be as follows: the device unique identifier is "T001", the device type is "temperature sensor", the spatial location is (x1, y1, z1), the attitude parameters are (α1, β1, γ1), the target location area is "cable tray area", the real-time monitoring data stream channel is "Channel1", the associated camera is "C002", the video viewing angle parameters are (θ1, φ1), and the historical status index address is "Index001".

[0099] In one embodiment, these mapping records can be established through database operations. Relevant information for each monitoring device is stored in a table in the database, with each row corresponding to a mapping relationship for one monitoring device. By establishing these mapping records, various information about the monitoring devices can be integrated and correlated.

[0100] Step S52: Based on the mapping relationship record, establish a first index from the spatial location and target location area identifier to the device unique identifier, and a second index from the device unique identifier to the data stream, video viewpoint and historical status index.

[0101] In this embodiment, the first index is used to quickly find the corresponding unique device identifier based on the spatial location and target location area identifier, and the second index is used to quickly find the corresponding data stream, video viewpoint, and historical status index based on the unique device identifier. The establishment of these indexes can improve the efficiency of data retrieval.

[0102] For example, the first index can map the spatial location (x1, y1, z1) and the target location area identifier "cable tray area" to the device unique identifier "T001". The second index can map the device unique identifier "T001" to the real-time monitoring data stream channel "Channel1", the video viewpoint parameters (θ1, φ1), and the historical status index address "Index001".

[0103] In one embodiment, the database's indexing capabilities can be used to create these two indexes. For the first index, a composite index can be created in the database table, containing spatial location and target location region identifier fields. For the second index, an index can be created in the database table, containing a device unique identifier field. Creating indexes can speed up data retrieval.

[0104] Step S53: Write the alarm threshold, update time stamp, version identifier, and valid status stamp into the mapping relationship record.

[0105] In this embodiment, the alarm threshold is a critical value used to determine whether the monitoring device is abnormal. For example, the alarm threshold for a temperature sensor can be set to 50°C. The update time stamp records the last update time of the mapping relationship record, the version identifier indicates the version number of the mapping relationship record, and the validity status flag is used to indicate whether the mapping relationship record is valid.

[0106] For example, for the mapping record of temperature sensor "T001", the alarm threshold is written as 50℃, the update time is marked as "2024-01-01 10:00:00", the version is marked as "V1.0", and the valid status is marked as "valid".

[0107] Step S54: Construct a spatiotemporal mapping relationship library based on the first index, the second index, the alarm threshold, the update time stamp, the version identifier, and the valid status stamp.

[0108] In this embodiment, the spatiotemporal mapping relationship database is a database containing mapping relationship records of all monitoring devices, as well as related indexes and tagging information. By constructing the spatiotemporal mapping relationship database, efficient management and rapid retrieval of monitoring device data can be achieved.

[0109] For example, all mapping records of monitoring devices, first index, second index, alarm thresholds, update timestamps, version identifiers, and valid status markers are integrated into a single database to form a spatiotemporal mapping relationship library. When querying information about a specific monitoring device, the relevant record can be quickly located using this library.

[0110] In one embodiment, a relational database can be used to construct a spatiotemporal mapping relation library. Mapping relationship records are stored in tables within the database, with indexes and tagging information serving as the database's indexes and fields. Constructing this spatiotemporal mapping relation library provides strong support for subsequent data analysis and processing.

[0111] In one embodiment, reference Figure 7 Step S60 can specifically include S61-S64, which will be described in detail below: Step S61: Parse the device unique identifier, monitoring value, timestamp, data quality marker and sampling sequence number in the real-time monitoring data stream.

[0112] In this embodiment, the real-time monitoring data stream is the data collected and transmitted by the monitoring device in real time. A unique device identifier is used to distinguish different monitoring devices, and the monitored values ​​are the actual data collected by the monitoring device, such as temperature and humidity values. A timestamp records the collection time of the monitored values, a data quality marker indicates the quality of the monitored values, such as whether they are valid or contain errors, and a sampling sequence number identifies the sampling order of the monitored values.

[0113] For example, a record in the real-time monitoring data stream is: "T001,25.5,2024-01-01 10:00:00, Valid, 100", where "T001" is the unique identifier of the device, "25.5" is the monitoring value, "2024-01-01 10:00:00" is the timestamp, "Valid" is the data quality marker, and "100" is the sampling sequence number.

[0114] Step S62: Extract the parameterized device model, alarm threshold, video viewpoint parameters, and historical status index address corresponding to the unique device identifier from the spatiotemporal mapping relationship library.

[0115] In this embodiment, the spatiotemporal mapping relationship library is a database containing mapping relationships of all monitoring devices. Based on the unique identifier of the device, the corresponding parameterized device model, alarm threshold, video viewpoint parameters, and historical status index address can be quickly found from the spatiotemporal mapping relationship library.

[0116] For example, based on the device's unique identifier "T001", the corresponding parameterized device model is extracted from the spatiotemporal mapping relationship library as a temperature sensor model, with an alarm threshold of 50℃, video viewing angle parameters of (θ1, φ1), and historical status index address of "Index001".

[0117] In one embodiment, relevant information can be extracted from a spatiotemporal mapping database using database query statements. The query is performed based on the device's unique identifier field to find the corresponding record, and then the required parameterized device model, alarm threshold, video viewpoint parameters, and historical status index address are extracted.

[0118] Step S63: Determine the device status level based on the monitored value, the alarm threshold, the data quality marker, and the sampling sequence number, and drive the parameterized device model to perform the corresponding status update.

[0119] In this embodiment, the device status level is determined comprehensively based on factors such as the comparison result between the monitored value and the alarm threshold, data quality markers, and sampling sequence number. Different device status levels reflect different operating states of the monitored device, such as normal, warning, and fault. The parameterized device model is a model obtained by abstracting and modeling the monitored device. Based on the device status level, the model can be driven to perform corresponding status updates to intuitively display the operating status of the device.

[0120] For example, for temperature sensor "T001", its alarm threshold is 50℃. When the monitored value is 25.5℃ and the data quality is marked as "valid", the device status level is normal. At this time, the parameterized device model can be driven to display green, indicating a normal state. If the monitored value reaches 45℃, approaching the alarm threshold, the device status level is warning, and the parameterized device model is driven to display yellow. When the monitored value exceeds 50℃, the device status level is fault, and the parameterized device model is driven to display red.

[0121] In one embodiment, the device status level can be determined by writing status judgment logic. The monitored value is compared with the alarm threshold, and factors such as data quality markers and sampling sequence numbers are considered to determine the device status level according to preset rules. Then, the device status level is converted into a corresponding display status by calling the status update interface of the parameterized device model. By determining the device status level and updating the status of the parameterized device model, the operating status of the monitored equipment can be reflected in a timely manner, facilitating monitoring and handling by inspection personnel.

[0122] Step S64: Write the updated device status into the corresponding historical status index according to the timestamp, and generate a linkage retrieval pointer associated with the video viewpoint parameters and alarm level.

[0123] In this embodiment, the historical status index is used to store historical status data of the monitoring equipment. Updating the equipment status and writing it to the historical status index according to a timestamp records the process of equipment status changes. The linked retrieval pointer is a type of association information used to associate video viewpoint parameters, alarm levels, and historical status data, facilitating linked retrieval when needed.

[0124] For example, for temperature sensor "T001", the device status updated at 10:00:00 on January 1, 2024 is normal, and this status is written to the historical status index "Index001" according to the timestamp. At the same time, a linked retrieval pointer is generated to associate the video viewing angle parameters (θ1, φ1) and the alarm level "normal" with this historical status data.

[0125] In one embodiment, database operations can be used to write the updated device status to the historical status index. A timestamp and device status field are added to the database table, and relevant information is inserted into the corresponding records. For linked retrieval pointers, a relational table can be created in the database to record the relationship between video viewpoint parameters, alarm levels, and the historical status index. By writing to the historical status index and generating linked retrieval pointers, effective management and linked retrieval of historical device status data can be achieved.

[0126] In one embodiment, reference Figure 8 Step S70 can specifically include S71-S74, which will be described in detail below: Step S71: Generate the main inspection path based on the tunnel centerline in the three-dimensional model of the cable tunnel structure, and determine the inspection direction along the main inspection path.

[0127] In this embodiment, the tunnel centerline is a crucial feature in the 3D model of the cable tunnel structure, reflecting the tunnel's orientation. The main inspection path is the primary route for inspection within the tunnel; generating the main inspection path based on the tunnel centerline ensures that the inspection covers the entire tunnel. The inspection direction refers to the direction of travel along the main inspection path; determining the inspection direction makes the inspection process more organized.

[0128] For example, in a cable tunnel, the tunnel centerline is straight. Starting from the tunnel entrance, a main inspection path is generated along the tunnel centerline, and the inspection direction is determined to be from the entrance to the exit.

[0129] In one embodiment, a path planning algorithm can be used to generate a main inspection path based on the tunnel centerline. First, the tunnel centerline is sampled to obtain a series of points. Then, these points are connected to form a continuous path as the main inspection path. The inspection direction can be set according to actual needs, such as from one end of the tunnel to the other, or in a clockwise or counterclockwise direction. By generating the main inspection path and determining the inspection direction, a clear route and direction can be provided for the inspection.

[0130] Step S72: Based on the spatial distribution density of the parameterized device model in the updated state, the historical alarm frequency, and the manually set key areas, set multiple inspection and observation nodes on the main inspection path.

[0131] In this embodiment, the updated parameterized device model reflects the real-time status of the monitoring equipment. Spatial distribution density refers to the density of the monitoring equipment distribution within the tunnel; historical alarm frequency refers to the number of times the monitoring equipment has triggered alarms in the past; manually set key areas are areas designated for focused attention based on actual conditions; and inspection and observation nodes are locations set along the main inspection path for stopping and observing.

[0132] For example, in a cable tunnel, a certain area has a high spatial density of monitoring equipment and a high historical alarm frequency, and this area has been manually designated as a key area. An inspection and observation node is set up at the location corresponding to this area along the main inspection path.

[0133] In one embodiment, the locations where inspection and observation nodes need to be set can be determined by analyzing the spatial distribution density and historical alarm frequency of the parameterized device model with updated status, combined with manually set key areas. These locations are then marked as inspection and observation nodes on the main inspection path. By setting inspection and observation nodes, inspection personnel can stop at key locations to observe the status of the monitoring equipment in more detail.

[0134] Step S73: Configure preset video viewing angle parameters, line of sight direction, and set of visual devices for each of the aforementioned inspection and observation nodes.

[0135] In this embodiment of the application, the preset video viewing angle parameter refers to the viewing angle parameter used to obtain video monitoring images at the inspection and observation node, the line of sight direction refers to the observation direction at the node, and the set of visible devices refers to the set of monitoring devices that can be observed at the node.

[0136] For example, at a patrol observation node, the preset video viewing angle parameters are configured as (θ2, φ2), the line of sight is towards the left side of the tunnel, and the set of visual devices includes temperature sensor "T001", humidity sensor "H002", etc.

[0137] In one embodiment, preset video viewing angle parameters and line-of-sight directions can be configured based on the location of the inspection and observation node and the distribution of surrounding monitoring equipment. By analyzing the 3D model of the cable tunnel structure and the parameterized equipment model, the monitoring equipment that can be observed at the node is determined, forming a set of visible equipment. By configuring this information, inspection personnel can obtain more comprehensive information at the inspection and observation node.

[0138] Step S74: Control the patrol view to automatically roam according to the patrol observation node, and preload the equipment status information, associated video source and historical status summary of the corresponding area when reaching the patrol observation node.

[0139] In this embodiment, automatic roaming of the inspection view refers to automatically controlling the movement of the inspection view within the 3D model of the cable tunnel structure according to the order of the inspection observation nodes. Preloading the equipment status information, associated video sources, and historical status summaries for the corresponding areas can improve inspection efficiency, enabling inspection personnel to obtain relevant information promptly upon reaching the inspection observation nodes.

[0140] For example, following the order of the inspection and observation nodes, the inspection perspective automatically moves from one node to the next. When a certain inspection and observation node is reached, the equipment status information of the corresponding area is preloaded, such as the current temperature value of the temperature sensor, the humidity value of the humidity sensor, etc., the video footage from the associated video source, and the historical status summary of the monitoring equipment in that area.

[0141] In one embodiment, animation control technology can be used to achieve automatic roaming of the inspection viewpoint. Before reaching the inspection observation node, the device status information, associated video sources, and historical status summaries for the corresponding area are preloaded via network request. Automatic roaming and preloaded information make the inspection process more efficient and convenient.

[0142] In one embodiment, reference Figure 9 Step S70 may specifically include S81-S84, which will be described in detail below: Step S81: When the monitoring data meets the preset alarm conditions during the inspection, extract the spatial location, video viewpoint parameters, historical status index and associated camera set of the alarm device from the spatiotemporal mapping relationship.

[0143] In this embodiment, the preset alarm conditions are thresholds set based on the safety requirements of the cable tunnel and the characteristics of the monitoring equipment. When the monitoring data exceeds the preset alarm conditions, it indicates that the monitoring equipment may be malfunctioning. The spatiotemporal mapping relationship is a relational database storing various information about the monitoring equipment, from which relevant information about the alarm equipment can be extracted.

[0144] For example, the preset alarm condition for temperature sensor "T001" is a temperature exceeding 50℃. When the monitoring data shows a temperature of 55℃, the preset alarm condition is met. The spatial location of "T001" is extracted from the spatiotemporal mapping relationship as (x1, y1, z1), the video viewpoint parameters are (θ1, φ1), the historical state index is "Index001", and the associated camera set is {C002, C003}.

[0145] In one embodiment, database query statements can be used to extract relevant information about alarm devices from spatiotemporal mapping relationships. Based on the device's unique identifier and preset alarm conditions, records that meet the conditions are filtered out, and then the required spatial location, video viewpoint parameters, historical status index, and associated camera set are extracted. By extracting this information, alarm devices can be quickly located and relevant data obtained.

[0146] Step S82: Calculate the coverage matching degree of the alarm device based on the spatial location and the video viewing angle parameters, viewing distance and occlusion relationship of each camera, and determine the target camera.

[0147] In this embodiment, coverage matching degree refers to the extent to which the camera covers the alarm device, which is related to the camera's video viewing angle parameters, viewing distance, and occlusion relationship. The target camera refers to the camera that can best observe the alarm device.

[0148] For example, for alarm device "T001", there are cameras C002 and C003. Camera C002 has video viewing angle parameters of (θ2, φ2) and a viewing distance of 50 meters, while camera C003 has video viewing angle parameters of (θ3, φ3) and a viewing distance of 60 meters. Based on the spatial location of "T001" and the camera parameters, the coverage matching degree of C002 to "T001" is calculated to be 0.8, and the coverage matching degree of C003 to "T001" is 0.9. Therefore, the target camera is determined to be C003.

[0149] In one embodiment, a geometric calculation method can be used to calculate the coverage matching degree. The camera's field of view is determined based on its video viewing angle parameters and viewing distance. Occlusion is considered to determine if the alarm device is within the camera's field of view. By comparing the coverage matching degrees of different cameras, the camera with the highest coverage matching degree is selected as the target camera. By determining the target camera, the clearest video image of the alarm device can be obtained.

[0150] Step S83: Control the current inspection view to jump to the location of the alarm device and lock the parameterized device model corresponding to the alarm device.

[0151] In this embodiment, the current inspection viewpoint refers to the viewpoint from which an inspection is being conducted within the 3D model of the cable tunnel structure. Jumping the current inspection viewpoint to the location of the alarm device allows inspection personnel to quickly focus on the alarm device. Locking the parameterized device model corresponding to the alarm device facilitates detailed observation of the alarm device by inspection personnel.

[0152] For example, when alarm device "T001" issues an alarm, the current inspection view is controlled to quickly jump from the current position to the spatial position (x1, y1, z1) where "T001" is located, and the parameterized device model corresponding to "T001" is locked so that it is highlighted in the screen.

[0153] In one embodiment, the viewpoint control interface of 3D modeling software can be used to switch and lock the viewpoint. Based on the spatial location of the alarm device, the position and direction of the inspection viewpoint are adjusted to align it with the alarm device. Then, by setting a locking function, the parameterized device model corresponding to the alarm device is kept fixed in the display. By switching the viewpoint and locking the model, inspection personnel can promptly focus on the alarm device.

[0154] Step S84: Synchronously retrieve the real-time video footage of the target camera and the historical status data within a preset time period before and after the alarm, and display them in a linked manner.

[0155] In this embodiment, the real-time video feed from the target camera can intuitively display the on-site status of the alarm device, and the historical status data within a preset time period before and after the alarm can help inspection personnel understand the status change process of the alarm device. Linked display refers to displaying the real-time video feed and historical status data on the same interface, facilitating comprehensive analysis by inspection personnel.

[0156] For example, for alarm device "T001", the target camera is identified as C003. Real-time video feed from C003 is retrieved simultaneously, and historical status data of "T001" within 10 minutes before and after the alarm, such as temperature change curves and alarm counts, is obtained. The real-time video feed and historical status data are then displayed on the same interface.

[0157] In one embodiment, video streaming technology can be used to retrieve real-time video footage from the target camera, and historical status data for a preset time period before and after the alarm can be obtained through database queries. An interface design tool is then used to layout and display the real-time video footage and historical status data. Through synchronized retrieval and linked display, more comprehensive information can be provided to inspection personnel, facilitating accurate judgment of alarm situations.

[0158] In one embodiment, outputting the inspection and control results in step S80 may include: P1: Stores status snapshots, alarm event markers, and corresponding area identifiers of alarm devices and their associated devices according to timestamps.

[0159] In this embodiment, a status snapshot refers to the status information of the alarm device and its associated devices at a certain moment, such as temperature, humidity, and current. Alarm event markers are used to identify the type and severity of alarm events, and corresponding area markers indicate the area where the alarm device is located. Storing this information according to timestamps can record the occurrence process of alarm events and the status changes of related devices.

[0160] For example, when temperature sensor "T001" issues an alarm, a snapshot of the status of "T001" and its associated devices (such as the adjacent humidity sensor "H002") at that moment is recorded, including temperature and humidity values. This alarm event is marked as "High Temperature Alarm, Severe," and the corresponding area is recorded as "Cable Tray Area." This information is stored in the database according to the timestamp (e.g., 2024-01-01 10:30:00).

[0161] P2: Upon receiving a backtracking instruction, the device status change process within the target time period before and after the alarm is reconstructed based on the historical status index, alarm event marker, and corresponding area identifier.

[0162] In this embodiment, the backtracking command is a user-issued command to view the device status changes before and after an alarm. The historical status index stores the device's historical status data, and alarm event markers and corresponding area identifiers are used to locate relevant alarm events and areas. This information allows for the reconstruction of the device status changes within a target time period before and after the alarm.

[0163] For example, upon receiving a traceback command, requesting to view the equipment status changes in the "cable tray area" from 10:00:00 to 11:00:00 on January 1, 2024, the system extracts the status data of alarm devices and their associated devices within that time period from the database based on the corresponding area identifier and historical status index. Then, it reconstructs the equipment status change process in chronological order, such as plotting curves showing the changes in parameters like temperature and humidity over time.

[0164] P3: Calculate the handling priority based on the location of the alarm device, the alarm level, the affected area, and the relationship with adjacent devices.

[0165] In this embodiment, the location of the alarm device determines its impact on the surrounding environment and equipment; the alarm level reflects the severity of the alarm event; the affected area refers to the range that the alarm event may affect; and the adjacent device association represents the mutual influence relationship between the alarm device and adjacent devices. By comprehensively considering these factors, the handling priority can be calculated.

[0166] For example, if an alarm device located at a critical position in a cable tunnel has a severe alarm level, affects a large area, and is closely related to multiple adjacent devices, then the handling priority for this alarm device is high.

[0167] In one embodiment, a handling priority calculation model can be established, using the location of the alarm device, alarm level, affected area, and correlation with adjacent devices as input parameters. The handling priority is calculated based on preset weights and rules. By calculating the handling priority, inspection and handling tasks can be rationally arranged, improving processing efficiency.

[0168] P4: Generate on-site handling tasks or robot inspection tasks that include task objects, inspection paths and handling time limits based on the handling priority and preset inspection strategy.

[0169] In this embodiment, the preset inspection strategy is an inspection rule formulated based on the actual conditions and safety requirements of the cable tunnel. Specific on-site handling tasks or robot inspection tasks can be generated based on the handling priority and the preset inspection strategy. The task object refers to the equipment that needs to be inspected or handled, the inspection path is the travel route of the inspection personnel or robot, and the handling time limit is the time limit for completing the task.

[0170] For example, for an alarm device with a high priority, an on-site handling task is generated according to a preset inspection strategy. The task targets the alarm device and its associated devices, the inspection path is the shortest path from the current location of the inspection personnel to the location of the alarm device, and the handling time limit is 30 minutes.

[0171] In one embodiment, a task generation algorithm can be used to generate on-site handling tasks or robotic inspection tasks based on handling priorities and preset inspection strategies. Information such as task objects, inspection paths, and handling time limits is stored in a task management system. By generating tasks, it is ensured that alarm events are handled promptly, guaranteeing the safe operation of the cable tunnel.

[0172] Accordingly, to better implement the above methods, this application also provides a digital inspection and control system for cable tunnels based on three-dimensional point clouds. For example... Figure 10 As shown, the cable tunnel digital inspection and control system 90 based on 3D point cloud includes:

[0173] The data acquisition module 91 is used to acquire the original three-dimensional point cloud data of the cable tunnel, the asset identification data of the monitoring equipment in the tunnel, the real-time monitoring data stream, and the video surveillance data. Point cloud processing module 92 is used to perform denoising, registration and stitching processing on the original three-dimensional point cloud data to obtain tunnel point cloud; The coordinate modeling module 93 is used to establish a unified tunnel coordinate system based on the tunnel point cloud, and to construct a three-dimensional model of the cable tunnel structure under the tunnel coordinate system. The equipment attachment module 94 is used to determine the spatial position and attitude parameters of the monitoring equipment based on the three-dimensional model of the cable tunnel structure, and to attach the corresponding parameterized equipment model to the target position. The spatiotemporal mapping module 95 is used to associate the device identifier, real-time monitoring data stream channel, video viewpoint parameters and historical status index based on the spatial position and attitude parameters of the monitoring device to establish a spatiotemporal mapping relationship; The status update module 96 is used to parse the parameterized device model corresponding to the real-time monitoring data stream based on the spatiotemporal mapping relationship, update the status data of the parameterized device model according to the timestamp, and write the corresponding historical status index. The patrol linkage module 97 is used to perform patrols in a scene composed of the three-dimensional model of the cable tunnel structure and the parameterized equipment model. When the monitoring data meets the preset alarm conditions, it synchronously determines the spatial location of the alarm equipment, the corresponding video monitoring screen and historical status data based on the spatiotemporal mapping relationship, performs linkage display, and outputs the patrol control results.

[0174] The implementation details of each module are provided in the preceding method embodiments and will not be repeated here. The technical effects achieved by each module and device are described in the foregoing method embodiments.

[0175] The above-disclosed embodiments are merely preferred embodiments of this application and should not be construed as limiting the scope of this application. Therefore, any equivalent variations made in accordance with the claims of this application are still within the scope of this application.

Claims

1. A method for digital inspection and control of cable tunnels based on three-dimensional point clouds, characterized in that, The method includes: Acquire raw 3D point cloud data of the cable tunnel, asset identification data of monitoring equipment inside the tunnel, real-time monitoring data stream, and video surveillance data; The original 3D point cloud data is denoised, registered, and stitched together to obtain the tunnel point cloud. A unified tunnel coordinate system is established based on the tunnel point cloud, and a three-dimensional model of the cable tunnel structure is constructed under the tunnel coordinate system. Based on the three-dimensional model of the cable tunnel structure, the spatial position and attitude parameters of the monitoring equipment are determined, and the corresponding parameterized equipment model is attached to the target position. Based on the spatial location and attitude parameters of the monitoring device, the device identifier, real-time monitoring data stream channel, video viewpoint parameters and historical status index are associated to establish a spatiotemporal mapping relationship; Based on the spatiotemporal mapping relationship, the parameterized device model corresponding to the real-time monitoring data stream is parsed, and the status data of the parameterized device model is updated according to the timestamp, while the corresponding historical status index is written. The inspection is performed in the scene consisting of the three-dimensional model of the cable tunnel structure and the parameterized equipment model; When the monitoring data corresponding to the real-time monitoring data stream meets the preset alarm conditions, the spatial location of the alarm device, the corresponding video monitoring screen and historical status data are determined synchronously based on the spatiotemporal mapping relationship, and then displayed in a linked manner to output the inspection and control results.

2. The method according to claim 1, characterized in that, The establishment of a unified tunnel coordinate system based on the tunnel point cloud includes: Extract the tunnel centerline, multiple cross-sectional profiles, and preset reference control points from the tunnel point cloud along the tunnel extension direction; Based on the tunnel centerline and multiple cross-sectional profiles, determine the mileage reference, normal reference, and height reference corresponding to each cross-section; A tunnel coordinate system is constructed using the mileage reference as the longitudinal reference, the normal reference as the lateral reference, and the height reference as the vertical reference, in conjunction with the preset reference control points. Based on the tunnel coordinate system, a unified coordinate transformation is performed on the point cloud data, monitoring equipment location data, and video surveillance pose data to obtain a spatial reference that can be uniformly retrieved.

3. The method according to claim 2, characterized in that, The construction of a three-dimensional model of the cable tunnel structure in the tunnel coordinate system includes: The tunnel point cloud is subjected to voxel downsampling, noise point removal, and reflection intensity normalization according to a preset voxel size. The tunnel inner wall, supports, cable trays and cable bodies are segmented based on point cloud normal vectors, curvature features and reflection intensity features; Perform surface reconstruction and meshing on the segmented structural point cloud to generate a 3D model of the cable tunnel structure containing region identifiers; For areas with dense equipment installation, retain local high-density point clouds and establish the correspondence between the local high-density point clouds, area identifiers, and the three-dimensional model of the cable tunnel structure.

4. The method according to claim 3, characterized in that, The step of determining the spatial position and attitude parameters of the monitoring equipment based on the three-dimensional model of the cable tunnel structure, and attaching the corresponding parameterized equipment model to the target position, includes: Based on the area identification and asset identification data in the three-dimensional model of the cable tunnel structure, the candidate installation areas corresponding to the monitoring equipment are determined. The initial position of the monitoring equipment is determined by combining the mileage range, installation height, and installation side of the candidate installation areas; Extract the local high-density point cloud retained within the candidate installation area, and perform coarse and fine registration with the preset equipment template to obtain the spatial position and attitude parameters of the monitoring equipment; When the registration confidence level is lower than a preset threshold, the spatial position and attitude parameters are corrected by calling the associated video pose data or the spatial constraint relationship of adjacent anchoring devices. Based on the corrected spatial position and attitude parameters, the corresponding parameterized device model is attached to the target position in the three-dimensional model of the cable tunnel structure, and the corresponding area identifier is written.

5. The method according to claim 4, characterized in that, The establishment of the spatiotemporal mapping relationship includes: Based on the parameterized device model after the connection, a mapping relationship record is established for each monitoring device, including the device's unique identifier, device type, spatial location, attitude parameters, target location area identifier, real-time monitoring data stream channel, associated camera identifier, video viewpoint parameters, and historical status index address. Based on the mapping relationship, a first index is established from the spatial location and target location area identifier to the device unique identifier, and a second index is established from the device unique identifier to the data stream, video viewpoint, and historical status index. The alarm threshold, update time stamp, version identifier, and valid status stamp are written into the mapping relationship record; A spatiotemporal mapping relationship library is constructed based on the first index, the second index, the alarm threshold, the update time stamp, the version identifier, and the valid status stamp.

6. The method according to claim 5, characterized in that, Based on the spatiotemporal mapping relationship, the parameterized device model corresponding to the real-time monitoring data stream is parsed, and the status data of the parameterized device model is updated according to the timestamp, while the corresponding historical status index is written, including: The device unique identifier, monitoring value, timestamp, data quality marker, and sampling sequence number in the real-time monitoring data stream are parsed. Extract the parameterized device model, alarm threshold, video viewpoint parameters, and historical status index address corresponding to the unique device identifier from the spatiotemporal mapping relationship library; The device status level is determined based on the monitored value, the alarm threshold, the data quality marker, and the sampling sequence number, and the parameterized device model is driven to perform the corresponding status update. The updated device status is written into the corresponding historical status index according to the timestamp, and a linkage retrieval pointer associated with the video viewpoint parameters and alarm level is generated.

7. The method according to claim 6, characterized in that, The inspection performed in the scene composed of the three-dimensional model of the cable tunnel structure and the parameterized equipment model includes: The main inspection path is generated based on the tunnel centerline in the three-dimensional model of the cable tunnel structure, and the inspection direction along the main inspection path is determined. Based on the spatial distribution density of the parameterized equipment model with the updated status, the frequency of historical alarms, and the key areas set manually, multiple inspection and observation nodes are set on the main inspection path. Configure preset video viewing angle parameters, line of sight direction, and set of visual devices for each of the aforementioned inspection and observation nodes; The patrol viewpoint is automatically roamed according to the patrol observation node, and the equipment status information, associated video source and historical status summary of the corresponding area are preloaded when the patrol observation node is reached.

8. The method according to claim 7, characterized in that, The process of synchronously determining the spatial location of the alarm device, the corresponding video surveillance footage, and historical status data based on the spatiotemporal mapping relationship, and displaying them in a linked manner, includes: When the monitoring data meets the preset alarm conditions during the patrol, the spatial location, video view parameters, historical status index and associated camera set of the alarm device are extracted from the spatiotemporal mapping relationship. Based on the spatial location and the video viewing angle parameters, viewing distance and occlusion relationship of each camera, the coverage matching degree of the alarm device is calculated, and the target camera is determined. Control the current inspection view to jump to the location of the alarm device and lock the parameterized device model corresponding to the alarm device; The system simultaneously retrieves real-time video feeds from the target camera and historical status data within a preset time period before and after the alarm, and displays them in a linked manner.

9. The method according to claim 8, characterized in that, The output inspection and control results include: Store the status snapshots of alarm devices and their associated devices, alarm event markers, and corresponding area identifiers according to timestamps; Upon receiving a backtracking instruction, the device status change process within the target time period before and after the alarm is reconstructed based on the historical status index, alarm event marker, and corresponding area identifier. The priority of handling is calculated based on the location of the alarm device, the alarm level, the area affected, and the relationship with adjacent devices; Based on the aforementioned handling priority and preset inspection strategy, an on-site handling task or robot inspection task is generated, which includes the task object, inspection path, and handling time limit.

10. A digital inspection and control system for cable tunnels based on three-dimensional point clouds, characterized in that, The system includes: The data acquisition module is used to acquire the original three-dimensional point cloud data of the cable tunnel, the asset identification data of the monitoring equipment in the tunnel, the real-time monitoring data stream, and the video surveillance data; The point cloud processing module is used to perform noise reduction, registration and stitching on the original three-dimensional point cloud data to obtain the tunnel point cloud. The coordinate modeling module is used to establish a unified tunnel coordinate system based on the tunnel point cloud, and to construct a three-dimensional model of the cable tunnel structure under the tunnel coordinate system. The equipment mounting module is used to determine the spatial position and attitude parameters of the monitoring equipment based on the three-dimensional model of the cable tunnel structure, and to mount the corresponding parameterized equipment model to the target position. The spatiotemporal mapping module is used to associate the device identifier, real-time monitoring data stream channel, video viewpoint parameters, and historical status index based on the spatial location and attitude parameters of the monitoring device to establish a spatiotemporal mapping relationship; The status update module is used to parse the parameterized device model corresponding to the real-time monitoring data stream based on the spatiotemporal mapping relationship, update the status data of the parameterized device model according to the timestamp, and write the corresponding historical status index. The patrol linkage module is used to perform patrols in a scene composed of the three-dimensional model of the cable tunnel structure and the parameterized equipment model. When the monitoring data meets the preset alarm conditions, it synchronously determines the spatial location of the alarm equipment, the corresponding video monitoring screen and historical status data based on the spatiotemporal mapping relationship, and displays them in a linked manner, outputting the patrol control results.

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