An Optimization and Evaluation Method for Line Relocation Schemes Based on Multidimensional Constraints

Abstract

This application provides a method for optimizing and evaluating pipeline relocation schemes based on multidimensional constraints, including: acquiring urban underground pipeline location information; extracting and marking the burial depth of unmarked pipelines and intersections based on the underground pipeline location information to obtain pipeline distribution data containing three-dimensional coordinates and burial depth deviations; extracting pipeline horizontal spacing information from the preliminary optimized layered scheme; determining whether the spacing meets safety requirements through a spatial geometric algorithm; if not, adjusting the horizontal spacing parameters to obtain the adjusted horizontal spacing parameters; acquiring damage level data of the protective layer; if the damage level exceeds a preset threshold, establishing a real-time responsive pipeline layout model by dynamically updating the vertical layering standard and horizontal spacing parameters; calculating the pipeline space utilization efficiency index based on the optimized pipeline collaborative distribution scheme; iteratively updating the pipeline layout model to obtain pipeline layout optimization results adapted to the dynamic environment.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to an optimization and evaluation method for line relocation schemes based on multidimensional constraints. Background Technology

[0002] The rational utilization of urban underground space is a key area of ​​modern urban planning and construction, directly related to the safety, sustainability, and operational efficiency of urban infrastructure. With the acceleration of urbanization, the complexity of underground pipeline networks is increasing, making pipeline relocation and coordinated planning crucial for ensuring urban functions. However, existing solutions often lack dynamic adaptability and comprehensive optimization capabilities when dealing with planning for intersections of various types of pipelines, making it difficult for planning schemes to meet the actual needs of complex scenarios. Current methods mainly rely on static pipeline detection data and fixed layering and spacing standards, making it difficult to adapt to new problems exposed by improved detection data accuracy. For example, the discovery of unmarked pipeline locations, deviations in intersection burial depth, and updates to the condition of protective layers all challenge the applicability of existing planning schemes. These limitations lead to low planning efficiency and may even cause safety hazards during construction. In this context, the core challenge stems from the dynamic changes in pipeline detection data accuracy and the insufficient adaptability of planning models. Improved detection data accuracy reveals the locations of unmarked pipelines, thus exposing deviations between the actual burial depth at intersections and the design values. This deviation not only affects the vertical layering design of pipelines but may also lead to a reassessment of horizontal safety spacing. If the planning model cannot dynamically adjust the stratification standards and safety distances, it will be difficult to meet the collaborative planning needs of complex intersection sections, further exacerbating the low utilization efficiency of underground space. Therefore, how to dynamically adjust the vertical stratification standards and horizontal safety distances of different types of pipelines based on multi-dimensional constraints to adapt to the changes brought about by the improvement of detection data accuracy has become a key issue in improving the utilization efficiency of urban underground space. Summary of the Invention

[0003] This invention provides a method for optimizing and evaluating line relocation schemes based on multidimensional constraints, mainly including:

[0004] Obtain the location information of urban underground pipelines, extract the burial depth of unmarked pipelines and intersections based on the location information of underground pipelines, and mark them to obtain pipeline distribution data containing three-dimensional coordinates and burial depth deviations;

[0005] Statistical analysis of pipeline distribution data yields pipeline type and burial depth distribution characteristics. Based on these characteristics, the vertical distribution characteristics of various pipeline types in intersection sections are analyzed. These vertical distribution characteristics include vertical spacing between pipelines and burial depth deviation at intersections. Vertical stratification standards are then adjusted based on these vertical distribution characteristics.

[0006] Extract the horizontal spacing information of pipelines from the preliminary optimized layered scheme, and use a spatial geometric algorithm to determine whether the spacing meets the safety requirements. If not, adjust the horizontal spacing parameters to obtain the adjusted horizontal spacing parameters.

[0007] Data on the degree of damage to the protective layer is obtained. If the degree of damage exceeds a preset threshold, a pipeline layout model with real-time response is established by dynamically updating the vertical layering standard and horizontal spacing parameters.

[0008] By using a real-time response pipeline layout model, the vertical crossing risk and horizontal collision risk of unmarked pipelines in the crossing section are analyzed. Based on the risk assessment results, the pipelines of various types are reordered to obtain an optimized pipeline collaborative distribution scheme.

[0009] Based on the optimized pipeline collaborative distribution scheme, the pipeline space utilization efficiency index is calculated, and the pipeline layout model is iteratively updated to obtain the pipeline layout optimization result that adapts to the dynamic environment.

[0010] Extract the trend data of vertical layering standards and horizontal safety distances from the pipeline layout optimization results. Based on real-time environmental perception capabilities, continuously monitor the burial depth deviation and protective layer damage status of the intersection section to obtain assessment parameters for quantifying the long-term safety risk targets of the pipeline.

[0011] Furthermore, the location information of urban underground pipelines is obtained. Based on this information, the burial depths of unmarked pipelines and intersections are extracted and marked, resulting in pipeline distribution data containing three-dimensional coordinates and burial depth deviations. This includes: acquiring echo signals from one to five meters underground using a ground-penetrating radar array scanner; performing layered processing on the echo signals based on the soil dielectric constant and pipeline material reflection coefficient; and calculating the initial pipeline burial depth data using echo delay. An image enhancement operator is used to perform spatial filtering on the echo signals, and histogram equalization is used to extract the coordinate set of pipeline edge feature points. A three-dimensional reference point set for the pipelines is constructed based on the feature point coordinate set and the initial pipeline burial depth data. Three-dimensional coordinates of ground control points are collected using a total station, and ground elevation data is generated through interpolation. Elevation correction is applied to the pipeline three-dimensional reference point set to obtain the measured three-dimensional point set for the pipelines. For pipeline intersection areas in the echo signals, peak detection methods are used to extract the signal features of the intersecting pipelines. The positional relationship between upper and lower pipelines is distinguished based on signal amplitude differences, and the burial depth data of the intersecting pipelines is obtained. Curve fitting is performed on the measured three-dimensional point set of the pipeline using the least squares method. The location of the pipeline spatial direction change is determined by the rate of curvature change, and the pipeline spatial distribution data is obtained. The burial depth data of the intersecting pipelines is compared with the existing pipeline labeling data to identify the location of unlabeled pipelines. The three-dimensional coordinates and burial depth deviation values ​​are calculated using the measured three-dimensional point set of the pipeline and the standard point set.

[0012] Furthermore, statistical analysis is performed on the pipeline distribution data to obtain pipeline type and burial depth distribution characteristics. Based on these characteristics, the vertical distribution characteristics of various pipeline types in intersection sections are analyzed. These vertical distribution characteristics include pipeline vertical spacing and intersection point burial depth deviation. The vertical stratification standard is adjusted based on these characteristics, including: dividing the pipeline burial depth dataset into grid units per square meter based on the pipeline spatial distribution data; using kernel density calculation to obtain pipeline burial density distribution data; and marking areas with pipeline burial density exceeding a preset threshold as densely populated pipeline sections. A point cloud density algorithm is used to extract the coordinates of pipeline intersection nodes in these densely populated sections. The nearest neighbor search method is used to obtain the spatial location data of pipelines surrounding the intersection nodes, and the three-dimensional spatial angles of the intersecting pipelines are calculated. Minimum burial depth specifications for different types of pipelines are extracted from the pipeline attribute database. Combined with the pipeline type information, an initial vertical stratification table is constructed, recording the correspondence between pipeline type, diameter, material, and backfill requirements. Measured pipeline burial depth values ​​are extracted from the coordinates of the intersection nodes. A random forest classifier is used to perform correlation analysis between pipeline type and burial depth data to obtain the vertical distribution pattern of pipelines in the intersection section. The vertical projection distance is calculated using the three-dimensional spatial angles of the intersection pipelines. The measured pipeline burial depth values ​​are compared with the initial vertical stratification table specifications to obtain the intersection point burial depth deviation data. The vertical projection distances are grouped using a layer-by-layer iterative method to calculate the vertical spacing between adjacent pipelines. The initial vertical stratification table is corrected based on the intersection point burial depth deviation data to generate an updated vertical stratification standard.

[0013] Furthermore, pipeline types are classified according to safety risk level and facility importance. The deviation between existing vertical stratification standards and actual vertical distribution is compared, and the minimum safe vertical distance between different types of pipelines is calculated. Differentiated stratification parameters are set for pipeline intersections in densely populated areas and under special geological conditions. The adjusted vertical stratification scheme is entered into the underground pipeline information system to update pipeline layout standards. This includes: using the analytic hierarchy process (AHP) to quantify pipeline operating pressure values ​​and facility service life data; constructing a safety risk scoring table using a decision tree algorithm; extracting facility importance indicators from the pipeline type database to obtain pipeline safety level classification results; calculating soil bearing capacity values ​​based on geological drilling data; obtaining the number of pipelines per unit area using a pipeline density detector; and determining the initial value of the minimum safe distance based on the pipeline safety level classification results; extracting standard spacing data from existing vertical stratification standards; calculating vertical distribution deviation values ​​using measured pipeline burial depth coordinates; and generating vertical stratification correction parameters based on the initial value of the minimum safe distance; collecting soil stress distribution data in pipeline intersection areas; obtaining soil stratification structure characteristics using a geological detector; and calculating geological influence coefficients based on the vertical stratification correction parameters. A stress correction model is established by combining pipeline intersection angle values ​​with soil stress distribution data. The vertical stratification correction parameters are adjusted using the geological influence coefficient to obtain a differentiated stratification scheme. The pipeline safety level classification results are graded and sorted according to the facility importance index. A pipeline layout specification data package is generated using the differentiated stratification scheme and written into the information database through the data interface.

[0014] Furthermore, the horizontal spacing information of pipelines in the initially optimized layered scheme is extracted. A spatial geometric algorithm is used to determine whether the spacing meets safety requirements. If not, the horizontal spacing parameters are adjusted to obtain the adjusted parameters. This includes: extracting the horizontal spacing values ​​of pipelines from the plan layout based on pipeline density data; classifying and labeling pipelines according to pipeline protection level rules; and obtaining pipeline parallel segment data using spatial vector calculation to obtain the basic parameters for horizontal pipeline layout. The minimum safe distance requirements between pipelines of different protection levels are extracted from the pipeline safety specification library. A horizontal safe distance threshold is generated using a pipeline type lookup table, and the basic parameters for horizontal pipeline layout are compared with the safe distance threshold to determine if the requirements are met. The nearest neighbor search algorithm is used to locate the coordinate point set of underground obstacles. A pipeline crossing path map is constructed using triangulation. The avoidance distance value is calculated based on the pipeline bending radius limit to generate an initial pipeline avoidance scheme. For sections that do not meet the safety distance requirements, pipeline coordinate data is extracted. The pipeline position adjustment amount is calculated using a recursive optimization method, and the position adjustment amount is constrained using the underground space utilization rate index to obtain the optimized pipeline spacing parameters. The horizontal pipeline layout data is updated based on the pipeline spacing optimization parameters. The convex hull algorithm is used to calculate the envelope area of ​​adjacent pipelines. The minimum pipeline spacing is verified to meet the safety requirements by the grid division method. A pipeline protection zoning map is constructed for the pipeline avoidance scheme. The pipeline safety protection range is divided by the buffer analysis method. The horizontal spacing adjustment parameter record is generated according to the protection level requirements.

[0015] Furthermore, damage data of the protective layer is obtained. If the damage exceeds a preset threshold, a real-time pipeline layout model is established by dynamically updating the vertical stratification standard and horizontal spacing parameters. This includes: acquiring protective layer thickness data using an ultrasonic detector, measuring material corrosion degree using an electromagnetic induction scanner, calculating environmental corrosion factors based on the soil erosion index, constructing a damage assessment function using a neural network algorithm, and generating a protective layer damage dataset. Damage area and depth parameters are extracted from the protective layer damage dataset. Stress distribution maps of the pipeline are collected using stress sensors. A damage warning function is established based on the damage propagation rate to determine the damage degree threshold. The detection time series is extracted from the damage warning function, and the damage propagation trend is calculated using regression analysis. The vertical stratification parameter curve is fitted using the least squares method to establish a pipeline spacing dynamic response function. The updated values ​​of the vertical stratification parameters are calculated using the pipeline spacing dynamic response function. The horizontal spacing adjustment amount is obtained based on the damage warning function, and a pipeline spatial layout map is constructed using a grid search method. The Particle Swarm Optimization (PSO) algorithm is used to optimize the pipeline spatial layout diagram. The layout adjustment range is set according to the damage propagation trend to generate a real-time response pipeline layout model. The safety assessment of the pipeline layout data is performed using a mesh verifier. The rationality of the layout adjustment is verified by stress distribution diagram. The parameters of the damage warning function are updated based on the verification results.

[0016] Furthermore, using a real-time response pipeline layout model, the vertical crossing risk and horizontal collision risk of unmarked pipelines in intersection sections are analyzed. Based on the risk assessment results, multiple types of pipelines are reordered to obtain an optimized pipeline collaborative distribution scheme. This includes: extracting a set of three-dimensional coordinate points for unmarked pipelines from real-time underground pipeline monitoring data; calculating the vertical crossing angle using spatial vector calculations; calculating pipeline stress distribution using pipeline operating pressure parameters to generate vertical crossing risk data; obtaining pipeline density values ​​using a grid density calculation method; locating horizontal collision point coordinates using a collision detector; and statistically analyzing the number of pipeline intersections based on the unmarked proportion index to construct a horizontal collision risk function. A risk assessment matrix is ​​established based on the vertical crossing risk data and the horizontal collision risk function. The risk level value is calculated using the analytic hierarchy process (AHP) to generate a pipeline risk classification table. High-risk pipeline data is extracted from the pipeline risk classification table, protection level thresholds are set according to pipeline operation safety specifications, and pipelines are classified and sorted using the risk level values. A grid-based approach is used to calculate the space utilization rate of the high-risk pipeline area. The minimum safe distance between pipelines is determined based on pipeline protection level thresholds, generating pipeline spatial layout rules. The pipeline sorting results are optimized and adjusted according to these rules. A collaborative layout scheme is generated using a particle swarm optimization algorithm, and the layout safety is assessed using a collision risk verifier. The pipeline protection level parameters are updated based on the verification results, and the collaborative layout scheme is fine-tuned using the latest protection level data to obtain pipeline distribution data.

[0017] Furthermore, data on the aging degree of various pipeline materials, pressure level, medium hazard, and operating status in the intersection section are extracted. Priority weights are assigned to various pipeline types, and the safety margin for each pipeline type at different locations is calculated. High-risk pipelines are placed in locations that are easy to maintain and less susceptible to external interference, while low-risk pipelines are arranged in confined spaces, forming a target pipeline collaborative distribution scheme that meets safety distance requirements and maximizes space utilization. This includes: using an ultrasonic detector to obtain pipeline material aging indicators, collecting pressure level values ​​through pressure sensors, extracting medium hazard classification standards from a pipeline attribute database, collecting operating status data using online monitoring equipment, and generating a pipeline basic characteristic table. A pipeline risk assessment function is constructed based on the pipeline basic characteristic table. The priority weight values ​​for various pipeline types are calculated using a random forest algorithm. Safety distance requirements are read from a pipeline safety specification library to obtain pipeline safety assessment data. A spatial grid division method is used to classify underground spaces into regions. External interference is calculated using vibration sensor data. The maintenance difficulty coefficient is determined based on historical maintenance frequency records, and pipeline spatial location scores are calibrated. Pipelines are classified into risk levels using the pipeline safety assessment data, and their deployment order is determined based on priority weight values. An initial layout scheme is generated using a grid search method. Spatial constraint analysis is performed on the initial layout scheme. High-risk pipelines are prioritized for convenient maintenance locations, while low-risk pipelines are placed in confined spaces, generating differentiated pipeline layout data. A grid validator is used to perform a safety check on the differentiated layout data. The rationality of the layout is determined through space utilization calculations, and spacing requirements are verified using the pipeline safety assessment data to obtain a collaborative layout scheme. Based on the verification results, the collaborative layout scheme is optimized and adjusted. Safety hazards are eliminated through fine-tuning of pipeline spacing, generating a pipeline distribution data package.

[0018] Furthermore, based on the optimized pipeline collaborative distribution scheme, the pipeline space utilization efficiency index is calculated, and the pipeline layout model is iteratively updated to obtain pipeline layout optimization results adapted to the dynamic environment. This includes: extracting pipeline spatial coordinate data based on the pipeline collaborative distribution scheme, obtaining pipeline occupied space values ​​through volume calculation methods, calculating space utilization index using grid partitioning methods, and generating a pipeline spatial distribution status table. Temperature and humidity parameters of underground space are obtained from environmental monitoring sensors, soil stress distribution data are collected using pressure monitoring instruments, and groundwater level changes are recorded by groundwater level monitors to construct an environmental parameter monitoring dataset. A Kalman filter is used to correct the pipeline spatial distribution status table in real time, environmental change trends are calculated using the environmental parameter monitoring dataset, and a pipeline layout prediction function is generated using data assimilation methods. An iterative optimization interval is set based on the pipeline layout prediction function, the layout adjustment range is determined through spatial constraints, and pipeline layout update parameters are generated using a particle filter algorithm. A deep reinforcement learning method is used to dynamically optimize the pipeline layout. The update time period is determined according to the trend of environmental changes, and a pipeline layout scheme that adapts to environmental changes is generated. The spatial rationality of the pipeline layout scheme is checked by a grid validator, and the layout optimization effect is verified by comparing with historical data to obtain the pipeline layout optimization result.

[0019] Furthermore, the changing trend data of vertical stratification standards and horizontal safety distances in the pipeline layout optimization results are extracted. Based on real-time environmental perception capabilities, the burial depth deviation and protective layer damage status of intersection sections are continuously monitored to obtain assessment parameters for quantifying the long-term safety risk targets of the pipeline. This includes: decomposing the pipeline layout optimization results using a time-series data processor, extracting the vertical stratification distance change data and horizontal safety distance fluctuation values, constructing a pipeline layout trend prediction function using a long short-term memory network, and generating a pipeline spatial parameter change table. Based on the actual burial depth data of the pipeline collected by the burial depth measuring instrument, the soil layer distribution characteristics are obtained through ground-penetrating radar scanning, and the soil moisture content and groundwater level are monitored using environmental sensors to generate a real-time environmental monitoring dataset. An adaptive filter is used to process the real-time environmental monitoring dataset, and the burial depth deviation correction value is calculated in conjunction with the pipeline spatial parameter change table to obtain the pipeline burial depth status parameters. An ultrasonic detector is used to obtain the pipeline protective layer thickness value, and an electromagnetic induction scanner is used to measure the material corrosion degree. A protective layer status assessment function is established based on the damage monitoring data. A correlation analysis was conducted between the pipeline burial depth state parameters and the protective layer state assessment function. A risk quantification calculation model was constructed using a deep neural network to generate safety risk assessment indicators. These indicators were validated using historical monitoring data. Risk correction coefficients were determined based on environmental change trends to obtain the target assessment model parameter set. A long-term safety risk prediction function was established based on this parameter set, and continuous monitoring and verification were performed using a sliding time window method to generate optimized risk assessment parameters.

[0020] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0021] This invention discloses a method for optimizing and evaluating pipeline relocation schemes based on multi-dimensional constraints. It acquires pipeline location information using high-precision detection technology, extracts the burial depth of unmarked pipelines and intersections, and obtains a dataset containing three-dimensional coordinates and burial depth deviations. Based on pipeline type and burial depth distribution characteristics, it analyzes the vertical distribution characteristics of various pipeline types in intersection sections, adjusting vertical stratification standards and horizontal spacing parameters. It acquires data on the degree of protective layer damage, establishes a real-time response pipeline layout model, analyzes the vertical intersection risk and horizontal collision risk of unmarked pipelines, and reorders various pipeline types. Iterative updates to the model are performed using data assimilation technology to obtain optimized results adapted to the dynamic environment. Based on real-time environmental perception capabilities, continuous monitoring of intersection sections is conducted to obtain assessment model parameters that quantify the long-term safety risks of pipelines. This invention can improve the space utilization efficiency of underground pipelines, reduce safety risks, and achieve dynamic optimization of pipeline layout. Attached Figure Description

[0022] Figure 1 This is a flowchart of an optimization and evaluation method for line relocation schemes based on multidimensional constraints, according to the present invention.

[0023] Figure 2 This is a schematic diagram of an optimization and evaluation method for line relocation schemes based on multidimensional constraints according to the present invention. Detailed Implementation

[0024] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0025] The complexity of urban underground pipeline networks necessitates planning schemes that can dynamically adapt to changes in detection data. Pipeline types include water supply, gas, electricity, and communications, and various pipelines may pose safety risks at intersections due to depth discrepancies or the presence of unmarked pipelines. This invention optimizes pipeline layering and spacing design through high-precision detection and multi-dimensional constraint analysis to improve the utilization efficiency of underground space. The following is combined with... Figures 1 to 2 This document details the implementation process of an optimization and evaluation method for line relocation schemes based on multidimensional constraints.

[0026] S101. Obtain the location information of urban underground pipelines through high-precision detection technology, extract and label the burial depth of unmarked pipelines and intersections, and form a pipeline distribution dataset containing three-dimensional coordinates and burial depth deviations.

[0027] Underground pipeline detection is the first step in optimization assessment, and data accuracy must be ensured to support subsequent analysis. The detection process uses high-precision equipment to acquire spatial distribution information of pipelines, and then processes this information through multiple steps to form a standardized dataset. The specific implementation method can be determined according to the actual scenario; for example, in densely populated urban areas or areas with complex geological conditions, detection parameters can be adjusted to improve data reliability.

[0028] S1011. Use a ground-penetrating radar array scanner to collect echo signals within a range of one to five meters underground. Based on the soil dielectric constant and the reflection coefficient of the pipeline material, perform layered processing to generate initial pipeline burial depth data.

[0029] Ground-penetrating radar (GPR) detects the location of underground pipelines by emitting high-frequency electromagnetic waves and receiving echo signals. The echo signal is affected by the soil's dielectric constant; for example, when the soil moisture content is 15%, the dielectric constant is approximately 9, making a 400MHz frequency suitable to balance detection depth and signal loss. Different pipeline materials have different reflection coefficients; for example, metal pipelines have a coefficient of 0.9, plastic pipelines 0.3, and concrete pipelines 0.5. The signal amplitude is used to distinguish pipeline types and calculate initial burial depth data.

[0030] S1012. An image enhancement operator is used to perform spatial filtering on the echo signal, extract the coordinate set of pipeline edge feature points, and construct a three-dimensional reference point set of the pipeline by combining the initial burial depth data.

[0031] Image enhancement operators are used to improve signal clarity. For example, the Laplacian operator is used to handle signal abrupt changes, which is suitable for setting 20 edge feature points per meter when the pipeline diameter is 300 mm. Combined with the initial burial depth data, a three-dimensional reference point is taken every 0.5 meters along the pipeline route to form a preliminary description of the pipeline's spatial distribution.

[0032] S1013. Collect the three-dimensional coordinates of ground control points by measuring with a total station, generate ground elevation data, perform elevation correction on the pipeline three-dimensional reference point set, and generate the pipeline measured three-dimensional point set.

[0033] Ground elevation data was obtained through total station measurements, with control point spacing set at 10 meters. An elevation grid was generated using the Kriging interpolation method, with a grid spacing of 1 meter and an interpolation radius of 30 meters. Elevation correction considered ground undulations, with the correction amount being the elevation difference between the vertical projection point and the reference point, thus generating a high-precision 3D point set for pipeline measurements.

[0034] S1014. For the echo signal in the pipeline crossing area, the peak detection method is used to extract the signal characteristics of the crossing pipeline, and the positional relationship of the upper and lower pipelines is determined according to the difference in signal amplitude to obtain the burial depth data of the crossing pipeline.

[0035] The signal in the intersection area exhibits a bimodal characteristic. For example, when the upper pipeline is buried at a depth of 1.5 meters and the lower pipeline is buried at a depth of 2 meters, the time difference between the peaks corresponds to a depth difference of 0.5 meters, and the amplitude of the upper signal is approximately 1.5 times that of the lower signal. The peak detection method distinguishes pipeline locations through amplitude analysis, ensuring the accuracy of the burial depth data.

[0036] S1015. Based on the least squares method, curve fitting is performed on the measured three-dimensional point set of the pipeline, and the spatial direction of the pipeline is determined by the rate of change of curvature, generating pipeline spatial distribution data.

[0037] Curve fitting uses a cubic spline function, with a curvature change rate threshold set to 0.1. Points exceeding this threshold are identified as pipeline inflection points, typically with an angle of 90 or 45 degrees. The fitting results reflect the spatial orientation of the pipeline, providing a basis for subsequent layered design.

[0038] S1016. Compare the burial depth data of intersecting pipelines with the existing pipeline labeling data, identify the locations of unlabeled pipelines, calculate the three-dimensional coordinates and burial depth deviation values, and generate a pipeline distribution dataset.

[0039] Unlabeled pipelines are identified based on differences in burial depth; for example, pipelines with a difference exceeding 0.3 meters are classified as unlabeled. A comparison between the measured 3D point set and the standard point set shows that the planar coordinate deviation is controlled within 0.2 meters, and the burial depth deviation is controlled within 0.1 meters. Deviations are slightly higher in the intersection areas, at 0.3 meters and 0.15 meters respectively. A pipeline distribution dataset containing 3D coordinates and burial depth deviations is generated to provide data support for subsequent optimization.

[0040] Through multi-step detection and data processing, this method can accurately obtain information on the spatial distribution of pipelines, identify unmarked pipelines and burial depth deviations, and lay the foundation for optimizing the design of vertical layering and horizontal spacing. Compared with traditional detection methods, this method significantly improves data accuracy and reliability, reduces planning risks caused by data deviations, and thus provides technical support for the efficient utilization of urban underground space.

[0041] S102. Perform statistical analysis on the pipeline distribution dataset to extract pipeline type and burial depth distribution characteristics, analyze the vertical distribution characteristics of multiple types of pipelines in the intersection section, adjust the vertical stratification standard according to the characteristics, classify the pipeline safety level, optimize the stratification scheme and update the layout specifications.

[0042] To achieve efficient and collaborative planning of urban underground pipelines, this invention systematically analyzes pipeline distribution datasets, extracts key features, and optimizes hierarchical design. The statistical analysis and classification process comprehensively considers multi-dimensional constraints such as pipeline type, burial depth deviation, and intersection angles to ensure the planning scheme adapts to complex scenarios. The implementation steps are detailed below; specific parameters or algorithms can be adjusted according to actual application scenarios.

[0043] S1021. The pipeline spatial distribution data is gridded using the kernel density estimation method to generate the pipeline burial density distribution and mark dense sections to support subsequent node extraction.

[0044] Pipeline spatial distribution data is divided into 1-square-meter grid cells, and the pipeline density within each cell is calculated using a kernel density estimation method. Areas with a density exceeding a preset threshold, such as 3 pipelines per square meter, are marked as dense sections. Dense sections are typically located in urban commercial areas or road intersections, occupying approximately 35% of the underground space. This method accurately reflects pipeline clustering characteristics by smoothing the data distribution, providing a basis for extracting intersection nodes.

[0045] S1022. The point cloud density analysis algorithm is used to extract the coordinates of the intersection nodes in the dense pipeline section, and the spatial angle of the intersection pipeline is calculated by combining the nearest neighbor point search to obtain the three-dimensional distribution characteristics.

[0046] Point cloud density analysis is based on a 2-meter search radius. When the point cloud density exceeds 80 points per cubic meter, it is identified as an intersecting node. The nearest neighbor search algorithm extracts pipeline location data within a 50-centimeter radius of the node and calculates the spatial angle between the intersecting pipelines, with common values ​​being 90 degrees or 60 degrees. The angle data reflects the three-dimensional distribution characteristics of the pipelines, providing support for calculating the vertical projection distance.

[0047] S1023. Extract the minimum burial depth specification value from the pipeline attribute database, construct an initial vertical layer table, and record the pipeline type, pipe diameter, and soil cover requirements.

[0048] The initial vertical stratification table is generated based on the pipeline attribute database and includes standard values ​​such as a minimum burial depth of 1.2 meters for water supply pipelines, 0.9 meters for gas pipelines, and 0.7 meters for power pipelines. The table records the relationship between pipe diameter and backfill requirements; for example, an increase of 100 mm in pipe diameter corresponds to an increase of 0.1 meters in burial depth; and the backfill thickness for steel pipelines is 0.2 meters greater than that for plastic pipelines. The stratification table provides a benchmark for deviation correction.

[0049] S1024. Analyze the correlation between pipeline type and burial depth data using the random forest algorithm, extract the vertical distribution pattern of intersection sections, and calculate the deviation between the measured burial depth and the standard value.

[0050] The Random Forest algorithm analyzes the correlation between pipeline type and burial depth data through the ensemble of multiple decision trees. For example, power lines tend to be located on the upper level, with a burial depth of approximately 0.8 meters; water supply lines are in the middle, with a burial depth of approximately 1.5 meters; and gas lines are located at the bottom, with a burial depth of approximately 2.2 meters. Actual measurement data shows that 25% of the intersections have burial depth deviations, with an average deviation of 0.15 meters, of which 80% are due to insufficient burial depth. The analysis reveals vertical spacing patterns; for example, the spacing between pipelines of the same type does not exceed 0.3 meters, while the spacing between pipelines of different types is 0.5 to 0.6 meters.

[0051] S1025. Calculate the vertical projection distance based on the spatial angle of the intersecting pipelines, optimize the vertical spacing by combining the layer-by-layer iterative method, and generate the updated vertical layering standard.

[0052] The vertical projection distance is calculated based on the intersection angle; at 90 degrees, it equals the actual distance, and at 60 degrees, it is 0.87 times the actual distance. A layer-by-layer iterative method is used to calculate the spacing between adjacent pipelines in groups; for example, the minimum spacing between power lines is 0.2 meters, between water supply lines 0.3 meters, and between gas lines 0.4 meters. Combining burial depth deviation data, the layering standard is optimized, adding a 0.1-meter safety distance at intersections to ensure the layering scheme meets safety requirements.

[0053] S1026. The analytic hierarchy process (AHP) and decision tree algorithm are used to quantify the pipeline operating pressure and service life, generate safety level classification results, and determine the initial value of the minimum safety distance.

[0054] The analytic hierarchy process (AHP) quantifies operating pressure and service life, while a decision tree algorithm generates a safety risk rating table. For example, a gas pipeline pressure of 0.4 MPa is considered high risk, and a water supply pipeline pressure of 0.6 MPa is considered medium risk; the risk level is raised if the pipeline has been in use for more than 20 years or if the material has aged by 50%. The initial minimum safe distance is based on pipeline density detection: 1 meter for high-risk pipelines, 0.8 meters for medium-risk pipelines, and 0.6 meters for low-risk pipelines. The classification results provide a priority basis for hierarchical optimization.

[0055] S1027. Combining geological drilling data with measured burial depth coordinates, calculate vertical distribution correction parameters, establish a stress correction model, generate differentiated stratification schemes, and update layout specifications.

[0056] Geological drilling data provides soil bearing capacity, for example, 80 kPa in soft soil areas and 200 kPa in hard soil areas. Comparison of measured burial depth coordinates with standard spacing generates correction coefficients; for example, a deviation of 0.2 meters in a commercial area corresponds to a correction coefficient of 1.2. Soil stress distribution combined with intersection angles constructs a stress correction model, with a correction coefficient of 1.0 at 90 degrees and increasing to 1.3 at 45 degrees. The geological influence coefficient is 1.5 in soft soil areas, requiring an increase of 0.3 meters in spacing; and 0.8 in rocky areas, requiring a decrease of 0.1 meters. Differentiated stratification schemes adjust spacing based on facility importance; for example, adding 0.2 meters for top-tier facilities and 0.15 meters for first-tier facilities. The final scheme is entered into the information system, generating a data package containing 426 layout rules, covering pipeline intersection scenarios under 12 geological conditions.

[0057] Through statistical analysis and multidimensional constraint optimization, pipeline distribution characteristics can be accurately extracted and stratification standards can be dynamically adjusted. Compared with traditional methods, this method significantly improves the planning accuracy of intersection sections and reduces safety risks caused by burial depth deviations or geological conditions by using kernel density estimation, random forests, and stress correction models. Meanwhile, differentiated stratification schemes and standard updates ensure the adaptability of the planning scheme, providing reliable support for the efficient utilization of urban underground space.

[0058] S103. Based on the preliminary optimized layered scheme, extract the horizontal spacing information of pipelines, verify safety through spatial geometric algorithms, and dynamically adjust parameters.

[0059] To achieve a safe and efficient layout of urban underground pipeline networks, this invention generates a pipeline layout scheme that adapts to complex environments by systematically analyzing and optimizing horizontal spacing, combined with protection level classification and avoidance path planning.

[0060] S1031. Extract horizontal spacing values ​​based on pipeline layout density data, classify pipelines according to protection level rules, generate horizontal safety spacing thresholds, and verify whether they meet safety requirements.

[0061] Pipeline density data reflects the density of pipeline distribution in urban areas. For example, the density in urban road areas is approximately 5 pipelines per meter, with an initial horizontal spacing of 0.2 meters. Pipelines are classified into three protection levels: Special Grade, Grade I, and Grade II, corresponding to high-pressure gas pipelines, main water supply pipelines, and communication cables, respectively. Safety regulations stipulate a minimum spacing of 1.5 meters between Special Grade gas pipelines and Grade I water supply pipelines, and 1 meter between Special Grade gas pipelines and Grade II communication cables, with an additional 0.5 meters at intersections. For large-diameter pipelines, such as those exceeding 400 mm, an additional 0.3 meters of spacing is required. A spatial vector algorithm calculates the length of parallel segments; if a Special Grade pipeline runs parallel for more than 50 meters, the spacing needs to be specifically calculated. By comparing the actual spacing with safety thresholds, non-compliant sections are identified, providing a basis for subsequent optimization.

[0062] S1032. Use the nearest neighbor search algorithm to locate the coordinates of underground obstacles, use the triangulation method to construct the crossing path map, generate an initial avoidance scheme and calculate the avoidance distance.

[0063] Underground obstacle localization employs a gridded nearest neighbor search with a search radius of 2 meters and a coordinate point spacing of 0.5 meters to ensure precise location of obstacles such as structures. Triangulation divides the pipeline path into a triangular grid, limiting path curvature based on pipe material characteristics; for example, the minimum bending radius for steel pipes is 30 times the pipe diameter, and for plastic pipes, it is 15 times. Initial avoidance schemes are generated using path planning algorithms, such as increasing the avoidance distance by 0.8 meters when bypassing underground structures. This scheme provides spatial constraints for subsequent spacing optimization, reducing the risk of conflicts during path adjustments.

[0064] S1033. For sections that do not meet the safety spacing requirements, extract the pipeline coordinates, apply a recursive optimization algorithm to calculate the position adjustment amount, and generate optimized spacing parameters by combining space utilization constraints.

[0065] The coordinates of pipelines in non-compliant sections are extracted from a spatial database. A recursive optimization algorithm iteratively adjusts the pipeline positions in steps of 0.1 meters, stopping when the difference between adjacent iterations falls below 0.05 meters. The optimization process is constrained by underground space utilization rates; for example, the utilization rate limit is 85% in urban built-up areas and 60% in green areas. After adjustment, the horizontal spacing increment is typically between 0.2 and 0.6 meters, ensuring safety while avoiding excessive space occupation. The adjustment amounts and corresponding coordinates of the optimization parameters are recorded for subsequent verification and implementation.

[0066] S1034. Calculate the pipeline envelope region based on the convex hull algorithm, verify the minimum spacing compliance through mesh division, generate horizontal spacing adjustment parameters, and construct a protection zoning map.

[0067] The convex hull algorithm calculates the envelope area of ​​adjacent pipelines using a 20cm grid size, ensuring that the overlapping area does not exceed 5% of the total area. The grid division method further verifies the minimum spacing; the adjusted spacing is 0.1 meters higher than the standard value, allowing for a margin of error during construction. The protection zoning map uses buffer zone analysis: the protection range for Class A pipelines is 2 meters to both sides of the pipeline centerline, Class I is 1.5 meters, and Class II is 1 meter. For example, in a pipeline group, the protection areas of gas and water supply pipelines overlap by 15 meters; this overlap is eliminated by adjusting their routing. The adjustment parameters record the starting and ending coordinates, adjustment amount, and protection level; the generated data package supports information system updates and construction guidance.

[0068] By combining spatial geometric algorithms and recursive optimization, the horizontal spacing of pipelines can be accurately verified and adjusted to meet safety regulations. Compared to traditional static spacing design, this method significantly improves the adaptability and safety of pipeline layout through dynamic obstacle avoidance path planning and protection zone division. Obstacle location and envelope area analysis further reduce construction risks, providing technical support for the efficient utilization and long-term operational safety of urban underground space.

[0069] S104. By acquiring data on the degree of damage to the protective layer and combining it with multi-dimensional sensor data analysis, the vertical layering and horizontal spacing parameters are dynamically updated to establish a pipeline layout model with real-time response.

[0070] To address the potential threat to pipeline safety posed by protective layer damage, this invention employs high-precision detection technology and optimization algorithms to construct a dynamic response model and adjust pipeline layout parameters in real time.

[0071] S1041. Using an ultrasonic detector and an electromagnetic induction scanner, data on the thickness and degree of corrosion of the protective layer are obtained. Combined with soil environmental factors, a damage assessment function is constructed through a neural network algorithm to generate a protective layer damage dataset.

[0072] Damage detection of the protective layer employs multi-sensor collaborative measurement. An ultrasonic detector operating at 5MHz can detect damage at depths greater than 0.1 mm; for example, it triggers a primary warning when the protective layer thickness decreases from 3 mm to 2.5 mm. An electromagnetic induction scanner using pulsed eddy current technology with a scanning interval of 5 mm can detect the depth of corrosion pits, issuing a warning when the depth reaches, for example, 1.2 mm. Soil erosivity is assessed using chloride ion content, pH value, and moisture content; when the chloride ion content exceeds 300 ppm or the pH value is below 5.5, the environmental corrosion factor increases by 0.2. The neural network algorithm uses a three-layer structure: the input layer contains 12 features, such as thickness, corrosion depth, and soil parameters; the hidden layer has 24 nodes; and the output layer generates a damage severity score. The generated damage dataset records the damaged area; for example, a pipeline damage area of ​​15% with a maximum depth reaching 40% of the protective layer thickness provides data support for analysis.

[0073] S1042. Extract area and depth parameters based on the damage dataset, establish a damage early warning function by combining stress sensor data, analyze the damage expansion trend, and set an early warning threshold.

[0074] Damage area and depth parameters were extracted from the dataset. Stress sensors were deployed at 10-meter intervals to capture pipeline stress distribution, such as stress concentration in bending sections reaching 75% of the design value. A damage warning function was constructed based on the rate of expansion; for example, in a highly corrosive environment, if the damage depth increases by 0.3 mm per year, the area expansion rate reaches 20% of the initial area. The warning function has three threshold levels: a warning is initiated when the damage depth exceeds the protective layer thickness by 30%, a medium warning at 50%, and a high warning at 70%. Time-series data was collected weekly, and analysis over eight consecutive weeks showed an exponential growth trend in damage, providing a basis for dynamic adjustments.

[0075] S1043. By fitting the vertical stratification parameter curve through regression analysis and combining it with the grid search method to calculate the spacing adjustment, a real-time pipeline spatial layout diagram is generated.

[0076] Regression analysis employed the least squares method to fit a cubic spline function, achieving an accuracy of 0.95, and generated vertical stratification parameter curves. The dynamic response function was updated on a 24-hour cycle; when the damage rate exceeded the warning value, the vertical spacing increased by 10%. Horizontal spacing adjustments were calculated based on the damage warning function, for example, increasing by 0.3 meters in severely damaged sections. A grid search method was used to divide the three-dimensional space into 0.5-meter grids to construct a pipeline layout diagram. Optimization objectives included minimum safe spacing and space utilization, ensuring the layout adapts to dynamic damage changes.

[0077] S1044. The particle swarm optimization algorithm is used to iteratively optimize the pipeline space layout diagram, and the safety is evaluated and the damage warning function parameters are updated using the mesh verifier.

[0078] The particle swarm optimization algorithm was set to a population size of 200, with 500 iterations and a convergence accuracy of 0.001. The optimization interval was adjusted according to the degree of damage, with a 5% adjustment for minor damage and an increase to 15% for severe damage. A mesh verifier was used to analyze stress distribution, spacing changes, and clearance space with a mesh density four times higher. Verification results showed that the maximum stress decreased by 20% and the minimum safe clearance increased by 0.3 meters after optimization. The damage warning function parameters were updated synchronously with the detection cycle to ensure a warning accuracy of 90% and a false negative rate of less than 3%. The updated parameters support real-time response, improving the adaptability of the layout model to dynamic environments.

[0079] By integrating multi-sensor detection and optimization algorithms, this method enables real-time monitoring of protective layer damage and dynamic adjustment of pipeline layout. Compared to traditional static models, this approach significantly improves the timeliness of damage response and the safety of layout schemes through neural network evaluation, early warning functions, and particle swarm optimization, providing technical support for the long-term stable operation of urban underground pipeline networks.

[0080] S105. The pipeline layout model based on real-time response assesses the vertical and horizontal collision risks of unmarked pipelines in the intersection section, reorders the pipelines by combining multi-dimensional risk analysis, and optimizes the pipeline positions by priority weights to generate a collaborative distribution scheme that meets the requirements of safe spacing and space utilization.

[0081] To ensure the safe operation and efficient layout of urban underground pipeline networks, this embodiment dynamically optimizes pipeline distribution through real-time monitoring and multi-dimensional risk assessment, prioritizing the safety and maintenance convenience of high-risk pipelines.

[0082] S1051. Using real-time monitoring data, extract the three-dimensional coordinates of unlabeled pipelines, calculate the vertical intersection angle and stress distribution, construct a vertical intersection risk assessment model, and locate horizontal collision points through grid density analysis to generate a collision risk function.

[0083] Real-time monitoring of underground pipelines employs a multi-point sensor network with a 10-meter spacing between monitoring points, accurately capturing the 3D coordinates of unlabeled pipelines. A spatial vector algorithm calculates the vertical intersection angle; angles between 75 and 90 degrees indicate lower risk, while angles less than 45 degrees increase the risk coefficient to 1.8. Pipeline operating pressure data, such as 0.3 to 0.4 MPa for gas pipelines and 0.4 to 0.6 MPa for water supply pipelines, reveals stress concentration at intersection points through stress distribution calculations. A grid density calculation method divides the space into 0.5-meter grids; areas with more than three pipelines per grid are considered densely populated. The collision detector uses the envelope method with an accuracy of 0.1 meters. Statistics show that unlabeled pipelines account for 15% of the total, with 325 intersection points, of which 22% are high-risk. A horizontal collision risk function is constructed based on the collision point coordinates and density, providing a quantitative basis for risk assessment.

[0084] S1052. Integrate vertical intersection and horizontal collision risk data through the analytic hierarchy process to generate a risk classification table, and classify and sort high-risk pipelines according to the protection level threshold.

[0085] The risk assessment matrix integrates vertical intersection and horizontal collision risks, using a scoring system from 0 to 10. The analytic hierarchy process (AHP) is used to assign weights: operating pressure accounts for 0.4, intersection angle for 0.3, and collision frequency for 0.3. When the intersection angle is less than 30 degrees and the pressure exceeds 0.5 MPa, the risk value exceeds 8 points. The risk classification table divides pipelines into four levels: Level 1 (28 pipelines), Level 2 (47 pipelines), Level 3 (156 pipelines), and Level 4 (94 pipelines). Protection level thresholds are set according to pipeline type; for example, gas pipelines score 9 points, water supply pipelines 8 points, and power lines 7 points. High-risk pipelines are prioritized in the classification and sorting process to ensure their safety and optimized layout.

[0086] S1053. The space utilization rate of high-risk pipeline areas is calculated using the grid division method, and the safety distance is determined in combination with the protection level threshold. A collaborative layout scheme is generated by the particle swarm optimization algorithm and its safety is verified.

[0087] The grid-based method calculates space utilization using a 0.5-meter grid, achieving 75% utilization in commercial areas and 55% in residential areas. Safety spacing is set according to the protection level; level nine protection requires a horizontal spacing of 2 meters and a vertical spacing of 1 meter. The particle swarm optimization algorithm, with a population size of 100, 300 iterations, and a convergence accuracy of 0.001, generates an initial cooperative layout. Collision risk verification uses the Monte Carlo method, sampling 1000 points; after optimization, the collision risk is reduced by 35%. Verification results drive dynamic updates of the protection level parameters 24 hours a day, with positional offsets controlled within 0.3 meters to ensure the layout meets safety requirements.

[0088] S1054. By acquiring data on pipeline material aging and operating status through ultrasonic detection and pressure sensors, a risk assessment function is constructed, and priority weights are calculated using the random forest algorithm to optimize pipeline location and improve maintenance efficiency.

[0089] Ultrasonic testing using a 5MHz probe measures material aging; a 15% reduction in the wall thickness of steel pipelines is considered severe aging. Pressure sensors record the highest pressure for gas pipelines at 0.4 MPa, water supply pipelines at 0.6 MPa, and sewage pipelines at 0.2 MPa. The media hazard classification is: gas at level 9, corrosive chemicals at level 8, and sewage at level 4. Operational status monitoring captures vibration frequency and temperature changes; vibration amplitude exceeding 2 mm triggers an early warning. A risk assessment function weights aging, pressure, and media hazard levels, and a random forest algorithm is trained with 1000 samples and 12-dimensional features to generate priority weights. Safety distance regulations require a 1-meter distance between gas and power pipelines, and 0.5 meters between gas and water supply pipelines. High-risk pipelines are preferentially placed in easily accessible maintenance areas, such as within 5 meters of maintenance manholes, where ground vibration is less than 0.5 mm / s; low-risk pipelines are placed in confined spaces, such as around building foundations.

[0090] S1055. Utilize spatial grid generation and vibration sensor data to assess external disturbances, calibrate pipeline location scores, and generate the final pipeline distribution data package through constraint analysis and grid verification.

[0091] The spatial grid is used to classify underground areas in 0.5-meter increments, with vibration sensors spaced 10 meters apart. External disturbances are calculated using surface vibration waveforms. The maintenance difficulty coefficient is related to burial depth and pipeline density; the difficulty increases by 0.2 for every meter increase in burial depth and by 0.1 for every additional surrounding pipeline. Location scoring is out of 10 points, with easily accessible areas scoring 8 points or higher and confined spaces scoring below 4 points. Constraint analysis ensures high-risk pipelines are placed in locations with short replacement cycles and easy excavation. A grid verifier checks the spacing with a 0.2-meter grid, identifying 5 insufficient spacing points with a deviation of 0.1 meters, which are eliminated through fine-tuning in 0.05-meter increments. The final data package includes the pipeline's 3D coordinates, risk level, and maintenance difficulty, with an update cycle of 24 hours. In practical applications, this reduces disturbance events by 80% and improves space utilization by 15%.

[0092] Optimizing pipeline layout through real-time monitoring and multi-dimensional risk assessment significantly reduces vertical and horizontal risks in intersection sections. Prioritization and differentiated layout strategies ensure the safety and maintenance efficiency of high-risk pipelines, while collaborative distribution schemes maximize space utilization while meeting safety clearance requirements, providing technical support for the sustainable development of urban underground pipeline networks.

[0093] S106. Calculate space utilization efficiency based on the optimized pipeline collaborative distribution scheme, and iteratively update the layout model through environmental monitoring data and data assimilation technology to generate pipeline layout optimization results that adapt to the dynamic environment, so as to improve the operation efficiency and safety of the urban underground pipeline network.

[0094] To enable underground pipeline layouts to continuously adapt to dynamic environments, this invention employs high-precision spatial analysis and environmental data fusion to construct an adaptive optimization model, ensuring that the layout scheme balances safety and efficiency under complex conditions.

[0095] S1061. Use three-dimensional laser scanning technology to extract pipeline spatial coordinates, use the grid division method to calculate space utilization, and generate a pipeline spatial distribution status table to provide basic data for layout optimization.

[0096] The spatial coordinates of the pipelines are obtained through 3D laser scanning with a scanning accuracy of 1 mm, covering the pipeline body and the volume of its protected area. A grid-based method is used to calculate space utilization with a grid size of 0.5 meters; for example, the utilization rate is approximately 78% in commercial areas and approximately 56% in residential areas. A pipeline spatial distribution table records key parameters, such as the average vertical spacing of gas and water supply pipelines being 0.8 meters. This table quantifies the pipeline distribution characteristics, providing data support for correction and optimization, and ensuring the comprehensiveness and accuracy of the layout analysis.

[0097] S1062. Collect underground space temperature, humidity and soil stress data through environmental monitoring sensors, combine them with groundwater level monitoring values ​​to construct an environmental parameter dataset, and analyze the environmental change patterns.

[0098] Environmental monitoring sensors are deployed at 10-meter intervals, monitoring temperatures from -20 to 60 degrees Celsius and humidity from 0 to 100%. Soil stress data reflects the pressure around the pipeline, fluctuating between 200 and 400 kPa. Groundwater level monitoring shows an annual variation of up to 1.5 meters, with summer rises causing a 25% decrease in soil bearing capacity. The environmental parameter dataset integrates this data, revealing seasonal and periodic patterns; for example, when the groundwater level rises at a rate of 3 centimeters per day, the layout needs to be adjusted. The dataset provides multidimensional evidence for environmental adaptability analysis, enhancing the model's responsiveness to dynamic conditions.

[0099] S1063. The Kalman filter algorithm is used to correct the spatial distribution of pipelines in real time, and a pipeline layout prediction function is generated in combination with the trend of environmental changes to guide iterative optimization.

[0100] The Kalman filter corrects the spatial distribution with a 1-hour cycle, and the measurement noise variance is set to 0.01 to ensure data smoothness. Environmental change trends are analyzed using exponential smoothing with a smoothing coefficient of 0.3, identifying short-term and long-term patterns of groundwater level and stress changes. Data assimilation employs the ensemble Kalman method with 100 ensemble members and a 24-hour assimilation window to generate a pipeline layout prediction function. This function predicts the changing trends of layout parameters; for example, it indicates that vertical spacing needs to be increased by 10% when the water level rises, providing a quantitative basis for dynamic optimization.

[0101] S1064. Iteratively optimize pipeline layout using deep reinforcement learning methods, determine update cycle based on environmental change rate, generate optimization schemes that adapt to dynamic environment, and verify their rationality.

[0102] The particle filtering algorithm employs 200 particles, an adaptive resampling strategy, and an iteration step size of 0.1 meters to optimize the layout adjustment range. Deep reinforcement learning utilizes a dual network structure with a 12-dimensional state space and a 6-dimensional action space, optimizing for spatial constraints such as minimum safe spacing and cover depth. The update cycle is related to the rate of environmental change, lasting 72 hours in stable conditions and decreasing to 4 hours during drastic changes. A grid validator uses a 0.2-meter grid to test spatial interference, spacing compliance, and stress distribution. Validation shows that the optimized space utilization is improved by 15%, and the minimum spacing is increased by 0.2 meters. Under scenarios with drastic water level fluctuations, the layout scheme remains stable, with spacing fluctuations controlled within 5%, demonstrating excellent adaptability.

[0103] S107. By extracting the vertical layering and horizontal spacing variation trends from the pipeline layout optimization results, and combining real-time environmental monitoring data to continuously track the burial depth deviation and protective layer damage in the intersection sections, a target assessment model for quantifying long-term safety risks is constructed.

[0104] To achieve accurate assessment of long-term safety risks of underground pipelines, embodiments of the present invention dynamically monitor pipeline status and optimize risk models.

[0105] S1071. Decompose the pipeline layout optimization results, extract the dynamic change data of vertical layering and horizontal spacing, generate a pipeline layout trend prediction function through a long short-term memory network, and construct a pipeline spatial parameter change table.

[0106] The time-series data processor decomposes the pipeline layout data, extracting the fluctuation characteristics of vertical layer spacing and horizontal safety distance. The Long Short-Term Memory (LSTM) network employs a three-layer structure with 64 hidden nodes and a 30-day time window, suitable for capturing periodic and trend changes. For example, during the rainy season, vertical layer spacing can fluctuate by up to 0.2 meters, and horizontal spacing changes by approximately 0.15 meters. The generated pipeline spatial parameter change table records this dynamic data, including timestamps, spacing values, and change amplitudes, providing a basis for subsequent deviation correction and risk assessment.

[0107] S1072. Obtain the actual burial depth of the pipeline and the characteristics of soil layer distribution through a burial depth measuring instrument and a ground-penetrating radar scanner. Combine this with environmental sensors to monitor soil moisture content and water level changes, and generate a real-time environmental monitoring dataset to analyze external influencing factors.

[0108] The burial depth measuring instrument employs Doppler ranging technology with an accuracy of 1 mm and a sampling interval of 10 meters, ensuring the reliability of the burial depth data. Ground-penetrating radar scans at a frequency of 400 MHz, reaching a depth of 5 meters, capturing soil layer distribution characteristics, such as the layered structure of sandy and clayey soils. Environmental sensors monitor soil moisture content, ranging from 15% to 35%, and groundwater level, with an annual variation of approximately 1.5 meters. High moisture content, such as exceeding 30%, can lead to a decrease in soil bearing capacity, affecting burial depth stability. The real-time environmental monitoring dataset integrates these parameters to reveal the impact of the environment on pipeline conditions, providing support for dynamic correction.

[0109] S1073. Adaptive filters are used to process environmental monitoring data, and the burial depth deviation correction value is calculated in combination with the pipeline space parameter change table to generate pipeline burial depth state parameters that include absolute burial depth and stability.

[0110] The adaptive filter, based on the Kalman algorithm, has a measurement noise variance of 0.01 and a process noise variance of 0.005, effectively smoothing environmental data noise in real time. Burial depth deviation correction incorporates geological conditions; for example, the correction coefficient is increased by 0.2 in soft soil areas and decreased by 0.1 in rocky areas, ensuring the correction results adapt to different environments. Pipeline burial depth status parameters include absolute burial depth, relative displacement, and stability indices, reflecting the pipeline's real-time position. For instance, during the rainy season, burial depth deviation may increase by 0.15 meters, requiring dynamic adjustment of stratification standards to mitigate risk.

[0111] S1074. Obtain protective layer damage data through ultrasonic detection and electromagnetic induction scanning, construct a state assessment function, and use a deep neural network to integrate burial depth and damage data to generate safety risk assessment indicators.

[0112] Ultrasonic testing uses a 5MHz probe to measure the protective layer thickness with an accuracy of 0.1 mm, detecting damage depth. Electromagnetic induction scanning employs the pulsed eddy current method with a 5 mm interval to assess the corrosion rate; for example, a corrosion rate exceeding 0.1 mm per year triggers an early warning. A protective layer status assessment function quantifies damage depth and area, triggering an early warning when the depth exceeds the protective layer thickness by 30%. A deep neural network is input with 12 features, including burial depth, damage parameters, and environmental factors. Training data covers 365 days, and validation accuracy reaches 90%. Safety risk assessment indicators use a 0-10 scale, with 5 points as the warning line and 7 points as the danger line, providing a quantitative reference for risk management.

[0113] S1075. Validate risk assessment indicators using historical data, adjust risk correction coefficients in conjunction with environmental change trends, continuously monitor through a sliding time window, and generate a target assessment model parameter set to predict long-term safety risks.

[0114] Historical monitoring data is validated over a 365-day period, revealing the seasonal characteristics of environmental changes, such as a 0.3 increase in risk correction coefficient during the rainy season and a 0.2 decrease during the dry season. A sliding time window spans 30 days with a 1-day step size, dynamically tracking changes in burial depth and damage. The target assessment model parameter set includes short-term risk values ​​and long-term trends, updated every 24 hours. Continuous monitoring shows a risk assessment accuracy of 85%, an early warning lead time of approximately 7 days, and the model maintains high predictive accuracy even under extreme weather conditions, providing a reliable basis for long-term pipeline safety management.

[0115] By combining time-series analysis and multi-source data fusion, the assessment model dynamically monitors burial depth deviation and protective layer damage, effectively quantifying long-term safety risks. The combination of deep learning and adaptive filtering enhances the model's responsiveness to complex environments, providing technical assurance for the continuous safe operation of urban underground pipeline networks.

[0116] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for optimizing and evaluating line relocation schemes based on multidimensional constraints, characterized in that, The method includes: Obtain the location information of urban underground pipelines, extract the burial depth of unmarked pipelines and intersections based on the location information of underground pipelines, and mark them to obtain pipeline distribution data containing three-dimensional coordinates and burial depth deviations; Statistical analysis of pipeline distribution data yields pipeline type and burial depth distribution characteristics. Based on these characteristics, the vertical distribution characteristics of various pipeline types in intersection sections are analyzed. These vertical distribution characteristics include vertical spacing between pipelines and burial depth deviation at intersections. Vertical stratification standards are then adjusted based on these vertical distribution characteristics. Extract the horizontal spacing information of pipelines from the preliminary optimized layered scheme, and use a spatial geometric algorithm to determine whether the spacing meets the safety requirements. If not, adjust the horizontal spacing parameters to obtain the adjusted horizontal spacing parameters. Data on the degree of damage to the protective layer is obtained. If the degree of damage exceeds a preset threshold, a pipeline layout model with real-time response is established by dynamically updating the vertical layering standard and horizontal spacing parameters. By using a real-time response pipeline layout model, the vertical crossing risk and horizontal collision risk of unmarked pipelines in the crossing section are analyzed. Based on the risk assessment results, the pipelines of various types are reordered to obtain an optimized pipeline collaborative distribution scheme. Based on the optimized pipeline collaborative distribution scheme, the pipeline space utilization efficiency index is calculated, and the pipeline layout model is iteratively updated to obtain the pipeline layout optimization result that adapts to the dynamic environment. Extract the trend data of vertical layering standards and horizontal safety distances from the pipeline layout optimization results. Based on real-time environmental perception capabilities, continuously monitor the burial depth deviation and protective layer damage status of the intersection section to obtain assessment parameters for quantifying the long-term safety risk targets of the pipeline.

2. The method for optimizing and evaluating line relocation schemes based on multidimensional constraints according to claim 1, characterized in that, The process of acquiring urban underground pipeline location information, extracting the burial depth of unmarked pipelines and intersections based on the underground pipeline location information, and then marking them to obtain pipeline distribution data containing three-dimensional coordinates and burial depth deviations includes: Ground-penetrating radar array scanners are used to acquire underground echo signals. The initial pipeline signal data is obtained by layering the echo signals based on the soil dielectric constant and the pipeline material reflection coefficient. The initial pipeline signal data is spatially filtered using an image enhancement operator. If pipeline edge feature points are detected, a three-dimensional reference point set for the pipeline is constructed based on the feature points. The three-dimensional coordinates of ground control points are obtained by measuring with a total station. The three-dimensional reference point set of the pipeline is then subjected to elevation correction based on the three-dimensional coordinates of the ground control points to obtain the measured three-dimensional point set of the pipeline. The least squares curve fitting method is used to fit the measured three-dimensional point set of the pipeline, and the spatial orientation of the pipeline is determined based on the rate of curvature change of the curve fitting result to obtain the spatial distribution data of the pipeline.

3. The method for optimizing and evaluating line relocation schemes based on multidimensional constraints according to claim 1, characterized in that, The statistical analysis of pipeline distribution data yields pipeline type and burial depth distribution characteristics. Based on these characteristics, the vertical distribution characteristics of various pipeline types in intersection sections are analyzed. These vertical distribution characteristics include pipeline vertical spacing and intersection burial depth deviation. The vertical stratification standard is adjusted based on these characteristics, including: The pipeline spatial distribution data is processed using the kernel density calculation method to obtain pipeline burial density distribution data, and the dense pipeline sections are identified based on the pipeline burial density distribution data. The point cloud density algorithm is used to extract nodes in the dense pipeline section to obtain the coordinate data of pipeline intersection nodes, and the three-dimensional spatial angle of the intersecting pipelines is calculated by the nearest neighbor search method. Extract the minimum burial depth specification value of the pipeline from the pipeline attribute database, and construct an initial vertical layer table based on the minimum burial depth specification value of the pipeline; The vertical projection distance is calculated based on the three-dimensional spatial angle of the intersecting pipelines. The updated vertical stratification standard is obtained by comparing the measured burial depth value with the standard value in the initial vertical stratification table based on the vertical projection distance.

4. The method according to claim 3, characterized in that, Also includes: Pipeline types are classified according to safety risk level and facility importance. The deviation between existing vertical stratification standards and actual vertical distribution is compared. Minimum safe vertical distances between different types of pipelines are calculated. Differentiated stratification parameters are set for pipeline intersections in densely populated areas and under special geological conditions. The adjusted vertical stratification scheme is then entered into the underground pipeline information system to update pipeline layout standards. Specifically, this includes: The analytic hierarchy process was used to obtain quantitative data on pipeline operating pressure and facility service life, and the pipeline safety level classification results were obtained through the decision tree algorithm. The initial value of the minimum safe distance is calculated based on the pipeline safety level classification results and geological drilling data. The initial value of the minimum safe distance is generated by the pipeline density detector when obtaining the number of pipelines per unit area. The vertical distribution correction parameters are calculated using the initial value of the minimum safe distance and the measured pipeline burial depth coordinates. The vertical distribution correction parameters are generated when the vertical layering deviation value is determined by the standard spacing data. A stress correction model is established using the vertical distribution correction parameters and pipeline intersection angles. If the soil stress distribution data meets the geological influence coefficient requirements, a differentiated stratification scheme is generated, and pipeline layout specification data is generated using the differentiated stratification scheme.

5. The method for optimizing and evaluating line relocation schemes based on multidimensional constraints according to claim 1, characterized in that, The process involves extracting horizontal pipeline spacing information from the initially optimized layered scheme, using a spatial geometric algorithm to determine if the spacing meets safety requirements. If not, the horizontal spacing parameters are adjusted to obtain the adjusted horizontal spacing parameters, including: The horizontal spacing of pipelines is obtained based on the pipeline layout density data, and the pipelines are classified and labeled according to the pipeline protection level rules to obtain pipeline type data; Extract the minimum safe distance requirements between pipelines of different protection levels from the pipeline safety specification library, and generate horizontal safe distance thresholds based on pipeline type data; The nearest neighbor search algorithm is used to locate the coordinate point set of underground obstacles, and the triangulation method is used to construct the pipeline crossing path map to obtain the avoidance path data. For sections in the avoidance path data that do not meet the safety distance requirements, extract pipeline coordinate data and calculate pipeline position adjustment amount to obtain pipeline spacing optimization parameters.

6. The method for optimizing and evaluating line relocation schemes based on multidimensional constraints according to claim 1, characterized in that, The process involves acquiring damage data of the protective layer. If the damage exceeds a preset threshold, a real-time pipeline layout model is established by dynamically updating the vertical layering standard and horizontal spacing parameters, including: A damage assessment function is constructed based on the protective layer thickness data and corrosion degree. The damage area value and depth parameter are extracted. Stress sensor is used to collect pipeline stress distribution data. A damage early warning function is established based on the damage area value and depth parameter. Calculate the vertical layering parameter value based on the damage warning function, obtain the horizontal spacing adjustment amount, and construct the pipeline spatial layout diagram through the grid search method; The particle swarm optimization algorithm is used to optimize the pipeline spatial layout diagram. The layout adjustment range is set according to the damage propagation trend to generate a pipeline layout model with real-time response.

7. The method for optimizing and evaluating line relocation schemes based on multidimensional constraints according to claim 1, characterized in that, The method utilizes a real-time response pipeline layout model to analyze the vertical crossing and horizontal collision risks of unmarked pipelines in intersection sections. Based on the risk assessment results, various types of pipelines are reordered to obtain an optimized pipeline cooperative distribution scheme, including: Vertical stress distribution data is calculated based on pipeline operating pressure parameters. The vertical stress distribution data is generated by the spatial vector calculation module through vertical intersection angle values. The horizontal collision point coordinate set is obtained by a collision detector, and the horizontal collision point coordinate set is determined by the grid density calculation method based on the pipeline density value. The risk level assessment value is obtained by processing the vertical stress distribution data and the horizontal collision point coordinate set using the analytic hierarchy process. The risk level assessment value is generated by the risk assessment matrix. High-risk pipeline spatial layout data is obtained by using a grid partitioning method. The high-risk pipeline spatial layout data is generated by calculating the safe distance between pipelines based on the risk level assessment value according to the protection level threshold. An optimized pipeline cooperative distribution scheme is generated using a particle swarm optimization algorithm.

8. The method according to claim 7, characterized in that, Also includes: Data on the aging degree of various pipeline materials, pressure rating, medium hazard, and operating status in the intersection section are extracted. Priority weights are assigned to each type of pipeline, and the safety margin for each pipeline type at different locations is calculated. High-risk pipelines are placed in locations that are easy to maintain and minimally affected by external interference, while low-risk pipelines are arranged in confined spaces. This forms a target pipeline collaborative distribution scheme that meets safety distance requirements and maximizes space utilization. Specifically, this includes: An ultrasonic detector is used to obtain aging indicators of pipeline materials, and a pressure sensor is used to collect pressure level values. A pipeline characteristic table is generated based on the pipeline's basic characteristic data. A risk assessment function is constructed based on the pipeline feature table, the pipeline priority weight value is calculated using the random forest algorithm, and the spacing requirements are read from the pipeline safety specification library to obtain pipeline safety assessment data. The underground space area is classified using a spatial grid division method. External disturbance is calculated using vibration sensor data. The spatial location score of the pipeline is determined based on the pipeline safety assessment data. Constraint analysis is performed on the pipeline spatial location score, and a grid verifier is used to perform a security check on the differentiated layout data to form a target pipeline collaborative distribution scheme that meets the safety spacing requirements and space utilization. Pipeline distribution data packages are generated based on the priority weight values.

9. The method for optimizing and evaluating line relocation schemes based on multidimensional constraints according to claim 1, characterized in that, The process involves calculating pipeline space utilization efficiency indices based on the optimized pipeline cooperative distribution scheme, iteratively updating the pipeline layout model, and obtaining optimized pipeline layout results adapted to the dynamic environment, including: Based on the pipeline collaborative distribution scheme, the pipeline spatial coordinate parameters are collected, and the pipeline space utilization rate index is calculated through the spatial coordinate parameters. The space utilization rate index is generated by the grid division method to generate a pipeline spatial distribution status table. Temperature and humidity parameters of underground space are collected from environmental monitoring sensors. Environmental parameter monitoring datasets are constructed by combining the temperature and humidity parameters with pressure monitoring data. The environmental parameter monitoring datasets include groundwater level monitoring values. Real-time correction signals are obtained based on the pipeline spatial distribution status table, environmental change patterns are calculated using the environmental parameter monitoring dataset, and a pipeline layout prediction function is generated using a data assimilation algorithm. The pipeline layout prediction function is iteratively calculated using deep reinforcement learning methods. The pipeline layout update cycle is determined by the environmental change patterns, and an optimized pipeline spatial layout scheme is obtained.

10. The method for optimizing and evaluating line relocation schemes based on multidimensional constraints according to claim 1, characterized in that, The extracted pipeline layout optimization results show changing trends in vertical layering standards and horizontal safety clearances. Based on real-time environmental awareness, continuous monitoring of burial depth deviations and protective layer damage in intersection sections is conducted to obtain assessment parameters for quantifying long-term pipeline safety risk targets, including: Based on pipeline layout data collected by the time-series data processor, a pipeline layout trend prediction function is constructed through a long short-term memory network to obtain a pipeline spatial parameter change table; Soil layer distribution characteristic data are obtained by using a ground-penetrating radar scanner, and soil moisture content is monitored by environmental sensors to generate a real-time environmental monitoring dataset. Adaptive filtering is performed on the real-time environmental monitoring dataset, and the burial depth deviation correction value is calculated in conjunction with the pipeline space parameter change table to obtain the pipeline burial depth status parameters. A deep neural network model is constructed using the pipeline burial depth parameters. The parameters of the deep neural network model are verified using historical monitoring data, and safety risk assessment indicators are generated.

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