Intelligent wiring and avoidance optimization method and system for limited space pipe trench in old factory area

Abstract

The application provides an old factory limited space trench intelligent wiring and avoidance optimization method and system, relates to the technical field of facility intelligent reconstruction, and comprises the following steps: obtaining point cloud data through structured light laser scanning, extracting crack profiles through edge detection, obtaining stress hot spot distribution through elastic-plastic finite element calculation, analyzing spatial distribution of weak structure areas, calculating safe wiring intervals based on tensor field stress diffusion, and planning pipeline layout paths by using an adaptive tree search algorithm. The application can effectively avoid dangerous structure areas by combining micro trenches and intelligent wiring on the basis of the limited space of existing old trenches, for example, in the case of insufficient spacing when directly laying in the original space, improve pipeline layout safety, prolong the service life of infrastructure, and reduce maintenance costs.

Description

Technical Field

[0001] This invention relates to the field of intelligent facility renovation technology, and in particular to an intelligent wiring and avoidance optimization method and system for pipe trenches in confined spaces in old factory areas. Background Technology

[0002] With the advancement of industrialization, many industrial plants are facing aging issues. Underground utility trench systems, as crucial infrastructure, are responsible for laying power, communication, and energy pipelines. These aging trenches commonly exhibit structural damage, cracks, and water seepage during long-term use, posing significant challenges to the installation of new pipelines. Traditional trench pipeline installation relies primarily on on-site surveys and experience-based judgment by engineers, lacking scientific assessment and optimization methods, making it difficult to meet the secure wiring requirements in complex environments.

[0003] Currently, there are several problems in the field of confined space pipe trench cabling in old factory areas. Traditional pipe trench inspection methods rely on manual visual inspection, which is not only inefficient and poses safety hazards, but also makes it difficult to obtain comprehensive and accurate information on structural defects, resulting in a lack of scientific basis for cabling planning. Existing cabling methods lack in-depth analysis of the mechanical properties of pipe trench structures, cannot identify potential structural weak areas, and fail to establish an effective correlation between stress distribution and pipeline layout safety, increasing the risk of later operation. Traditional cabling schemes mostly adopt experience-based planning, which cannot achieve intelligent avoidance and path optimization for complex environments, making it difficult to maximize space utilization while ensuring safety, resulting in low cabling efficiency and insufficient reliability.

[0004] In summary, there is an urgent need for a technical method that can accurately acquire trench structure information, scientifically assess the structural safety status, and realize intelligent wiring planning, so as to improve the safety and efficiency of pipeline layout in the limited space of old factory areas. Summary of the Invention

[0005] This invention provides a method and system for intelligent wiring and obstacle avoidance optimization in confined space trenches in old factory areas, which can solve the problems in the prior art.

[0006] A first aspect of this invention provides a method for intelligent wiring and obstacle avoidance optimization in confined space trenches in old factory areas, comprising: Structured light laser scanning was used to acquire point cloud data of the pipe trench, and crack contours were extracted by edge detection to obtain a structural defect distribution map; Based on the structural defect distribution map, a stress field is constructed through nonlinear elastoplastic finite element calculation to obtain the stress hot spot distribution matrix of the pipe trench; using the stress hot spot distribution matrix, spatial distribution data of the structural weak area is obtained through stress field evolution curve analysis and stress field topological feature extraction. Based on the spatial distribution data of structurally weak areas, an iterative calculation method using tensor field stress diffusion and relaxation factor adjustment is used to obtain the stress diffusion path map under pipeline load; using the continuity index in the stress diffusion path map, the instability development area is divided to obtain safe wiring interval data. Based on the safety wiring interval data, an adaptive tree search algorithm is used for path planning, stress hotspots are used as avoidance constraints, and the pipeline spatial layout path is obtained by dynamically adjusting the search step size. Output the pipeline spatial layout path to the construction control system.

[0007] In one optional embodiment, based on the structural defect distribution map, a stress field is constructed using nonlinear elastoplastic finite element calculation to obtain the stress hotspot distribution matrix of the trench, including: By marking the crack locations and densely populated defect areas in the structural defect distribution map, a set of defect feature point coordinates is obtained; Based on the set of coordinates of the defect feature points, a non-uniform mesh partitioning result is obtained by setting quarter-node singular elements at the crack location and adding mesh nodes in the defect-dense region. Based on the non-uniform mesh division results, the initial stress and strain values ​​of the nodes are obtained by calculating the stress and strain components of each mesh node and determining the plastic state of the material. Based on the initial stress and strain values ​​of the node, the calculation step size is adjusted by detecting the strain energy value and iterative calculation is performed until the difference between two adjacent calculation results is less than the preset convergence accuracy, thereby obtaining the stress and strain distribution of the converged node. Based on the stress-strain distribution at the convergence nodes, the stress hotspot distribution matrix is ​​obtained by extracting the equivalent stress value, plastic deformation value, and damage value of each node.

[0008] In one optional embodiment, using the stress hotspot distribution matrix, and through stress field evolution curve analysis and stress field topological feature extraction, spatial distribution data of structurally weak regions are obtained, including: Based on the stress hotspot distribution matrix, the crack tip, the boundary of the dense defect area, and the region where the stress gradient is greater than a preset gradient threshold are selected as observation points to obtain the set of observation point coordinates. Based on the coordinate set of the observation points, the nodal stress time series data of the observation points during the loading process are collected, and the stress field evolution curve is obtained by interpolating and fitting the nodal stress time series data. Extract the stress rise rate, stress fluctuation amplitude, and stress residence time from the stress field evolution curve, and compare them with preset rate threshold, preset fluctuation threshold, and preset time threshold respectively to obtain the coordinates of the stress field distortion region. Based on the stress hotspot distribution matrix, equivalent stress contour lines are constructed. Based on the equivalent stress contour lines, local extreme points are extracted as singular points, and stress transmission inflection points are extracted as saddle points to obtain stress field feature point distribution data. Based on the coordinates of the stress field distortion region and the distribution data of the stress field feature points, it is determined whether each region simultaneously satisfies the following conditions: located within the stress field distortion region, containing singular points, and located on the line connecting two saddle points. Regions that meet these conditions are marked as structurally weak regions, thus obtaining the spatial distribution data of structurally weak regions.

[0009] In one optional embodiment, based on the spatial distribution data of structurally weak areas, an iterative calculation method using tensor field stress diffusion and relaxation factor adjustment is employed to obtain a stress diffusion path diagram under pipeline load, including: The spatial distribution data of the weak structural region is converted into the initial conditions of the tensor field. The stress state of discrete calculation points in the weak structural region is described by establishing a second-order stress tensor. The structural guidance factor determined by the material constitutive relation and the geometric characteristics of the structure is introduced to obtain the stress state data of the tensor field. Based on the tensor field stress state data, the diffusion tensor parameters and stress correction parameters are determined, and the tensor field stress state data is subjected to time-domain iterative processing to obtain time-domain iterative data of stress diffusion. The stress difference between two adjacent iterations is calculated, and the ratio of the stress difference to a preset stress reference threshold is determined as the stress change rate. An adjustment curve for the relaxation factor is constructed based on the stress change rate, wherein the value of the relaxation factor is positively correlated with the stress change rate. The value of the relaxation factor is determined according to the preset range of the stress change rate, and the stress distribution evolution data under pipeline load is obtained. Based on the stress distribution evolution data, the principal values ​​and principal directions of the stress tensor are calculated. At each discrete calculation point, the principal direction corresponding to the maximum principal stress is selected to determine the stress propagation direction. The corresponding vectors of the stress propagation directions of adjacent time steps are connected to form stress streamlines, thus obtaining the stress diffusion path diagram under pipeline load.

[0010] In one optional embodiment, the instability development region is delineated using a continuity index in the stress diffusion path diagram to obtain safe wiring interval data, including: The average value of the angle between the stress propagation direction vector of each discrete calculation point and its corresponding adjacent calculation point in the stress diffusion path diagram is used as the continuity index of the discrete calculation points to obtain the continuity distribution data of the calculation points. Based on the continuous distribution data of the calculation points, the combination of adjacent calculation points with a continuity index less than a preset continuous threshold is determined as the unstable development region, and the distribution data of the unstable region is obtained. Based on the distribution data of the unstable region, the minimum distance between the potential wiring path and the unstable development region is calculated. When the minimum distance is greater than the preset safety distance, the corresponding potential wiring path is determined as a safe wiring interval, and safe wiring interval data is obtained.

[0011] In one optional embodiment, based on the safety wiring interval data, an adaptive tree search algorithm is used for path planning, with stress hotspots as avoidance constraints. By dynamically adjusting the search step size, the pipeline spatial layout path is obtained, including: Based on the safe wiring interval data, the wiring interval is determined, the unstable region distribution data is extracted from the wiring interval, and the set of stress hotspot locations is determined based on the unstable region distribution data. Calculate the environmental complexity index within the wiring interval, and dynamically adjust the search step size based on the environmental complexity index to obtain the adaptive step size parameter; The set of stress hotspot locations is set as an avoidance constraint. An adaptive tree search algorithm is used for path planning. The adaptive tree search algorithm expands nodes according to the adaptive step size parameter and performs path search based on the avoidance constraint to obtain the search path. The search path is smoothed to obtain the pipeline spatial layout path.

[0012] In one optional embodiment, the adaptive tree search algorithm includes: Calculate the safe distance from each current node to the nearest unstable region in the wiring interval, obtain the variance of the stress gradient direction around the current node, and determine the environmental complexity index. The search step size is dynamically adjusted based on the environmental complexity index. When the environmental complexity index is greater than the preset complexity threshold, the search step size is adjusted to a preset multiple of the current search step size. When the environmental complexity index is less than or equal to the complexity threshold, the search step size is adjusted to a preset proportion of the current search step size. The adjustment range of the search step size is between the preset minimum step size and the preset maximum step size. Determine the starting point and the key point of the routing, set the starting point as the root node of the search tree, randomly sample to obtain the target node, determine the nearest neighbor node in the search tree that is closest to the target node, and expand from the nearest neighbor node to the target node according to the search step size to obtain the extended node; Calculate the avoidance distance between the extended node and each stress hotspot in the set of stress hotspot locations, obtain the minimum avoidance distance between the extended node and the set of stress hotspot locations, and when the minimum avoidance distance is greater than a preset safety margin, add the extended node to the search tree and record the parent node index of the extended node; Calculate the endpoint deviation between the extended node and the routing endpoint. Stop the search when the endpoint deviation is less than a preset endpoint threshold or when the maximum number of iterations is reached. Obtain the search path by connecting the current node to the root node level by level according to the parent node index.

[0013] A second aspect of the present invention provides an intelligent wiring and obstacle avoidance optimization system for confined space trenches in old factory areas, comprising: The first unit is used to acquire point cloud data of the trench using structured light laser scanning, extract crack contours through edge detection, and obtain a structural defect distribution map; The second unit is used to construct the stress field based on the structural defect distribution map through nonlinear elastoplastic finite element calculation, and obtain the stress hot spot distribution matrix of the pipe trench; using the stress hot spot distribution matrix, the spatial distribution data of the structural weak area is obtained through stress field evolution curve analysis and stress field topological feature extraction. The third unit is used to obtain the stress diffusion path map under pipeline load by using iterative calculation methods based on the spatial distribution data of structurally weak areas and the stress diffusion of tensor fields and relaxation factor adjustment; and to divide the instability development area by using the continuity index in the stress diffusion path map to obtain safe wiring interval data. The fourth unit is used to perform path planning based on the safety wiring interval data using an adaptive tree search algorithm. Stress hotspots are used as avoidance constraints, and the pipeline spatial layout path is obtained by dynamically adjusting the search step size. The fifth unit is used to output the pipeline spatial layout path to the construction control system.

[0014] A third aspect of the present invention provides an electronic device, comprising: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0015] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0016] In this embodiment of the invention, a defect distribution map of the trench structure is obtained through structured light laser scanning and edge detection technology. Combined with non-uniform mesh generation and elastoplastic finite element analysis, weak areas of the structure are analyzed, enabling accurate assessment of the internal structural state of the trench in the limited space of an old factory area. This provides a reliable basis for structural safety in cabling planning. An iterative calculation method based on tensor field stress diffusion and relaxation factor adjustment, combined with a stress hotspot distribution matrix, scientifically divides the instability development region and safe cabling intervals. This avoids potential safety hazards caused by traditional cabling methods neglecting the dynamic response characteristics of the structure, improving the long-term stability and reliability of the cabling system. Path planning is based on an adaptive tree search algorithm, using stress hotspots as avoidance constraints. By dynamically adjusting the search step size, intelligent and optimized pipeline spatial layout is achieved, reducing construction difficulty, lowering cabling costs, extending pipeline service life, and improving the utilization efficiency of trench space in old factory areas. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the intelligent wiring and obstacle avoidance optimization method for confined space trenches in old factory areas, as described in an embodiment of the present invention. Figure 2 This is a curve showing the relationship between the rate of change of stress and the relaxation factor. Figure 3 This is a simulation diagram of the wiring algorithm. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0020] Figure 1 This is a flowchart illustrating the intelligent wiring and obstacle avoidance optimization method for confined space trenches in old factory areas, as described in an embodiment of the present invention. Figure 1 As shown, the method includes: Structured light laser scanning was used to acquire point cloud data of the pipe trench, and crack contours were extracted by edge detection to obtain a structural defect distribution map; Based on the structural defect distribution map, a stress field is constructed through nonlinear elastoplastic finite element calculation to obtain the stress hot spot distribution matrix of the pipe trench; using the stress hot spot distribution matrix, spatial distribution data of the structural weak area is obtained through stress field evolution curve analysis and stress field topological feature extraction. Based on the spatial distribution data of structurally weak areas, an iterative calculation method using tensor field stress diffusion and relaxation factor adjustment is used to obtain the stress diffusion path map under pipeline load; using the continuity index in the stress diffusion path map, the instability development area is divided to obtain safe wiring interval data. Based on the safety wiring interval data, an adaptive tree search algorithm is used for path planning, stress hotspots are used as avoidance constraints, and the pipeline spatial layout path is obtained by dynamically adjusting the search step size. Output the pipeline spatial layout path to the construction control system.

[0021] In one optional implementation, based on the structural defect distribution map, a stress field is constructed using nonlinear elastoplastic finite element calculation to obtain the stress hotspot distribution matrix of the trench, including: By marking the crack locations and densely populated defect areas in the structural defect distribution map, a set of defect feature point coordinates is obtained; Based on the set of coordinates of the defect feature points, a non-uniform mesh partitioning result is obtained by setting quarter-node singular elements at the crack location and adding mesh nodes in the defect-dense region. Based on the non-uniform mesh division results, the initial stress and strain values ​​of the nodes are obtained by calculating the stress and strain components of each mesh node and determining the plastic state of the material. Based on the initial stress and strain values ​​of the node, the calculation step size is adjusted by detecting the strain energy value and iterative calculation is performed until the difference between two adjacent calculation results is less than the preset convergence accuracy, thereby obtaining the stress and strain distribution of the converged node. Based on the stress-strain distribution at the convergence nodes, the stress hotspot distribution matrix is ​​obtained by extracting the equivalent stress value, plastic deformation value, and damage value of each node.

[0022] In one specific implementation, a defect distribution map of the pipe trench structure is obtained by acquiring images of the inner wall of the pipe using a high-definition camera or laser scanner. The acquired images are preprocessed, including grayscale conversion, filtering, and binarization, to highlight structural defect features. Defect boundaries are extracted using an image edge detection algorithm (such as the Canny algorithm), forming a two-dimensional structural defect distribution map. In this distribution map, defective areas are represented by black pixels, and intact areas are represented by white pixels.

[0023] Feature points are marked on the structural defect distribution map. A pixel traversal method is used to detect each pixel in the defect distribution map. When a continuous black pixel region with a length exceeding 5mm is detected, it is determined to be a crack region; when a black pixel density of less than 10cm² is detected, it is considered a crack region. 2 When the defect coverage exceeds 40%, it is considered a densely defected area. For cracked areas, a feature point is marked every 2 mm along the crack centerline; for densely defected areas, feature points are marked at the center and boundary of the area. Each feature point is represented by two-dimensional coordinates (x, y), and all feature points form the defect feature point coordinate set {(x1, y1), (x2, y2), ..., (x...y1)}. n y n In one real-world case, 54 crack feature points and 126 defect-dense area feature points were detected in a certain pipeline section.

[0024] Non-uniform mesh generation was performed based on the coordinate set of defect feature points. Quadrilateral meshes were used as the basic mesh type, with a conventional mesh size of 10mm × 10mm used in defect-free areas. For crack locations, quarter-node singular elements were set within a circular region with a radius of 5mm, centered on the crack tip. These elements are characterized by offsetting the nodes around the crack tip by 1 / 4 of the mesh distance from their original positions towards the tip, forming a mesh structure with specific singularities. For areas with dense defects, the original 10mm × 10mm mesh was subdivided into smaller 2mm × 2mm meshes to increase local mesh density. In a typical case, the original total mesh size was 5000 elements; after non-uniform generation, the total mesh size increased to 12630 elements, with 270 quarter-node singular elements placed around the crack.

[0025] The initial stress and strain values ​​of each node were calculated based on the non-uniform mesh generation results. A matrix relating displacement and strain at the mesh nodes was established, and the pipe material parameters were set, including an elastic modulus E = 210 GPa, Poisson's ratio μ = 0.3, and a yield strength σs = 345 MPa. Boundary conditions were set according to actual working conditions, such as fixing one end of the pipe and applying an axial pressure of 10 MPa and an internal pressure of 5 MPa at the other end. The displacement field of each node was solved using finite element analysis software, and then the stress components (σx, σy, σxy) and strain components (εx, εy, εxy) of each node were calculated. It was determined whether the material had entered a plastic state. When the equivalent stress at a node exceeded the yield strength, an isotropic hardening model was used to correct the stress-strain relationship. For example, in the initial calculation, 237 nodes entered a plastic state, mainly distributed around the crack tip.

[0026] Iterative calculations are performed to obtain a convergent stress-strain distribution. A convergence precision of 0.1% is set, meaning convergence is considered achieved when the maximum difference between two consecutive calculation results is less than 0.1%. After each calculation, the system strain energy value is checked. When the strain energy change rate exceeds 5%, the calculation step size is halved to ensure calculation stability; when the change rate is less than 1%, the calculation step size is appropriately increased to improve calculation efficiency. The initial step size is set to 0.1, and the minimum step size is not less than 0.01. During the iteration process, the plastic state and stress-strain values ​​of the nodes are continuously updated until the convergence condition is met. For example, after 28 iterations, convergence is achieved, and ultimately 382 nodes enter the plastic deformation state.

[0027] Based on the convergent nodal stress-strain distribution, stress hotspot distribution information is extracted. The equivalent stress value σe of each node is calculated using the Von Mises criterion: σe is a specific combination of stress components, considering the combined effect of stress in all directions. The plastic deformation value εp of each node is calculated, i.e., the cumulative amount of plastic strain. The damage value D of each node is calculated based on the Lemaitre damage model, considering the effects of plastic deformation and stress triaxiality. These three parameters are combined to form a stress hotspot feature vector (σe, εp, D), and the feature vectors of all nodes constitute the stress hotspot distribution matrix M. When σe exceeds 0.9 times the material yield strength, or εp exceeds 0.02, or D exceeds 0.3, the node is marked as a stress hotspot. In a practical case, 47 stress hotspots were identified, mainly concentrated in three regions: 15 around the crack tip, 18 in two defect-dense areas, and 14 in two other areas.

[0028] By analyzing the stress hotspot distribution matrix, weak points and potential failure areas in the pipe trench structure can be identified, providing a scientific basis for subsequent maintenance and reinforcement plans. Verification has shown that the stress hotspot distribution obtained using this method matches the actual pipe failure locations with over 90% accuracy, improving prediction accuracy by 25% compared to traditional methods.

[0029] In one optional implementation, using the stress hotspot distribution matrix, and through stress field evolution curve analysis and stress field topological feature extraction, spatial distribution data of structurally weak regions are obtained, including: Based on the stress hotspot distribution matrix, the crack tip, the boundary of the dense defect area, and the region where the stress gradient is greater than a preset gradient threshold are selected as observation points to obtain the set of observation point coordinates. Based on the coordinate set of the observation points, the nodal stress time series data of the observation points during the loading process are collected, and the stress field evolution curve is obtained by interpolating and fitting the nodal stress time series data. Extract the stress rise rate, stress fluctuation amplitude, and stress residence time from the stress field evolution curve, and compare them with preset rate threshold, preset fluctuation threshold, and preset time threshold respectively to obtain the coordinates of the stress field distortion region. Based on the stress hotspot distribution matrix, equivalent stress contour lines are constructed. Based on the equivalent stress contour lines, local extreme points are extracted as singular points, and stress transmission inflection points are extracted as saddle points to obtain stress field feature point distribution data. Based on the coordinates of the stress field distortion region and the distribution data of the stress field feature points, it is determined whether each region simultaneously satisfies the following conditions: located within the stress field distortion region, containing singular points, and located on the line connecting two saddle points. Regions that meet these conditions are marked as structurally weak regions, thus obtaining the spatial distribution data of structurally weak regions.

[0030] In one specific implementation, the stress hotspot distribution matrix is ​​a two-dimensional data set characterizing the stress distribution in various regions of the structure, which can be obtained through finite element analysis. In the environment of old factory trenches, the resolution of the stress hotspot distribution matrix is ​​usually set to 10mm×10mm to ensure that local stress changes in the trench structure can be captured. Based on the obtained stress hotspot distribution matrix, three types of regions need to be selected as observation points: crack tips, boundaries of defect-dense areas, and areas where the stress gradient is greater than a preset gradient threshold. Crack tips can be identified by stress concentration points in the stress hotspot matrix, which are points where the local stress value suddenly increases. In old trenches, the stress value at the crack tip can typically reach 3-5 times the matrix stress. Boundaries of defect-dense areas are transitional regions where the stress value decreases from high to low, usually at the joints of the concrete structure of old trenches or at the edges of severely corroded areas. Areas where the stress gradient is greater than the preset gradient threshold refer to areas where the rate of stress change between adjacent grid points exceeds a set value. For old trench structures, the preset gradient threshold is usually set to 12MPa / mm. By selecting these three types of regions, a series of key observation points can be obtained, forming a set of observation point coordinates. In practical applications, a typical trench structure section may select 30-50 observation points, and the coordinates of these points are stored in the form of (x, y, z) in millimeters.

[0031] Based on the acquired set of observation point coordinates, time-series data of nodal stress at the observation points during the loading process were collected. The loading process simulates various loads that the trench may experience during actual use, including static and dynamic loads. Static loads include the pipe's self-weight, the weight of the medium, and the soil pressure, while dynamic loads include vehicle loads and vibrations. The loading process is divided into multiple stages, each corresponding to a different load level, gradually increasing from 0 to 1.5 times the design load to evaluate the trench structure's response under overload conditions. The stress value of each observation point was recorded at each loading stage, forming a time-stress data pair. In older trench structures, the typical loading time is 2 hours, divided into 120 time steps, with the stress value recorded once at each time step. Interpolation fitting was performed on the collected nodal stress time-series data to obtain the stress field evolution curve. The interpolation fitting used a cubic spline interpolation method to ensure that the first and second derivatives of the fitted curve are continuous at each observation point, thus obtaining a smooth stress field evolution curve. For each observation point in the trench structure, a corresponding stress field evolution curve is generated, reflecting the stress change at that point throughout the entire loading process.

[0032] Three key parameters were extracted from the stress field evolution curve: stress rise rate, stress fluctuation amplitude, and stress residence time. The stress rise rate refers to the increase in stress per unit time, calculated as the stress difference between adjacent time steps divided by the time interval, in MPa / s. In aging trench structures, the stress rise rate in normal areas is typically in the range of 0.05-0.2 MPa / s. The stress fluctuation amplitude is the ratio of the peak-to-valley difference of local fluctuations to the average stress, expressed as a percentage; the fluctuation amplitude in normal areas typically does not exceed 10%. The stress residence time is the ratio of the length of time the stress value remains constant within a specific range to the total loading time, also expressed as a percentage; the residence time in normal areas typically does not exceed 15%. These three extracted parameters were compared with preset thresholds: the preset rate threshold was set to 0.3 MPa / s, the preset fluctuation threshold to 15%, and the preset time threshold to 20%. When the parameters of a certain area exceed the corresponding threshold, that area is marked as a stress field distortion area. The coordinates of the stress field distortion area are recorded in the form of (x, y, z), forming a stress field distortion area coordinate set.

[0033] Based on the stress hotspot distribution matrix, equivalent stress contour lines are constructed. These contour lines connect points with equal stress values, forming closed curves that visually reflect the stress distribution. The contour line interval is set to 8% of the base stress to ensure that details of stress changes are captured without generating excessive redundant information. For older pipe trench structures, typically 10-15 equivalent stress contour lines are generated, covering the entire range from minimum to maximum stress. Based on the constructed equivalent stress contour lines, local extreme points are extracted as singularities. Singularities include local maximum and local minimum points, corresponding to the stress peaks and valleys in the stress field. In pipe trench structures, singularities typically appear at pipe support points, corners, valve areas, etc. Simultaneously, stress transmission inflection points are extracted as saddle points. Saddle points are special points in the stress field, exhibiting a maximum value in one direction and a minimum value in the vertical direction. They typically appear at inflection points in stress transmission paths, such as pipe intersections and changes in support structures. Through these extractions, stress field feature point distribution data are obtained, including the spatial coordinates of singular points and saddle points and their corresponding stress values.

[0034] Based on the coordinates of the stress field distortion region and the distribution data of stress field feature points, it is determined whether each region simultaneously meets three conditions: it is located within the stress field distortion region, contains a singular point, and lies on the line connecting two saddle points. For the first condition, it is directly checked whether the region coordinates are within the coordinate set of the stress field distortion region; for the second condition, it is checked whether the region contains at least one singular point; for the third condition, the distance from the center point of the region to the line connecting any two saddle points is calculated. If the distance is less than a preset threshold (usually set to 20% of the region size), the region is considered to be located on the line connecting the two saddle points. For regions that meet all three conditions, they are marked as structurally weak regions, and their spatial coordinate range is recorded to form spatial distribution data of structurally weak regions.

[0035] In a case study of a pipe trench in an old factory area, a stress hotspot distribution matrix with a resolution of 10mm×10mm was obtained for a 100m×2m×1.5m trench section. Eighteen crack tips, 23 boundary points of densely defected areas, and 35 areas with stress gradients greater than 12MPa / mm were selected as observation points, totaling 76 observation points. During a 2-hour simulated loading process, stress values ​​were collected at each observation point over 120 time steps. 76 stress field evolution curves were obtained through cubic spline interpolation. Analysis of these curves revealed that 12 areas had stress rise rates exceeding 0.3MPa / s, 15 areas had stress fluctuation amplitudes exceeding 15%, and 8 areas had stress residence times exceeding 20%, thus identifying 22 stress field distortion areas. Through equivalent stress contour analysis, 34 singular points and 27 saddle points were extracted. Finally, 9 areas that simultaneously met all three conditions were identified and marked as structurally weak areas. These vulnerable areas are mainly distributed at pipe support points, corners, and old joints, providing clear guidance for intelligent wiring and avoidance optimization in pipe trenches. New pipelines can be laid out in these areas, or these areas can be reinforced in a targeted manner, thereby improving the overall safety and reliability of the pipe trench system.

[0036] Table 1 shows a performance comparison of methods for identifying weak areas in pipe trench structures:

[0037] Table 1 clearly shows that this technical solution has achieved significant advantages in all key indicators. In terms of recognition accuracy, this solution reaches 91.8%, far exceeding the 67.5% of the traditional stress threshold method, the 78.3% of the improved FCM clustering method, and the 83.6% of the deep learning-based recognition method. Regarding the false negative rate, this solution is only 6.2%, significantly lower than the 21.4%, 15.7%, and 12.3% of the other three methods. Particularly noteworthy is the outstanding performance of this solution in terms of early warning lead time, reaching 78 days. This means it can predict potential structural weaknesses nearly three months in advance, providing ample preparation time for maintenance and preventative measures, far exceeding the 23 days, 45 days, and 52 days of other methods. Simultaneously, this solution maintains a moderate level of computational resource consumption, with a computational efficiency of 92 s / km, superior to the improved FCM clustering method and the deep learning-based recognition method, and only slightly lower than the traditional stress threshold method with limited computational power, demonstrating the optimized balance of the algorithm.

[0038] In one optional implementation, based on the spatial distribution data of structurally weak areas, an iterative calculation method using tensor field stress diffusion and relaxation factor adjustment is employed to obtain a stress diffusion path diagram under pipeline load, including: The spatial distribution data of the weak structural region is converted into the initial conditions of the tensor field. The stress state of discrete calculation points in the weak structural region is described by establishing a second-order stress tensor. The structural guidance factor determined by the material constitutive relation and the geometric characteristics of the structure is introduced to obtain the stress state data of the tensor field. Based on the tensor field stress state data, the diffusion tensor parameters and stress correction parameters are determined, and the tensor field stress state data is subjected to time-domain iterative processing to obtain time-domain iterative data of stress diffusion. The stress difference between two adjacent iterations is calculated, and the ratio of the stress difference to a preset stress reference threshold is determined as the stress change rate. An adjustment curve for the relaxation factor is constructed based on the stress change rate, wherein the value of the relaxation factor is positively correlated with the stress change rate. The value of the relaxation factor is determined according to the preset range of the stress change rate, and the stress distribution evolution data under pipeline load is obtained. Based on the stress distribution evolution data, the principal values ​​and principal directions of the stress tensor are calculated. At each discrete calculation point, the principal direction corresponding to the maximum principal stress is selected to determine the stress propagation direction. The corresponding vectors of the stress propagation directions of adjacent time steps are connected to form stress streamlines, thus obtaining the stress diffusion path diagram under pipeline load.

[0039] In one specific implementation, spatial distribution data of structurally weak areas is acquired, which can be obtained through non-destructive testing or structural analysis. For a steel pipeline with a diameter of 500 mm and a wall thickness of 20 mm, spatial distribution data of structurally weak areas was acquired through ultrasonic testing, including the location coordinates and corrosion depth of multiple corrosion points. For example, 15 obvious corrosion points were detected within a 100-meter range along the pipeline's length, with a maximum depth of 8 mm and a minimum depth of 2 mm.

[0040] The spatial distribution data of these structurally weak areas are converted into initial conditions for a tensor field. Specifically, the detection area is divided into a 1000×100×36 grid, with each grid point representing a discrete calculation point along the pipeline length, radial direction, and circumferential direction. For each discrete calculation point, a second-order stress tensor is established to describe its stress state. For example, for a corrosion point located at coordinates (50 m, 15 mm, 90°) with a depth of 5 mm, the principal values ​​of its initial stress tensor can be set to values ​​proportional to the corrosion depth, such as 500 MPa, 300 MPa, and 100 MPa.

[0041] A structural orientation factor is introduced to adjust the stress tensor. This structural orientation factor is determined based on the material's elastic modulus, Poisson's ratio, and the pipeline's geometric parameters. For the steel pipeline in this example, the elastic modulus is 210 GPa and the Poisson's ratio is 0.3. Combining the pipeline's curvature and wall thickness distribution, the structural orientation factor matrix is ​​calculated. The initial stress tensor is then multiplied with the structural orientation factor to obtain the corrected tensor field stress state data.

[0042] The diffusion tensor parameters are determined based on the corrected tensor field stress state data. The diffusion tensor parameters reflect the stress propagation characteristics in different directions. In this embodiment, the diffusion tensor parameters are related to the anisotropy of the material; for the pipeline structure, the diffusion coefficient is set to 0.8 along the pipeline axis, 0.5 radially, and 0.6 circumferentially. Furthermore, stress correction parameters are determined to control the stress adjustment amplitude during the iteration process, with an initial value set to 0.3.

[0043] The stress state data of the tensor field is processed using time-domain iterative processing. In each iteration, the stress change at each discrete point is calculated, and the stress state is updated accordingly. The iteration step size is set to 0.01 seconds, and a total of 500 iterations are performed. For example, for the first iteration, the stress diffusion at each discrete point is calculated, and then the stress tensor is updated to obtain the stress distribution at t=0.01 seconds.

[0044] Calculate the stress difference between two adjacent iterations. Taking the 100th and 101st iterations as an example, calculate the difference in stress tensors at each point after the two iterations, and then determine the stress change rate by the ratio of these differences to a preset stress reference threshold (set to 10 MPa). During implementation, it was observed that the stress change rate in most areas was between 0.01 and 0.1, while in stress concentration areas, the change rate could reach over 0.5.

[0045] An adjustment curve for the relaxation factor is constructed based on the stress change rate. The relaxation factor is used to dynamically adjust the stress update amplitude during the iteration process. In this embodiment, the relaxation factor is set to 0.2 when the stress change rate is less than 0.05; 0.5 when the stress change rate is between 0.05 and 0.3; and 0.8 when the stress change rate is greater than 0.3. This dynamic adjustment mechanism makes the iteration process more stable in regions of rapid stress change, avoiding numerical divergence.

[0046] After 500 iterations, stable stress distribution evolution data were obtained. Based on this data, the principal values ​​and principal directions of the stress tensor at each discrete point were calculated. For each point, the principal direction corresponding to the maximum principal stress was selected as the stress propagation direction. For example, at a calculation point at coordinates (55 m, 10 mm, 180°), the maximum principal stress is 350 MPa, and the corresponding principal direction is a unit vector pointing to coordinates (55.2 m, 10.5 mm, 185°).

[0047] By connecting the vectors corresponding to the stress propagation directions of adjacent time steps, stress streamlines are formed. Specifically, starting from each initial corrosion point, the stress propagation path is traced along its respective direction of maximum principal stress at a step size of 0.01 seconds until a complete stress streamline is obtained after 500 time steps. These stress streamlines constitute a stress diffusion path diagram under pipeline load.

[0048] In practical applications, this stress diffusion path diagram can visually display the stress distribution and propagation in pipeline structures. This diagram can identify stress concentration areas and potential failure paths. For example, in this embodiment, it was found that stress flow lines originating from a corrosion point with a depth of 8 mm exhibit a clear concentration trend, with multiple flow lines converging at a specific area of ​​the pipeline. This area may become the starting point of structural failure and requires focused attention and reinforcement.

[0049] like Figure 2The figure shows the relationship curves between the stress change rate and the relaxation factor in different calculation methods. The horizontal axis represents the stress change rate (0.05-1.00), and the vertical axis represents the corresponding relaxation factor value (0.2-1.0). The blue line in the figure represents the adaptive relaxation factor adjustment strategy adopted by this technical solution, the red dashed line represents the fixed relaxation factor strategy of the traditional SOR method, and the green line represents the traditional linear relaxation method. The data shows that the relaxation factor of this technical solution increases non-linearly with the stress change rate, forming a unique S-shaped curve. When the stress change rate is 0.25, the relaxation factor is 0.45; when the stress change rate increases to 0.75, the relaxation factor reaches 0.81; and in the middle region of the stress change rate of 0.50, the relaxation factor is 0.62, showing good adaptability to moderate stress changes. In contrast, the traditional SOR method uses a fixed value of 0.50 and cannot dynamically adjust according to stress changes, while the linear relaxation method, although it also increases linearly with the stress change rate, lacks targeted processing for different stress change ranges. This technical solution achieves fine-grained control over different stress variation ranges by constructing a nonlinear adjustment curve for the relaxation factor. In particular, it provides a larger relaxation factor (0.75-0.85) in the region of rapid stress change (0.65-0.85) to accelerate convergence, while a smaller relaxation factor (0.30-0.45) is used in the region of gentle stress change (0.05-0.25) to ensure stability. This comprehensively improves the efficiency and stability of stress diffusion calculation, and increases the calculation accuracy by approximately 42%.

[0050] In one alternative implementation, the instability development region is delineated using a continuity index in the stress diffusion path diagram to obtain safe wiring interval data, including: The average value of the angle between the stress propagation direction vector of each discrete calculation point and its corresponding adjacent calculation point in the stress diffusion path diagram is used as the continuity index of the discrete calculation points to obtain the continuity distribution data of the calculation points. Based on the continuous distribution data of the calculation points, the combination of adjacent calculation points with a continuity index less than a preset continuous threshold is determined as the unstable development region, and the distribution data of the unstable region is obtained. Based on the distribution data of the unstable region, the minimum distance between the potential wiring path and the unstable development region is calculated. When the minimum distance is greater than the preset safety distance, the corresponding potential wiring path is determined as a safe wiring interval, and safe wiring interval data is obtained.

[0051] In one specific implementation, a stress diffusion path diagram of the structure to be analyzed is obtained. This stress diffusion path diagram can be obtained through finite element analysis or experimental measurement, and contains information on the stress distribution and propagation direction at each point in the structure. The stress diffusion path diagram is discretized into multiple calculation points, each with a specific spatial location and stress direction vector.

[0052] For each discrete calculation point in the stress propagation path diagram, determine its set of neighboring calculation points. The determination of neighboring calculation points can be based on a spatial distance threshold, for example, setting the threshold to 5 mm. When the Euclidean distance between two calculation points is less than this threshold, they are considered neighboring points. For a specific calculation point P, assuming its set of neighboring points includes calculation points P1, P2, P3, and P4, calculate the angle between the stress propagation direction vector of calculation point P and the stress propagation direction vectors of its neighboring calculation points P1, P2, P3, and P4, respectively. For example, the stress propagation direction vector at calculation point P is (0.7, 0.7), the stress propagation direction vector at the adjacent calculation point P1 is (0.8, 0.6), and the angle between them is approximately 8.13 degrees; the stress propagation direction vector at the adjacent calculation point P2 is (0.6, 0.8), and the angle between them is approximately 14.04 degrees; the stress propagation direction vector at the adjacent calculation point P3 is (0.5, 0.9), and the angle between them is approximately 23.96 degrees; the stress propagation direction vector at the adjacent calculation point P4 is (0.9, 0.4), and the angle between them is approximately 21.80 degrees.

[0053] Calculate the average angle between the stress propagation direction vectors of calculation point P and all its adjacent calculation points, which is (8.13 + 14.04 + 23.96 + 21.80) / 4 = 16.98 degrees. Use this average value as the continuity index of calculation point P, representing the stability of the stress propagation direction at that point. Repeat the above process to calculate the continuity index of all discrete calculation points in the stress diffusion path diagram, obtaining the continuity distribution data of the calculation points.

[0054] For a stress diffusion path map containing 1000 discrete calculation points, a complete continuity distribution data table of calculation points is generated by traversing and calculating the continuity index of each point. This data table contains the coordinate information and corresponding continuity index value of each calculation point.

[0055] Based on the continuity distribution data of the calculated points, the instability development region in the structure is determined. A continuity threshold of 25 degrees is set. When the continuity index of a calculated point is less than this threshold, it indicates that the stress propagation direction near that point changes little, and the stress field is relatively stable. When the continuity index of a calculated point is greater than this threshold, it indicates that the stress propagation direction near that point changes significantly, and the stress field is relatively unstable. Adjacent calculated points with continuity indices greater than the preset continuity threshold are grouped together and identified as instability development regions.

[0056] For example, for adjacent calculation points A (30 degrees), B (35 degrees), C (28 degrees), and D (32 degrees), since their continuity indices are all greater than the preset threshold of 25 degrees and they are adjacent points, they are grouped together and identified as an instability development region. This process is repeated to identify all instability development regions in the stress diffusion path diagram, thus obtaining instability region distribution data.

[0057] In one instance, analysis of a stress diffusion path diagram containing 5000 calculation points identified 15 distinct instability development regions, labeled R1 to R15. Each region contained a different number of calculation points and varied in area. For example, region R1 contained 42 calculation points and had an area of ​​approximately 78 square millimeters; region R2 contained 29 calculation points and had an area of ​​approximately 53 square millimeters. The boundary coordinates, area size, and number of calculation points for these instability regions were recorded in an instability region distribution data table.

[0058] Based on the distribution data of unstable regions, the minimum distance between potential cabling paths and unstable development regions is calculated. Potential cabling paths can be straight lines or curves, designed according to actual requirements. For each potential cabling path, the minimum distance between it and all unstable development regions is calculated. For example, the minimum distance between potential cabling path L1 and unstable region R1 is 12 mm, the minimum distance to unstable region R2 is 8 mm, the minimum distance to unstable region R3 is 15 mm, and so on. The minimum value among all distances is taken as the minimum distance between the cabling path and the unstable region; that is, the minimum distance between L1 and the unstable region is 8 mm.

[0059] A preset safety distance of 10 mm is set. When the minimum distance between a potential wiring path and all unstable development areas is greater than this safety distance, the corresponding potential wiring path is identified as a safe wiring zone. In the example above, since the minimum distance of wiring path L1 from the unstable area (8 mm) is less than the preset safety distance of 10 mm, L1 does not belong to the safe wiring zone. However, if the minimum distance between another wiring path L2 and all unstable areas is 12 mm, which is greater than the preset safety distance of 10 mm, then L2 belongs to the safe wiring zone.

[0060] By evaluating multiple potential cabling paths, a set of cabling intervals that meet security requirements is ultimately determined, generating secure cabling interval data. In one example, 50 potential cabling paths were evaluated, and 28 secure cabling paths were identified, forming a complete secure cabling interval data table containing the start and end coordinates of each secure cabling path, as well as the coordinates of key points along the path.

[0061] In one optional implementation, based on the safety wiring interval data, an adaptive tree search algorithm is used for path planning, with stress hotspots as avoidance constraints. By dynamically adjusting the search step size, the pipeline spatial layout path is obtained, including: Based on the safe wiring interval data, the wiring interval is determined, the unstable region distribution data is extracted from the wiring interval, and the set of stress hotspot locations is determined based on the unstable region distribution data. Calculate the environmental complexity index within the wiring interval, and dynamically adjust the search step size based on the environmental complexity index to obtain the adaptive step size parameter; The set of stress hotspot locations is set as an avoidance constraint. An adaptive tree search algorithm is used for path planning. The adaptive tree search algorithm expands nodes according to the adaptive step size parameter and performs path search based on the avoidance constraint to obtain the search path. The search path is smoothed to obtain the pipeline spatial layout path.

[0062] In one specific implementation, the wiring interval is determined based on safety wiring interval data. The wiring interval can be represented as a geometric region in three-dimensional space, its boundary consisting of multiple coordinate points. For example, in an industrial scenario, the wiring interval can be defined as a cubic space with a length of 100 meters, a width of 50 meters, and a height of 30 meters. From this wiring interval, instability region distribution data is extracted through structural stress analysis. The instability region distribution data includes the spatial coordinates of each instability point and its instability degree value. For example, there may be 20 instability points within the wiring interval, each represented by its three-dimensional coordinates (x, y, z) and instability degree value p. By analyzing the instability region distribution data, a set of stress hotspot locations is determined. The set of stress hotspot locations can be represented as {H1, H2, ..., H...}. n}, where each hotspot H i From the center coordinates (x i y i , z i It consists of the radius of influence rᵢ. In practical applications, if the instability values ​​of 10 instability points exceed the preset threshold of 0.75, these 10 points will be identified as stress hotspots.

[0063] Calculate the environmental complexity index within the cabling section. The environmental complexity index C can be calculated by comprehensively considering obstacle density, path tortuosity, and stress hotspot distribution. In the specific implementation, the cabling section is divided into multiple grid cells, and the proportion of obstacles and the number of stress hotspots in each grid are statistically analyzed. Combined with the path turning frequency, the environmental complexity index is calculated. For example, if the cabling section is divided into 1000 grid cells, of which 300 contain obstacles, 50 are covered by stress hotspots, and the average path turning frequency is 0.2 times / meter, then the environmental complexity index C = 0.42 can be calculated. The search step size is dynamically adjusted based on the environmental complexity index C. When the C value is large, it indicates a complex environment, and a smaller step size is used; when the C value is small, it indicates a simple environment, and a larger step size is used. The adaptive step size parameter S can be expressed as a function of the basic step size and the environmental complexity. In practical applications, if the basic step size is set to 2 meters, when the environmental complexity C = 0.42, the adaptive step size S = 1.2 meters can be obtained through the mapping relationship.

[0064] The set of stress hotspot locations was then set as an avoidance constraint, and an adaptive tree search algorithm was used for path planning. The core of the adaptive tree search algorithm is to expand nodes from the starting point according to an adaptive step size S, avoiding stress hotspot regions during the expansion process. Specifically, starting from the starting point P0(x0, y0, z0), nodes are expanded in all feasible directions. For each node P(x, y, z) to be expanded, it is checked whether it meets the constraints: the node is not inside an obstacle; the distance between the node and any stress hotspot Hᵢ is greater than the influence radius r. i The node is located within the wiring interval. If the constraints are met, the node is added to the search tree. When expanding a node, the expansion distance is an adaptive step size S. For example, when the adaptive step size S = 1.2 meters, a new node P'(11.2, 15, 5) is obtained by expanding forward from the current node P(10, 15, 5). During the search, a heuristic function is used to evaluate node priority, prioritizing the expansion of nodes with higher scores. The heuristic function considers the distance from the node to the target, path length, and stress hotspot avoidance. By continuously expanding nodes and checking constraints, a search path from the starting point to the end point is finally found.

[0065] Finally, the search path is smoothed to obtain the pipeline spatial layout path. The smoothing process includes two steps: curve fitting and local optimization. In the curve fitting step, cubic spline interpolation is used to connect the key points of the search path to generate a smooth curve. For example, for the searched path point set {(0, 0, 0), (1.2, 0.5, 0), (2.4, 1.2, 0.3), (3.6, 1.8, 0.7), (4.8, 2.0, 1.0), (6.0, 2.5, 1.2)}, a continuous and smooth curve is obtained through cubic spline interpolation. In the local optimization step, high curvature regions of the path are detected, and these regions are locally adjusted to ensure that the curvature change of the path is gradual, facilitating actual pipeline layout. Specifically, if the curvature at the point (3.6, 1.8, 0.7) exceeds the threshold of 0.5, local adjustments are made to that point and its adjacent points to make the curvature change gradually. After smoothing, the final pipeline spatial layout path has good continuity and feasibility.

[0066] In this embodiment, a pipeline spatial layout path planning based on an adaptive tree search algorithm is implemented, effectively avoiding stress hotspot areas and ensuring the safety and reliability of pipeline layout. This method can adaptively adjust the search step size according to environmental complexity in complex environments, improving the efficiency and accuracy of path planning.

[0067] In one optional implementation, the adaptive tree search algorithm includes: Calculate the safe distance from each current node to the nearest unstable region in the wiring interval, obtain the variance of the stress gradient direction around the current node, and determine the environmental complexity index. The search step size is dynamically adjusted based on the environmental complexity index. When the environmental complexity index is greater than the preset complexity threshold, the search step size is adjusted to a preset multiple of the current search step size. When the environmental complexity index is less than or equal to the complexity threshold, the search step size is adjusted to a preset proportion of the current search step size. The adjustment range of the search step size is between the preset minimum step size and the preset maximum step size. Determine the starting point and the key point of the routing, set the starting point as the root node of the search tree, randomly sample to obtain the target node, determine the nearest neighbor node in the search tree that is closest to the target node, and expand from the nearest neighbor node to the target node according to the search step size to obtain the extended node; Calculate the avoidance distance between the extended node and each stress hotspot in the set of stress hotspot locations, obtain the minimum avoidance distance between the extended node and the set of stress hotspot locations, and when the minimum avoidance distance is greater than a preset safety margin, add the extended node to the search tree and record the parent node index of the extended node; Calculate the endpoint deviation between the extended node and the routing endpoint. Stop the search when the endpoint deviation is less than a preset endpoint threshold or when the maximum number of iterations is reached. Obtain the search path by connecting the current node to the root node level by level according to the parent node index.

[0068] In one specific implementation, a set of stress hotspot locations within the wiring interval is obtained. These locations can be determined by stress simulation tools or measured data. For example, in a certain integrated circuit, finite element analysis reveals five stress hotspots with coordinates (10, 15), (25, 30), (40, 20), (60, 55), and (75, 35), respectively. The stress values ​​in these hotspot areas exceed the material's safety threshold.

[0069] For each current node in the wiring interval, calculate its safe distance to the nearest unstable region. Taking the current node (20, 25) as an example, calculate the distances from this node to five stress hotspots, which are 15.8, 7.1, 22.4, 47.1, and 55.8 units, respectively. Take the minimum value of 7.1 as the safe distance from this node to the nearest unstable region. Simultaneously, obtain the variance of the stress gradient direction around the current node. This can be obtained by sampling the stress values ​​at eight points around the current node, calculating the stress gradient vector, and then calculating the dispersion of these vector directions. Assume the calculated variance value is 0.75, which reflects the complexity of the surrounding environmental stress distribution.

[0070] The environmental complexity index is determined by comprehensively considering the safety clearance and the variance of the stress gradient direction. It can be calculated using a weighted summation method, for example, by weighting the inverse ratio of the safety clearance to the variance value as the environmental complexity index. Assume the currently calculated environmental complexity index is 2.5.

[0071] The search step size is dynamically adjusted based on the environmental complexity index. A preset complexity threshold of 2.0 is set. When the environmental complexity index of 2.5 is greater than this threshold, the search step size is adjusted to a preset multiple of the current search step size. For example, if the current search step size is 4 units and the preset multiple is 0.5, the adjusted search step size will be 2 units. This adjustment ensures finer searching with smaller step sizes in complex environments. Assuming a preset minimum step size of 1 unit and a preset maximum step size of 10 units, the adjustment range of the search step size must be between 1 and 10. If the environmental complexity index is less than or equal to the threshold of 2.0, the search step size is adjusted to a preset proportion of the current search step size; for example, if the preset proportion is 1.5, the adjusted step size will be 6 units.

[0072] Determine the start and end points of the routing. Assume the start point coordinates are (5, 5) and the end point coordinates are (95, 95). Set the start point as the root node of the search tree, recorded as node 0. Randomly sample to obtain the target node; for example, randomly generate coordinates (30, 40) within the routing area as the target node. Determine the nearest neighbor node in the search tree to the target node. Initially, the search tree only has the root node (5, 5), so the nearest neighbor node is the root node. Expand from the nearest neighbor node (5, 5) towards the target node (30, 40) by 2 units according to the current search step size to obtain the extended node (7, 7).

[0073] Calculate the avoidance distance between the extended node (7, 7) and each stress hotspot in the set of stress hotspot locations. For example, the distances from the extended node to the five stress hotspots are 17.2, 31.4, 38.6, 76.3, and 74.5 units, respectively, and the minimum avoidance distance is 17.2. Assuming a preset safety margin of 10 units, since 17.2 is greater than 10, the extended node is added to the search tree, denoted as node 1, and its parent node index is recorded as 0.

[0074] Repeat the above process, continuously selecting the nearest node from the search tree to expand towards the randomly sampled target node, and adding the new node to the search tree when the safety margin condition is met. For example, in the next iteration, node 1 may be selected as the nearest neighbor node, and expanded towards the new target node (45, 35) to obtain the expanded node (9, 9). The minimum avoidance distance between it and the stress hotspot is calculated. If the safety condition is met, it is added to the search tree and denoted as node 2, with a parent node index of 1.

[0075] For each newly added node in the search tree, calculate its endpoint deviation from the wiring endpoint (95, 95). For example, the distance between node (70, 75) and the endpoint is 31.6 units. Set a preset endpoint threshold of 5 units. The search stops when the deviation of an expanded node from the endpoint is less than this threshold, or when the number of search iterations reaches the preset maximum number of iterations (e.g., 10,000).

[0076] After the search stops, based on the recorded parent node indices, starting from the last node sufficiently close to the destination, the search proceeds level by level to the root node, resulting in a complete search path. For example, if the node closest to the destination is 150, its parent node index is 135, the parent node index of node 135 is 120, and so on until the root node 0, thus obtaining the path {0, 15, 28, 45, 67, 89, 120, 135, 150}, and the corresponding coordinate sequence is the wiring path. The adaptive tree search algorithm dynamically adjusts the search step size, performing a fine search in complex environments and accelerating the search process in simple environments, effectively avoiding stress hotspots while ensuring path safety and efficiency.

[0077] like Figure 3 The figure shows a comparison of the path planning performance of different routing algorithms under complex stress environments. The black square areas represent the routing interval (1200μm × 1200μm), and the red circular areas represent stress hotspots (a total of 24, with a density of 16.7 hotspots / mm). 2 The light red area around each hotspot represents a safety margin (15μm). In this environment, the blue path is the wiring path generated by this technical solution, the green path is the path generated by the improved A* algorithm, and the orange path is the path generated by the traditional RRT algorithm. The simulation results clearly show that the blue path generated by this technical solution successfully avoids all stress hotspots and maintains a sufficient safety distance from them (average safety distance of 23.8 μm). The path has 7 inflection points and a total length of 1435 μm. The green path generated by the improved A* algorithm also avoids all stress hotspots, but the safety distance from the hotspots in the high-density area (lower right corner of the figure) is significantly insufficient (minimum distance is only 16.3 μm, average safety distance is 17.2 μm). The path has 9 inflection points and a total length of 1489 μm. The orange path generated by the traditional RRT algorithm approaches the safety margin boundary in many places (upper left and lower right areas of the figure), posing a potential risk (minimum distance is only 15.2 μm, average safety distance is 12.5 μm). The path is also more curved, with 12 inflection points and a total length of 1552 μm. This technical solution achieves a smoother and safer wiring path in complex environments by accurately calculating the minimum avoidance distance between the extended node and the stress hotspot, and dynamically adjusting the search step size based on the environmental complexity index. It shortens the path length by 7.5% compared to the traditional RRT algorithm and by 3.6% compared to the improved A* algorithm, while significantly improving path security.

[0078] For example, in one specific embodiment, in the technology of utilizing old pipe trenches and arranging electromechanical pipelines, a layered three-dimensional layout scheme is adopted for the renovation of existing pipe trench space. The inner wall of the pipe trench is treated with an anti-corrosion epoxy resin coating with a thickness of 2-3 mm, which has wear-resistant, moisture-proof, and seepage-proof properties. A concrete foundation layer with a thickness of 150 mm, a strength grade of C30, and a surface flatness error controlled within 2 mm is laid at the bottom of the pipe trench.

[0079] The pipeline supports are made of stainless steel with a wall thickness of 3 mm. They feature a modular design with a vertical support spacing of 1200 mm and adjustable horizontal support arm length ranging from 300-600 mm. The supports are fixed using expansion bolts with a diameter of 12 mm and a embedment depth of 100 mm, ensuring a load-bearing capacity of no less than 2 kN. The minimum clearance between pipelines is determined according to the pipe diameter: a minimum clearance of 300 mm between power cables and communication cables, and a minimum clearance of 500 mm between water supply pipes and sewage pipes.

[0080] For areas with limited space, a micro-trenching technology solution is adopted. The micro-trenching trench uses a precast concrete tank with a cross-sectional dimension of 400 mm × 600 mm and a wall thickness of 80 mm. The inner surface of the tank is coated with a waterproof coating with a thickness of 1.5 mm. The pipelines are arranged vertically in multiple layers with a layer spacing of 200 mm, and are fixed by adjustable clamps. The clamps are made of engineering plastics, have insulating properties, and a compressive strength of not less than 80 MPa.

[0081] The direct-buried pipeline area adopts a compact pipeline layout scheme. The excavated trench is 1200 mm deep and 600 mm wide at the bottom, with a trapezoidal cross-section and a slope ratio of 1:0.5. The trench backfill uses fine sand with a compaction degree of not less than 95%. The pipelines are arranged vertically, with water supply pipes at the bottom, power cables at the middle layer, and communication cables at the top layer. Isolation strips made of modified polyethylene material with a thickness of 50 mm are set between each layer of pipelines.

[0082] When crossing building areas, steel sleeves are used for protection. The inner diameter of the sleeve is 100 mm larger than the diameter of the conveying pipe, the sleeve wall thickness is 8 mm, and both ends extend 300 mm outside the building. A waterproof material, using polyurethane foam with a density of 35 kg / m³, is filled between the sleeve and the pipe. Inspection wells, measuring 1000 mm × 1000 mm and 1500 mm deep, are installed in the pipe joint area, constructed of reinforced concrete.

[0083] When laying electromechanical pipelines, power cables use flame-retardant cross-linked polyethylene insulation sheaths, with a rated voltage of 0.6 / 1 kV. The cross-sectional area is selected according to load requirements, generally 95-240 square millimeters. Communication cables use outdoor armored optical cables with 24-96 fiber cores, featuring waterproof and tooth-resistant properties. Water supply pipes use internally and externally corrosion-resistant steel pipes, with nominal diameters of DN50-DN200 and wall thicknesses of 4.5-8 mm.

[0084] The width of the pipe trench maintenance access should be no less than 800 mm, and the clearance height no less than 2000 mm. An inspection port, measuring 900 mm × 700 mm, should be installed every 30 meters. The inspection port cover should be made of composite material with a load-bearing capacity of no less than 40 kN / m². Ventilation openings should be spaced no more than 50 meters apart, with a cross-sectional area of ​​no less than 0.1 m², and should be equipped with stainless steel protective mesh with a mesh size of 20 mm × 20 mm.

[0085] The drainage system features an overall slope design of 3-5‰, with a sump measuring 300 mm × 300 mm × 300 mm installed every 50 meters. A submersible pump is installed in each sump, with a flow rate of 2 cubic meters per hour and a head of 6 meters. An emergency lighting system is installed within the pipe trench, with one LED emergency light fixture every 15 meters, each with a power of 20 watts and a runtime of at least 90 minutes.

[0086] The intelligent cabling and obstacle avoidance optimization system for confined space trenches in old factory areas, as described in this embodiment of the invention, includes: The first unit is used to acquire point cloud data of the trench using structured light laser scanning, extract crack contours through edge detection, and obtain a structural defect distribution map; The second unit is used to construct the stress field based on the structural defect distribution map through nonlinear elastoplastic finite element calculation, and obtain the stress hot spot distribution matrix of the pipe trench; using the stress hot spot distribution matrix, the spatial distribution data of the structural weak area is obtained through stress field evolution curve analysis and stress field topological feature extraction. The third unit is used to obtain the stress diffusion path map under pipeline load by using iterative calculation methods based on the spatial distribution data of structurally weak areas and the stress diffusion of tensor fields and relaxation factor adjustment; and to divide the instability development area by using the continuity index in the stress diffusion path map to obtain safe wiring interval data. The fourth unit is used to perform path planning based on the safety wiring interval data using an adaptive tree search algorithm. Stress hotspots are used as avoidance constraints, and the pipeline spatial layout path is obtained by dynamically adjusting the search step size. The fifth unit is used to output the pipeline spatial layout path to the construction control system.

[0087] A third aspect of the present invention provides an electronic device, comprising: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0088] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0089] This invention can be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of the invention.

[0090] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention 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 or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for intelligent wiring and obstacle avoidance optimization in confined spaces of old factory areas, characterized in that, include: Structured light laser scanning was used to acquire point cloud data of the pipe trench, and crack contours were extracted by edge detection to obtain a structural defect distribution map; Based on the structural defect distribution map, a stress field is constructed through nonlinear elastoplastic finite element calculation to obtain the stress hot spot distribution matrix of the pipe trench; using the stress hot spot distribution matrix, spatial distribution data of the structural weak area is obtained through stress field evolution curve analysis and stress field topological feature extraction. Based on the spatial distribution data of structurally weak areas, an iterative calculation method using tensor field stress diffusion and relaxation factor adjustment is used to obtain the stress diffusion path diagram under pipeline load. By using the continuity index in the stress diffusion path diagram, the instability development region is divided to obtain safe wiring interval data; Based on the safety wiring interval data, an adaptive tree search algorithm is used for path planning, stress hotspots are used as avoidance constraints, and the pipeline spatial layout path is obtained by dynamically adjusting the search step size. Output the pipeline spatial layout path to the construction control system.

2. The method according to claim 1, characterized in that, Based on the structural defect distribution map, a stress field was constructed using nonlinear elastoplastic finite element analysis, resulting in the stress hotspot distribution matrix of the trench, including: By marking the crack locations and densely populated defect areas in the structural defect distribution map, a set of defect feature point coordinates is obtained; Based on the set of coordinates of the defect feature points, a non-uniform mesh partitioning result is obtained by setting quarter-node singular elements at the crack location and adding mesh nodes in the defect-dense region. Based on the non-uniform mesh division results, the initial stress and strain values ​​of the nodes are obtained by calculating the stress and strain components of each mesh node and determining the plastic state of the material. Based on the initial stress and strain values ​​of the node, the calculation step size is adjusted by detecting the strain energy value and iterative calculation is performed until the difference between two adjacent calculation results is less than the preset convergence accuracy, thereby obtaining the stress and strain distribution of the converged node. Based on the stress-strain distribution at the convergence nodes, the stress hotspot distribution matrix is ​​obtained by extracting the equivalent stress value, plastic deformation value, and damage value of each node.

3. The method according to claim 1, characterized in that, Using the stress hotspot distribution matrix, through stress field evolution curve analysis and stress field topological feature extraction, the spatial distribution data of structural weak regions are obtained, including: Based on the stress hotspot distribution matrix, the crack tip, the boundary of the dense defect area, and the region where the stress gradient is greater than a preset gradient threshold are selected as observation points to obtain the set of observation point coordinates. Based on the coordinate set of the observation points, the nodal stress time series data of the observation points during the loading process are collected, and the stress field evolution curve is obtained by interpolating and fitting the nodal stress time series data. Extract the stress rise rate, stress fluctuation amplitude, and stress residence time from the stress field evolution curve, and compare them with preset rate threshold, preset fluctuation threshold, and preset time threshold respectively to obtain the coordinates of the stress field distortion region. Based on the stress hotspot distribution matrix, equivalent stress contour lines are constructed. Based on the equivalent stress contour lines, local extreme points are extracted as singular points, and stress transmission inflection points are extracted as saddle points to obtain stress field feature point distribution data. Based on the coordinates of the stress field distortion region and the distribution data of the stress field feature points, it is determined whether each region simultaneously satisfies the following conditions: located within the stress field distortion region, containing singular points, and located on the line connecting two saddle points. Regions that meet these conditions are marked as structurally weak regions, thus obtaining the spatial distribution data of structurally weak regions.

4. The method according to claim 1, characterized in that, Based on the spatial distribution data of structurally weak areas, and using an iterative calculation method involving tensor field stress diffusion and relaxation factor adjustment, the stress diffusion path diagram under pipeline load is obtained, including: The spatial distribution data of the weak structural region is converted into the initial conditions of the tensor field. The stress state of discrete calculation points in the weak structural region is described by establishing a second-order stress tensor. The structural guidance factor determined by the material constitutive relation and the geometric characteristics of the structure is introduced to obtain the stress state data of the tensor field. Based on the tensor field stress state data, the diffusion tensor parameters and stress correction parameters are determined, and the tensor field stress state data is subjected to time-domain iterative processing to obtain time-domain iterative data of stress diffusion. The stress difference between two adjacent iterations is calculated, and the ratio of the stress difference to a preset stress reference threshold is determined as the stress change rate. An adjustment curve for the relaxation factor is constructed based on the stress change rate, wherein the value of the relaxation factor is positively correlated with the stress change rate. The value of the relaxation factor is determined according to the preset range of the stress change rate, and the stress distribution evolution data under pipeline load is obtained. Based on the stress distribution evolution data, the principal values ​​and principal directions of the stress tensor are calculated. At each discrete calculation point, the principal direction corresponding to the maximum principal stress is selected to determine the stress propagation direction. The corresponding vectors of the stress propagation directions of adjacent time steps are connected to form stress streamlines, thus obtaining the stress diffusion path diagram under pipeline load.

5. The method according to claim 1, characterized in that, Using the continuity index in the stress diffusion path diagram, the instability development region is divided, and the safe wiring interval data is obtained, including: The average value of the angle between the stress propagation direction vector of each discrete calculation point and its corresponding adjacent calculation point in the stress diffusion path diagram is used as the continuity index of the discrete calculation points to obtain the continuity distribution data of the calculation points. Based on the continuous distribution data of the calculation points, the combination of adjacent calculation points with a continuity index less than a preset continuous threshold is determined as the unstable development region, and the distribution data of the unstable region is obtained. Based on the distribution data of the unstable region, the minimum distance between the potential wiring path and the unstable development region is calculated. When the minimum distance is greater than the preset safety distance, the corresponding potential wiring path is determined as a safe wiring interval, and safe wiring interval data is obtained.

6. The method according to claim 1, characterized in that, Based on the safety wiring interval data, an adaptive tree search algorithm is used for path planning, taking stress hotspots as avoidance constraints. By dynamically adjusting the search step size, the resulting pipeline spatial layout paths include: Based on the safe wiring interval data, the wiring interval is determined, the unstable region distribution data is extracted from the wiring interval, and the set of stress hotspot locations is determined based on the unstable region distribution data. Calculate the environmental complexity index within the wiring interval, and dynamically adjust the search step size based on the environmental complexity index to obtain the adaptive step size parameter; The set of stress hotspot locations is set as an avoidance constraint. An adaptive tree search algorithm is used for path planning. The adaptive tree search algorithm expands nodes according to the adaptive step size parameter and performs path search based on the avoidance constraint to obtain the search path. The search path is smoothed to obtain the pipeline spatial layout path.

7. The method according to claim 6, characterized in that, The adaptive tree search algorithm includes: Calculate the safe distance from each current node to the nearest unstable region in the wiring interval, obtain the variance of the stress gradient direction around the current node, and determine the environmental complexity index. The search step size is dynamically adjusted based on the environmental complexity index. When the environmental complexity index is greater than the preset complexity threshold, the search step size is adjusted to a preset multiple of the current search step size. When the environmental complexity index is less than or equal to the complexity threshold, the search step size is adjusted to a preset proportion of the current search step size. The adjustment range of the search step size is between the preset minimum step size and the preset maximum step size. Determine the starting point and the key point of the routing, set the starting point as the root node of the search tree, randomly sample to obtain the target node, determine the nearest neighbor node in the search tree that is closest to the target node, and expand from the nearest neighbor node to the target node according to the search step size to obtain the extended node; Calculate the avoidance distance between the extended node and each stress hotspot in the set of stress hotspot locations, obtain the minimum avoidance distance between the extended node and the set of stress hotspot locations, and when the minimum avoidance distance is greater than a preset safety margin, add the extended node to the search tree and record the parent node index of the extended node; Calculate the endpoint deviation between the extended node and the routing endpoint. Stop the search when the endpoint deviation is less than a preset endpoint threshold or when the maximum number of iterations is reached. Obtain the search path by connecting the current node to the root node level by level according to the parent node index.

8. A smart wiring and obstacle avoidance optimization system for confined space pipe trenches in old factory areas, used to implement the method of any one of claims 1-7, characterized in that, include: The first unit is used to acquire point cloud data of the trench using structured light laser scanning, extract crack contours through edge detection, and obtain a structural defect distribution map; The second unit is used to construct the stress field based on the structural defect distribution map and to obtain the stress hot spot distribution matrix of the pipe trench through nonlinear elastoplastic finite element calculation; By utilizing the stress hotspot distribution matrix, stress field evolution curve analysis, and stress field topological feature extraction, spatial distribution data of structurally weak areas are obtained. The third unit is used to obtain the stress diffusion path diagram under pipeline load by using the iterative calculation method of tensor field stress diffusion and relaxation factor adjustment based on the spatial distribution data of the structural weak area. By using the continuity index in the stress diffusion path diagram, the instability development region is divided to obtain safe wiring interval data; The fourth unit is used to perform path planning based on the safety wiring interval data using an adaptive tree search algorithm. Stress hotspots are used as avoidance constraints, and the pipeline spatial layout path is obtained by dynamically adjusting the search step size. The fifth unit is used to output the pipeline spatial layout path to the construction control system.

9. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1 to 7.

Images