Multi-dimensional data processing and modeling method for urban lifeline safety construction

Abstract

The invention discloses a multi-dimensional data processing and modeling method for urban lifeline safety construction, which belongs to the technical field of gas line maintenance and comprises the following steps: acquiring multi-dimensional data, preprocessing urban regional map and gas pipe network distribution data, performing coordinate alignment, and establishing a multi-dimensional model; associating the gas pipe network attribute data with the urban gas pipe network distribution map to obtain maintenance accident data, mapping a position corresponding to the maintenance accident data to the urban gas pipe network distribution map to obtain a coordinate of an accident position in a three-dimensional coordinate system, and integrating historical accident data into a three-dimensional model according to a time sequence to obtain a three-dimensional model; a three-dimensional urban gas pipe network model is obtained; in the coordinate alignment process, technologies such as feature point extraction and space duplicate removal are adopted, the optimal feature point is selected as a reference for coordinate conversion, the workload of reference object marking in advance is reduced, meanwhile, it is guaranteed that the space coverage of coordinate alignment meets the engineering specification requirement, and the efficiency and precision of coordinate alignment are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of gas line maintenance, and more specifically to a multidimensional data processing and modeling method for urban lifeline safety construction. Background Art

[0002] Urban lifeline safety construction refers to the use of technical means and management strategies to ensure the safe operation of key urban infrastructure, prevent accidents, and enhance urban resilience. Its core goal is to reduce the probability of accidents such as gas explosions, bridge collapses, and urban flooding through real-time monitoring, risk assessment, and intelligent early warning, thereby ensuring the normal operation of urban functions and the safety of residents' lives and property. By building models based on real-time monitoring data and environmental data from lifeline systems such as urban gas, water supply, drainage, and bridges, early identification of urban hazards can be achieved. However, for mountainous cities, due to the rugged terrain and complex geological conditions, underground pipelines are at high risk of corrosion and displacement, and bridges and tunnels are vulnerable to landslides and subsidence. In addition, data comes from a wide range of sources, so it is necessary to model various types of urban lifelines, including gas lines, water lines, drainage lines, and bridges. Among them, gas lines are particularly important as they are important and pose the greatest risk of accidents. In the actual modeling process, the city map and gas pipeline map need to be aligned. In order to meet the spatial coverage, the existing technology will select large areas and reference objects as the reference basis for the coordinate alignment, especially in mountainous areas, which increases the workload. Moreover, urban gas pipeline networks are distributed at different depths, including surface pressure regulating stations, meters and other facilities, shallow underground trunk pipelines, and deep underground pipeline sections. The traditional single plane coordinate system is difficult to accurately express the complex spatial relationships between these pipelines at different depths, such as the three-dimensional intersection and upper and lower layer avoidance. This can easily lead to problems such as ambiguous pipeline positioning and collisions. For example, shallow trunk pipelines and deep river-crossing pipelines may appear to overlap when projected onto the same plane coordinate system. Summary of the Invention

[0003] To solve the above problems, the present invention provides a multi-dimensional data processing and modeling method for urban lifeline safety construction.

[0004] The present invention provides a multidimensional data processing and modeling method for urban lifeline safety construction, comprising the following steps: S1. Collect multidimensional data, including urban area map data, gas pipeline network distribution data, gas pipeline network attribute data, and maintenance accident data; S2. Preprocessing the urban area map and the gas pipeline network distribution data, then performing coordinate alignment, and overlaying the gas pipeline network distribution data onto the urban area map; S3. Associating the gas pipe network attribute data with the city gas pipe network distribution map, and binding the gas pipe network attribute data to the corresponding gas pipe network line segment; S4. For each independent management area, select the feature point with the largest coverage density from the reference feature points selected in S2 as a candidate node, ensuring that there is at least one candidate point in each management area, and obtain a candidate point set; The typical depth value of each independent management area is extracted from the candidate point set. Then, the hierarchical boundary threshold is calculated based on the gas pipeline network attribute data and the candidate point set. The urban gas pipeline network distribution map is divided into surface layer, shallow layer and deep layer according to the hierarchical boundary threshold. Each layer is assigned an independent coordinate system, and the three-level coordinate system with a hierarchical relationship is output. A three-dimensional coordinate system is established for the urban gas pipeline network distribution map, and the three-dimensional position coordinates of the gas pipeline network segment in each independent management area are obtained. Obtain maintenance accident data, map the location corresponding to the maintenance accident data to the city gas pipeline network distribution map, and obtain the coordinates of the accident location in the three-dimensional coordinate system; Integrate historical accident data into the three-dimensional model in time series to obtain a three-dimensional city gas pipeline network model; S5. Based on the three-dimensional city gas pipeline network model, a risk assessment model is constructed. The risk assessment model receives data provided by the three-dimensional city gas pipeline network model and outputs a risk assessment result for each independent management area in the three-dimensional city gas pipeline network model.

[0005] Preferably, said S2 comprises the following steps: S21. Collect urban area map data and gas pipeline network distribution data, perform integrity checks on them, and supplement any missing information. S22. Select a unified geographic coordinate system, convert the urban area map data and the gas pipeline network distribution data into the unified coordinate system, and complete coordinate alignment; S23. Overlaying the gas pipeline network distribution data after the coordinates are aligned with the urban area map, integrating the gas pipeline network distribution data into the urban area map, and generating a city gas pipeline network distribution map.

[0006] Preferably, the specific working steps of S22 also include the following: First, feature points are extracted. For urban area maps, road intersections, independent building corners, and bridge endpoints are extracted as the first type of feature points. For gas pipeline network distribution data, valve nodes and pipeline inflection points are extracted as the second type of feature points; Then set a distance threshold , perform spatial deduplication on the first type of feature points and the second type of feature points; The first and second feature points are grouped into two feature point sets respectively. The points closest to the boundary in the first and second feature points are selected as base points respectively. With the base point as the origin, the polar angles of all other points relative to the base point are calculated and sorted in a counterclockwise direction. For both feature point sets, a stack is used to construct the convex hull. The base point and the first two points after sorting are pushed into the stack. Then, the sorted feature point sets are traversed in sequence. For each new point, check whether the three points at the top of the stack form a right turn. If it is a right turn, the point at the top of the stack is popped out. Otherwise, the new point is pushed into the stack. The points in the stack constitute the vertices of the convex hull. All the vertices of the convex hull are used as the selected reference feature points to obtain the first type of reference feature points and the second type of reference feature points. The urban area map data and the gas pipe network distribution data are respectively converted into the unified coordinate system according to the first type of reference feature points and the second type of reference feature points.

[0007] Preferably, the specific steps of converting the urban area map data and the gas pipe network distribution data into the unified coordinate system according to the first type of reference feature points and the second type of reference feature points are as follows: The first type of reference feature points and the second type of reference feature points obtained previously are taken as point pairs with the same name; Use the seven-parameter conversion function of the GIS software to input these pairs of points with the same name and calculate the parameters that need to be adjusted; The calculated seven parameters are applied to the entire gas pipeline network distribution data set, and the gas pipeline network data are batch converted from the original coordinate system to the target unified coordinate system through the conversion tool or programming tool of the GIS software.

[0008] Preferably, said S3 comprises the following steps: S31, obtaining the boundary of the city gas supply area, and extracting the boundary information of the city gas supply area from the city area map data; Based on the extracted urban gas supply area boundaries, the urban gas network distribution map is divided into multiple independent management areas, each of which represents a specific gas supply area; S32. Identify the starting point and end point of each gas pipeline network segment in the gas pipeline network distribution data, and mark a continuous gas pipeline network segment from the starting point to the end point as a gas pipeline network segment; S33. Organize attribute information related to the gas pipeline network, including gas pipeline material, pipe diameter, and wall thickness data; The collected gas network attribute data is associated with the corresponding network segments, and a database is created to store each network segment and its associated attribute data. A unique ID is assigned to each gas network segment. Generate a preliminarily processed city gas pipeline network distribution map, combine the established pipeline network attribute database with the city gas pipeline network distribution map, and generate a preliminarily processed city gas pipeline network distribution map containing detailed attribute information.

[0009] Preferably, said S4 comprises the following steps: S41. For each independent management area, select the feature point with the largest coverage density from the reference feature points selected in S2 as a candidate node, ensuring that there is at least one candidate point in each management area, and obtain a candidate point set; S42 extracts the typical depth value of each independent management area from the candidate point set, then calculates the hierarchical boundary threshold based on the gas pipeline network attribute data and the candidate point set, divides the urban gas pipeline network distribution map into a surface layer, a shallow layer, and a deep layer according to the hierarchical boundary threshold, assigns an independent coordinate system to each layer, and outputs a three-level coordinate system with a hierarchical relationship; Extract the branch structure of each independent management area in the gas pipeline network distribution data. Starting from the main pipeline, generate a local coordinate system tree according to the branch hierarchy relationship. Merge the local coordinate system with the overall three-level coordinate system to obtain the three-dimensional position coordinates of the gas pipeline network segment in each independent management area. S43. Extracting an accident location description from the maintenance accident data, obtaining location information of the maintenance accident based on the accident location description, and mapping the location corresponding to the maintenance accident data to a three-level coordinate system to obtain the three-dimensional location coordinates of the accident location in the gas pipeline network segment; S44. A three-dimensional urban gas pipeline network model is established based on the three-level coordinate systems and the three-dimensional position coordinates of the gas pipeline network segments. The historical accident data are integrated in time series to obtain a maintenance accident data dataset. The maintenance accident data dataset is marked at the coordinate position of the gas pipeline network segment corresponding to the accident occurrence, specifically including the three-dimensional coordinates of the accident point, the time of occurrence and the maintenance accident data.

[0010] Preferably, the specific steps of S42 are as follows: First, the typical depth value of each independent management area is extracted from the candidate point set; Then, the layer boundary threshold is calculated based on the gas network attributes and typical depth values. ; The urban gas pipeline network distribution map is divided into surface layer, shallow layer and deep layer according to the layer boundary threshold; Calculated level boundary threshold The dividing value used to distinguish shallow and deep layers, with each layer given an independent coordinate system; The branch structure of each independent management area in the gas pipeline network distribution data is extracted. Starting from the main pipeline, a local coordinate system tree is generated according to the branch hierarchical relationship, and the local coordinate system is integrated with the overall three-level coordinate system.

[0011] Preferably, the specific steps of S43 are as follows: Based on the accident location description and gas pipeline network distribution data, determine whether the accident occurred in the surface layer, shallow layer, or deep layer; convert the geographic coordinates of the accident location into coordinates in the three-level coordinate system; based on the common reference point and coordinate conversion parameters, convert the latitude and longitude coordinates of the accident location into three-dimensional coordinates in the surface layer, shallow layer, or deep layer coordinate system; The specific steps of S44 are as follows: Integrate the obtained three-level coordinate systems and the 3D position coordinate data of the gas pipeline network segments, and use 3D modeling software to construct a 3D model of the gas pipeline network based on the 3D coordinates of the pipeline network segments. Add gas network attribute data to each gas network segment in the gas network model; The collected maintenance accident data is sorted by occurrence time to form a time series. Based on the sorted historical accident data, a maintenance accident data dataset is created. The maintenance accident data dataset is marked at the coordinate position of the gas pipeline line segment corresponding to the accident occurrence; In the 3D city gas network model, the maintenance accident data set is associated with the corresponding network segment according to the 3D coordinates of the accident point.

[0012] Preferably, the S5 comprises the following steps: S51. Install a sensor on each gas pipe network segment to collect the operating status of each gas pipe network segment; Based on the 3D city gas network model, the operating status of each gas network segment is input to build a risk assessment model; S52. The risk assessment model receives data provided by the three-dimensional urban gas pipeline network model in real time, including the pipeline network topology structure of each independent management area; protection data and equipment operating status, and uses the constructed risk assessment model to perform risk assessment calculations on the gas pipeline network of each independent management area. According to the risk assessment calculation results of each independent management area, the independent management areas with higher risks and major risk factors are identified, and the parameters of the risk assessment model are dynamically adjusted according to the assessment results.

[0013] Preferably, the specific working steps of S52 are as follows: Construct a feature vector based on the pre-processed operating status data, pipe network topology and maintenance data; Input the constructed feature vector into the risk assessment model to perform risk assessment calculations on the gas pipeline network in each independent management area; Analyze the risk values output by the model to determine the risk level. Set risk thresholds based on the actual situation and safety standards of the gas pipeline network, and divide the risk level into three levels: low, medium, and high. Calculate the risk level distribution of each independent management area and identify the independent management area with the highest risk. Based on the assessment results, the parameters of the risk assessment model are dynamically adjusted, and the monitoring frequency is increased for the independent management areas with the highest risks.

[0014] Beneficial effects: Through multi-dimensional data collection, comprehensive integration of information related to urban gas pipeline networks provides a rich and accurate data foundation for subsequent modeling and risk assessment; During the coordinate alignment process, we use techniques such as feature point extraction and spatial deduplication to select the optimal feature points as references for coordinate transformation, reducing the workload of pre-annotating reference objects while ensuring that the spatial coverage of the coordinate alignment meets engineering specifications. This improves the efficiency and accuracy of coordinate alignment and lays the foundation for the accuracy of subsequent modeling. Rationally partition the urban gas pipeline network distribution map into several independent management areas, associate and bind pipeline network attribute data with line segments, establish a pipeline network attribute database, and achieve refined management and efficient query of pipeline network information, providing strong support for subsequent analysis and decision-making; By introducing a three-dimensional coordinate system, the urban gas pipeline network distribution map is divided into surface, shallow, and deep layers, and each layer is assigned an independent coordinate system. This accurately expresses the complex spatial relationships between pipelines at different depths, avoiding positioning ambiguity and collisions caused by the traditional single-plane coordinate system. This improves pipeline network positioning accuracy, provides clear spatial guidance for new construction, renovation, and maintenance of pipeline networks, and reduces the risk of construction errors. Maintenance accident data is mapped into a three-dimensional coordinate system. The three-dimensional coordinates, occurrence time and maintenance accident data of the accident point are marked on the gas pipeline network segment. A three-dimensional urban gas pipeline network model is constructed to realize the visualization and spatiotemporal correlation analysis of accident data, which helps to quickly locate the accident point and improve the efficiency and pertinence of maintenance work. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0016] like Figure 1 As shown: A multidimensional data processing and modeling method for urban lifeline safety construction includes the following steps: S1. Collect multidimensional data, including urban area map data, gas pipeline network distribution data, gas pipeline network attribute data, and maintenance accident data. It should be noted that urban area map data can be obtained through the city map app or through the city surveying and mapping department. Gas pipe network distribution data, including pipeline start and end coordinates and valve location information; obtained from drawings provided by the city gas department, usually in DWG format; Gas pipeline network attribute data, including gas pipeline material, diameter, and wall thickness; also provided by the city gas department; Maintenance accident data, including accident type, maintenance time and maintenance results; obtained through maintenance order data of city gas departments; S2. Preprocess the urban area map and gas pipeline network distribution data, and then perform coordinate alignment. It should be noted that this reduces the workload of pre-reference object annotation and ensures that the spatial coverage of the coordinate alignment meets the requirements of engineering specifications. Overlaying the gas pipeline network distribution data onto the urban area map, spatially connecting the gas pipeline network distribution with the urban area map, and obtaining an urban area map showing the gas pipeline network distribution data, which is labeled as an urban gas pipeline network distribution map; S3. Zoning the city gas pipeline network distribution map, dividing the city gas pipeline network distribution map into several independent management areas according to the boundaries of the city gas supply areas in the city area map; The gas pipeline network attribute data is associated with the gas pipeline network segments within several independently managed areas. The gas pipeline network attribute data is bound to the corresponding gas pipeline network segments to establish a pipeline network attribute database. Each pipeline network segment corresponds to a unique ID, and a preliminary processed urban gas pipeline network distribution map is obtained. It should be noted that the starting point and end point of each gas pipeline network segment are marked in the gas pipeline network distribution data. The gas pipeline network segment from the starting point to the end point is marked as a gas pipeline network segment. S4. For each independent management area, select the feature point with the largest coverage density from the reference feature points selected in S2 as a candidate node, ensuring that there is at least one candidate point in each management area, and obtain a candidate point set; The typical depth value of each independent management area is extracted from the candidate point set. Then, the hierarchical boundary threshold is calculated based on the gas pipeline network attribute data and the candidate point set. The urban gas pipeline network distribution map is divided into surface layer, shallow layer and deep layer according to the hierarchical boundary threshold. Each layer is assigned an independent coordinate system, and the three-level coordinate system with a hierarchical relationship is output. A three-dimensional coordinate system is established for the urban gas pipeline network distribution map, and the three-dimensional position coordinates of the gas pipeline network segment in each independent management area are obtained. Obtain maintenance accident data, map the location corresponding to the maintenance accident data to the city gas pipeline network distribution map, and obtain the coordinates of the accident location in the three-dimensional coordinate system; Integrate historical accident data into the 3D model in time series, mark the 3D coordinates of the accident points, occurrence time and maintenance accident data on the gas pipeline network segments, and obtain a 3D city gas pipeline network model; S5. Based on the three-dimensional urban gas pipeline network model, a risk assessment model is constructed. The three-dimensional urban gas pipeline network model updates data in real time. The risk assessment model receives data provided by the three-dimensional urban gas pipeline network model, outputs risk assessment results for each independent management area in the three-dimensional urban gas pipeline network model, and marks them in the three-dimensional urban gas pipeline network model.

[0017] Urban lifeline safety construction refers to the use of technical means and management strategies to ensure the safe operation of key urban infrastructure, prevent accidents, and enhance urban resilience. Its core goal is to reduce the probability of accidents such as gas explosions, bridge collapses, and urban flooding through real-time monitoring, risk assessment, and intelligent early warning, thereby ensuring the normal operation of urban functions and the safety of residents' lives and property. By building models based on real-time monitoring data and environmental data from lifeline systems such as urban gas, water supply, drainage, and bridges, early identification of urban hazards is possible. However, for mountainous cities, the high terrain and complex geological conditions lead to high risks of corrosion and displacement of underground pipelines, and bridges and tunnels are vulnerable to landslides and subsidence. Moreover, the data comes from a wide range of sources, and it is necessary to model various types of urban lifelines, including gas lines, water supply lines, drainage lines, and bridges. Among them, gas lines are important lines in urban lifelines and pose greater risks in the event of accidents, so modeling them is particularly important.

[0018] As an optional embodiment: S2 includes the following steps: S21. Collect urban area map data and gas pipeline network distribution data, perform integrity checks on them, and supplement any missing information. It should be noted that for urban area map data, it is necessary to confirm that the map data covers the entire urban area and check whether the boundaries are complete. For example, check whether the boundaries of the urban administrative divisions are closed and whether all relevant sub-regions (such as urban districts, counties, etc.) are included; Check road connectivity and look for road breakpoints, where gaps exist between two roads that should be connected. These potential breakpoints can be discovered using methods such as buffer analysis. For example, a narrow buffer zone can be created for each road to see if the buffer zones of adjacent roads touch each other. If not, a break may exist. Fix topology errors at road intersections to ensure that correct nodes are formed when roads intersect. There are no nodes missing or inaccurate node positions at road intersections. If there are errors, the road geometry needs to be re-edited to create the correct nodes. For gas pipeline network distribution data, check whether the starting and ending points of the gas pipeline are complete, ensure that each section of the pipeline has a clear starting and ending point, and there are no unwarranted interruptions to the pipeline. If a pipeline interruption is found, it is necessary to find the missing pipeline section information. This can be supplemented by consulting the gas company's pipeline network construction records, construction drawings, etc. Check the connectivity of the pipelines to see if the entire gas pipeline network forms a coherent network without isolated pipeline segments. Network connectivity analysis can be used to identify isolated pipeline segments and then take appropriate measures to repair them, such as adding missing connecting pipes or correcting incorrect connection relationships. S22. Select a unified geographic coordinate system, convert the urban area map data and the gas pipeline network distribution data into the unified coordinate system, and complete coordinate alignment; It should be noted that the problem of inconsistent coordinate systems of multidimensional data can be solved and the spatial position deviation caused by differences in coordinate systems can be eliminated; S23. Overlay the aligned gas pipeline network distribution data with the urban area map, integrating the gas pipeline network distribution data into the urban area map to generate a city gas pipeline network distribution map. It should be noted that spatially associating the gas pipeline network with urban geographic elements to generate a visual city gas pipeline network distribution map supports subsequent risk analysis and decision-making.

[0019] As an optional embodiment, the specific working steps of S22 further include the following: First, feature point extraction is performed. For urban area maps, road intersections, independent building corners, and bridge endpoints are extracted as the first type of feature points. It should be noted that these are obtained through map annotation and recognition. For the gas pipe network distribution data, the valve nodes and pipeline inflection points are extracted as the second type of feature points. It should be noted that the second type of feature points are obtained by marking the valve nodes and pipeline inflection points in the gas pipe network distribution data. Then set a distance threshold , perform spatial deduplication on the first type of feature points and the second type of feature points; It should be noted that spatial deduplication is specifically used to remove duplicate points or merge multiple points within a certain range into one point to reduce the amount of data and improve data processing efficiency and accuracy. The distance threshold is used to determine whether two points are close enough to be merged or considered as the same feature point. The specific steps are as follows: The adjacent distance is less than the distance threshold The first or second type of feature points are merged into a single point; It should be noted that the distance threshold Adjust according to the accuracy of the map. Initially it is 5m and can be adjusted according to actual needs. The first and second types of feature points are respectively formed into two feature point sets. The points closest to the boundary of the first and second types of feature points are selected as base points. With the base point as the origin, the polar angles of all other points relative to the base point are calculated and sorted in a counterclockwise direction. It should be noted that when urban feature points are widely distributed and have low density, the point set is arranged in order by polar angle sorting with the base point as the center, which facilitates the construction of the convex hull and can effectively process scattered feature points. It should be noted that the steps for calculating the polar angles of other points relative to the base point are as follows: According to the formula Get the polar angles of other points relative to the base point; in is the coordinate of the other i-th point in its own coordinate system, ; For both feature point sets, a stack is used to construct the convex hull, the base point and the first two points after sorting are pushed into the stack, and then the sorted feature point sets are traversed in sequence. For each new point, check whether the three points at the top of the stack form a right turn. If it is a right turn, pop the point at the top of the stack, otherwise, push the new point into the stack. The points in the stack constitute the vertices of the convex hull. All the vertices of the convex hull are used as the selected reference feature points to obtain the first type of reference feature points and the second type of reference feature points. Through the above method, the first type of feature points and the second type of feature points are formed into a feature point set, the point closest to the boundary is selected as the base point, and the extreme angle is sorted with the base point as the origin. Then, the stack is used to construct the convex hull. The number of convex hull vertices finally obtained depends on the distribution of the feature point set, which is usually much smaller than the total number of feature point sets. These vertices can effectively reflect the spatial distribution range of the feature point set and provide a quantitative basis for the subsequent optimal control point selection. The convex hull is the smallest convex polygon that contains all feature points. Its vertices are extreme points selected from the feature point set. These vertices are very representative in spatial distribution and can reflect the overall range and shape of the feature point set. In the selection of optimal control points, the convex hull vertices can be used as reference feature points for the following reasons: The convex hull vertices are the extreme points of the feature point set. Located on the boundary of the entire feature point set, they are highly representative of the spatial distribution and effectively cover the area occupied by the feature point set, reflecting its shape and extent. Using these vertices as reference points ensures that control points are evenly distributed across the urban area, improving the stability and accuracy of spatial alignment.

[0020] The convex hull can contain all feature points. Based on this property, using the convex hull vertices as reference feature points can ensure that the optimal control point set finally selected also has the ability to contain all feature points. In this way, when performing subsequent spatial alignment and coordinate transformation, the representativeness of the entire feature point set can be maximized, thereby improving positioning accuracy and spatial relationship preservation; The urban area map data and the gas pipe network distribution data are respectively converted into the unified coordinate system according to the first type of reference feature points and the second type of reference feature points.

[0021] In order to meet the spatial coverage requirements in the existing technology, large areas and reference objects are selected as reference bases for benchmarking during the coordinate alignment process, especially in mountainous areas. This technical solution selects the optimal feature points as references through calculation. On the basis of reducing the workload of pre-annotating reference objects, it ensures that the spatial coverage of coordinate alignment meets the requirements of engineering specifications, which is conducive to the accuracy of subsequent modeling.

[0022] As an optional embodiment, the specific steps of converting the urban area map data and the gas pipe network distribution data into the unified coordinate system according to the first type of reference feature points and the second type of reference feature points are as follows: The first type of reference feature points and the second type of reference feature points obtained previously are taken as point pairs with the same name; Use the seven-parameter conversion function of the GIS software to input these pairs of points with the same name and calculate the parameters that need to be adjusted. It should be noted that the parameters that need to be adjusted are mainly seven parameters, including the offset of the origin of the target coordinate system relative to the origin of the source coordinate system in the X, Y, and Z axis directions, the angles by which the target coordinate system needs to be rotated around the X, Y, and Z axes to align the coordinate axes, and the scale ratio difference of the target coordinate system relative to the source coordinate system. The calculated seven parameters are applied to the entire gas pipeline network distribution data set, and the gas pipeline network data are batch converted from the original coordinate system to the target unified coordinate system through the conversion tool or programming tool of the GIS software.

[0023] As an optional embodiment: S3 includes the following steps: S31. Obtaining the boundaries of the urban gas supply areas, extracting the boundary information of the urban gas supply areas from the urban area map data; it should be noted that these boundaries are usually determined by urban planning departments or gas companies based on factors such as administrative divisions, geographical characteristics, and gas supply demand; Based on the extracted boundaries of the urban gas supply areas, the urban gas network distribution map is divided into multiple independent management areas. Each management area represents a specific gas supply area. It should be noted that this provides a basic unit for subsequent refined management and analysis. S32. In the gas pipeline network distribution data, identify the starting point and end point of each gas pipeline network segment, and mark a continuous gas pipeline network segment from the starting point to the end point as a gas pipeline network segment. Ensure that each segment has a clear starting and ending point and direction, providing a clear spatial unit for subsequent attribute association and management. S33. Organize attribute information related to the gas pipeline network, including data on gas pipeline material, diameter, and wall thickness; this data comes from the gas company's operating records and construction data; The collected gas network attribute data is associated with the corresponding network segments, creating a database in which each network segment and its associated attribute data are stored, and each gas network segment is assigned a unique ID. Attribute information related to the gas network is scanned and extracted from paper documents. The collected attribute data such as gas pipeline material, diameter, and wall thickness is entered into the database to facilitate rapid query, update, and management of network information in the database. Assign a unique ID to each gas network segment, and match and associate the entered attribute data with the corresponding gas network segment ID; the ID format is area code + start point ID + end point ID + material code; The regional code is the code of the independent management area, and is sorted and coded in the order from top to bottom and from left to right; Generate a preliminary processed city gas pipeline network distribution map by combining the established pipeline network attribute database with the city gas pipeline network distribution map to generate a preliminary processed city gas pipeline network distribution map containing detailed attribute information. This provides comprehensive and accurate data support for subsequent analysis, management, and decision-making; It should be noted that gas company records may be paper documents or unstructured electronic files that need to be manually entered. The matching of pipeline network segment IDs and attribute tables relies on manual verification, which is prone to omissions or misalignment. After the pipeline network is renovated, the spatial data and attributes need to be updated synchronously, otherwise "map-attribute inconsistency" will occur. Through the above steps, this technical solution can reduce the error of combining the established pipeline network attribute database with the urban gas pipeline network distribution map. Each pipeline network segment not only has spatial location information, but also comes with its material, pipe diameter and other attribute data, providing comprehensive and accurate information support for subsequent analysis, management and decision-making.

[0024] As an optional embodiment: S4 includes the following steps: S41. For each independent management area, select the feature point with the largest coverage density from the reference feature points selected in S2 as a candidate node, ensuring that there is at least one candidate point in each management area, and obtain a candidate point set; It should be noted that the specific steps are as follows: Input S2 and the reference feature point set that has completed coordinate alignment. Use the Voronoi diagram iteration algorithm to select the feature point with the highest coverage density in the reference feature point set as the candidate node. Ensure that there is at least one candidate point in each independent management area. Divide the city gas pipeline network distribution map into multiple independent management areas. Each independent management area contains and only contains all the points closest to a specific point. For a given reference feature point set, each reference feature point corresponds to a Voronoi polygon area. The distance between all points in the area and the reference feature point is closer than the distance to any other reference feature point. S42 extracts the typical depth value of each independent management area from the candidate point set, then calculates the hierarchical boundary threshold based on the gas pipeline network attribute data and the candidate point set, divides the urban gas pipeline network distribution map into a surface layer, a shallow layer, and a deep layer according to the hierarchical boundary threshold, assigns an independent coordinate system to each layer, and outputs a three-level coordinate system with a hierarchical relationship; Extract the branch structure of each independent management area in the gas pipeline network distribution data. Starting from the main pipeline, generate a local coordinate system tree according to the branch hierarchy relationship. Merge the local coordinate system with the overall three-level coordinate system to obtain the three-dimensional position coordinates of the gas pipeline network segment in each independent management area. It should be noted that urban gas pipeline networks are distributed at different depths, including surface pressure regulating stations, meters, and other facilities, shallow underground trunk pipelines, and deep underground pipelines. The traditional single plane coordinate system is difficult to accurately express the complex spatial relationships between these pipelines at different depths, such as the three-dimensional intersections and upper and lower layer avoidances. This can easily lead to problems such as ambiguous pipeline positioning and collisions. For example, when projected onto the same plane coordinate system, shallow trunk pipelines and deep river-crossing pipelines may appear to overlap, even though they are actually three-dimensionally layered and do not interfere with each other. During the construction, renovation, and maintenance of gas pipelines, different construction phases involve operations at different depths. The layered coordinate system can clearly indicate to construction workers the depth level of the target pipeline and its relative position to other layers of pipelines, thus avoiding mis-digging or accidentally hitting pipelines at other layers due to the coordinate system. When repairing shallow trunk pipelines, construction workers can use the layered coordinate system to accurately avoid deep pipelines crossing subway tracks, reducing the risk of accidents caused by construction errors and improving construction safety and accuracy. S43. Extracting an accident location description from the maintenance accident data, obtaining location information of the maintenance accident based on the accident location description, and mapping the location corresponding to the maintenance accident data to a three-level coordinate system to obtain the three-dimensional position coordinates of the accident location in the gas pipeline network segment. It should be noted that by mapping the accident location to a three-level coordinate system and annotating the maintenance accident data on the three-dimensional model, the accident point can be located more quickly, thereby improving the efficiency and pertinence of maintenance work. S44. A three-dimensional urban gas pipeline network model is established based on the three-level coordinate systems and the three-dimensional position coordinates of the gas pipeline network segments. The historical accident data are integrated in time series to obtain a maintenance accident data dataset. The maintenance accident data dataset is marked at the coordinate position of the gas pipeline network segment corresponding to the accident occurrence, specifically including the three-dimensional coordinates of the accident point, the time of occurrence and the maintenance accident data.

[0025] As an optional embodiment, the specific steps of S42 are as follows: First, the typical depth value of each independent management area is extracted from the candidate point set; The specific steps are as follows: According to the formula ; Then, the layer boundary threshold is calculated based on the gas network attributes and typical depth values. ; According to the formula: Calculate the level boundary threshold ,in is the gas pipeline diameter of the i-th gas pipeline, is the material coefficient of the i-th gas pipeline, where steel is 1.2 and PE is 1; is the actual buried depth of the i-th gas pipeline; in is the regional depth standard deviation, which is calculated by the depth values of all benchmark points in the region to reflect the geological heterogeneity; The urban gas pipeline network distribution map is divided into surface layer, shallow layer and deep layer according to the layer boundary threshold; Calculated level boundary threshold The boundary value used to distinguish shallow and deep layers is to mark the range of [−1m,1m] as the surface layer and [−1m, m] is marked as shallow, and will be greater than The marks above m are deep layers; Each layer is assigned an independent coordinate system. For the origin of the surface layer, the point whose horizontal projection is closest to the geometric center of the region among the reference points of the layer in the candidate point set is selected; For shallow origins, the vertical offset is directly below the surface origin. minus ; For deep origins, vertical offset directly below the surface origin minus ; Extract the branch structure of each independent management area in the gas network distribution data, start from the main pipeline, generate a local coordinate system tree according to the branch hierarchy relationship, and integrate the local coordinate system with the overall three-level coordinate system. It should be noted that the specific steps are as follows: Select the main pipeline in each independent management area, specifically the gas pipeline with the largest diameter: Starting from the main pipeline, analyze its branch structure and determine the level of each branch. According to the connection relationship of the pipeline network, the branches are divided into first-level branches, second-level branches, and so on. The first-level branch is the pipeline directly branched from the main pipeline, and the second-level branch is the pipeline branched from the first-level branch. At the starting point of the main pipeline in each independent management area, determine the origin of the local coordinate system, specifically the second type of reference feature point closest to the public reference point; Establish a local coordinate system with the direction of the main pipeline as the X axis, the horizontal direction perpendicular to the main pipeline as the Y axis, and the direction perpendicular to the ground as the height axis Z axis; For each local coordinate system, determine the transformation parameters between it and the three-level coordinate system. These parameters include translation vector, rotation matrix and scaling factor. Specifically, the transformation parameters are determined by the common reference point and the origin of the local coordinate system. The detailed transformation parameters are the same as those of the first and second types of reference feature points mentioned above. According to the determined conversion parameters, each point in the local coordinate system is converted to the three-level coordinate system of the whole system. The three-dimensional position coordinates of the gas pipeline network segment in each independent management area are converted from the local coordinate system to the global coordinate system to ensure that the converted coordinates are consistent with other data in the global coordinate system. It should be noted that the traditional method uses fixed depth division. This solution combines the steps in S2 and uses the three-dimensional benchmark point depth distribution and pipeline network attributes to dynamically calculate the boundary, which improves the accuracy of the hierarchical division and is beneficial to the accuracy of subsequent modeling. The X / Y axis direction of each layer's coordinate system strictly inherits the determined main pipeline direction to ensure the spatial topological consistency of the cross-layer pipeline network.

[0026] As an optional embodiment: the specific steps of S43 are as follows; Based on the accident location description and gas pipeline network distribution data, determine whether the accident occurred at the surface, shallow, or deep layer. For example, if the accident location description mentions "near a pressure regulating station," it may have occurred at the surface layer. If it mentions "main pipeline" and the main pipeline in this area is typically laid 3-5 meters underground, it may have occurred at the shallow layer. If the accident involves a pipeline crossing a river or subway track, it may have occurred at a deep layer. The geographical coordinates of the accident location are converted into coordinates in the three-level coordinate system. Based on the common reference point and coordinate conversion parameters, the latitude and longitude coordinates of the accident location are converted into three-dimensional coordinates in the surface layer, shallow layer or deep layer coordinate system. Given the geographical coordinates of the common reference point and the coordinates in the three-level coordinate system, the coordinate values of the accident location in the corresponding level coordinate system are calculated through the coordinate conversion algorithm. The specific steps of S44 are as follows: Integrate the obtained three-level coordinate systems and the 3D position coordinate data of the gas pipeline network segments, and use 3D modeling software to construct a 3D model of the gas pipeline network based on the 3D coordinates of the pipeline network segments. Add gas network attribute data to each gas network segment in the gas network model; The collected maintenance accident data is sorted by occurrence time to form a time series. Based on the sorted historical accident data, a maintenance accident data dataset is created. The maintenance accident data dataset is marked at the coordinate position of the gas pipeline line segment corresponding to the accident occurrence; In a 3D city gas network model, maintain accident data sets and associate them with corresponding network segments based on the 3D coordinates of the accident points. You can create markers or annotations for each accident point in the model, annotating the details of the accident. Different accident types are represented in the model using icons of different colors or shapes, with the time of occurrence and a brief description displayed next to the icons.

[0027] As an optional embodiment: S5 includes the following steps: S51. Install a sensor on each gas pipe network segment to collect the operating status of each gas pipe network segment. It should be noted that the operating status includes pressure, flow, temperature, and methane concentration. Based on the 3D city gas network model, the operating status of each gas network segment is input to build a risk assessment model; The specific steps for building a risk assessment model are as follows: Select input parameters, including real-time operating status: pressure, flow, temperature, methane concentration, and data acquisition frequency of once per second; Pipeline network attributes: gas pipeline material, diameter, wall thickness data; three-dimensional coordinates, adjacent pipeline density, number of intersections; Historical accident data, leakage frequency, accident type; repair response time; Select a suitable long short-term memory network (LSTM), use the extracted historical data features and corresponding risk factors as input, and use historical accident data as output labels to train the model; Build the LSTM network structure, determine the input size, select the number of hidden units of 100 and the number of layers, generally 1 to 3 layers; Output layer design: Design the output layer based on the risk assessment task type. For regression tasks, the output layer consists of one neuron. Model compilation: Select mean square error (MSE) as the loss function and use Adam optimizer for parameter update; Train the model using the training set, setting the appropriate number of training rounds, batch size, and early stopping mechanism; Model evaluation: Use the test set to evaluate the trained model and calculate indicators such as mean square error, root mean square error, and mean absolute error.

[0028] Plot the training and validation loss curves to observe the convergence of the model and whether there is overfitting or underfitting; Risk levels are determined based on the risk value output by the model, combined with the actual conditions of the gas pipeline network and safety standards; Compare the risk value predicted by the model with the set threshold, divide the gas pipeline network in each independently managed area into corresponding risk levels, and use different colors or icons to mark and visualize them in the 3D city gas pipeline network model; S52. The risk assessment model receives data provided by the three-dimensional urban gas pipeline network model in real time, including the pipeline network topology structure of each independent management area; protection data and equipment operating status, and uses the constructed risk assessment model to perform risk assessment calculations on the gas pipeline network of each independent management area. According to the risk assessment calculation results of each independent management area, the independent management areas with higher risks and major risk factors are identified, and the parameters of the risk assessment model are dynamically adjusted according to the assessment results.

[0029] As an optional embodiment: the specific working steps of S52 are as follows: Construct a feature vector based on pre-processed operating status data, pipeline network topology, and maintenance data. The feature vector should include multiple features that can reflect the risk status of the gas pipeline network, such as pressure change rate, flow fluctuation amplitude, temperature anomaly, methane concentration trend, pipeline density, and adjacent pipeline intersections. Input the constructed feature vector into the risk assessment model to perform risk assessment calculations on the gas pipeline network in each independent management area; Analyze the risk values output by the model to determine the risk level. Set risk thresholds based on the actual situation and safety standards of the gas pipeline network, and divide the risk level into three levels: low, medium, and high. Calculate the risk level distribution of each independent management area and identify the independent management area with the highest risk. By analyzing the internal structure of the model and the weight of the eigenvectors, the main risk factors that have a greater impact on the risk assessment results are identified; If the methane concentration characteristic of a region has a high weight in the model and the methane concentration in the region has recently shown an increasing trend, then the methane concentration can be considered as a major risk factor for the region; Based on the assessment results, the parameters of the risk assessment model are dynamically adjusted, and the monitoring frequency is increased for the independent management areas with the highest risks. It should be noted that this is achieved by adjusting the collection frequency of the sensors.

[0030] The above are only preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions that fall within the scope of protection of the present invention are within the scope of protection of the present invention. It should be pointed out that for ordinary technical personnel in this technical field, certain improvements and modifications that do not depart from the principles of the present invention should also be considered as the scope of protection of this template.

Claims

1. A multi-dimensional data processing and modeling method for urban lifeline safety construction, characterized by: The following steps are involved: S1. Collect multidimensional data, including urban area map data, gas pipeline network distribution data, gas pipeline network attribute data, and maintenance accident data; S2. Preprocessing the urban area map and the gas pipeline network distribution data, then performing coordinate alignment, and overlaying the gas pipeline network distribution data onto the urban area map; S3. Associating the gas pipe network attribute data with the city gas pipe network distribution map, and binding the gas pipe network attribute data to the corresponding gas pipe network line segment; S4. For each independent management area, select the feature point with the largest coverage density from the reference feature points selected in S2 as a candidate node, ensuring that there is at least one candidate point in each management area, and obtain a candidate point set; The typical depth value of each independent management area is extracted from the candidate point set. Then, the hierarchical boundary threshold is calculated based on the gas pipeline network attribute data and the candidate point set. The urban gas pipeline network distribution map is divided into surface layer, shallow layer and deep layer according to the hierarchical boundary threshold. Each layer is assigned an independent coordinate system, and the three-level coordinate system with a hierarchical relationship is output. A three-dimensional coordinate system is established for the urban gas pipeline network distribution map, and the three-dimensional position coordinates of the gas pipeline network segment in each independent management area are obtained. Obtain maintenance accident data, map the location corresponding to the maintenance accident data to the city gas pipeline network distribution map, and obtain the coordinates of the accident location in the three-dimensional coordinate system; Integrate historical accident data into the three-dimensional model in time series to obtain a three-dimensional city gas pipeline network model; S5. Based on the three-dimensional city gas pipeline network model, a risk assessment model is constructed. The risk assessment model receives data provided by the three-dimensional city gas pipeline network model and outputs a risk assessment result for each independent management area in the three-dimensional city gas pipeline network model.

2. A multi-dimensional data processing and modeling method for urban lifeline safety construction according to claim 1, characterized in that: The S2 comprises the following steps: S21. Collect urban area map data and gas pipeline network distribution data, perform integrity checks on them, and supplement any missing information. S22. Select a unified geographic coordinate system, convert the urban area map data and the gas pipeline network distribution data into the unified coordinate system, and complete coordinate alignment; S23. Overlaying the gas pipeline network distribution data after the coordinates are aligned with the urban area map, integrating the gas pipeline network distribution data into the urban area map, and generating a city gas pipeline network distribution map.

3. The multidimensional data processing and modeling method for urban lifeline safety construction according to claim 2 is characterized in that: The specific working steps of S22 also include the following: First, feature points are extracted. For urban area maps, road intersections, independent building corners, and bridge endpoints are extracted as the first type of feature points. For gas pipeline network distribution data, valve nodes and pipeline inflection points are extracted as the second type of feature points; Then set a distance threshold , perform spatial deduplication on the first type of feature points and the second type of feature points; The first and second feature points are grouped into two feature point sets respectively. The points closest to the boundary in the first and second feature points are selected as base points respectively. With the base point as the origin, the polar angles of all other points relative to the base point are calculated and sorted in a counterclockwise direction. For both feature point sets, a stack is used to construct the convex hull. The base point and the first two points after sorting are pushed into the stack. Then, the sorted feature point sets are traversed in sequence. For each new point, check whether the three points at the top of the stack form a right turn. If it is a right turn, the point at the top of the stack is popped out. Otherwise, the new point is pushed into the stack. The points in the stack constitute the vertices of the convex hull. All the vertices of the convex hull are used as the selected reference feature points to obtain the first type of reference feature points and the second type of reference feature points. The urban area map data and the gas pipe network distribution data are respectively converted into the unified coordinate system according to the first type of reference feature points and the second type of reference feature points.

4. The multidimensional data processing and modeling method for urban lifeline safety construction according to claim 3 is characterized in that: The specific steps of converting the urban area map data and the gas pipe network distribution data into the unified coordinate system according to the first type of reference feature points and the second type of reference feature points are as follows: The first type of reference feature points and the second type of reference feature points obtained previously are taken as point pairs with the same name; Use the seven-parameter conversion function of the GIS software to input these pairs of points with the same name and calculate the parameters that need to be adjusted; The calculated seven parameters are applied to the entire gas pipeline network distribution data set, and the gas pipeline network data are batch converted from the original coordinate system to the target unified coordinate system through the conversion tool or programming tool of the GIS software.

5. The multidimensional data processing and modeling method for urban lifeline safety construction according to claim 1 is characterized in that: The S3 includes the following steps: S31, obtaining the boundary of the city gas supply area, and extracting the boundary information of the city gas supply area from the city area map data; Based on the extracted urban gas supply area boundaries, the urban gas network distribution map is divided into multiple independent management areas, each of which represents a specific gas supply area; S32. Identify the starting point and end point of each gas pipeline network segment in the gas pipeline network distribution data, and mark a continuous gas pipeline network segment from the starting point to the end point as a gas pipeline network segment; S33. Organize attribute information related to the gas pipeline network, including gas pipeline material, pipe diameter, and wall thickness data; The collected gas network attribute data is associated with the corresponding network segments, and a database is created to store each network segment and its associated attribute data. A unique ID is assigned to each gas network segment. Generate a preliminarily processed city gas pipeline network distribution map, combine the established pipeline network attribute database with the city gas pipeline network distribution map, and generate a preliminarily processed city gas pipeline network distribution map containing detailed attribute information.

6. The multidimensional data processing and modeling method for urban lifeline safety construction according to claim 3 is characterized in that: The S4 comprises the following steps: S41. For each independent management area, select the feature point with the largest coverage density from the reference feature points selected in S2 as a candidate node, ensuring that there is at least one candidate point in each management area, and obtain a candidate point set; S42 extracts the typical depth value of each independent management area from the candidate point set, then calculates the hierarchical boundary threshold based on the gas pipeline network attribute data and the candidate point set, divides the urban gas pipeline network distribution map into a surface layer, a shallow layer, and a deep layer according to the hierarchical boundary threshold, assigns an independent coordinate system to each layer, and outputs a three-level coordinate system with a hierarchical relationship; Extract the branch structure of each independent management area in the gas pipeline network distribution data. Starting from the main pipeline, generate a local coordinate system tree according to the branch hierarchy relationship. Merge the local coordinate system with the overall three-level coordinate system to obtain the three-dimensional position coordinates of the gas pipeline network segment in each independent management area. S43. Extracting an accident location description from the maintenance accident data, obtaining location information of the maintenance accident based on the accident location description, and mapping the location corresponding to the maintenance accident data to a three-level coordinate system to obtain the three-dimensional location coordinates of the accident location in the gas pipeline network segment; S44. A three-dimensional urban gas pipeline network model is established based on the three-level coordinate systems and the three-dimensional position coordinates of the gas pipeline network segments. The historical accident data are integrated in time series to obtain a maintenance accident data dataset. The maintenance accident data dataset is marked at the coordinate position of the gas pipeline network segment corresponding to the accident occurrence, specifically including the three-dimensional coordinates of the accident point, the time of occurrence and the maintenance accident data.

7. The multidimensional data processing and modeling method for urban lifeline safety construction according to claim 6 is characterized in that: The specific steps of S42 are as follows: First, the typical depth value of each independent management area is extracted from the candidate point set; Then, the layer boundary threshold is calculated based on the gas network attributes and typical depth values. ; The urban gas pipeline network distribution map is divided into surface layer, shallow layer and deep layer according to the layer boundary threshold; Calculated level boundary threshold The dividing value used to distinguish shallow and deep layers, with each layer given an independent coordinate system; The branch structure of each independent management area in the gas pipeline network distribution data is extracted. Starting from the main pipeline, a local coordinate system tree is generated according to the branch hierarchical relationship, and the local coordinate system is integrated with the overall three-level coordinate system.

8. The multi-dimensional data processing and modeling method for urban lifeline safety construction according to claim 6 is characterized in that: The specific steps of S43 are as follows: Based on the accident location description and gas pipeline network distribution data, determine whether the accident occurred in the surface layer, shallow layer, or deep layer; convert the geographic coordinates of the accident location into coordinates in the three-level coordinate system; based on the common reference point and coordinate conversion parameters, convert the latitude and longitude coordinates of the accident location into three-dimensional coordinates in the surface layer, shallow layer, or deep layer coordinate system; The specific steps of S44 are as follows: Integrate the obtained three-level coordinate systems and the 3D position coordinate data of the gas pipeline network segments, and use 3D modeling software to construct a 3D model of the gas pipeline network based on the 3D coordinates of the pipeline network segments. Add gas network attribute data to each gas network segment in the gas network model; The collected maintenance accident data is sorted by occurrence time to form a time series. Based on the sorted historical accident data, a maintenance accident data dataset is created. The maintenance accident data dataset is marked at the coordinate position of the gas pipeline line segment corresponding to the accident occurrence; In the 3D city gas network model, the maintenance accident data set is associated with the corresponding network segment according to the 3D coordinates of the accident point.

9. The multidimensional data processing and modeling method for urban lifeline safety construction according to claim 6 is characterized in that: The S5 comprises the following steps: S51. Install a sensor on each gas pipe network segment to collect the operating status of each gas pipe network segment; Based on the three-dimensional city gas pipeline network model, the operating status of each gas pipeline network segment is input to build a risk assessment model; S52. The risk assessment model receives data provided by the three-dimensional urban gas pipeline network model in real time, including the pipeline network topology structure of each independent management area; protection data and equipment operating status, and uses the constructed risk assessment model to perform risk assessment calculations on the gas pipeline network of each independent management area. According to the risk assessment calculation results of each independent management area, the independent management areas with higher risks and major risk factors are identified, and the parameters of the risk assessment model are dynamically adjusted according to the assessment results.

10. The multi-dimensional data processing and modeling method for urban lifeline safety construction according to claim 9 is characterized in that: The specific working steps of S52 are as follows: Construct a feature vector based on the pre-processed operating status data, pipe network topology and maintenance data; Input the constructed feature vector into the risk assessment model to perform risk assessment calculations on the gas pipeline network in each independent management area; Analyze the risk values output by the model to determine the risk level. Set risk thresholds based on the actual situation and safety standards of the gas pipeline network, and divide the risk level into three levels: low, medium, and high. Calculate the risk level distribution of each independent management area and identify the independent management area with the highest risk. Based on the assessment results, the parameters of the risk assessment model are dynamically adjusted, and the monitoring frequency is increased for the independent management areas with the highest risks.

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