A method and system for optimizing the relocation of underground transportation pipelines.

Abstract

This application provides a method and system for optimizing the relocation of underground transportation pipelines. Specifically, this application acquires the spatial location, cross-sectional dimensions, joint locations, and maintenance records of existing cables, and constructs a spatial layout model based on the tunnel structure. Using non-contact detectors, it scans the tunnel sidewalls and floor, dynamically updating the model based on the identified obstacle locations, dimensional parameters, and structural boundaries to generate a tunnel distribution map. The distribution map is divided into a grid, and using cross-sectional dimensions to calculate safe distances as constraints, the grid connections are traversed to avoid high-risk areas marked with joint locations and determine the initial installation path. Subsequently, fault locations and maintenance frequencies are extracted, and the initial path is bound to the spatial location using a historical model to calculate path risks and output maintenance parameters. The path direction is adjusted to generate the target scheme and output a cable layout diagram. This application achieves precise optimization of cable paths, avoids construction collision risks, and reduces long-term operation and maintenance costs.

Description

Technical Field

[0001] This application relates to the field of cable or wire installation engineering technology, and in particular to a method and system for optimizing the relocation of underground transportation pipelines. Background Technology

[0002] When embedding new pipelines into an existing underground utility tunnel system, the existing pipeline layout inside the tunnel is complex and dense, the construction space is extremely limited, and there are many hidden structures, including undocumented metal embedded parts, concrete cavities, and areas prone to water seepage, which pose hidden risks. The new pipelines must ensure that they maintain a strict safety distance from existing facilities, avoid collision accidents during construction, and meet the physical deformation requirements of thermal expansion and contraction of the equipment.

[0003] Currently, the mainstream solution employs a semi-automated design approach combining Building Information Modeling (BIM) and path optimization algorithms. This approach first integrates the utility tunnel structural drawings and pipeline as-built data to construct a 3D static model containing pipeline coordinates and design spacing thresholds. Finally, after manual review of the feasibility of the generated paths, construction plan drawings are directly output and delivered for on-site implementation.

[0004] However, this technology has some key limitations. First, because it relies entirely on as-built drawings from the construction phase, it cannot identify dynamically changing structures such as newly added metal supports or hidden cracks during the operation phase, leading to a significant deviation between the model and the actual on-site conditions. Second, using only fixed geometric spacing as a rigid constraint exposes the installation process to the risk of collision accidents. Summary of the Invention

[0005] This application provides a method and system for optimizing the relocation of underground transportation pipelines, which addresses the problems of uncontrolled construction safety risks and long-term maintenance costs in existing technologies.

[0006] Firstly, this application provides a method for optimizing the relocation of underground transportation pipelines, including:

[0007] Obtain the spatial location, cross-sectional dimensions, joint locations, and maintenance records of existing cables within the cable channel, and construct a spatial layout model based on the channel's structural parameters;

[0008] Non-contact detectors are used to scan the side walls and bottom plate of the passage to identify obstacles and hidden structures. Based on the identified obstacle positions, size parameters and structural boundaries, the spatial layout model is dynamically updated to obtain the passage distribution map.

[0009] Based on the spatial location, the internal space of the channel distribution map is divided into a grid. Based on the cross-sectional dimensions, the safety distance is calculated. With the safety distance as a constraint, the connection relationship of the grid is traversed through the path calculator. Based on the joint position, high-risk areas are marked. The grid nodes corresponding to the high-risk areas are avoided to determine the initial installation path of the new cable.

[0010] Extract the fault location and maintenance frequency from the maintenance records, train a historical model based on the historical cable case library, bind the initial installation path with the spatial location, input the model into the historical model, calculate the path risk by combining the fault location and maintenance frequency, and output the maintenance parameters corresponding to the initial installation path.

[0011] Based on the comparison results between the maintenance parameters and the preset cost threshold, the direction and node distribution of the initial installation path in space are adjusted to generate the target scheme and output the corresponding cable layout diagram.

[0012] Optionally, the fault location and maintenance frequency are extracted from the maintenance records, a historical model is trained based on a historical cable case library, the initial installation path is bound to the spatial location, input into the historical model, and the path risk is calculated by combining the fault location and maintenance frequency. The maintenance parameters corresponding to the initial installation path are then output, including:

[0013] The coordinates of the fault location and the value of the maintenance frequency are extracted from the maintenance records, and a historical prediction model is trained based on the entire dataset of the historical cable case library.

[0014] The initial installation path is bound to the spatial location coordinates to generate a bound path dataset. The bound path dataset, the fault location coordinates, and the maintenance frequency value are then input into the historical prediction model.

[0015] Using the historical prediction model, combined with the distribution density of the fault location coordinates and the maintenance frequency, the path risk value is calculated. The path risk value is then converted into maintenance parameter values ​​using a preset mapping function, and the maintenance parameter values ​​for the initial installation path are output.

[0016] Optionally, using the historical prediction model, combined with the distribution density of the fault location coordinates and the maintenance frequency, a path risk value is calculated. This path risk value is then converted into maintenance parameter values ​​using a preset mapping function, outputting the maintenance parameter values ​​for the initial installation path, including:

[0017] Using the historical prediction model, a spherical region with a fixed radius is generated with the coordinates of each fault location point as the center. The ratio of the total number of fault points in the spherical region to the volume of the spherical region is calculated as the location distribution density data.

[0018] The initial installation path is divided into continuous path segments, and the location distribution density data and maintenance weight value corresponding to each path segment are matched. Based on the location distribution density data and maintenance weight value, the path risk value is calculated according to the linear weighting formula.

[0019] The path risk values ​​are mapped to maintenance parameter values ​​using a piecewise linear function, and the maintenance parameter values ​​are output in the order of path segment identifiers.

[0020] Optionally, a non-contact detector is used to scan the sidewalls and floor of the passage to identify obstacles and hidden structures. Based on the identified obstacle locations, size parameters, and structural boundaries, the spatial layout model is dynamically updated to obtain a passage distribution map, including:

[0021] The non-contact detector is used to scan the side walls and bottom surface of the channel to generate scanning signal data. The scanning signal data is then analyzed to identify the obstacle location points and the structural boundary points of the concealed structure.

[0022] Measure the three-dimensional coordinate information of the obstacle's location point, calculate the obstacle's size parameter value, extract the contour data of the structural boundary point, and input the three-dimensional coordinate information, size parameter value, and contour data into the spatial layout model;

[0023] Adjust the parameters at the corresponding positions in the spatial layout model and update the internal structure data of the channel to generate a channel distribution map that includes the distribution of obstacles and structural boundaries.

[0024] Optionally, the spatial location, cross-sectional dimensions, joint locations, and maintenance records of existing cables within the cable channel are obtained. Combined with the channel's structural parameters, a spatial layout model is constructed, including:

[0025] The spatial location of existing cables within the cable channel is collected, the cross-sectional dimensions of all cables are obtained, the joint location information of cable joints and historical maintenance records are recorded, and the channel structure parameters are obtained, including the channel length range, width dimension value, and height data value.

[0026] By combining the spatial location with the channel structure parameter data, a three-dimensional geometric framework of the channel is established. In the three-dimensional geometric framework, the cross-sectional dimension data of the cable is set, all joint location information is marked, and historical maintenance record data is added to obtain the constructed spatial layout model.

[0027] Optionally, based on the comparison result between the maintenance parameters and the preset cost threshold, the spatial orientation and node distribution of the initial installation path are adjusted to generate a target scheme and output the corresponding cable layout diagram, including:

[0028] Compare the relationship between the maintained parameters and the preset cost threshold, and adjust the spatial orientation curve and path node distribution density of the initial installation path according to the comparison results;

[0029] Based on the adjusted spatial orientation curve and path node distribution density, target path scheme data that meets the cost threshold is generated.

[0030] The target path scheme data is parsed into a set of cable centerline coordinates and a set of pipe diameter parameters. Based on the set of cable centerline coordinates and pipe diameter parameters, a drawing tool is driven to draw a cable layout diagram of the cable in the channel, and the cable layout diagram is output to a display device or construction control terminal.

[0031] Optionally, based on the spatial location, the internal space of the channel distribution map is divided into a grid; based on the cross-sectional dimensions, a safety distance is calculated; using the safety distance as a constraint, the connection relationship of the grid is traversed through a path calculator; high-risk areas are marked based on the joint locations; and grid nodes corresponding to the high-risk areas are avoided to determine the initial installation path of the new cable, including:

[0032] The internal space of the channel distribution map is divided into an equidistant set of grid cells according to the spatial location, and the safety distance between adjacent cables is calculated based on the cross-sectional size data of the cables.

[0033] Using the safety distance value as a movement constraint, the path calculator iterates through the connection relationship data between each grid cell in all the grid cell sets, and records the set of feasible path points that satisfy the safety distance value.

[0034] Based on the grid nodes corresponding to the high-risk areas marked by the joint locations, all grid nodes corresponding to the high-risk areas are avoided from the set of feasible path points. The continuous path point sequence of the new cable is calculated to generate the initial installation path of the new cable.

[0035] Secondly, this application provides an underground transportation pipeline relocation and optimization system, including:

[0036] The acquisition module is used to acquire the spatial location, cross-sectional dimensions, joint locations, and maintenance records of existing cables within the cable channel, and to construct a spatial layout model by combining the channel structural parameters.

[0037] The update module is used to scan the sidewalls and bottom plate of the passage with a non-contact detector to identify obstacles and hidden structures. Based on the identified obstacle positions, size parameters and structural boundaries, the spatial layout model is dynamically updated to obtain the passage distribution map.

[0038] The calculation module is used to divide the internal space of the channel distribution map into a grid based on the spatial location, calculate the safety distance based on the cross-sectional dimensions, use the safety distance as a constraint, traverse the connection relationship of the grid through the path calculator, mark high-risk areas based on the joint positions, avoid the grid nodes corresponding to the high-risk areas, and determine the initial installation path of the new cable.

[0039] The output module is used to extract the fault location and maintenance frequency from the maintenance record, train a historical model based on the historical cable case library, bind the initial installation path with the spatial location, input the historical model, calculate the path risk by combining the fault location and maintenance frequency, and output the maintenance parameters corresponding to the initial installation path.

[0040] The adjustment module is used to adjust the spatial orientation and node distribution of the initial installation path based on the comparison result between the maintenance parameters and the preset cost threshold, generate the target scheme, and output the corresponding cable layout diagram.

[0041] Thirdly, this application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement the underground transportation construction pipeline relocation and optimization method as described in the first aspect above.

[0042] Fourthly, this application provides a computer storage medium storing a computer program, which, when executed by a computer, implements the method for optimizing the relocation of underground transportation construction pipelines as described in the first aspect.

[0043] This application integrates channel structure and cable parameters through a spatial layout model to accurately establish a three-dimensional spatial reference coordinate system; non-contact detection and dynamic model updating effectively identify the coordinates of concealed structural boundaries and metal obstacles, eliminating positioning deviations caused by outdated drawing data; the combination of grid division and safety distance constraints ensures that the path calculator physically avoids existing facilities and areas with insufficient safety distances during traversal; high-risk areas are marked based on joint locations and corresponding nodes are avoided, specifically reducing the risk of interference to key weak points during construction operations; the historical model binds the initial path with spatial location and extracts maintenance record parameters, converting fault frequency into path risk coefficient output, achieving quantitative prediction of long-term maintenance costs; finally, by comparing maintenance parameters with cost thresholds, the path direction is dynamically adjusted to generate a cable layout diagram that meets construction safety requirements and ensures full-cycle economic efficiency.

[0044] Furthermore, by extracting the coordinates of fault locations and maintenance frequency values ​​from maintenance records as data input, a historical prediction model is trained based on a complete dataset from a historical cable case library to eliminate sampling bias. Subsequently, the initial installation path is bound to spatial locations with coordinate points to generate a path dataset, and the fault coordinates and maintenance frequency are input into the model. By analyzing the distribution density of fault locations and maintenance frequency, the path risk value is calculated. Finally, the risk value is converted into maintenance parameter values ​​through a preset mapping function, so that the parameter values ​​accurately reflect the risk level of high-risk fault clusters within the path segment, driving the path to actively avoid high-frequency fault areas, significantly reducing long-term maintenance costs and balancing resource allocation.

[0045] These or other aspects of this application will become more apparent in the following description of the embodiments. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 A flowchart of an optimization method for relocating underground transportation pipelines provided in this application is shown;

[0048] Figure 2 A scenario diagram is shown, illustrating an optimization method for relocating underground transportation pipelines provided in this application.

[0049] Figure 3 This application provides a schematic diagram of the structure of an underground transportation pipeline relocation and optimization system.

[0050] Figure 4 A schematic diagram of the structure of a computing device provided in this application is shown. Detailed Implementation

[0051] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0052] In some of the processes described in the specification, claims, and accompanying drawings of this application, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not themselves represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a chronological order, nor do they limit "first" and "second" to different types.

[0053] Researchers have found that current underground pipeline relocation technologies primarily rely on as-built drawings from the construction phase, failing to effectively identify dynamically changing structures such as newly added metal supports and hidden cracks during the operational phase. This leads to significant deviations between the spatial layout model and actual on-site conditions. Furthermore, these technologies use fixed geometric spacing as a rigid constraint, neglecting to fully consider actual maintenance risks, thus exposing the installation process to potential collision hazards. Therefore, there is an urgent need for an optimization method for underground transportation pipeline relocation that can dynamically update the model and intelligently optimize the path.

[0054] To address the aforementioned issues, this invention proposes a method for optimizing the relocation of underground transportation pipelines. Its core lies in dynamically updating the spatial layout model using non-contact detectors and optimizing path planning based on risk assessment fusion with historical maintenance data. Specifically, firstly, the spatial location, cross-sectional dimensions, joint locations, and maintenance records of existing cables are acquired, and an initial model is constructed by combining these with channel structure parameters. Next, obstacles and hidden structures are identified through non-contact detector scanning, and the model is dynamically updated to generate an accurate channel distribution map. Subsequently, the internal space is gridded, and the grid path is traversed with safety distance constraints to avoid high-risk areas marked at joint locations to determine the initial installation path. Then, fault locations and maintenance frequencies from maintenance records are extracted, and the historical model is trained to calculate path risks and output maintenance parameters. Finally, the path direction is dynamically adjusted based on maintenance parameters and cost thresholds to generate and output an optimized cable layout map. This method ensures consistency between the model and the actual site conditions by dynamically scanning and identifying dynamically changing structures added during the operation phase, solving the model deviation problem. Simultaneously, by combining risk assessment with safety distance optimization of path planning, the potential for collision accidents is significantly reduced, achieving intelligent and highly reliable pipeline relocation optimization.

[0055] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0056] Figure 1 This application provides a flowchart of an optimization method for relocating underground transportation pipelines, as shown in the embodiments. Figure 1 As shown, the method includes:

[0057] 101. Obtain the spatial location, cross-sectional dimensions, joint locations, and maintenance records of existing cables within the cable channel, and construct a spatial layout model based on the channel structural parameters;

[0058] Optionally, step 101 above may specifically include the following steps:

[0059] 1011. Collect the spatial location of existing cables in the cable channel, obtain the cross-sectional dimensions of all cables, record the joint location information of cable joints and historical maintenance records, and obtain the channel structure parameters, including the channel length range value, width dimension value, and height data value;

[0060] 1012. Combine the spatial location with the channel structure parameter data to establish a three-dimensional geometric framework of the channel. Set the cross-sectional dimension data of the cable in the three-dimensional geometric framework, mark the location information of all joints, add historical maintenance record data, and obtain the constructed spatial layout model.

[0061] In the above steps, spatial location refers to the specific coordinate data of the cable within the channel, including three-dimensional coordinate point values; cross-sectional dimensions refer to the external dimensions of the cable cross-section, such as the diameter of a circular cable; joint location information refers to the specific coordinate data of the cable connection point; maintenance records are historical data of past cable repairs and inspections, such as maintenance dates and descriptions; channel structure parameters include the channel length range value (representing the channel length interval range), width value (representing the channel width), and height value (representing the channel height); the spatial layout model is a three-dimensional geometric model framework used to visualize the channel, cable, and related information.

[0062] In this embodiment, the basic data within the cable channel is first collected in step 1011: a laser scanner is used to acquire the spatial coordinates of all existing cables; a measuring tool is used to record the cable cross-sectional dimensions; the three-dimensional coordinates of the joint locations are read using a positioning device; historical maintenance record text is extracted from the database; and channel structural parameters are analyzed from the construction drawings. For example, for a 10-meter-long channel, the spatial coordinates of a cable are measured as X0.5m / Y1m / Z0.8m, with a cross-sectional diameter of 5cm. The joint is located at X8m / Y0.6m / Z1m, the maintenance record is "No abnormalities detected during maintenance in 2023," and the channel parameters are a length range of 10-15 meters, a width of 1 meter, and a height of 2 meters.

[0063] Secondly, a spatial layout model is constructed through step 1012: Based on the data from step 1011, a rectangular box frame is created using 3D modeling software by inputting channel parameters; the spatial coordinates of the cables are mapped to the frame to generate positioning points; corresponding geometry is generated based on the cross-sectional dimension data; marker symbols are added at the joint position coordinates; and maintenance record text is associated with the corresponding positions. For example, for the above data, a basic frame with a length of 12 meters (values ​​within the range of 10-15 meters), a width of 1 meter, and a height of 2 meters is first created; a cylinder with a diameter of 5 centimeters is set at coordinates 0.5 / 1 / 0.8 to represent the cable; a red sphere is added at coordinates 8 / 0.6 / 1 to mark the joint; a label "No abnormalities found during maintenance in 2023" is displayed on the side of the model, and finally, a complete 3D model is output.

[0064] In a practical application, during an underground pipeline upgrade project for a power grid, the project team employed the following method to construct a three-dimensional spatial layout model of the cable tunnel. First, staff delved into the tunnel and used precision measuring instruments to accurately obtain the spatial coordinates and center-point spacing data of all six existing cables within the tunnel. They also meticulously measured and recorded the cross-sectional dimensions of each cable, with the important tunnel branches having cross-sectional dimensions of 0.5m × 0.3m and 0.4m × 0.25m respectively. By reviewing the archival system, they comprehensively collected the three-dimensional coordinates and historical maintenance information of cable joints located at 12 key locations within the tunnel, including the record of joint number 3 being replaced in 2022. Subsequently, the measurement team obtained key parameters of the tunnel structure: the main tunnel section is 47.5 meters long and 2.5 meters wide, with a vertical shaft connection height of 2.2 meters. Technicians input this basic data into the three-dimensional modeling system, generating a 1:1 scale tunnel frame model based on the measured spatial parameters. In this digital model, each cable was accurately modeled according to its measured cross-sectional dimensions, and all joint information was clearly marked at specific coordinate points. Technicians also linked 35 historical maintenance events to corresponding cable model nodes, creating a visualized spatial layout model that includes spatial structure, facility attributes, and maintenance status. This model provides a visual representation of the passageway's condition, offering an accurate basis for subsequent construction planning.

[0065] In the overall scheme of step 101 above, the spatial location and cross-sectional dimensions of existing cables in the cable channel are collected, combined with cable joint location information and historical maintenance records. At the same time, channel structural parameters such as channel length range, width dimension, and height data are obtained. This information is integrated to establish a three-dimensional geometric framework of the channel. Cable cross-sectional dimension data is set in this framework, all joint locations are marked, and historical maintenance record data is added. Finally, a spatial layout model containing channel structure, cable layout, and key information is constructed, thereby achieving precise visualization and management of the cable channel.

[0066] 102. Use a non-contact detector to scan the sidewalls and bottom plate of the passage to identify obstacles and hidden structures. Based on the identified obstacle positions, size parameters and structural boundaries, dynamically update the spatial layout model to obtain the passage distribution map.

[0067] Optionally, step 102 above may specifically include the following steps;

[0068] 1021. Use a non-contact detector to scan the sidewalls and bottom surface of the channel, generate scanning signal data, analyze the scanning signal data, and identify the obstacle location points of obstacles and the structural boundary points of concealed structures;

[0069] 1022. Measure the three-dimensional coordinate information of the obstacle location points, calculate the size parameter values ​​of the obstacle, extract the contour data of the structural boundary points, and input the three-dimensional coordinate information, size parameter values ​​and contour data into the spatial layout model;

[0070] 1023. Adjust the parameters at the corresponding positions in the spatial layout model, update the internal structure data of the channel, and generate a channel distribution map containing the distribution of obstacles and structural boundaries.

[0071] In the above steps, non-contact detectors refer to devices that can scan without contacting the channel surface, such as lidar instruments; channel sidewalls and bottom plates refer to the walls on both sides of the channel and the ground area at the bottom; scanning signal data refers to the signal waveform or image data output by the detector during scanning; obstacles refer to obstructions within the channel, such as pipes or equipment bodies; concealed structures refer to hidden auxiliary structures, such as recesses or holes; obstacle location points refer to the location coordinate point data of the obstacles; structural boundary points refer to the outline coordinate point data of the concealed structures; three-dimensional coordinate information refers to the X-axis, Y-axis, and Z-axis values ​​of the location points; size parameter values ​​refer to the length, width, and height values ​​of the obstacles; outline data refers to the set of shape point data of the concealed structures; and the channel distribution map refers to the updated three-dimensional visualization model map containing obstacle and structural boundary information.

[0072] In this embodiment, firstly, in step 1021, a non-contact detector, such as a lidar, is used to scan the sidewalls and bottom surface of the passage to obtain raw data. The laser beam emitted by the detector is reflected to generate scanning signal data, such as point cloud waveform information. Then, the signal analysis algorithm in the computer system is used to process these data to identify obstacle location points and structural boundary points, forming intermediate data for identifying locations and boundaries for subsequent steps. For example, in an actual 10-meter-long passage, after scanning, the algorithm analyzes and identifies a rock obstacle location point with coordinates of 5 meters in length, 1 meter in width, and 0.5 meters in height. At the same time, a set of structural boundary points of a recessed and concealed structure is identified, with the starting point at 7 meters in length, 0.8 meters in width, and 0.2 meters in height, and the ending point at 7.5 meters in length, 0.8 meters in width, and 0.4 meters in height. These point data are directly stored as the input basis for the next step.

[0073] Secondly, based on the location point and boundary point data in step 1022, the three-dimensional coordinate information of the obstacle's location points is directly extracted and recorded, such as 5 meters in length, 1 meter in width, and 0.5 meters in height. Then, a geometric calculation algorithm is used to calculate the obstacle's size parameters based on the multi-location point data. For example, the length, width, and height values ​​are obtained by subtracting the minimum and maximum values ​​among all relevant points. The specific calculation process is as follows: Assume that multiple scan points include point 1 with coordinates of 4.8 meters in length, 0.9 meters in width, and 0.4 meters in height, and point 2 with coordinates of 5.2 meters in length, 1.1 meters in width, and... In the height direction (0.6 meters), the maximum point (5.2 meters) minus the minimum point (4.8 meters) equals 0.4 meters, which is taken as the length value. In the width direction (1.1 meters) minus the minimum point (0.9 meters) equals 0.2 meters, which is taken as the width value. In the height direction (0.6 meters) minus the minimum point (0.4 meters) equals 0.2 meters, which is taken as the height value. Thus, the dimensional parameter values ​​are obtained: length 0.4 meters, width 0.2 meters, height 0.2 meters. At the same time, the boundary point set of the hidden structure is extracted to form the contour data, such as the list of recessed points. Finally, these three-dimensional coordinate information, dimensional parameter values, and contour data are input into the spatial layout model through the data interface for subsequent updates.

[0074] Finally, in step 1023, based on the 3D coordinate information, size parameter values, and contour data, the spatial layout model is dynamically updated. Computer modeling software is used to add obstacle objects at corresponding locations and set size values ​​to simulate new elements. At the same time, based on the contour data, hidden structural areas are added to adjust the internal structure and generate a channel distribution map. For example, a cube with a length of 0.4 meters, a width of 0.2 meters, and a height of 0.2 meters is placed at a coordinate point of 5 meters, a length of 1 meter, a width of 0.5 meters, and a height of 0.2 meters. This represents the obstacle at the concave position point set. After applying a surface deformation algorithm to generate the shape area, a 3D view file containing the new elements is output as the final channel distribution map. The entire process is coherent, from data recognition to size calculation to model update, ensuring seamless integration.

[0075] In practical application, at the site of a city's integrated utility tunnel renovation project, technicians implemented the following non-contact detection process to refine the tunnel model. The work team evenly deployed 23 detection points along the 130-meter-long tunnel, using ground-penetrating radar to continuously scan the concrete sidewalls and basalt base at 0.3-meter intervals. After receiving the reflected electromagnetic wave signal, the instrument identified an abnormal signal area at a depth of 0.5 meters at a distance of 35.6 meters from the starting point, which the system determined to be a cast iron tubular obstacle with a diameter of approximately 0.45 meters. Simultaneously, the scan data revealed an irregular structural boundary extending 5.2 meters on the west side of the tunnel, which, after comparison with construction drawings, was confirmed as the entrance to an abandoned civil defense project. The surveying team then set up a total station to accurately map the obstacle, obtaining its center point spatial coordinates as X=35.612, Y=2.304, Z=-1.258 (in meters), and marking the obstacle's axial length as 2.1 meters. For the concealed structure boundary contour, a total of 143 feature points with 3D coordinates were extracted using point cloud capture technology. This data, including the pose parameters of three obstacles and the concealed structure contour, was transmitted to the modeling platform via a data interface. The model system automatically integrated the newly acquired data, accurately marking the tubular obstacles and their boundary ranges in the 7th detection zone within the original spatial layout model, and simultaneously optimizing the 3D contour line at the southwest corner of the passage. After data iteration, the system generated a passage distribution map containing the newly added obstacle layer and structural boundary heatmap. The map clearly displays seven different spatial feature elements within the passage, significantly improving the accuracy of visual management of the underground space environment.

[0076] In the overall scheme of step 102 above, a non-contact detector is used to perform a comprehensive scan of the sidewalls and bottom plate of the cable channel, acquire scan signal data, and analyze and identify the location points of obstacles and the boundary points of hidden structures. The three-dimensional coordinate information of the obstacles is accurately measured and their size parameters are calculated. At the same time, the structural boundary contour data is extracted. The above information is dynamically input into the spatial layout model to adjust the corresponding position parameters and update the internal structural data of the channel synchronously. Finally, a channel distribution map containing accurate obstacle distribution and structural boundary features is generated, realizing the dynamic optimization and visualization upgrade of the spatial model.

[0077] 103. Based on the spatial location, the internal space of the channel distribution map is divided into a grid. Based on the cross-sectional dimensions, the safety distance is calculated. With the safety distance as a constraint, the connection relationship of the grid is traversed through the path calculator. Based on the joint position, high-risk areas are marked. The grid nodes corresponding to the high-risk areas are avoided to determine the initial installation path of the new cable.

[0078] Optionally, step 103 above may specifically include the following steps:

[0079] 1031. Divide the internal space of the channel distribution map into an equidistant set of grid cells according to the spatial location, and calculate the safe distance between adjacent cables based on the cross-sectional size data of the cables;

[0080] 1032. Using the safety distance value as the movement constraint, the path calculator traverses the connection relationship data between each grid cell in all the grid cell sets and records the set of feasible path points that satisfy the safety distance value.

[0081] 1033. Based on the location of the joint, mark the grid nodes corresponding to the high-risk areas, avoid all grid nodes corresponding to the high-risk areas from the set of feasible path points, calculate the continuous path point sequence of the new cable, and generate the initial installation path of the new cable.

[0082] In the above steps, the grid cell set refers to the set of equally sized regions into which the internal space of the channel is divided; the safety distance value refers to the minimum safety distance that cables need to maintain; the path calculator refers to the algorithm tool used for path search in the computer system; the connection relationship data refers to the relationship information between the grid cells; the feasible path point set refers to the set of all grid location points that meet the safety distance conditions; the high-risk area refers to the dangerous area near the cable joint location; the grid node refers to the center point or connection point of the grid cell; the continuous path point sequence refers to a series of connected grid point location sequences; the initial installation path refers to the first route path of the new cable planning and wiring; the cross-sectional dimension data refers to the diameter parameter of the cable cross-section; and the joint location refers to the specific coordinate location of the cable connection point.

[0083] In this embodiment, firstly, step 1031 uniformly divides the internal space of the channel into a set of equidistant grid cells based on spatial location data, and then calculates the safety distance value based on the cable cross-sectional size data. Specifically, the process is as follows: input the three-dimensional spatial data of the channel distribution map, use a grid division algorithm to generate grid cells of uniform size, for example, using a 1m × 1m grid to divide a 10m long channel into 100 cells, with the center point of each cell as the node position; then calculate the safety distance value, defined by the formula: Safety distance value = Safety factor × Cable cross-sectional diameter, where the safety factor is fixed at 1.5 by industry standards, and the cable cross-sectional diameter is a measured value. For example, when the cable diameter is 0.1m, the calculation process is: Safety distance value = 1.5 × 0.1 = 0.15m. This value constrains the subsequent path node spacing to be ≥ 0.15m. All grid cell sets and safety distance values ​​are output to the next process.

[0084] Secondly, using the safety distance of 0.15 meters as a movement constraint in step 1032, the path calculator iterates through the connections between grid cells to filter the set of feasible path points. The specific process is as follows: input the grid node positions and connections from step 1031, and use the path search algorithm in the computer to traverse the grid nodes; the algorithm checks whether the distance between adjacent nodes satisfies ≥ 0.15 meters, and nodes that meet the condition are retained. For example, if the distance between node A (1.0m, 1.0m) and its adjacent node B (1.0m, 1.2m) is |1.2-1.0|=0.2m>0.15m, then node B is retained; conversely, if the distance between node C (1.0m, 1.1m) is 0.1m<0.15m, then it is discarded. Finally, the set of feasible path points is output and passed to step 1033.

[0085] Finally, in step 1033, high-risk area grid nodes are marked based on the joint location, and an initial installation path is generated by avoiding these nodes in the set of feasible path points. Specifically, this includes: inputting the joint location coordinates from step 101, such as (8.0m, 0.6m), setting a circular area with a radius of 0.5 meters centered on this point as a high-risk area, and marking the grid nodes within the covered area; deleting high-risk nodes from the set of feasible path points; using a path planning algorithm to calculate the connected path sequence, such as the continuous set of points from the starting point (0m, 0.5m) to the ending point (10m, 0.5m), which must skip high-risk nodes; and finally, outputting the initial installation path as a coordinate sequence text file for construction reference.

[0086] In a practical application, during an underground cable optimization project in a certain city, engineers performed the following path planning operation to determine the installation scheme for new cables. First, based on spatial location data from the channel distribution map, they divided the internal space of the channel into equidistant grid cells, ensuring that each grid cell uniformly covered the entire area. Then, using the cross-sectional dimensions of existing cables, they calculated the required safe distance between adjacent cables, serving as a critical path constraint. The team input this data into a path calculator, which automatically traversed the connections between all grid cells, filtering out all feasible path points that met the safe distance requirements, forming a potential path library. Simultaneously, based on known cable joint location information, it accurately marked the grid nodes corresponding to high-risk areas and actively avoided these risk nodes during path selection. Through algorithmic processing, the path calculator generated a continuous sequence of path points that successfully avoided all high-risk areas and strictly adhered to the safe distance constraints, thus determining the initial installation path of the new cable and improving the reliability and safety of the planning.

[0087] In the overall scheme of step 103 above, the internal space is divided into equidistant grid cells according to the channel distribution map. The safe distance between adjacent cables is calculated in combination with the cable cross-sectional dimensions and used as a movement constraint. The path calculator traverses the connection relationship data of each cell in the grid cell set to filter out the set of feasible path points that meet the safe distance. Then, according to the joint position, the grid nodes corresponding to the high-risk area are marked, and all high-risk grid nodes are excluded from the set of feasible path points. The initial installation path is automatically generated by calculating the continuous path point sequence of the new cable.

[0088] 104. Extract the fault location and maintenance frequency from the maintenance record, train a historical model based on the historical cable case library, bind the initial installation path with the spatial location, input the model into the historical model, calculate the path risk by combining the fault location and maintenance frequency, and output the maintenance parameters corresponding to the initial installation path.

[0089] Optionally, step 104 above may specifically include the following steps:

[0090] 1041. Extract the coordinates of the fault location and the value of the maintenance frequency from the maintenance record, and train a historical prediction model based on the entire dataset of the historical cable case library.

[0091] 1042. Bind the initial installation path to the spatial location coordinates to generate a bound path dataset, and input the bound path dataset, the fault location coordinates, and the maintenance frequency value into the historical prediction model.

[0092] 1043. Using the historical prediction model, combined with the distribution density of the fault location coordinates and the maintenance frequency, calculate the path risk value, convert the path risk value into maintenance parameter value through a preset mapping function, and output the maintenance parameter value of the initial installation path.

[0093] Step 1043 may specifically include the following process: using the historical prediction model, generating a spherical region with a fixed radius, centered on the coordinates of each fault location point; calculating the ratio of the total number of fault points within the spherical region to the volume of the spherical region, as location distribution density data; dividing the initial installation path into continuous path segments; matching the location distribution density data and maintenance weight value corresponding to each path segment; calculating the path risk value based on the location distribution density data and maintenance weight value using a linear weighting formula; mapping the path risk value to maintenance parameter values ​​using a piecewise linear function; and outputting the maintenance parameter values ​​in the order of path segment identifiers.

[0094] In the above steps, the grid cell set is a collection of equally sized regions divided within the channel space; the safety distance value is the minimum safe distance that cables must maintain; the path calculator is a tool in the computer system used to search for paths; the connection relationship data is a collection of information about the connections between grid points; the feasible path point set is the set of all grid point locations that meet the safety distance requirements; the high-risk area is the potentially dangerous area around the cable joint; the grid node is the location of the center point of the grid cell; the continuous path point sequence is the sequence of locations of consecutive points in the cable path; the initial installation path is the first route planned for the new cable; the cross-sectional dimension data is the diameter value of the cable cross-section; the joint location is the specific coordinate location of the cable connection point; and the fault location is the coordinate point where the fault occurred in the past. The following parameters are defined: Maintenance frequency (number of repairs required for each fault point); Historical cable case library (dataset storing historical fault cases); Historical model (prediction tool trained on historical cases); Maintenance parameter (time interval required for cable maintenance); Path risk (risk magnitude along the cable path); Path-bound dataset (dataset combining path point sequences with coordinate locations); Path risk value (calculated risk value); Maintenance parameter value (specific maintenance value mapped to risk); Location distribution density data (ratio of the number of fault points near the fault point to the area volume); Maintenance weight value (weighting factor of maintenance frequency on risk); Piecewise linear function (tool for mapping risk values ​​to maintenance parameters in segments); Path segment identifier (unique identifier after path segmentation).

[0095] In this embodiment, firstly, through step 1041, data is extracted and a model is trained. The computer system reads the maintenance record file, parses the three-dimensional coordinates of the fault location (e.g., (8.0, 0.6, 0.5)) from the text records, and simultaneously extracts the corresponding maintenance frequency value 3. Next, the historical cable case library is called to load the entire historical dataset. Then, the random forest algorithm is used to train the historical prediction model, automatically learning feature associations by inputting historical fault coordinates and maintenance frequency data. Finally, the trained model file and a list of fault point coordinates are output. For example, the fault point coordinates (8.0, 0.6, 0.5) and maintenance frequency 3 are actually read, a historical database containing 100 records is called to train the model, and finally, a model file is generated and the coordinate list is stored for the next step.

[0096] Secondly, data binding and model input are completed in step 1042. The system obtains the initial installation path point sequence, such as [(0.0,0.5,0.2),(5.0,1.0,0.3)]. A spatial matching algorithm is used to verify and bind the coordinates of each path point with the spatial location data in step 101 to generate a dataset. Then, this dataset is merged and packaged with the list of fault location coordinates. Finally, the maintenance frequency value is input into the historical prediction model. For example, the path point (0.0,0.5,0.2) is bound as the identifier P001, merged with the fault point (8.0,0.6,0.5), and input into the model. The maintenance frequency value 3 is submitted simultaneously.

[0097] Finally, risk calculation and parameter output are performed in step 1043. The model generates a spherical region with a radius of 0.5m centered on each fault point, such as (8.0, 0.6, 0.5), and calculates the volume V = 4 / 3 × 3.1416 × (0.5)³ = 0.523m³. The number of fault points in the region is counted, such as 5, and the density D = 5 / 0.523 ≈ 9.56 times / m³ is calculated. The installation path is divided into segments, such as P001 (0m-5m), and the density value is matched. The maintenance weight W = 3 × 0.1 = 0.3 is calculated. The risk value (α = 0.2, β = 0.3) is calculated using the formula R = α × D + β × W, resulting in R = 0.2 × 9.56 + 0.3 × 0.3 = 2.002. The maintenance parameter value 40 is output by mapping R ≥ 2 through the segmentation rule. Finally, the result file P001:40 is generated. Complete calculation example: Within a volume of 0.523m³ in the fault point (8.0,0.6,0.5) region, there are 5 fault points with a density of 9.56 times / m³. The P001 segment matches this density and a weight of 0.3, calculating R=2.002. Since ≥2, the mapping output is 40.

[0098] In a practical application of a power grid infrastructure renovation project, the engineering team implemented the following path risk assessment process. Technicians first extracted the 3D coordinates and corresponding maintenance frequency data of 17 historical fault points from cable maintenance records, and trained a prediction model using a historical database containing over 200 cable fault cases. Subsequently, the initial installation path of the new cable was point-to-point bound to the channel spatial coordinate system, forming a dataset containing 83 path nodes. This bound dataset, along with the fault location coordinates and maintenance frequency parameters, was input into the prediction model. During model processing, the system automatically generated a spherical analysis area with a radius of 0.8 meters centered on each fault point, calculating the fault point density within the area; for example, area 3 contained 1.2 fault points per cubic meter. Simultaneously, the initial path was divided into 12 continuous segments, and a corresponding fault density value and maintenance frequency weight coefficient were matched to each segment. Using a linear weighting algorithm, with a density weight of 0.6 and a maintenance frequency weight of 0.4, the system calculated the risk value for each path segment. These risk values, after being mapped by a piecewise function, were converted into a set of maintenance parameters including maintenance cycle levels (e.g., Level A requires 3-month inspection) and risk indicators (e.g., red high-risk segments). The final output includes an evaluation report containing maintenance parameter values ​​for each path segment, providing a quantitative basis for subsequent construction decisions.

[0099] In the overall scheme of step 104 above, by extracting the coordinates of fault locations and maintenance frequency values ​​from the maintenance records, and training a historical prediction model in conjunction with a historical cable case library, the initial installation path is bound to the spatial location coordinates to generate a bound path dataset. The bound data, fault coordinates, and maintenance frequency are then input into the historical prediction model. This model generates a spherical region with a fixed radius centered on each fault point coordinate, calculates the number of fault points per unit volume within the region as the location distribution density, then divides the initial path into continuous path segments, dynamically matches the location distribution density and maintenance weight value corresponding to each segment, calculates the path risk value using a linear weighting formula, and finally maps the risk value to maintenance parameter values ​​using a piecewise linear function, outputting the maintenance parameter values ​​of each segment in the order of the path segments, thereby quantitatively assessing the operation and maintenance risk level of the new cable installation path.

[0100] 105. Based on the comparison results between the maintenance parameters and the preset cost threshold, adjust the direction and node distribution of the initial installation path in space, generate the target scheme, and output the corresponding cable layout diagram.

[0101] Optionally, step 105 above may specifically include the following steps:

[0102] 1051. Compare the relationship between the maintained parameters and the preset cost threshold, and adjust the spatial orientation curve and path node distribution density of the initial installation path according to the comparison results;

[0103] 1052. Based on the adjusted spatial orientation curve and path node distribution density, generate target path scheme data that meets the cost threshold.

[0104] 1053. Parse the target path scheme data into a set of cable centerline coordinates and a set of pipe diameter parameters, and based on the set of cable centerline coordinates and pipe diameter parameters, drive the drawing tool to draw a cable layout diagram of the cable in the channel, and output the cable layout diagram to a display device or construction control terminal.

[0105] In the above steps, the maintenance parameter is a quantitative value of cable maintenance requirements, such as 40; the preset cost threshold is a preset budget control value, such as 50; the spatial path curve is the continuous curve shape of the path in three-dimensional space; the node distribution density is the number of nodes per unit length of the path, such as 1.2 points / meter; the target path scheme is the optimized path planning dataset; the cable centerline coordinate set is the three-dimensional coordinate sequence of the path center point; the pipe diameter parameter set is the set of cable duct size parameters; the drawing tool is a software tool that automatically generates drawings; the cable layout diagram is a construction drawing file containing the cable path; the display device is a display screen or projection device; and the construction control terminal is the on-site construction control device.

[0106] In this embodiment, firstly, parameter comparison and path adjustment are performed in step 1051. The computer system reads the maintenance parameter file to obtain the value 40, and simultaneously obtains the preset cost threshold value 50 for comparison. When the maintenance parameter value 40 is greater than the preset cost threshold 30, the path adjustment algorithm is automatically triggered. This algorithm first modifies the spatial orientation curve of the initial installation path, using a curve optimization tool to reduce the path curvature, for example, changing a 90° turn to a gentle 45° turn. Simultaneously, it adjusts the path node distribution density, increasing the node density value through a density calculation function, for example, from the original density of 1 points / m to 1.2 points / m, making the path straighter and the nodes denser, ultimately generating the adjusted spatial orientation curve file and node density parameter file. For example, when the maintenance parameter 45 is greater than the preset cost threshold 40, the system calculates a new bending radius of 2 m, reduces the turning angle, and increases the node density from 1 points / m to 1.5 points / m to adapt to the new path shape.

[0107] Secondly, in step 1052, the target path scheme is generated. Based on the spatial orientation curve file output in step 1051 and the new node density parameter values, the computer system calls the path generation algorithm to recalculate the path point sequence. The algorithm first draws the basic path trajectory according to the shape of the new curved curve, and then evenly sets path points on the trajectory at a node density of 1.2 points / m to generate a continuous coordinate sequence to form the target path scheme data file. When the maintenance parameter 40 does not exceed the cost threshold 50, the original path is directly output as the target scheme. For example, based on a gentle curved curve trajectory, path points are set at a node density of 1.2 points / m, generating a total of 12 node coordinate sequences in a 10 m channel to complete the path planning, and finally outputting the scheme data file to the next step.

[0108] Finally, the layout diagram is generated and output through step 1053. After the system reads the target path scheme data file, the parsing module converts the path point sequence into a cable centerline coordinate set, such as the coordinate sequence [(0 m, 0 m, 0 m) → (1 m, 1 m, 1 m)]. Simultaneously, it calculates the pipe diameter parameter set from the cross-sectional dimension data, such as a pipe diameter of 0.1 m. Then, the computer's CAD drawing tool is activated to draw continuous path segments in three-dimensional space based on the coordinate set, and pipe models of corresponding thicknesses are added according to the pipe diameter parameters to complete the cable layout diagram. Finally, the graphic file is output to a display screen or construction equipment terminal. For example, a blue line segment from coordinate point (0 m, 0 m, 0 m) to (1 m, 1 m, 1 m) is drawn in the software, and a pipe thickness of 0.1 m is set to generate a zoomable vector drawing, which is then sent to a tablet device for construction use.

[0109] In a practical application, during an underground cable laying optimization project in a certain city, the engineering team implemented path optimization based on maintenance parameter analysis results. When the system detected that the maintenance parameter value of the third segment in the initial path exceeded the preset cost threshold, the technicians immediately adjusted the spatial layout: changing the original straight cable route crossing the high-risk area to a smooth curve offset by 2.3 meters along the west wall, while increasing the node density from 1.2 to 1.8 per meter to optimize the bending radius. For the fifth segment, two transition nodes were added to shorten the span, ultimately forming a target path scheme containing 126 three-dimensional coordinate points. These spatial coordinate data and the 0.28-meter pipe diameter parameter were simultaneously input into the modeling system, automatically generating a three-dimensional cable layout diagram. The diagram clearly shows the 15° turn of the main cable at 7.5 meters with a solid red line, and the blue circular markers accurately indicate the pipe diameter change points. High-voltage joint risk areas are highlighted in yellow. System verification showed that this scheme successfully avoided three high-risk structures while strictly controlling the overall maintenance cost index within the preset threshold range. Finally, the drawing data was transmitted to the construction terminal to guide precise on-site operations.

[0110] In the overall scheme of step 105 above, by comparing the relationship between the maintenance parameter values ​​and the preset cost threshold, the spatial orientation curve of the initial installation path and the distribution density of path nodes are dynamically adjusted to generate target path scheme data that meets cost constraints. The scheme is then parsed into a precise set of cable centerline coordinates and pipe diameter parameters, which drives the drawing tool to automatically generate a three-dimensional spatial layout diagram of the cable in the channel. The final scheme is then output to the construction control terminal or display device, realizing quantitative cost control and construction visualization collaboration of cable installation path.

[0111] The following is a specific embodiment for steps 101 to 105:

[0112] like Figure 2 As shown, in a subway tunnel pipeline renovation project in a certain city, the specific process of cable relocation and optimization implemented by the construction team is as follows: The engineering team first went deep into the existing cable channel, which is 127 meters long, and accurately collected the three-dimensional coordinates of nine running cables using a total station, including the X-axis position of 35.12 meters, the Y-axis height of 1.78 meters, and the Z-axis depth of -2.15 meters. At the same time, the cross-sectional dimensions of the main cable, which are 0.4 meters wide and 0.25 meters high, were recorded. The team retrieved the coordinates of seventeen joints distributed in the channel and an archive containing fifty-three maintenance records. Combining the measured channel parameters, namely the basalt structure characteristics of the 127-meter length, 2.8-meter width, and 2.5-meter height, a spatial layout system integrating the cable solid model was constructed on a three-dimensional modeling platform. Subsequently, pulse ground-penetrating radar was used to scan the sidewall structure at 0.2-meter intervals. A concrete obstacle with a depth of 0.6 meters was identified at a distance of 42.3 meters from the south entrance. The obstacle was 1.2 meters long and 0.8 meters wide. At the same time, an unexplored cavity boundary extending 3.5 meters in the northern section was also discovered. The system updated the channel distribution map and marked the newly added risk areas.

[0113] Based on the digital model, technicians divided the channel space into a grid array of 0.5-meter square cells and calculated a minimum safe distance of 0.35 meters based on the cable cross-section. After avoiding three high-risk joint areas, the path planning algorithm generated an initial installation path through the channel, containing eighty-nine discrete coordinate points. The system called a database covering ten years of cable fault cases to train a prediction model, performing spatial correlation analysis between the initial path coordinates and thirty-five historical fault points: a spherical analysis domain with a radius of 0.7 meters was constructed with each fault point as the center, and spatial fault density data was calculated. For example, the third analysis area had 0.4 fault points per cubic meter. A two-factor weighted calculation was performed using a maintenance frequency weighting coefficient to obtain the path risk value. When the maintenance index of the fifth path segment reached 0.83, exceeding the preset safety threshold of 0.75, the engineering team dynamically adjusted the path spatial topology—transforming the original straight path into a curved path with a radius of 4.5 meters. This curve deviated from the original trajectory by 2.1 meters and added three path nodes, reducing the maintenance index of this segment to 0.68.

[0114] The final design resulted in a 3D cabling path with 112 precise coordinate nodes, exhibiting a smooth 22-degree bend at 56.7 meters. The system output design drawings incorporating pipe diameter parameters, with the main cable diameter set at 0.32 meters. In the visualization interface, red pipe lines indicate the main cable route, seven pipe diameter change points are marked with blue halos, and three high-risk joint areas are displayed with yellow warning boxes. Field testing verified that this design, while avoiding all survey obstacles and risk areas, strictly controlled all path maintenance parameters within cost thresholds. The design was transmitted to the tunnel construction equipment terminal via a high-speed network, guiding the robotic arm to complete cable laying operations with millimeter-level precision.

[0115] Figure 3 This application provides a schematic diagram of the structure of an underground transportation pipeline relocation and optimization system, as shown in the embodiment. Figure 3 As shown, the system includes:

[0116] The acquisition module 31 is used to acquire the spatial location, cross-sectional dimensions, joint locations and maintenance records of existing cables in the cable channel, and to construct a spatial layout model in combination with the channel structure parameters;

[0117] Update module 32 is used to scan the sidewalls and bottom plate of the channel with a non-contact detector to identify obstacles and hidden structures. Based on the identified obstacle positions, size parameters and structural boundaries, the spatial layout model is dynamically updated to obtain the channel distribution map.

[0118] The calculation module 33 is used to divide the internal space of the channel distribution map into a grid based on the spatial location, calculate the safety distance based on the cross-sectional dimensions, use the safety distance as a constraint, traverse the connection relationship of the grid through the path calculator, mark high-risk areas based on the joint positions, avoid the grid nodes corresponding to the high-risk areas, and determine the initial installation path of the new cable.

[0119] Output module 34 is used to extract the fault location and maintenance frequency from the maintenance record, train a historical model based on the historical cable case library, bind the initial installation path with the spatial location, input the historical model, calculate the path risk in combination with the fault location and maintenance frequency, and output the maintenance parameters corresponding to the initial installation path.

[0120] The adjustment module 35 is used to adjust the direction and node distribution of the initial installation path in space according to the comparison result between the maintenance parameters and the preset cost threshold, generate the target scheme and output the corresponding cable layout diagram.

[0121] Figure 3 The aforementioned underground transportation pipeline relocation and optimization system can perform... Figure 1The implementation principle and technical effects of the underground transportation pipeline relocation and optimization method described in the illustrated embodiment will not be repeated here. The specific operation methods of each module and unit in the underground transportation pipeline relocation and optimization system in the above embodiments have been described in detail in the embodiments related to this method, and will not be elaborated upon here.

[0122] In one possible design, Figure 3 The underground transportation pipeline relocation and optimization system of the embodiment shown can be implemented as a computing device, such as... Figure 4 As shown, the computing device may include a storage component 41 and a processing component 42;

[0123] The storage component 41 stores one or more computer instructions, wherein the one or more computer instructions are invoked and executed by the processing component 42.

[0124] The processing component 42 is used for the above Figure 1 The embodiment describes the method for relocating and optimizing underground transportation pipelines.

[0125] The processing component 42 may include one or more processors to execute computer instructions to complete all or part of the steps in the above-described method. Alternatively, the processing component may be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described method.

[0126] Storage component 41 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0127] Of course, computing devices may also include other components, such as input / output interfaces, display components, communication components, etc.

[0128] Input / output interfaces provide interfaces between processing components and peripheral interface modules, which can be output devices, input devices, etc.

[0129] The communication components are configured to facilitate wired or wireless communication between computing devices and other devices.

[0130] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform. In this case, the computing device can refer to a cloud server, and the aforementioned processing components, storage components, etc., can be basic server resources rented or purchased from the cloud computing platform.

[0131] This application also provides a computer storage medium storing a computer program, which, when executed by a computer, can perform the above-described functions. Figure 1 The illustrated embodiment presents a method for optimizing the relocation of underground transportation pipelines.

[0132] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0133] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0134] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0135] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for optimizing the relocation of underground transportation pipelines, characterized in that, include: Obtain the spatial location, cross-sectional dimensions, joint locations, and maintenance records of existing cables within the cable channel, and construct a spatial layout model based on the channel's structural parameters; Non-contact detectors are used to scan the side walls and bottom plate of the passage to identify obstacles and hidden structures. Based on the identified obstacle positions, size parameters and structural boundaries, the spatial layout model is dynamically updated to obtain the passage distribution map. Based on the spatial location, the internal space of the channel distribution map is divided into a grid. Based on the cross-sectional dimensions, the safety distance is calculated. With the safety distance as a constraint, the connection relationship of the grid is traversed through the path calculator. Based on the joint position, high-risk areas are marked. The grid nodes corresponding to the high-risk areas are avoided to determine the initial installation path of the new cable. Extract the fault location and maintenance frequency from the maintenance records, train a historical model based on the historical cable case library, bind the initial installation path with the spatial location, input the model into the historical model, calculate the path risk by combining the fault location and maintenance frequency, and output the maintenance parameters corresponding to the initial installation path. The step of binding the initial installation path to the spatial location, inputting it into the historical model, and calculating the path risk based on the fault location and maintenance frequency includes: Using the historical model, a spherical region with a fixed radius is generated with the coordinates of each fault location as the center. The ratio of the total number of fault points within the spherical region to the volume of the spherical region is calculated as the location distribution density data. The initial installation path is divided into continuous path segments, and the location distribution density data and maintenance weight value corresponding to each path segment are matched. Based on the location distribution density data and maintenance weight value, the path risk value is calculated using a linear weighting formula. The maintenance weight value is a weighting factor for the impact of maintenance frequency on risk. Based on the comparison results between the maintenance parameters and the preset cost threshold, the direction and node distribution of the initial installation path are adjusted to generate the target scheme and output the corresponding cable layout diagram.

2. The method according to claim 1, characterized in that, Extract the fault location and maintenance frequency from the maintenance records, train a historical model based on a historical cable case library, bind the initial installation path to the spatial location, input the model, calculate the path risk based on the fault location and maintenance frequency, and output the maintenance parameters corresponding to the initial installation path, including: The coordinates of the fault location and the value of the maintenance frequency are extracted from the maintenance records, and a historical prediction model is trained based on the entire dataset of the historical cable case library. The initial installation path is bound to the spatial location coordinates to generate a bound path dataset. The bound path dataset, the fault location coordinates, and the maintenance frequency value are then input into the historical prediction model. Using the historical prediction model, combined with the distribution density of the fault location coordinates and the maintenance frequency, the path risk value is calculated. The path risk value is then converted into maintenance parameter values ​​using a preset mapping function, and the maintenance parameter values ​​for the initial installation path are output.

3. The method according to claim 2, characterized in that, The path risk value is converted into maintenance parameter value using a preset mapping function, and the maintenance parameter value of the initial installation path is output, including: The path risk values ​​are mapped to maintenance parameter values ​​using a piecewise linear function, and the maintenance parameter values ​​are output in the order of path segment identifiers.

4. The method according to claim 1, characterized in that, Non-contact detectors are used to scan the sidewalls and floor of the passageway to identify obstacles and hidden structures. Based on the identified obstacle locations, size parameters, and structural boundaries, the spatial layout model is dynamically updated to obtain a passageway distribution map, including: The non-contact detector is used to scan the side walls and bottom surface of the channel to generate scanning signal data. The scanning signal data is then analyzed to identify the obstacle location points and the structural boundary points of the concealed structure. Measure the three-dimensional coordinate information of the obstacle's location point, calculate the obstacle's size parameter value, extract the contour data of the structural boundary point, and input the three-dimensional coordinate information, size parameter value, and contour data into the spatial layout model; Adjust the parameters at the corresponding positions in the spatial layout model and update the internal structure data of the channel to generate a channel distribution map that includes the distribution of obstacles and structural boundaries.

5. The method according to claim 1, characterized in that, Obtain the spatial location, cross-sectional dimensions, joint locations, and maintenance records of existing cables within the cable tunnel. Combine this with tunnel structural parameters to construct a spatial layout model, including: The spatial location of existing cables within the cable channel is collected, the cross-sectional dimensions of all cables are obtained, the joint location information of cable joints and historical maintenance records are recorded, and the channel structure parameters are obtained, including the channel length range, width dimension value, and height data value. By combining the spatial location with the channel structure parameter data, a three-dimensional geometric framework of the channel is established. In the three-dimensional geometric framework, the cross-sectional dimension data of the cable is set, all joint location information is marked, and historical maintenance record data is added to obtain the constructed spatial layout model.

6. The method according to claim 1, characterized in that, Based on the comparison results between the maintenance parameters and the preset cost threshold, the direction and node distribution of the initial installation path are adjusted to generate the target scheme and output the corresponding cable layout diagram, including: Compare the relationship between the maintained parameters and the preset cost threshold, and adjust the spatial orientation curve and path node distribution density of the initial installation path according to the comparison results; Based on the adjusted spatial orientation curve and path node distribution density, target path scheme data that meets the cost threshold is generated. The target path scheme data is parsed into a set of cable centerline coordinates and a set of pipe diameter parameters. Based on the set of cable centerline coordinates and pipe diameter parameters, a drawing tool is driven to draw a cable layout diagram of the cable in the channel, and the cable layout diagram is output to a display device or construction control terminal.

7. The method according to claim 1, characterized in that, Based on the spatial location, the internal space of the channel distribution map is divided into a grid. Based on the cross-sectional dimensions, a safety distance is calculated. Using the safety distance as a constraint, the connection relationships of the grid are traversed using a path calculator. High-risk areas are marked based on the joint locations, and the grid nodes corresponding to the high-risk areas are avoided to determine the initial installation path of the new cable, including: The internal space of the channel distribution map is divided into an equidistant set of grid cells according to the spatial location, and the safety distance between adjacent cables is calculated based on the cross-sectional size data of the cables. Using the safety distance value as a movement constraint, the path calculator iterates through the connection relationship data between each grid cell in all the grid cell sets, and records the set of feasible path points that satisfy the safety distance value. Based on the grid nodes corresponding to the high-risk areas marked by the joint locations, all grid nodes corresponding to the high-risk areas are avoided from the set of feasible path points. The continuous path point sequence of the new cable is calculated to generate the initial installation path of the new cable.

8. An underground transportation pipeline relocation and optimization system, used to execute the method for underground transportation pipeline relocation and optimization as described in any one of claims 1 to 7, characterized in that, include: The acquisition module is used to acquire the spatial location, cross-sectional dimensions, joint locations, and maintenance records of existing cables within the cable channel, and to construct a spatial layout model by combining the channel structural parameters. The update module is used to scan the sidewalls and bottom plate of the passage with a non-contact detector to identify obstacles and hidden structures. Based on the identified obstacle positions, size parameters and structural boundaries, the spatial layout model is dynamically updated to obtain the passage distribution map. The calculation module is used to divide the internal space of the channel distribution map into a grid based on the spatial location, calculate the safety distance based on the cross-sectional dimensions, use the safety distance as a constraint, traverse the connection relationship of the grid through the path calculator, mark high-risk areas based on the joint positions, avoid the grid nodes corresponding to the high-risk areas, and determine the initial installation path of the new cable. The output module is used to extract the fault location and maintenance frequency from the maintenance record, train a historical model based on the historical cable case library, bind the initial installation path with the spatial location, input the historical model, calculate the path risk by combining the fault location and maintenance frequency, and output the maintenance parameters corresponding to the initial installation path. The adjustment module is used to adjust the direction and node distribution of the initial installation path based on the comparison result between the maintenance parameters and the preset cost threshold, generate the target scheme and output the corresponding cable layout diagram.

9. A computing device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement the underground transportation construction pipeline relocation and optimization method as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that, The system contains a computer program that, when executed by a computer, implements a method for optimizing the relocation of underground transportation pipelines as described in any one of claims 1 to 7.

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