Underground traffic construction pipeline relocation optimization method and system

Abstract

The invention provides an underground traffic construction pipeline relocation optimization method and system. According to the method, the spatial position, the cross section size, the joint position and the maintenance record of the existing cable are acquired, and a channel structure is combined to construct a spatial layout model; scanning the side wall and the bottom plate of the channel by using a non-contact detector, and generating a channel distribution diagram based on the identified obstacle position, size parameter and structure boundary dynamic updating model; dividing the distribution map into grids, calculating a safety distance by taking a section size as a constraint, traversing a grid connection relation, and determining an initial installation path by avoiding a high-risk area marked at a joint position; then extracting a fault position and a maintenance frequency, binding an initial path with a spatial position by means of a historical model, calculating a path risk, and outputting a maintenance parameter; and adjusting the path trend, generating a target scheme and outputting a cable arrangement diagram. According to the invention, accurate optimization of the cable path is realized, the construction collision risk is avoided, and the long-term operation and maintenance cost is reduced.

Description

Technical Field

[0001] The present application relates to the technical field of cable or wire installation engineering, and in particular to a method and system for optimizing the relocation of underground transportation construction pipelines. Background Art

[0002] When inserting new pipelines into an existing underground integrated pipeline corridor system, the existing pipeline layout inside the corridor is complex and tightly packed, with extremely limited construction space and numerous hidden structures, including undocumented embedded metal parts, concrete voids, and water seepage areas, among other hidden risk points. New pipelines must maintain a strict safe distance from existing facilities to avoid collisions during construction and accommodate the physical deformation requirements of equipment due to thermal expansion and contraction.

[0003] Currently, the mainstream solution utilizes a semi-automated design approach that combines building information modeling with a path optimization algorithm. This approach first integrates the corridor's structural drawings and pipeline as-built data to construct a 3D static model that includes pipeline coordinates and design spacing thresholds. Finally, after manual review of the generated path's feasibility, the resulting construction plan is directly output and delivered to the site for implementation.

[0004] However, this technology has some key limitations. First, because it relies entirely on as-built drawings from the construction phase, it cannot recognize dynamically changing structures such as newly added metal supports or hidden cracks during the operation phase, resulting in significant deviations from actual on-site conditions. Second, the use of only fixed geometric spacing as a hard constraint exposes the installation process to potential collision accidents. Summary of the Invention

[0005] The present application provides a method and system for optimizing the relocation of underground transportation construction pipelines, which are used to solve the problems of construction safety risks and uncontrolled long-term maintenance costs in the prior art.

[0006] In a first aspect, the present application provides a method for optimizing the relocation of underground transportation construction pipelines, comprising: Obtain the spatial position, cross-sectional dimensions, joint locations, and maintenance records of existing cables in the cable channel, and build a spatial layout model based on channel structural parameters; A non-contact detector is used to scan the side walls and bottom plate of the channel 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 a channel distribution map. Dividing the internal space in the channel distribution map into grids based on the spatial position, calculating a safety distance based on the cross-sectional dimensions, traversing the connection relationships of the grids using the safety distance as a constraint using a path calculator, marking high-risk areas based on the joint positions, avoiding grid nodes corresponding to the high-risk areas, and determining an initial installation path for the new cable; Extracting the fault location and repair frequency from the maintenance record, training a historical model based on a historical cable case library, binding the initial installation path to the spatial location, inputting the historical model, calculating the path risk based on the fault location and repair frequency, and outputting maintenance parameters corresponding to the initial installation path; According to the comparison result of the maintenance parameters and the preset cost threshold, the direction and node distribution of the initial installation path in the spatial position are adjusted, a target solution is generated and a corresponding cable layout diagram is output.

[0007] Optionally, extracting the fault location and maintenance frequency from the maintenance record, training a historical model based on a historical cable case library, binding the initial installation path to the spatial location, inputting the historical model, calculating the path risk in combination with the fault location and maintenance frequency, and outputting maintenance parameters corresponding to the initial installation path, including: Extracting the coordinates of the fault location and the numerical values ​​of the maintenance frequency from the maintenance records, and training a historical prediction model based on the entire data set of the historical cable case library; Binding the initial installation path to the spatial position by coordinate points to generate a bound path data set, and inputting the bound path data set, the fault location coordinates, and the maintenance frequency value into a historical prediction model; The path risk value is calculated by combining the distribution density of the fault location coordinates and the maintenance frequency through the historical prediction model, and the path risk value is converted into a maintenance parameter value through a preset mapping function to output the maintenance parameter value of the initial installation path.

[0008] Optionally, the path risk value is calculated by combining the distribution density of the fault location coordinates and the maintenance frequency through the historical prediction model, the path risk value is converted into a maintenance parameter value through a preset mapping function, and the maintenance parameter value of the initial installation path is output, including: Using the historical prediction model, a spherical area with a fixed radius is generated with the coordinates of each fault location point as the sphere center, and the ratio of the total number of fault points in the spherical area to the volume of the spherical area is calculated as the location distribution density data; Dividing the initial installation path into continuous path segments, matching location distribution density data and maintenance weight values ​​corresponding to each path segment, and calculating a path risk value based on the location distribution density data and maintenance weight values ​​using a linear weighted formula; The path risk value is mapped to a maintenance parameter value through a piecewise linear function, and the maintenance parameter value is output in the order of path segment identifiers.

[0009] Optionally, a non-contact detector is used to scan the side walls and floor of the channel 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 a channel distribution map, including: Scanning the sidewalls and bottom plate surfaces of the channel using a non-contact detector to generate scanning signal data, analyzing the scanning signal data to identify obstacle locations and structural boundary points of concealed structures; Measuring the three-dimensional coordinate information of the obstacle location point, calculating the size parameter value of the obstacle, extracting the contour data of the structure boundary point, and inputting the three-dimensional coordinate information, size parameter value and contour data into the spatial layout model; The parameters of the corresponding positions in the spatial layout model are adjusted, and the internal structure data of the channel is updated to generate a channel distribution map including obstacle distribution and structure boundaries.

[0010] Optionally, obtain the spatial position, cross-sectional dimensions, joint positions, and maintenance records of existing cables in the cable channel, and combine them with channel structural parameters to construct a spatial layout model, including: Collect the spatial position 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 channel structural parameters, including the length range value, width dimension value, and height data value of the channel; The spatial position is combined with the channel structure parameter data to establish a three-dimensional geometric framework of the channel. The cross-sectional dimension data of the cable is set in the three-dimensional geometric framework, all joint position information is marked, and historical maintenance record data is added to obtain a constructed spatial layout model.

[0011] Optionally, based on the comparison result of the maintenance parameter and a preset cost threshold, adjusting the direction and node distribution of the initial installation path in the spatial position, generating a target solution and outputting a corresponding cable layout diagram, including: Comparing the maintained parameters with a preset cost threshold, and adjusting the spatial trend curve and path node distribution density of the initial installation path according to the comparison result; Generate target path plan data that meets the cost threshold based on the adjusted spatial trend curve and path node distribution density; The target path plan data is parsed into a cable centerline coordinate set and a pipe diameter parameter set, and based on the cable centerline coordinate set and the pipe diameter parameter set, 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 a construction control terminal.

[0012] Optionally, dividing the internal space in the channel distribution map into grids based on the spatial position, calculating a safety distance based on the cross-sectional size, traversing the connection relationship of the grid using the safety distance as a constraint using a path calculator, marking high-risk areas based on the joint positions, avoiding grid nodes corresponding to the high-risk areas, and determining an initial installation path for the new cable, including: Dividing the internal space of the channel distribution diagram into a set of equally spaced grid units according to the spatial position, and calculating a safety spacing value between adjacent cables based on the cross-sectional dimension data of the cables; Using the safety distance value as a movement constraint, traversing the connection relationship data between all grid cells in the set of grid cells through a path calculator, and recording a set of feasible path points that meet the safety distance value; Mark the grid nodes corresponding to the high-risk areas according to the joint position, avoid all grid nodes corresponding to the high-risk areas from the feasible path point set, calculate the continuous path point sequence of the new cable, and generate the initial installation path of the new cable.

[0013] In a second aspect, the present application provides an underground transportation construction pipeline relocation optimization system, comprising: The acquisition module is used to obtain the spatial position, cross-sectional dimensions, joint location, and maintenance records of existing cables in the cable channel, and build a spatial layout model based on the channel structure parameters; An updating module is configured to scan the sidewalls and floor of the channel using a non-contact detector to identify obstacles and hidden structures, and dynamically update the spatial layout model based on the identified obstacle positions, size parameters, and structural boundaries to obtain a channel distribution map; a calculation module, configured to divide the internal space in the channel distribution map into a grid based on the spatial position, calculate a safety distance based on the cross-sectional dimensions, traverse the connection relationship of the grid using the safety distance as a constraint using a path calculator, mark high-risk areas based on the joint positions, avoid grid nodes corresponding to the high-risk areas, and determine an initial installation path for the new cable; An output module is configured to extract the fault location and repair frequency from the maintenance record, train a historical model based on a historical cable case library, bind the initial installation path to the spatial location, input the historical model, calculate the path risk based on the fault location and repair frequency, and output 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 in the spatial position according to the comparison result of the maintenance parameter and the preset cost threshold, generate a target solution and output a corresponding cable layout diagram.

[0014] In the third aspect, the present application provides a computing device comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a method for optimizing the relocation of underground transportation construction pipelines as described in the first aspect above.

[0015] In a fourth aspect, the present application provides a computer storage medium storing a computer program. When the computer program is executed by a computer, it implements an underground transportation construction pipeline relocation optimization method as described in the first aspect.

[0016] 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 update operations effectively identify the coordinates of hidden structure boundaries and metal obstacles, eliminating positioning deviations caused by lagging drawing data; grid division is combined with safety spacing constraints to ensure that the path calculator physically avoids existing facilities and areas with insufficient safety spacing when traversing; high-risk areas are marked based on joint locations and corresponding nodes are avoided, specifically reducing the risk of interference from construction operations on key weak points; the historical model binds the initial path to the spatial position and extracts maintenance record parameters, converts the fault frequency into a path risk coefficient output, and realizes quantitative prediction of long-term maintenance costs; finally, the path direction is dynamically adjusted by comparing maintenance parameters with cost thresholds to generate a cable layout diagram that meets construction safety requirements and ensures full-cycle economy.

[0017] Furthermore, by extracting the coordinates of the fault location points and the repair frequency values ​​in the maintenance records as data input, a historical prediction model is trained based on the complete data set of the historical cable case library to eliminate sampling bias; then, the initial installation path and the spatial position are bound by coordinate points to generate a path dataset, and the fault coordinates and repair frequency are combined into the input model to calculate the path risk value by analyzing the distribution density of the fault location and the repair frequency; finally, the risk value is converted into a maintenance parameter value output through a preset mapping function, so that the parameter value accurately reflects the risk level of the high-risk fault cluster area in the path segment, driving the path direction to actively avoid the high-frequency fault area, significantly reducing long-term maintenance costs and balancing resource allocation.

[0018] These and other aspects of the present application will become more readily apparent from the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0020] Figure 1 A flowchart of an optimization method for underground transportation construction pipeline relocation provided by the present application is shown; Figure 2 A scenario diagram showing an optimization method for underground transportation construction pipeline relocation provided by this application is shown; Figure 3 The following is a schematic diagram showing the structure of an underground transportation construction pipeline relocation optimization system provided by the present application; Figure 4 A schematic structural diagram of a computing device provided by the present application is shown. DETAILED DESCRIPTION

[0021] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0022] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to being different types.

[0023] 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 results in significant deviations between spatial layout models and actual on-site conditions. Furthermore, these technologies employ only fixed geometric spacing as hard constraints, failing to fully account for actual maintenance risks and exposing the installation process to potential collision accidents. Therefore, a pipeline relocation optimization method for underground transportation construction that can dynamically update models and intelligently optimize routes is urgently needed.

[0024] In response to the above problems, the present invention proposes a method for optimizing the relocation of underground transportation construction pipelines, the core of which is to use non-contact detectors to dynamically update the spatial layout model, and to optimize the path planning based on the integration of historical maintenance data and risk assessment. Specifically, the spatial position, cross-sectional dimensions, joint positions and maintenance records of existing cables are first obtained, and an initial model is constructed in combination with the channel structure parameters; then, obstacles and hidden structures are scanned and identified by non-contact detectors, and the model is dynamically updated to generate an accurate channel distribution map; then, the internal space is gridded, and the grid path is traversed with the safety distance as a constraint to avoid the high-risk areas marked by the joint position to determine the initial installation path; then, the fault location and maintenance frequency of the maintenance record are extracted, the historical model is trained to calculate the path risk, and the maintenance parameters are output; finally, the path direction is dynamically adjusted according to the maintenance parameters and cost thresholds, and an optimized cable layout diagram is generated and output. This method dynamically scans and identifies the newly added dynamically changing structures in the operation phase to ensure that the model is consistent with the actual situation on site, thereby solving the problem of model deviation; at the same time, the risk of collision accidents is significantly reduced by combining risk assessment with safety distance optimization path planning, thus realizing intelligent and highly reliable pipeline relocation optimization.

[0025] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0026] Figure 1 A flowchart of a method for optimizing the relocation of underground transportation construction pipelines is provided for the embodiment of the present application. Figure 1 As shown, the method includes: 101. Obtain the spatial position, cross-sectional dimensions, joint location, and maintenance records of existing cables in the cable channel, and build a spatial layout model based on the channel structural parameters; Optionally, the above step 101 may specifically include the following steps: 1011. Collect the spatial positions of existing cables in the cable channel, obtain the cross-sectional dimensions of all cables, record the joint location information of the cable joints and historical maintenance records, and obtain channel structural parameters, wherein the channel structural parameters include a length range value, a width dimension value, and a height data value of the channel; 1012. Combine the spatial position 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 all joint position information, add historical maintenance record data, and obtain a constructed spatial layout model.

[0027] In the above steps, spatial position refers to the specific position coordinate data of the cable in the channel, including the three-dimensional coordinate point values; cross-sectional dimensions refer to the external dimension data of the cable cross section, such as the diameter value of a circular cable; connector location information refers to the specific position coordinate data of the cable connection point; maintenance records are historical data of past repairs and inspections of the cable, such as maintenance dates and descriptions; channel structure parameters include the channel length range value representing the channel length interval range value, the width dimension value representing the channel width value, and the height data value representing the channel height value; the spatial layout model is a geometric model framework in a three-dimensional space, which is used to visualize channels, cables, and related information.

[0028] In this embodiment, step 1011 first collects basic data within the cable channel: a laser scanner is used to obtain the spatial coordinates of all existing cables; a measuring tool is used to record the cable cross-sectional dimensions; a positioning device is used to read the three-dimensional coordinates of the joint locations; a database is accessed to retrieve historical maintenance records; and the channel structural parameters are analyzed from construction drawings. For example, for a 10-meter-long channel, the spatial coordinates of a cable are measured to be X0.5m / Y1m / Z0.8m, with a cross-sectional diameter of 5cm, a joint located at X8m / Y0.6m / Z1m, and a maintenance record of "No abnormalities in 2023 maintenance." The channel parameters are: length range 10-15m, width 1m, and height 2m.

[0029] Next, construct the spatial layout model through step 1012: Based on the data from step 1011, use 3D modeling software to input channel parameters to create a rectangular box frame. Map the cable's spatial position coordinates to generate positioning points within the frame. Generate corresponding geometric bodies based on the cross-sectional dimensions. Add marker symbols at the joint location coordinates. Associate maintenance record text with the corresponding locations. For example, for the above data, first create a base frame with a length of 12 meters (within the range of 10-15 meters), a width of 1 meter, and a height of 2 meters. Set 5-centimeter diameter cylinders at coordinates 0.5 / 1 / 0.8 to represent the cables. Add red spheres at coordinates 8 / 0.6 / 1 to mark the joints. Display a "2023 Maintenance No Abnormality" label on the side of the model to finally output the complete 3D model.

[0030] In a practical application, the project team employed the following method to construct a three-dimensional spatial layout model of a power grid's underground pipeline upgrade project. First, personnel penetrated the tunnel interior and, using precision measuring instruments, accurately obtained the spatial coordinates and center-to-center spacing of all six existing cables. They also measured and recorded the cross-sectional dimensions of each cable. The cross-sectional dimensions of the cables in key branch lines were 0.5 m x 0.3 m and 0.4 m x 0.25 m, respectively. By accessing the archive system, they collected the 3D coordinates and historical maintenance information of 12 key cable joints located throughout the tunnel, including a record of joint No. 3 being replaced in 2022. The surveying team then determined key tunnel structural parameters: the main tunnel section was 47.5 meters long, 2.5 meters wide, and 2.2 meters high at the shaft connection. Technicians input this basic data into a 3D modeling system, generating a 1:1 scale tunnel framework model based on the measured tunnel spatial parameters. In this digital model, each cable was accurately modeled according to the measured cross-sectional dimensions, with all joint information clearly labeled at specific coordinates. Technicians also linked 35 historical maintenance events to corresponding cable model nodes, creating a visual spatial layout model that includes spatial structure, facility attributes, and maintenance status. This model visualizes channel conditions and provides an accurate basis for subsequent construction plan development.

[0031] In the overall solution of the above step 101, by collecting the spatial position and cross-sectional dimensions of the existing cables in the cable channel, combining the cable joint position information and historical maintenance records, and obtaining the channel structure parameters such as the length range value, width dimension value, and height data value of the channel, this information is integrated and a three-dimensional geometric framework of the channel is established. The cable cross-sectional dimension data is set in the framework, all joint positions are marked, and historical maintenance record data is added. Finally, a spatial layout model including the channel structure, cable layout and key information is constructed, thereby realizing accurate visualization and management of the cable channel.

[0032] 102. Scan the sidewalls and floor of the channel using a non-contact detector to identify obstacles and hidden structures, and dynamically update the spatial layout model based on the identified obstacle positions, size parameters, and structural boundaries to obtain a channel distribution map; Optionally, the above step 102 may specifically include the following steps: 1021. Scan the sidewalls and the bottom plate surface of the channel using a non-contact detector to generate scanning signal data, analyze the scanning signal data, and identify the obstacle location points of the obstacles and the structural boundary points of the hidden structure; 1022. Measure the three-dimensional coordinate information of the obstacle location point, calculate the size parameter value of the obstacle, extract the contour data of the structure boundary point, and input the three-dimensional coordinate information, size parameter value and contour data into the spatial layout model; 1023. Adjust the parameters of the corresponding positions in the spatial layout model, and update the internal structure data of the channel to generate a channel distribution map including obstacle distribution and structure boundaries.

[0033] In the above steps, the non-contact detector refers to a device that can scan without contacting the channel surface, such as a lidar instrument; the channel sidewall and bottom plate refer to the walls on both sides of the channel and the bottom ground area; the scanning signal data refers to the signal waveform or image data output by the detector during scanning; the obstacle refers to an obstruction in the channel, such as a pipe or equipment body; the hidden structure refers to a hidden auxiliary structure such as a depression or a hole; the obstacle position point refers to the position coordinate point data of the obstacle; the structure boundary point refers to the contour coordinate point data of the hidden structure; the three-dimensional coordinate information refers to the X-axis value, Y-axis value, and Z-axis value of the position point; the size parameter value refers to the length value, width value, and height value of the obstacle; the contour data refers to the set data of the outline points of the hidden structure; the channel distribution map refers to the updated three-dimensional visualization model map containing obstacle and structure boundary information.

[0034] In an embodiment of the present application, first, through step 1021, a non-contact detector such as a lidar is used to scan the side walls and bottom plate surface of the channel to obtain raw data. After reflection, the laser beam emitted by the detector generates 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, and form intermediate data for identifying positions and boundaries for subsequent steps. For example, in an actual 10-meter-long channel, after scanning, a rock obstacle location point is identified through algorithm analysis, 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 concave hidden structure is identified, with a starting point of 7 meters in length, 0.8 meters in width, and 0.2 meters in height, and an ending point of 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.

[0035] Secondly, in step 1022, based on the position point and boundary point data, the three-dimensional coordinate information record value of the obstacle position point is directly extracted, such as 5 meters in the length direction, 1 meter in the width direction, and 0.5 meters in the height direction. Then, a geometric calculation algorithm is used to calculate the size parameter value of the obstacle based on the multi-position point data. For example, by finding the minimum point and the maximum point in all relevant points and subtracting them, the length value, width value, and height value are obtained. The specific calculation process is as follows: Assume that multiple scanning points include point 1 coordinates 4.8 meters in length, 0.9 meters in width, and 0.4 meters in height, point 2 coordinates 5.2 meters in length, 1.1 meters in width, and In the height direction of 0.6 meters, in the length direction, the maximum point 5.2 meters minus the minimum point 4.8 meters equals 0.4 meters as the length value, in the width direction, the maximum point 1.1 meters minus the minimum point 0.9 meters equals 0.2 meters as the width value, and in the height direction, the maximum point 0.6 meters minus the minimum point 0.4 meters equals 0.2 meters as the height value, and the size parameter values ​​​​of length 0.4 meters, width 0.2 meters, and height 0.2 meters are obtained. At the same time, the boundary point set of the hidden structure is extracted to form contour data such as a list of concave points. Finally, these three-dimensional coordinate information size parameter values ​​​​and contour data are input into the spatial layout model through the data interface for subsequent updates.

[0036] Finally, through step 1023, the spatial layout model is dynamically updated based on the three-dimensional coordinate information size parameter values ​​and contour data, and the obstacle object is added at the corresponding position using computer modeling software, and the size numerical value is set to simulate the new element. At the same time, based on the contour data, the hidden structure area is added to adjust the internal structure to 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 the coordinate point of 5 meters. The surface deformation algorithm is applied to the point set at the recessed position of the obstacle. After the shape area is generated, the three-dimensional view file containing the new element is output as the final channel distribution map. The entire process is coherent from data recognition to size calculation to model update to ensure seamless connection.

[0037] In practice, at a certain city's integrated utility corridor renovation project, technicians performed the following non-contact survey process to refine the corridor model. The team evenly spaced 23 survey points along the 130-meter corridor, using ground-penetrating radar (GPR) equipment to continuously scan the concrete sidewalls and basalt floor at 0.3-meter intervals. After receiving the electromagnetic wave reflection signal, the instrument identified an abnormal signal area at a depth of 0.5 meters at a location 35.6 meters from the starting point. The system identified this as a cast iron pipe-shaped obstacle with a diameter of approximately 0.45 meters. The scan data also revealed an irregular structural boundary extending 5.2 meters to the west of the corridor. Comparison with construction drawings confirmed this to be the entrance to an abandoned civil air defense project. The surveying team then set up a total station to accurately map the obstacle, obtaining the spatial coordinates of its center point as X=35.612, Y=2.304, and Z=-1.258 (in meters), and noting the axial length of the obstacle as 2.1 meters. For the boundary contours of hidden structures, the three-dimensional coordinates of a total of 143 feature points were extracted using point cloud capture technology. These data, including the pose parameters of three obstacles and the contours of hidden structures, were transmitted to the modeling platform through a data interface. The model system automatically integrated the newly collected data, accurately marked the tubular obstacles and their boundary ranges in the No. 7 detection area within the original spatial layout model, and simultaneously optimized the three-dimensional contour line at the southwest corner of the channel. After completing the data iteration, the system generated a channel distribution map containing the newly added obstacle layer and the structure boundary heat map. The map clearly shows the spatial characteristic elements of seven different attributes in the channel, significantly improving the accuracy of visual management of the underground space environment.

[0038] In the overall solution of the above step 102, the side walls and bottom plate of the cable channel are fully scanned by a non-contact detector to obtain scanning signal data and analyze and identify the obstacle location points and hidden structure boundary points, accurately measure the three-dimensional coordinate information of the obstacle and calculate its size parameters, and extract the structural boundary contour data at the same time. The above information is dynamically input into the spatial layout model to adjust the corresponding position parameters, and the internal structure data of the channel is synchronously updated. Finally, a channel distribution map containing accurate obstacle distribution and structural boundary characteristics is generated, realizing dynamic optimization and visualization upgrade of the spatial model.

[0039] 103. Divide the internal space in the channel distribution map into a grid based on the spatial position, calculate a safety distance based on the cross-sectional dimensions, traverse the connection relationship of the grid using the safety distance as a constraint using a path calculator, mark high-risk areas based on the joint positions, avoid the grid nodes corresponding to the high-risk areas, and determine an initial installation path for the new cable; Optionally, the above step 103 may specifically include the following steps: 1031. Divide the internal space of the channel distribution diagram into a set of equally spaced grid units according to the spatial position, and calculate a safety spacing value between adjacent cables based on the cross-sectional dimension data of the cables; 1032. Using the safety distance value as a movement constraint, traverse the connection relationship data between all grid cells in the set of grid cells using a path calculator, and record a set of feasible path points that meet the safety distance value. 1033. Mark the grid nodes corresponding to the high-risk areas according to the joint position, avoid all grid nodes corresponding to the high-risk areas from the feasible path point set, calculate the continuous path point sequence of the new cable, and generate the initial installation path of the new cable.

[0040] In the above steps, the grid unit set refers to the set of equal-sized area blocks into which the internal space of the channel is divided; the safety distance value refers to the minimum safety distance value that needs to be maintained between cables; 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 units; the feasible path point set refers to the set of all grid position 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 location of the grid unit; the continuous path point sequence refers to a series of connected grid point position 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 numerical parameter of the diameter of the cable cross section; the joint location refers to the specific coordinate location of the cable connection point.

[0041] In an embodiment of the present application, first, step 1031 is used to evenly divide the internal space of the channel into a set of equidistant grid cells based on the spatial position data, and then the safety distance value is calculated based on the cable cross-sectional size data. The specific process is: input the three-dimensional spatial data of the channel distribution map, use the grid division algorithm to generate grid cells of uniform size, for example, use a 1 meter × 1 meter grid to divide a 10-meter long channel into 100 units, and the center point of each unit is used as a node position; then calculate the safety distance value, which is defined by the formula: safety distance value = safety factor × cable cross-sectional diameter, where the safety factor is fixed to 1.5 by industry standards, and the cable cross-sectional diameter is a measured value. For example, when the cable diameter is 0.1 meter, the calculation process is: safety distance value = 1.5 × 0.1 = 0.15 meters. This value constrains the subsequent path node spacing to be ≥ 0.15 meters, and all grid unit sets and safety distance values ​​are output to the next process.

[0042] Next, in step 1032, the safety distance value of 0.15 meters is used as a movement constraint. The path calculator traverses the connections between grid cells to select a set of feasible path points. The specific process is as follows: The grid node locations and connections from step 1031 are input, and the path search algorithm in the computer traverses the grid nodes. The algorithm checks whether the distance between adjacent nodes is ≥ 0.15 meters, and nodes that meet the condition are retained. For example, if the distance between node A (1.0m, 1.0m) and adjacent node B (1.0m, 1.2m) is |1.2 - 1.0| = 0.2m > 0.15m, node B is retained. Conversely, if the distance between node C (1.0m, 1.1m) is 0.1m < 0.15m, node C is eliminated. The final set of feasible path points is output and passed to step 1033.

[0043] Finally, in step 1033, grid nodes in high-risk areas are marked based on the joint locations, and an initial installation path is generated by avoiding these nodes in the set of feasible path points. Specifically, the following steps are performed: The joint location coordinates from step 101, such as (8.0m, 0.6m), are input, and a circular area with a radius of 0.5 meters is defined as the high-risk area centered on this point. The grid nodes within this area are marked; high-risk nodes are removed from the set of feasible path points; a path planning algorithm is used to calculate a connected path sequence, such as a continuous set of points from the starting point (0m, 0.5m) to the end point (10m, 0.5m), which must skip high-risk nodes; and finally, the initial installation path is output as a coordinate sequence text file for construction reference.

[0044] In a real-world application, during a city underground cable optimization project, engineers performed the following path planning operations to determine the installation plan for the new cables. First, based on the spatial location data in the channel distribution map, they divided the internal space of the channel into a set of equally spaced grid cells, ensuring that each grid cell evenly covered the entire area. Subsequently, using the cross-sectional dimensions of the existing cables, they calculated the required safe spacing between adjacent cables as a critical path constraint. The team input this data into a path calculator, which automatically traversed the connections between all grid cells, screening out a set of feasible path points that met the safe spacing requirements and forming a potential path library. Simultaneously, based on known cable connector location information, the system accurately marked the grid nodes corresponding to high-risk areas, proactively avoiding these risky nodes during the path selection process. 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 spacing constraints, thereby determining the initial installation path for the new cables and improving the reliability of the plan and the safety of its implementation.

[0045] In the overall solution of the above step 103, the internal space is divided into equidistant grid units according to the channel distribution map, and the safety distance value between adjacent cables is calculated in combination with the cable cross-sectional dimensions. This is used as a movement constraint condition, and the connection relationship data of each unit in the grid unit set is traversed through the path calculator to screen out a set of feasible path points that meet the safety distance. Then, the grid nodes corresponding to the high-risk areas are marked according to the joint positions, and all high-risk grid nodes are excluded from the feasible path point set. The initial installation path is automatically generated by calculating the continuous path point sequence of the new cable.

[0046] 104. Extract the fault location and maintenance frequency from the maintenance record, train a historical model based on a historical cable case library, bind the initial installation path to the spatial location, input the historical model, calculate the path risk based on the fault location and maintenance frequency, and output maintenance parameters corresponding to the initial installation path; Optionally, the above step 104 may specifically include the following steps: 1041. Extracting the coordinates of the fault location and the numerical values ​​of the maintenance frequency from the maintenance record, and training a historical prediction model based on the entire data set of the historical cable case library; 1042. Bind the initial installation path and the spatial position by coordinate points to generate a bound path dataset, and input the bound path dataset, the fault location coordinates, and the maintenance frequency value into a historical prediction model. 1043. Calculate the path risk value through the historical prediction model, combined with the distribution density of the fault location coordinates and the maintenance frequency, convert the path risk value into a maintenance parameter value through a preset mapping function, and output the maintenance parameter value of the initial installation path.

[0047] Among them, step 1043 may specifically include the following processes: using the historical prediction model, taking the coordinates of each fault location point as the center of the sphere, generating a spherical area with a fixed radius, and calculating the ratio of the total number of fault points in the spherical area to the volume of the spherical area as the position distribution density data; dividing the initial installation path into continuous path segments, matching the position distribution density data and maintenance weight value corresponding to each path segment, and calculating the path risk value based on the position distribution density data and maintenance weight value according to the linear weighted formula; mapping the path risk value to a maintenance parameter value through a piecewise linear function, and outputting the maintenance parameter value in the order of the path segment identifier.

[0048] In the above steps, the grid unit set is a set of equal-sized areas divided in the internal space of the channel; the safety distance value is the minimum safety distance value that must be maintained between cables; the path calculator is a tool used to search for paths in the computer system; the connection relationship data is a set of information connected between grid points; the feasible path point set is a set of all grid point positions that meet the safety distance requirements, and the high-risk area is a potentially dangerous area around the cable joint; the grid node is the center point position of the grid unit; the continuous path point sequence is the position sequence of continuous 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 position of the cable connection point; the fault location is the coordinate point position of the past fault The maintenance parameter is the time interval required for cable maintenance; the path risk is the risk value on the cable path, and the bound path dataset is a data set that combines the path point sequence with the coordinate position; the path risk value is the risk value obtained by calculation, and the maintenance parameter value is the specific maintenance value after risk mapping; the location distribution density data is the ratio of the number of fault points near the fault point to the area volume; the maintenance weight value is the weight factor of the maintenance frequency affecting the risk; the piecewise linear function is a tool for mapping the risk value to the maintenance parameter in segments; the path segment identifier is the unique identifier after the path is segmented.

[0049] In the embodiment of the present application, first, data is extracted and a training model is performed through step 1041. The computer system reads the maintenance record file, parses the three-dimensional coordinates of the fault location, such as (8.0, 0.6, 0.5), from the text record, and extracts the corresponding maintenance frequency value 3. Then, the historical cable case library is called to load the entire historical data set. Then, the historical prediction model is trained using the random forest algorithm, inputting the historical fault coordinates and maintenance frequency data to automatically learn feature associations. Finally, the trained model file and the list of fault point coordinates are output. For example, the actual fault point coordinates (8.0, 0.6, 0.5) and the number of repairs 3 are read, and the historical database containing 100 records is called to train the model. Finally, the model file is generated and the coordinate list is stored for the next step.

[0050] Next, data binding and model input are completed in step 1042. The system obtains an initial installation path point sequence, such as [(0.0, 0.5, 0.2), (5.0, 1.0, 0.3)]. Using a spatial matching algorithm, the coordinates of each path point are verified and bound to the spatial location data from step 101 to generate a dataset. This dataset is then combined and packaged with the list of fault location coordinates. Finally, this dataset, along with the maintenance frequency value, is input into the historical prediction model. For example, the path point (0.0, 0.5, 0.2) is bound to the identifier P001, combined with the fault point (8.0, 0.6, 0.5), and then input into the model. The maintenance frequency value of 3 is also submitted simultaneously.

[0051] Finally, step 1043 performs risk calculation and parameter output. The model generates a spherical area with a radius of 0.5 m with each fault point (e.g., (8.0, 0.6, 0.5)) as the center, and calculates the volume V = 4 / 3 × 3.1416 × (0.5)³ = 0.523 m³. The number of fault points in the area is counted, e.g., 5, and the density D = 5 / 0.523 ≈ 9.56 times / m³ is calculated. The installation path is divided into segments, e.g., P001 (0 m - 5 m), and the density values ​​are 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, and the value R = 0.2 × 9.56 + 0.3 × 0.3 = 2.002 is obtained. The maintenance parameter value 40 is output through segmentation rule mapping R ≥ 2. Finally, the result file P001:40 is generated. Complete calculation example: Within the fault point (8.0, 0.6, 0.5) area with a volume of 0.523 m³, the density of five fault points is 9.56 times / m³. The P001 segment matches this density and a weight of 0.3 to calculate R=2.002. Because ≥2 is mapped, the output is 40.

[0052] In practice, during a power grid facility renovation project, the engineering team implemented the following path risk assessment process. Technicians first extracted the 3D coordinates and corresponding maintenance frequency data for 17 historical fault points from cable maintenance records. A predictive model was trained using a historical database of over 200 cable fault cases. The initial installation path of the new cable was then bound point-by-point to the spatial coordinate system of the channel, generating a dataset containing 83 path nodes. This bound dataset, along with the fault location coordinates and maintenance frequency parameters, was input into the predictive model. During model processing, the system automatically generated a spherical analysis area with a radius of 0.8 meters centered on each fault point and calculated the fault point density within the area. For example, area 3 contained 1.2 fault points per cubic meter. The initial path was then divided into 12 contiguous segments, each of which was assigned a corresponding fault density value and maintenance frequency weighting coefficient. 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 ​​were then mapped using a piecewise function to a maintenance parameter set that included maintenance interval levels (e.g., Level A requiring three-month inspections) and risk indicators (e.g., red high-risk segments). The final output includes an evaluation report of the maintenance parameter values ​​of each path segment, providing a quantitative basis for subsequent construction decisions.

[0053] In the overall solution of the above step 104, by extracting the coordinates of the fault location points and the maintenance frequency values ​​in the maintenance records, combining with the historical cable case library to train the historical prediction model, the initial installation path and the spatial position are bound by coordinate points to generate a bound path data set, and then the bound data, fault coordinates and maintenance frequency are input into the historical prediction model. The model generates a spherical area with a fixed radius with each fault point coordinate as the center of the sphere, calculates the number of fault points per unit volume in the area as the location distribution density, and then divides the initial path into continuous path segments. The location distribution density and maintenance weight value corresponding to each segment are dynamically matched, and the path risk value is calculated by a linear weighted formula. Finally, the risk value is mapped to a maintenance parameter value using a piecewise linear function, and the maintenance parameter value of each segment is output in the order of the path segments, so as to quantitatively evaluate the operation and maintenance risk level of the new cable installation path.

[0054] 105. According to the comparison result of the maintenance parameter and the preset cost threshold, the direction and node distribution of the initial installation path in the spatial position are adjusted, a target solution is generated, and a corresponding cable layout diagram is output.

[0055] Optionally, the above step 105 may specifically include the following steps: 1051. Compare the maintained parameters with a preset cost threshold, and adjust the spatial trend curve and path node distribution density of the initial installation path according to the comparison result; 1052. Generate target path plan data that meets the cost threshold based on the adjusted spatial trend curve and path node distribution density; 1053. Parse the target path plan data into a cable centerline coordinate set and a pipe diameter parameter set, and based on the cable centerline coordinate set and the pipe diameter parameter set, drive a 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 a construction control terminal.

[0056] In the above steps, the maintenance parameter is a quantitative value of the cable maintenance demand, such as 40, the preset cost threshold is a preset budget control value, such as 50, the spatial trend curve is the continuous curve shape of the path in three-dimensional space, the node distribution density is the number of nodes on a unit length path, such as 1.2 points / meter, the target path plan is the optimized path planning data set, the cable centerline coordinate set is the three-dimensional coordinate sequence of the path center point, the pipe diameter parameter set is the cable pipe size parameter set, the drawing tool is a software tool for automatically generating 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 an on-site construction control device.

[0057] In an embodiment of the present application, first, parameter comparison and path adjustment are performed through step 1051, and the computer system reads the maintenance parameter file to obtain a value 40, and simultaneously obtains a preset cost threshold value 50 for comparison operation. When the maintenance parameter value 40 is greater than the preset cost threshold value 30, the path adjustment algorithm is automatically triggered. The algorithm first modifies the spatial trend curve of the initial installation path, and uses a curve optimization tool to reduce the curvature of the path, for example, changing a 90° turn to a 45° gentle turn. At the same time, the path node distribution density is adjusted, and the node density value is increased through the density calculation function, for example, from the original density 1 points / m to 1.2 points / m, so that the path direction is straighter and the nodes are denser, and finally the adjusted spatial trend curve file and node density parameter file are generated. For example, when the maintenance parameter 45 is greater than the preset cost threshold 40, the system calculates the new bending radius to be 2 m to reduce the turning angle, and increases the node density from 1 points / m to 1.5 points / m to adapt to the new path shape.

[0058] Next, in step 1052, a target path solution is generated. Based on the spatial trend curve file output in step 1051 and the new node density parameter value, the computer system invokes the path generation algorithm to recalculate the path point sequence. The algorithm first draws a basic path trajectory based on the new curved curve shape. Then, using a node density of 1.2 points / m, it evenly sets path points along the trajectory to generate a continuous coordinate sequence, forming the target path solution data file. If the maintenance parameter 40 does not exceed the cost threshold of 50, the original path is directly output as the target solution. For example, based on a gently 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-meter channel to complete the path planning. The solution data file is then output to the next step.

[0059] Finally, step 1053 generates and outputs the layout diagram. After the system reads the target path plan 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, the pipe diameter parameter set, such as the 0.1 m pipe diameter data set, is calculated from the cross-sectional dimension data. The CAD drawing tool in the computer then draws continuous path segments in three-dimensional space based on the coordinate set, adding pipe models of corresponding thicknesses based on 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 drawn from the coordinate points (0 m, 0 m, 0 m) to (1 m, 1 m, 1 m) in the software sets the pipe thickness to 0.1 m, generating a scalable vector drawing that can be sent to a tablet device for construction use.

[0060] In a practical application, during a city's underground cable optimization project, the engineering team implemented route optimization based on maintenance parameter analysis. When the system detected that the maintenance parameter value for the third segment of the initial route exceeded a preset cost threshold, technicians immediately adjusted the spatial layout: the cable route, originally a straight line through a high-risk area, was changed to a smooth curve offset 2.3 meters along the west wall. The node density was also increased from 1.2 per meter to 1.8 to optimize the bending radius. For the fifth segment, two transition nodes were added to shorten the span, ultimately resulting in a target route plan consisting of 126 three-dimensional coordinate points. These spatial coordinate data, along with the 0.28-meter pipe diameter parameter, were simultaneously input into the modeling system, automatically generating a 3D cable layout diagram. The diagram clearly shows the 15° turn of the main cable at 7.5 meters with a solid red line, and blue circular markers precisely identify the pipe diameter change points. High-voltage joint risk areas are also highlighted in yellow. System verification confirmed that this plan successfully avoided three high-risk structures while keeping the overall maintenance cost index within the preset threshold. The final drawing data was then transmitted to the construction terminal for precise on-site operation.

[0061] In the overall solution of the above step 105, by comparing the size relationship between the maintenance parameter value and the preset cost threshold, the spatial trend curve and path node distribution density of the initial installation path are dynamically adjusted to generate target path solution data that meets the cost constraint conditions. The solution is parsed into an accurate cable centerline coordinate set and pipe diameter parameter set, and the drawing tool is driven to automatically generate a three-dimensional spatial layout diagram of the cable in the channel. The final solution is output to the construction control terminal or display device, realizing the quantitative cost control and construction visualization collaboration of the cable installation path.

[0062] The following is a specific embodiment of steps 101 to 105: like Figure 2As shown in the figure, in a certain city's subway tunnel supporting pipeline renovation project, the construction team implemented the following detailed process for cable relocation optimization: The engineering team first penetrated the existing 127-meter-long cable tunnel and used a total station to accurately collect the three-dimensional coordinates of nine operating cables, 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. The team also recorded the cross-sectional dimensions of the main cable, which were 0.4 meters wide and 0.25 meters high. The team retrieved the coordinates of 17 joints distributed throughout the tunnel and an archive containing 53 maintenance records. Combining the measured tunnel parameters—namely, the basalt structural characteristics of 127 meters in length, 2.8 meters in width, and 2.5 meters in height—they constructed a spatial layout system that integrated the cable physical model on a 3D modeling platform. Subsequently, pulse geological radar was used to scan the side wall structure at intervals of 0.2 meters, and a concrete barrier with a depth of 0.6 meters was identified 42.3 meters from the south entrance. The barrier was 1.2 meters long and 0.8 meters wide. At the same time, an unsurveyed cavity boundary extending 3.5 meters in the northern section was discovered. The system updated the channel distribution map and marked the new risk areas.

[0063] Based on the digital model, technicians divided the tunnel space into a grid array of 0.5-meter square cells and calculated a minimum safe spacing 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 tunnel, consisting of 89 discrete coordinate points. The system then used a prediction model trained on a database covering ten years of cable failure cases to perform a spatial correlation analysis between the initial path coordinates and 35 historical fault points. A spherical analysis domain with a radius of 0.7 meters was constructed centered on each fault point, 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, combined with a maintenance frequency weighting factor, was used to determine 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—converting the original straight path into a curved path with a radius of 4.5 meters. This curve was offset by 2.1 meters from the original trajectory and added three new nodes, reducing the maintenance index for this segment to 0.68.

[0064] The final solution resulted in a three-dimensional routing path with 112 precise coordinate nodes, featuring a smooth 22-degree bend at 56.7 meters. The system output a design drawing incorporating pipe diameter parameters, with the main cable diameter set at 0.32 meters. In the visualization interface, red lines indicate the main cable path, seven diameter change points are marked with blue halos, and three high-risk joint areas are displayed as yellow warning boxes. Field testing verified that the solution avoided all survey obstacles and risk areas while maintaining parameter values ​​throughout the entire path within strict cost thresholds. The design was transmitted via a high-speed network to the tunnel construction equipment terminal, guiding the robotic arm to complete the cable laying operation with millimeter-level precision.

[0065] Figure 3The present application provides a schematic diagram of the structure of an underground transportation construction pipeline relocation optimization system. Figure 3 As shown, the system includes: An acquisition module 31 is used to acquire the spatial position, cross-sectional dimensions, joint positions, and maintenance records of existing cables in the cable channel, and to construct a spatial layout model based on the channel structure parameters; An updating module 32 is configured to scan the sidewalls and floor of the channel using a non-contact detector to identify obstacles and hidden structures, and dynamically update the spatial layout model based on the identified obstacle positions, size parameters, and structural boundaries to obtain a channel distribution map; a calculation module 33 configured to divide the internal space in the channel distribution map into a grid based on the spatial position, calculate a safety distance based on the cross-sectional dimensions, traverse the connection relationships of the grid using the safety distance as a constraint using a path calculator, mark high-risk areas based on the joint positions, avoid grid nodes corresponding to the high-risk areas, and determine an initial installation path for the new cable; Output module 34 is configured to extract the fault location and maintenance frequency from the maintenance record, train a historical model based on a historical cable case library, bind the initial installation path to the spatial location, input the historical model, calculate the path risk based on the fault location and maintenance frequency, and output maintenance parameters corresponding to the initial installation path; The adjustment module 35 is used to adjust the direction and node distribution of the initial installation path in the spatial position according to the comparison result of the maintenance parameters and the preset cost threshold, generate a target solution and output a corresponding cable layout diagram.

[0066] Figure 3 The underground transportation construction pipeline relocation optimization system can execute Figure 1 The implementation principles and technical effects of the underground transportation construction pipeline relocation optimization method described in the illustrated embodiment will not be repeated here. The specific manner in which each module and unit performs operations in the underground transportation construction pipeline relocation optimization system in the above embodiment has been described in detail in the embodiment of the method and will not be elaborated on here.

[0067] In one possible design, Figure 3 The underground transportation construction pipeline relocation 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; The storage component 41 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 42 .

[0068] The processing component 42 is used for the above Figure 1The embodiment of the invention provides an optimization method for relocating underground transportation construction pipelines.

[0069] 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 method. Of course, the processing component may also 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 method.

[0070] The 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 memory 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 memory, flash memory, magnetic disk, or optical disk.

[0071] Of course, a computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0072] The input / output interface provides an interface between the processing component and the peripheral interface module, which can be an output device, an input device, etc.

[0073] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0074] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0075] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 The embodiment shown is a method for optimizing the relocation of underground transportation construction pipelines.

[0076] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0077] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0078] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion 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, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer or server) to execute the methods described in each embodiment or certain portions of the embodiments.

[0079] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for optimizing the relocation of underground transportation construction pipelines, characterized in that: include: Obtain the spatial position, cross-sectional dimensions, joint locations, and maintenance records of existing cables in the cable channel, and build a spatial layout model based on channel structural parameters; A non-contact detector is used to scan the side walls and bottom plate of the channel 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 a channel distribution map. Dividing the internal space in the channel distribution map into grids based on the spatial position, calculating a safety distance based on the cross-sectional dimensions, traversing the connection relationships of the grids using the safety distance as a constraint using a path calculator, marking high-risk areas based on the joint positions, avoiding grid nodes corresponding to the high-risk areas, and determining an initial installation path for the new cable; Extracting the fault location and repair frequency from the maintenance record, training a historical model based on a historical cable case library, binding the initial installation path to the spatial location, inputting the historical model, calculating the path risk based on the fault location and repair frequency, and outputting maintenance parameters corresponding to the initial installation path; According to the comparison result of the maintenance parameters and the preset cost threshold, the direction and node distribution of the initial installation path in the spatial position are adjusted, a target solution is generated and a corresponding cable layout diagram is output.

2. The method according to claim 1, characterized in that Extract the fault location and maintenance frequency from the maintenance record, train a historical model based on a historical cable case library, bind the initial installation path to the spatial location, input the historical model, calculate the path risk based on the fault location and maintenance frequency, and output maintenance parameters corresponding to the initial installation path, including: Extracting the coordinates of the fault location and the numerical values ​​of the maintenance frequency from the maintenance records, and training a historical prediction model based on the entire data set of the historical cable case library; Binding the initial installation path to the spatial position by coordinate points to generate a bound path data set, and inputting the bound path data set, the fault location coordinates, and the maintenance frequency value into a historical prediction model; The path risk value is calculated by combining the distribution density of the fault location coordinates and the maintenance frequency through the historical prediction model, and the path risk value is converted into a maintenance parameter value through a preset mapping function to output the maintenance parameter value of the initial installation path.

3. The method according to claim 2, characterized in that The path risk value is calculated by combining the distribution density of the fault location coordinates and the maintenance frequency through the historical prediction model, and the path risk value is converted into a maintenance parameter value through a preset mapping function, and the maintenance parameter value of the initial installation path is output, including: Using the historical prediction model, a spherical area with a fixed radius is generated with the coordinates of each fault location point as the sphere center, and the ratio of the total number of fault points in the spherical area to the volume of the spherical area is calculated as the location distribution density data; Dividing the initial installation path into continuous path segments, matching location distribution density data and maintenance weight values ​​corresponding to each path segment, and calculating a path risk value based on the location distribution density data and maintenance weight values ​​using a linear weighted formula; The path risk value is mapped to a maintenance parameter value through a piecewise linear function, and the maintenance parameter value is output in the order of path segment identifiers.

4. The method according to claim 1, wherein A non-contact detector is used to scan the side walls and floor of the channel 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 a channel distribution map, including: Scanning the sidewalls and bottom plate surfaces of the channel using a non-contact detector to generate scanning signal data, analyzing the scanning signal data to identify obstacle locations and structural boundary points of concealed structures; Measuring the three-dimensional coordinate information of the obstacle location point, calculating the size parameter value of the obstacle, extracting the contour data of the structure boundary point, and inputting the three-dimensional coordinate information, size parameter value and contour data into the spatial layout model; The parameters of the corresponding positions in the spatial layout model are adjusted, and the internal structure data of the channel is updated to generate a channel distribution map including obstacle distribution and structure boundaries.

5. The method according to claim 1, wherein Obtain the spatial location, cross-sectional dimensions, joint locations, and maintenance records of existing cables in the cable channel. Combined with channel structural parameters, a spatial layout model is constructed, including: Collect the spatial position 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 channel structural parameters, including the length range value, width dimension value, and height data value of the channel; The spatial position is combined with the channel structure parameter data to establish a three-dimensional geometric framework of the channel. The cross-sectional dimension data of the cable is set in the three-dimensional geometric framework, all joint position information is marked, and historical maintenance record data is added to obtain a constructed spatial layout model.

6. The method according to claim 1, wherein According to the comparison result of the maintenance parameter and the preset cost threshold, the direction and node distribution of the initial installation path in the spatial position are adjusted, a target solution is generated and a corresponding cable layout diagram is output, including: Comparing the maintained parameters with a preset cost threshold, and adjusting the spatial trend curve and path node distribution density of the initial installation path according to the comparison result; Generate target path plan data that meets the cost threshold based on the adjusted spatial trend curve and path node distribution density; The target path plan data is parsed into a cable centerline coordinate set and a pipe diameter parameter set, and based on the cable centerline coordinate set and the pipe diameter parameter set, 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 a construction control terminal.

7. The method according to claim 1, characterized in that Dividing the internal space in the channel distribution map into grids based on the spatial position, calculating a safety distance based on the cross-sectional size, traversing the connection relationship of the grid using the safety distance as a constraint using a path calculator, marking high-risk areas based on the joint positions, avoiding grid nodes corresponding to the high-risk areas, and determining an initial installation path for the new cable, including: Dividing the internal space of the channel distribution diagram into a set of equally spaced grid units according to the spatial position, and calculating a safety spacing value between adjacent cables based on the cross-sectional dimension data of the cables; Using the safety distance value as a movement constraint, traversing the connection relationship data between all grid cells in the set of grid cells through a path calculator, and recording a set of feasible path points that meet the safety distance value; Mark the grid nodes corresponding to the high-risk areas according to the joint position, avoid all grid nodes corresponding to the high-risk areas from the feasible path point set, calculate the continuous path point sequence of the new cable, and generate the initial installation path of the new cable.

8. An underground transportation construction pipeline relocation optimization system, characterized by: include: The acquisition module is used to obtain the spatial position, cross-sectional dimensions, joint location, and maintenance records of existing cables in the cable channel, and build a spatial layout model based on the channel structure parameters; An updating module is configured to scan the sidewalls and floor of the channel using a non-contact detector to identify obstacles and hidden structures, and dynamically update the spatial layout model based on the identified obstacle positions, size parameters, and structural boundaries to obtain a channel distribution map; a calculation module, configured to divide the internal space in the channel distribution map into a grid based on the spatial position, calculate a safety distance based on the cross-sectional dimensions, traverse the connection relationship of the grid using the safety distance as a constraint using a path calculator, mark high-risk areas based on the joint positions, avoid grid nodes corresponding to the high-risk areas, and determine an initial installation path for the new cable; An output module is configured to extract the fault location and repair frequency from the maintenance record, train a historical model based on a historical cable case library, bind the initial installation path to the spatial location, input the historical model, calculate the path risk based on the fault location and repair frequency, and output 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 in the spatial position according to the comparison result of the maintenance parameter and the preset cost threshold, generate a target solution and output a 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 used to be called and executed by the processing component to implement an underground transportation construction pipeline relocation optimization method as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that A computer program is stored, and when the computer program is executed by a computer, an underground transportation construction pipeline relocation optimization method as described in any one of claims 1 to 7 is implemented.

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