A park pipeline intelligent management method and system based on topology optimization

Abstract

This invention relates to the field of industrial park infrastructure management technology, and more particularly to a method and system for intelligent management of industrial park pipelines based on topology optimization. The method includes preprocessing heterogeneous source data; grouping a standardized set of pipeline objects according to pipeline type; constructing a minimum spanning tree for each subset of pipeline objects based on the Kruskal algorithm and disjoint-set data structure; traversing each logical edge of the minimum spanning tree; generating grid-aligned paths using a grid alignment strategy; generating a pipeline network topology structure conforming to engineering laying habits based on the grid-aligned paths; identifying spatial conflicts of pipelines based on the pipeline network topology structure through range interference detection and path planning detection; dynamically filtering pipelines according to map zoom level and multi-level filtering strategies; and performing differentiated visual dynamic rendering of pipelines and conflicts on the pipeline network topology structure. This invention achieves optimal connection of pipeline nodes and high-precision spatial conflict identification.

Description

Technical Field

[0001] This invention relates to the field of industrial plant infrastructure management technology, and in particular to a method and system for intelligent management of industrial park pipelines based on topology optimization. Background Technology

[0002] Underground pipelines in industrial plants are a crucial component of the plant's infrastructure, encompassing various types such as water supply, drainage, gas, electricity, and communications. With the acceleration of digitalization and the advancement of smart plant construction, the number and complexity of underground pipelines have increased dramatically. Traditional paper-based drawing management and 2D CAD management methods can no longer meet the demands of modern, refined management, and the following technical problems currently exist:

[0003] 1. Lack of pipeline topology: Most existing systems only store discrete point data of pipelines, lacking the expression of connection relationships between pipelines of the same type, and cannot intuitively show the overall structure and direction of the pipeline network.

[0004] 2. Low efficiency of conflict detection: During the pipeline planning and construction phase, it is necessary to detect spatial conflicts between new pipelines and existing pipelines. Existing methods mostly use simple Euclidean distance calculations, which do not take into account the influence of the Earth's curvature, and cannot support both point measurement and path planning scenarios at the same time, resulting in insufficient detection accuracy and application limitations.

[0005] 3. Difficulty in integrating heterogeneous data sources: Pipeline data from different departments and systems use different field naming conventions (Chinese, English, camelCase, etc.). The existing system lacks an intelligent mapping mechanism, resulting in a high data import error rate and seriously affecting data quality.

[0006] 4. Performance bottleneck of large-scale data rendering: Campus-level pipeline data usually contains tens of thousands of nodes. The existing system has lag issues when rendering the full amount of data and lacks optimization strategies for dynamic filtering based on map zoom level.

[0007] While existing technologies have made some progress in pipeline 3D modeling and collision detection, a complete technical solution integrating topology optimization, multi-mode conflict detection, multi-source data fusion, and adaptive rendering has not yet been formed. Summary of the Invention

[0008] This invention aims to address at least one of the technical problems existing in related technologies. To this end, this invention provides a method and system for intelligent management of campus pipelines based on topology optimization, achieving optimal connection of pipeline nodes and high-precision spatial conflict identification. While ensuring large-scale data visualization performance, it also ensures the integrity of data access. This invention solves the key problems in existing pipeline management systems, such as missing network topology relationships, insufficient conflict detection accuracy, difficulty in integrating heterogeneous data, and performance bottlenecks in large-scale data rendering.

[0009] This invention provides a method for intelligent management of park pipelines based on topology optimization, comprising: S1: Obtain heterogeneous source data of water, electricity and gas pipelines, and preprocess the heterogeneous source data to obtain a standardized pipeline object set; S2: Group the standardized pipeline object set according to pipeline type to obtain multiple pipeline object subsets, and construct the minimum spanning tree of each pipeline object subset based on Kruskal's algorithm and disjoint set union; S3: Traverse each logical edge of the minimum spanning tree, generate a grid-aligned path using a grid alignment strategy, and generate a pipeline network topology that conforms to engineering laying habits based on the grid-aligned path. S4: Based on the pipeline network topology, spatial conflicts of pipelines are identified through range interference detection and path planning detection; S5: Dynamically filter pipelines based on map zoom level and multi-level filtering strategy, and perform differentiated visualization and dynamic rendering of pipelines and conflicts on the pipeline network topology.

[0010] Furthermore, step S1 includes: S11: Read the header of the heterogeneous source data and extract the set of field names; S12: The field name in the field name set is standardized by using a field mapping function; the field mapping function supports the recognition of Chinese, English, camelCase and underscore naming styles; S13: Perform type conversion and validation on each row of heterogeneous source data to generate a standardized pipeline object set. The type conversion includes converting coordinate fields and numerical fields to double-precision floating-point numbers and converting status fields to integers.

[0011] Furthermore, in step S2, the construction of the minimum spanning tree for each subset of pipeline objects based on Kruskal's algorithm and disjoint-set data structure includes: S21: Take the node set of the pipeline object subset as the node set of the complete graph, calculate the spherical distance between any two nodes using the Haversine formula, and construct the complete graph using the spherical distance as the edge weight. S22: Set a distance threshold to filter the edges of the complete graph, and retain the edges whose distance is within the threshold to form a candidate edge set; S23: Sort the candidate edge set by weight from smallest to largest, initialize the disjoint set and the minimum spanning tree edge set, and traverse the sorted candidate edge set by combining the initialized disjoint set with Kruskal's algorithm. Add edges that do not form a cycle to the edge set until the number of edges in the edge set is the number of nodes minus 1, and obtain the minimum spanning tree edge set. S24: Merge the complete graph node set and the minimum spanning tree edge set to obtain the minimum spanning tree for each pipeline object subset.

[0012] Furthermore, step S3 includes: S31: Traverse each logical edge of the minimum spanning tree to generate candidate paths for horizontal-then-vertical grid alignment and candidate paths for vertical-then-horizontal grid alignment. S32: Calculate the total path length of candidate paths for horizontal-then-vertical grid alignment and the total path length of candidate paths for vertical-then-horizontal grid alignment. S33: Select the shorter of the total path lengths of the candidate paths for horizontal-then-vertical grid alignment and the candidate paths for vertical-then-horizontal grid alignment as the grid alignment path.

[0013] Furthermore, range interferometry detection includes: S41: Calculate the spherical distance from each pipeline node to the center point; S42: Set the search radius and select pipeline nodes whose spherical distance from the pipeline node to the center point is less than or equal to the search radius. This is for pipeline interference conflicts within the construction range.

[0014] Furthermore, path planning detection includes: For each segment of the planned path, calculate the shortest distance from all pipeline nodes to the segment; If the line segment degenerates into a point, then the shortest distance is the spherical distance from the pipeline node to that point; If the line segment is not degenerate, then the pipeline node is projected onto the line where the line segment is located to obtain the projection parameters; By limiting the projection parameters to the range [0,1], the restricted projection parameters are obtained. Based on the restricted projection parameters, calculate the point on the line segment closest to the pipeline node; Calculate the distance between the point on the line segment closest to the pipeline node and the pipeline node to obtain the shortest projected distance; If the shortest projection distance is less than or equal to the conflict threshold, it is determined to be a path planning conflict.

[0015] Furthermore, the projection parameters represent the relative positions of the projection points on the line segment; If the projection parameter is 0, the projection point coincides with the starting point of the line segment; If the projection parameter is equal to 1, the projection point coincides with the endpoint of the line segment. If the projection parameter is greater than 0 and less than 1, the projection point is in the middle of the line segment; If the projection parameter is less than 0, the projection point is before the starting point of the line segment; If the projection parameter is greater than 1, the projection point will be after the end point of the line segment.

[0016] Furthermore, the multi-level filtering strategy is as follows:

[0017] in, This is the set of pipeline nodes visible at map zoom level z. For pipeline nodes The number of nodes in the topology network to which it belongs, where z is the map zoom level. For the set of all pipeline nodes, For all pipeline nodes In the process, nodes with a network topology of more than 10 nodes are selected. For all pipeline nodes In the process, nodes with a network topology of more than 5 nodes are selected.

[0018] Furthermore, when a pipeline node is in normal operation, the corresponding line segment is rendered as a solid line; when a pipeline node is in a deactivated state, the corresponding line segment is rendered as a dashed line. When a user clicks on a filtered and hidden pipeline, the system uses a click-force display mechanism to temporarily load and highlight the filtered and hidden pipeline.

[0019] This invention also includes a topology-optimized intelligent management system for park pipelines, used to execute the aforementioned topology-optimized intelligent management method for park pipelines, comprising: The data preprocessing module acquires heterogeneous source data of water, electricity and gas pipelines, and preprocesses the heterogeneous source data to obtain a standardized pipeline object set. A minimum spanning tree construction module is used to group the standardized pipeline object set according to pipeline type to obtain multiple pipeline object subsets, and to construct the minimum spanning tree of each pipeline object subset based on Kruskal's algorithm and disjoint set union. The pipeline network topology construction module traverses each logical edge of the minimum spanning tree, generates a grid alignment path through a grid alignment strategy, and generates a pipeline network topology structure that conforms to engineering laying habits based on the grid alignment path. The conflict detection module identifies spatial conflicts in pipelines based on the pipeline network topology through range interference detection and path planning detection. The visualization rendering module dynamically filters pipelines based on map zoom level and multi-level filtering strategies, and performs differentiated visualization rendering of pipelines and conflicts on the pipeline network topology.

[0020] The above-described one or more technical solutions in the embodiments of the present invention have at least one of the following technical effects: A minimum spanning tree topology construction method based on Kruskal's algorithm and disjoint-set data structure is proposed. This method automatically connects discrete pipeline nodes to form a network structure. Combined with Haversine spherical distance formula and grid alignment path generation strategy, it greatly improves the accuracy of pipeline network topology relationships and the visualization of engineering practicality.

[0021] By using field mapping functions to automatically integrate heterogeneous data sources, the workload of manual data processing and import error rate can be effectively reduced, and the integration efficiency and data quality of data from multiple departments and systems can be improved.

[0022] By utilizing vector projection algorithm and dual-mode conflict detection mechanism, a high-precision spatial conflict identification scheme is proposed. Compared with traditional single-mode detection methods, it can simultaneously meet the needs of two typical construction scenarios: point measurement and path planning, providing reliable decision support for pipeline planning and risk assessment.

[0023] Employing a multi-level adaptive rendering optimization strategy, the density of displayed objects is dynamically adjusted according to the map zoom level. This results in significant performance optimization in large-scale campus pipeline data scenarios containing tens of thousands of nodes (at different zoom levels). At the same time, the number of rendered objects is reduced by about 70%, and the frame rate is increased from 15fps to 60fps, while the integrity of data access is ensured through a click-force display mechanism.

[0024] With its versatility and scalability, it is not only suitable for water supply, electricity, and gas pipeline management in industrial plants, but also for various infrastructure management systems that require network topology analysis and spatial conflict detection, such as municipal pipe networks, petrochemical pipelines, and mine ventilation pipe networks. It is easy to implement in engineering projects and is easy to promote and use.

[0025] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0027] Figure 1 This is a flowchart illustrating a smart management method for park pipelines based on topology optimization provided by the present invention.

[0028] Figure 2 This is a schematic diagram of the structure of a campus pipeline intelligent management system based on topology optimization provided by the present invention.

[0029] Figure label: 101. Data preprocessing module; 102. Minimum spanning tree construction module; 103. Pipeline network topology construction module; 104. Conflict detection module; 105. Visualization rendering module. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention. The following embodiments are used to illustrate this invention but cannot be used to limit the scope of this invention.

[0031] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0032] The following is combined Figures 1 to 2 This invention describes a method and system for intelligent management of park pipelines based on topology optimization.

[0033] like Figure 1 As shown, a smart management method for park pipelines based on topology optimization includes: S1: Obtain heterogeneous source data of water, electricity and gas pipelines, and preprocess the heterogeneous source data to obtain a standardized pipeline object set; Preprocess heterogeneous source data containing n rows of water, electricity, and gas pipelines: S11: Read the header of the heterogeneous source data and extract the set of field names; Read the header data of heterogeneous sources containing n rows of water, electricity, and gas pipelines, and extract the set of field names. , ; S12: The field name in the field name set is standardized by using a field mapping function; the field mapping function supports the recognition of Chinese, English, camelCase and underscore naming styles; Check the core field set Check if the mapping is complete; if not, return the list of missing fields and terminate data reading.

[0034] S13: Perform type conversion and validation on each row of heterogeneous source data to generate a standardized pipeline object set. The type conversion includes converting coordinate fields and numerical fields to double-precision floating-point numbers and converting status fields to integers. For each line of data in the source data file Perform type conversion and validation: Convert coordinate fields to double-precision floating-point numbers, such as latitude and longitude coordinates; Convert numeric fields to double-precision floating-point numbers, such as diameter, depth, and height; Convert the status field to an integer; Output a collection of standardized pipeline objects , ,in, For the first Standardized pipeline objects.

[0035] S2: Group the standardized pipeline object set according to pipeline type to obtain multiple pipeline object subsets, and construct the minimum spanning tree of each pipeline object subset based on Kruskal's algorithm and disjoint set union; Establish network connections between pipelines of the same type to provide a topology structure for visualization and basic data for network analysis.

[0036] The standardized pipeline object set is grouped according to pipeline type to obtain multiple pipeline object subsets. The minimum spanning tree of each pipeline object subset is constructed based on Kruskal's algorithm and disjoint set union. With subset Let's take an example to describe the construction process: S21: Take the node set of the pipeline object subset as the node set of the complete graph, calculate the spherical distance between any two nodes using the Haversine formula, and construct the complete graph using the spherical distance as the edge weight. Treat the set of pipeline nodes as the set of nodes in a graph. , , For the first There are several pipeline nodes, and any two nodes may be connected. Therefore, it is necessary to calculate the distance between all pairs of nodes.

[0037] Construct a complete graph , ,in For edge set, , To connect the first The pipeline node and the first The edges of each pipeline node, weight For the first The pipeline node and the first The distance between pipeline nodes.

[0038] Because pipelines are distributed across the Earth's surface, using planar Euclidean distances would introduce significant errors. Therefore, the Haversine formula is used to calculate spherical distances, with the following expression:

[0039] in, For the Earth's radius, , For pipeline nodes Latitude corresponds to radians. , For pipeline nodes latitude, For pipeline nodes Latitude corresponds to radians. , For pipeline nodes latitude, For pipeline nodes and pipeline nodes The difference in radians corresponding to inter-latitude, , For pipeline nodes and pipeline nodes The difference in radians corresponding to the longitude between them , For pipeline nodes longitude, For pipeline nodes Longitude.

[0040] Compared to the Euclidean distance formula, the Haversine formula improves the accuracy of spherical distance calculation by about 0.5% within a range of 1000 meters, and by more than 2% in high-latitude regions (latitude > 60°).

[0041] S22: Set a distance threshold to filter the edges of the complete graph, and retain the edges whose distance is within the threshold to form a candidate edge set; In a complete graph, connections between certain pairs of nodes that are too far apart (such as pipelines several kilometers apart) are meaningless in engineering. To improve algorithm efficiency and ensure the rationality of the generated network, it is necessary to filter these long-distance edges and construct a candidate edge set. .

[0042] Candidate edge set The calculation expression is:

[0043] in, This is the distance threshold.

[0044] In some specific embodiments of the present invention Only node pairs within 1000 meters are considered as candidate connections, resulting in the filtered candidate edge set. Its size is much smaller than the edge set of a complete graph, which significantly improves computational efficiency.

[0045] S23: Sort the candidate edge set by weight from smallest to largest, initialize the disjoint set and the minimum spanning tree edge set, and traverse the sorted candidate edge set by combining the initialized disjoint set with Kruskal's algorithm. Add edges that do not form a cycle to the edge set until the number of edges in the edge set is the number of nodes minus 1, and obtain the minimum spanning tree edge set. Since Kruskal's algorithm prioritizes adding edges with shorter distances to the spanning tree, the edge weights are sorted from smallest to largest. The disjoint-set data structure (Union-Find) is initialized synchronously. Union-set data structures are efficient data structures used for maintaining set merging and querying operations. In Kruskal's algorithm, they are used to determine if two pipeline nodes are connected, thus avoiding loops. In actual code operations, each pipeline node is treated as an independent set.

[0046] in, For pipeline nodes The parent node index initially points to itself; For pipeline nodes The rank (approximate depth) of the tree is used for rank-based merging optimization, while also initializing the minimum spanning tree edge set. , .

[0047] The path compression optimization of the disjoint-set data structure reduces the amortized time complexity of a single search operation to [missing information]. ,in, Let be the algorithm complexity function. It is the inverse function of the Ackermann function. In practical applications, it is approximately a constant.

[0048] Traverse the sorted edge set For each edge, Kruskal's algorithm is used to calculate and add edges that do not form cycles to the edge set until the number of edges in the edge set is the number of nodes minus 1, thus obtaining the minimum spanning tree edge set.

[0049] S24: Merge the complete graph node set and the minimum spanning tree edge set to obtain the minimum spanning tree for each pipeline object subset.

[0050] S3: Traverse each logical edge of the minimum spanning tree, generate a grid-aligned path using a grid alignment strategy, and generate a pipeline network topology that conforms to engineering laying habits based on the grid-aligned path. S31: Traverse each logical edge of the minimum spanning tree to generate candidate paths for horizontal-then-vertical grid alignment and candidate paths for vertical-then-horizontal grid alignment. Each edge in This represents the logical connection between two pipeline nodes. Drawing a straight line connection directly on the map may not reflect the actual pipeline layout (actual pipelines typically follow street lines). Therefore, this invention introduces a grid alignment strategy to generate connection paths containing only horizontal and vertical line segments, including candidate paths for horizontal-then-vertical grid alignment and candidate paths for vertical-then-horizontal grid alignment.

[0051] S32: Calculate the total path length of candidate paths for horizontal-then-vertical grid alignment and the total path length of candidate paths for vertical-then-horizontal grid alignment. Path 1: Generate candidate paths for horizontal-then-vertical grid alignment:

[0052] in, For pipeline nodes , For pipeline nodes , From arrive The first intermediate node on the path, for latitude, for longitude, From through arrive The total path length, This represents the distance between the two pipeline nodes.

[0053] Path 2: Candidate paths for vertical-then-horizontal grid alignment:

[0054] in, From arrive The second intermediate node on the path, for latitude, for longitude, From through arrive The total length of the path.

[0055] S33: Select the shorter of the total path lengths of the candidate paths for horizontal-then-vertical grid alignment and the candidate paths for vertical-then-horizontal grid alignment as the grid alignment path. The selection formula is as follows:

[0056] in, This is the path with the shortest total length.

[0057] S4: Based on the pipeline network topology, spatial conflicts of pipelines are identified through range interference detection and path planning detection; A dual-mode spatial conflict detection system, consisting of range interference detection and route planning detection, provides decision support for construction planning and risk assessment by detecting spatial conflicts between pipelines. Range interference detection focuses on pipeline interference around the construction site; route planning detection focuses on whether the planned pipeline route conflicts with existing pipelines.

[0058] Range interference detection includes: S41: Calculate the spherical distance from each pipeline node to the center point; S42: Set the search radius and select pipeline nodes whose spherical distance from the pipeline node to the center point is less than or equal to the search radius. This is for pipeline interference within the construction range.

[0059] For each pipeline node Calculate the spherical distance from the center point. Using the Haversine formula, after calculating the distances of all nodes, filter out pipeline nodes whose distances are less than or equal to the search radius. The calculation expression is:

[0060] in, For the set of pipeline nodes that interfere with the construction scope, For the first The spherical distance from each pipeline node to the center point The search radius is [value].

[0061] Path planning detection includes: For each segment of the planned path, calculate the shortest distance from all pipeline nodes to the segment; The planned path consists of m pipeline nodes, with m-1 line segments between adjacent pipeline nodes. For each line segment, the shortest distance from all existing pipeline nodes to that line segment needs to be calculated. The calculation expression is:

[0062] in, This is the distance between two adjacent pipeline nodes. No. Longitude of each pipeline node For the first Longitude of each pipeline node No. The latitude of each pipeline node For the first The latitude of each pipeline node.

[0063] If the line segment degenerates into a point, then the shortest distance is the spherical distance from the pipeline node to that point; like This means that the line segment has degenerated into a single point (where the starting and ending points coincide), and the shortest distance is the point. arrive Distance:

[0064] in, For the shortest distance, It is the starting point of the line segment.

[0065] If the line segment is not degenerate, then the pipeline node is projected onto the line where the line segment is located to obtain the projection parameters; Will To line segment The projection of the line in question, where, For the endpoint of the line segment, the expression for calculating the projection parameters is:

[0066] in, For projection parameters, for longitude, for Latitude; The projection parameters represent the relative positions of the projection points on the line segment; If the projection parameter is 0, the projection point coincides with the starting point of the line segment; If the projection parameter is equal to 1, the projection point coincides with the endpoint of the line segment. If the projection parameter is greater than 0 and less than 1, the projection point is in the middle of the line segment; If the projection parameter is less than 0, the projection point is before the starting point of the line segment; If the projection parameter is greater than 1, the projection point will be after the end point of the line segment.

[0067] By limiting the projection parameters to the range [0,1], the restricted projection parameters are obtained. Due to practical engineering requirements, the projection must lie on the line segment. Therefore, the projection parameters are restricted to the range [0,1], and the calculation expression is as follows:

[0068] in, These are the restricted projection parameters. To find the function with the maximum value, To find the minimum value of the function.

[0069] Based on the restricted projection parameters, calculate the point on the line segment closest to the pipeline node; This point represents the linear interpolation between the start and end points of the line segment, and the calculation expression is:

[0070] in, Distance on line segment The nearest point.

[0071] Calculate the distance between the point on the line segment closest to the pipeline node and the pipeline node to obtain the shortest projected distance; Calculate points using the Haversine formula arrive The spherical distance is used to obtain the shortest projected distance.

[0072] If the shortest projection distance is less than or equal to the conflict threshold, it is determined to be a path planning conflict.

[0073] S5: Dynamically filter pipelines based on map zoom level and multi-level filtering strategy, and perform differentiated visualization and dynamic rendering of pipelines and conflicts on the pipeline network topology.

[0074] By dynamically filtering displayed objects based on the map zoom level, performance is guaranteed while ensuring the integrity of data access.

[0075] Based on the scaling level z, the system applies the following filtering rules:

[0076] in, This is the set of pipeline nodes visible at map zoom level z. For pipeline nodes The number of nodes in the topology network to which it belongs, where z is the map zoom level. For the set of all pipeline nodes, For all pipeline nodes In the process, nodes with a network topology of more than 10 nodes are selected. For all pipeline nodes In the process, nodes with a network topology of more than 5 nodes are selected.

[0077] A larger z value indicates that the closer to the ground, and a smaller z value indicates that the farther away from the ground. When a pipeline node is in normal operation, the corresponding line segment is rendered as a solid line; when a pipeline node is in a deactivated state, the corresponding line segment is rendered as a dashed line. When a user clicks on a filtered and hidden pipeline, the system uses a click-force display mechanism to temporarily load and highlight the filtered and hidden pipeline.

[0078] right For each pipeline, the system needs to determine the rendering style for each segment. During pipeline maintenance, some nodes may be deactivated due to repairs, scrapping, or other reasons. The segments containing these deactivated nodes should be displayed as dashed lines for quick identification by administrators. For the segment connecting pipeline node i and pipeline j, the rendering style is determined to be a solid line. While filtering rules improve performance, they may also cause some pipelines to be hidden. To ensure users can still access all data, the system provides a click-force display mechanism: when a user clicks on a pipeline... Even at that time If a pipeline is filtered out and hidden due to insufficient node count, the system will temporarily load and highlight it.

[0079] Multi-level filtering strategies at scaling levels At that time, the number of rendered objects was reduced by about 70%, and the frame rate increased from 15fps to 60fps, resulting in a significant performance improvement.

[0080] This invention primarily addresses key issues in the management of underground pipelines in industrial plants, such as missing topological relationships, insufficient collision detection accuracy, difficulties in integrating heterogeneous data, and performance bottlenecks in large-scale data rendering. By constructing a network topology architecture based on the Kruskal minimum spanning tree algorithm and a disjoint-set data structure, combined with a dual-mode collision detection mechanism using Haversine spherical distance calculation and vector projection algorithms, and a multi-level adaptive rendering optimization strategy, it achieves optimal connection of pipeline nodes, high-precision spatial collision identification, and high-performance visualization of large-scale data.

[0081] like Figure 2 As shown, a topology-optimized intelligent management system for park pipelines, used to execute the aforementioned topology-optimized intelligent management method for park pipelines, includes: The data preprocessing module 101 acquires heterogeneous source data of water, electricity and gas pipelines, and preprocesses the heterogeneous source data to obtain a standardized pipeline object set. Minimum spanning tree construction module 102 groups the standardized pipeline object set according to pipeline type to obtain multiple pipeline object subsets, and constructs the minimum spanning tree of each pipeline object subset based on Kruskal's algorithm and disjoint set union; The pipeline network topology construction module 103 traverses each logical edge of the minimum spanning tree, generates a grid alignment path through a grid alignment strategy, and generates a pipeline network topology structure that conforms to engineering laying habits based on the grid alignment path. The conflict detection module 104 identifies spatial conflicts in pipelines based on the pipeline network topology through range interference detection and path planning detection. The visualization rendering module 105 dynamically filters pipelines based on map zoom level and multi-level filtering strategy, and performs differentiated visualization and dynamic rendering of pipelines and conflicts on the pipeline network topology.

[0082] Through the collaborative work of the aforementioned modules, automatic fusion of heterogeneous data sources is achieved via field mapping functions. This supports the recognition and conversion of various naming styles, including Chinese, English, camelCase, and underscore, ensuring accurate import of pipeline data from different departments and systems. The Kruskal minimum spanning tree algorithm is employed to construct the network topology of pipelines of the same type. The Haversine formula is used to calculate spherical distances to account for the Earth's curvature. Efficient topology network generation is achieved through distance threshold filtering and disjoint-set data structure path compression optimization. A grid alignment strategy is introduced to generate horizontal and vertical connection paths that conform to actual laying habits. A dual-mode spatial conflict detection mechanism is implemented. The range interference detection mode filters pipelines around the construction point using spherical distances, while the path planning conflict detection mode accurately calculates the shortest distance from pipeline nodes to planned line segments using vector projection algorithms. These two modes complement each other to ensure comprehensive and accurate conflict identification. Finally, a three-level adaptive filtering rule based on map zoom level is constructed. The display density is dynamically adjusted according to the number of nodes in the pipeline's topology network, and a click-force display mechanism ensures the integrity of data access, maintaining user experience while ensuring large-scale data visualization performance.

[0083] This invention uses the Haversine spherical distance formula to replace the traditional planar Euclidean distance, improving accuracy by approximately 0.5% within a 1000-meter range and by over 2% in high-latitude regions (latitude > 60°). It combines the Kruskal algorithm and a disjoint-set data structure to construct the optimal network topology, reducing the amortized time complexity of a single search operation through distance threshold filtering and path compression optimization techniques, significantly improving the construction efficiency of large-scale pipeline networks. Two candidate path schemes—horizontal first then vertical and vertical first then horizontal—are selected through total length optimization to generate connection paths containing only horizontal and vertical straight segments. This ensures the visualization results align with actual pipeline laying practices along streets, improving managers' intuitive understanding and decision-making efficiency. This invention designs two complementary modes: range interference detection and path planning conflict detection. The former uses spherical distance to screen pipelines around the construction point, while the latter innovatively applies a vector projection algorithm to accurately calculate the shortest distance from the point to the line segment. By limiting the projection parameters to the [0,1] interval, it ensures that the calculation point is located on the line segment. The two modes, combined with the Haversine formula, achieve high-precision conflict identification and meet the needs of different construction planning scenarios. This invention establishes a three-tiered dynamic filtering rule based on map zoom level z (z≥16 displays all pipelines, 14≤z<16 displays networks with ≥5 nodes, and z<14 displays networks with ≥10 nodes). When the zoom level z<14, the number of rendered objects is reduced by approximately 70%, and the frame rate is increased from 15fps to 60fps, resulting in a significant performance improvement. It also provides a click-forced display mechanism and a solid / dashed line rendering style based on node status, ensuring data access integrity and visualization of operation and maintenance information while optimizing performance.

[0084] Example: An industrial park needs to digitally manage its water supply pipelines, and data from multiple existing pipeline nodes needs to be imported into the system and a topology network needs to be built.

[0085] S1: Obtain heterogeneous source data of water, electricity and gas pipelines, and preprocess the heterogeneous source data to obtain a standardized pipeline object set; The Excel file contains 7 pipeline node records, with the original field names being: node_id (node ​​number), Longitude (longitude), Latitude (latitude), pipe_type (pipeline type), and status (status).

[0086] Read the table header and extract the field name set H={node_id, Longitude, Latitude, pipe_type,status}. Perform intelligent conversion through field mapping functions: Longitude→lon (longitude), Latitude→lat (latitude), pipe_type→type (pipe type), status→status (status). Perform core field validation simultaneously. Some data is shown in Table 1.

[0087] Table 1 Partial Data

[0088] Perform floating-point type conversion and verification, taking node 1K301A-0 as an example. Coordinate field conversion: lon=116.866127 (double-precision floating-point number), lat=38.576318 (double-precision floating-point number); Pipeline type: type="water supply pipeline" (string) Status field: status=1 (integer, 1 indicates normal operation, 0 indicates disabled).

[0089] S2: Group the standardized pipeline object set according to pipeline type to obtain multiple pipeline object subsets, and construct the minimum spanning tree of each pipeline object subset based on Kruskal's algorithm and disjoint set union; Network topology generation based on minimum spanning tree (taking 7 pipelines as an example).

[0090] Treat the 7 pipeline nodes as a set of nodes in a graph. There may be a connection between any two nodes, and it is necessary to calculate the spherical distance between all pairs of nodes.

[0091] Taking node 1 (lon1, lat1) and node 2 (lon2, lat2) as an example: Node 1 (1K301A-0): lon1=116.866127°, lat1=38.576318° Node 2 (1K301A-1): lon2=116.866092°, lat2=38.576320° Convert coordinates to radians: lat1 = 38.576318° × π / 180 = 0.67335847 radians lat² = 38.576320° × π / 180 = 0.67335851 radians Latitude difference Δlat = lat2 - lat1 = 0.00000004 radians Longitude difference Δlon = (116.866092° - 116.866127°) × π / 180 = -0.00000611 radians; Calculate the Haversine spherical distance: sin²(Δlat / 2)=sin²(0.00000002)≈0.0000000000000004 cos(lat1)=cos(0.67335847)≈0.77956 cos(lat2)=cos(0.67335851)≈0.77956 sin²(Δlon / 2)=sin²(-0.00000306)≈0.0000000000093636

[0092] The spherical distance between nodes 1 and 2 is 3.87 meters, which is a typical spacing between adjacent pipeline nodes. The same applies to other nodes.

[0093] Since the seven nodes are relatively concentrated within the park, and the distance between all node pairs is less than the distance threshold, all 21 candidate edges are retained. Next, the candidate edges are sorted by distance in ascending order and initialized, with each node forming its own independent set.

[0094] Apply Kruskal's algorithm to the sorted edge sets sequentially: The first edge (3.87 meters): Find(UF,1)=1, Find(UF,2)=2, the two nodes are not in the same connected component, so the edge... Add minimum spanning tree: Union(UF,1,2): Merge two sets, parent[2]=1, one edge has been added.

[0095] second edge (Approximately 40 meters): Find(UF,2)=1, Find(UF,3)=3, the two nodes are not in the same connected component, so the edge... Add minimum spanning tree: Union(UF,2,3): parent[3]=1, 2 edges have been added.

[0096] S3: Traverse each logical edge of the minimum spanning tree, generate a grid-aligned path using a grid alignment strategy, and generate a pipeline network topology that conforms to engineering laying habits based on the grid-aligned path. When the spanning tree contains 6 edges (n-1=7-1=6), all nodes are fully connected, and the algorithm terminates.

[0097] S4: Based on the pipeline network topology, spatial conflicts of pipelines are identified through range interference detection and path planning detection; Suppose the park plans to carry out construction at coordinates C (116.8661, 38.5768), and it is necessary to check whether there are any pipeline conflicts in the surrounding area.

[0098] Range interference detection: Construction center point C: lon0=116.8661°, lat0=38.5768°; search radius is 50 meters.

[0099] With nodes Taking (1K301A-3) as an example, the node is calculated. Coordinates: lon = 116.866077°, lat = 38.576658°, d(C, )≈15.78 meters, and compare the search radius, meeting the conditions.

[0100] Path planning conflict detection: Suppose a new pipeline is planned to be laid, and the path is defined by two endpoints: Starting point S: lon_s = 116.8660°, lat_s = 38.5763°; End point E: lon_e = 116.8663°, lat_e = 38.5770°, calculated as:

[0101] At this time, it is proved that the line segment is not degenerate, and projection parameter calculation is carried out:

[0102] Then there is:

[0103]

[0104] It can be obtained that 0 < t < 1, the projection point is on the line segment, and the shortest distance d_min ≈ 5.12 meters is calculated. Suppose the set threshold is 3 meters, and no conflict occurs at this time.

[0105] S5: Dynamically filter the pipelines according to the map zoom level and multi-level filtering strategy, and perform differential visualization dynamic rendering of the pipelines and conflicts on the pipeline network topology.

[0106] For the newly laid pipeline, the rendering style is determined to be a solid line.

[0107] Finally, it should be noted that: The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: They can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; And these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for intelligent management of park pipelines based on topology optimization, characterized in that, include: S1: Obtain heterogeneous source data of water, electricity and gas pipelines, and preprocess the heterogeneous source data to obtain a standardized pipeline object set; S2: Group the standardized pipeline object set according to pipeline type to obtain multiple pipeline object subsets, and construct the minimum spanning tree of each pipeline object subset based on Kruskal's algorithm and disjoint set union; S3: Traverse each logical edge of the minimum spanning tree, generate a grid-aligned path using a grid alignment strategy, and generate a pipeline network topology that conforms to engineering laying habits based on the grid-aligned path. S4: Based on the pipeline network topology, spatial conflicts of pipelines are identified through range interference detection and path planning detection; S5: Dynamically filter pipelines based on map zoom level and multi-level filtering strategy, and perform differentiated visualization and dynamic rendering of pipelines and conflicts on the pipeline network topology.

2. The intelligent management method for park pipelines based on topology optimization according to claim 1, characterized in that, Step S1 includes: S11: Read the header of the heterogeneous source data and extract the set of field names; S12: The field name in the field name set is standardized by using a field mapping function; the field mapping function supports the recognition of Chinese, English, camelCase and underscore naming styles; S13: Perform type conversion and validation on each row of heterogeneous source data to generate a standardized pipeline object set. The type conversion includes converting coordinate fields and numerical fields to double-precision floating-point numbers and converting status fields to integers.

3. The intelligent management method for park pipelines based on topology optimization according to claim 1, characterized in that, In step S2, the minimum spanning tree for each subset of pipeline objects is constructed using Kruskal's algorithm and disjoint-set data structure, including: S21: Take the node set of the pipeline object subset as the node set of the complete graph, calculate the spherical distance between any two nodes using the Haversine formula, and construct the complete graph using the spherical distance as the edge weight. S22: Set a distance threshold to filter the edges of the complete graph, and retain the edges whose distance is within the threshold to form a candidate edge set; S23: Sort the candidate edge set by weight from smallest to largest, initialize the disjoint set and the minimum spanning tree edge set, and traverse the sorted candidate edge set by combining the initialized disjoint set with Kruskal's algorithm. Add edges that do not form a cycle to the edge set until the number of edges in the edge set is the number of nodes minus 1, and obtain the minimum spanning tree edge set. S24: Merge the complete graph node set and the minimum spanning tree edge set to obtain the minimum spanning tree for each pipeline object subset.

4. The intelligent management method for park pipelines based on topology optimization according to claim 1, characterized in that, Step S3 includes: S31: Traverse each logical edge of the minimum spanning tree to generate candidate paths for horizontal-then-vertical grid alignment and candidate paths for vertical-then-horizontal grid alignment. S32: Calculate the total path length of candidate paths for horizontal-then-vertical grid alignment and the total path length of candidate paths for vertical-then-horizontal grid alignment. S33: Select the shorter of the total path lengths of the candidate paths for horizontal-then-vertical grid alignment and the candidate paths for vertical-then-horizontal grid alignment as the grid alignment path.

5. The intelligent management method for park pipelines based on topology optimization according to claim 1, characterized in that, Range interference detection includes: S41: Calculate the spherical distance from each pipeline node to the center point; S42: Set the search radius and select pipeline nodes whose spherical distance from the pipeline node to the center point is less than or equal to the search radius. This is for pipeline interference conflicts within the construction range.

6. The intelligent management method for park pipelines based on topology optimization according to claim 1, characterized in that, Path planning detection includes: For each segment of the planned path, calculate the shortest distance from all pipeline nodes to the segment; If the line segment degenerates into a point, then the shortest distance is the spherical distance from the pipeline node to that point; If the line segment is not degenerate, then the pipeline node is projected onto the line where the line segment is located to obtain the projection parameters; By limiting the projection parameters to the range [0,1], the restricted projection parameters are obtained. Based on the restricted projection parameters, calculate the point on the line segment closest to the pipeline node; Calculate the distance between the point on the line segment closest to the pipeline node and the pipeline node to obtain the shortest projected distance; If the shortest projection distance is less than or equal to the conflict threshold, it is determined to be a path planning conflict.

7. The intelligent management method for park pipelines based on topology optimization according to claim 6, characterized in that, The projection parameters represent the relative positions of the projection points on the line segment; If the projection parameter is 0, the projection point coincides with the starting point of the line segment; If the projection parameter is equal to 1, the projection point coincides with the endpoint of the line segment. If the projection parameter is greater than 0 and less than 1, the projection point is in the middle of the line segment; If the projection parameter is less than 0, the projection point is before the starting point of the line segment; If the projection parameter is greater than 1, the projection point will be after the end point of the line segment.

8. The intelligent management method for park pipelines based on topology optimization according to claim 1, characterized in that, The multi-level filtering strategy is as follows: ； in, This is the set of pipeline nodes visible at map zoom level z. For pipeline nodes The number of nodes in the topology network to which it belongs, where z is the map zoom level. For the set of all pipeline nodes, For all pipeline nodes In the process, nodes with a network topology of more than 10 nodes are selected. For all pipeline nodes In the process, nodes with a network topology of more than 5 nodes are selected.

9. The intelligent management method for park pipelines based on topology optimization according to claim 1, characterized in that, When a pipeline node is in normal operation, the corresponding line segment is rendered as a solid line; when a pipeline node is in a deactivated state, the corresponding line segment is rendered as a dashed line. When a user clicks on a filtered and hidden pipeline, the system uses a click-force display mechanism to temporarily load and highlight the filtered and hidden pipeline.

10. A campus pipeline intelligent management system based on topology optimization, characterized in that, To implement the intelligent management method for campus pipelines based on topology optimization as described in any one of claims 1 to 9, comprising: The data preprocessing module acquires heterogeneous source data of water, electricity and gas pipelines, and preprocesses the heterogeneous source data to obtain a standardized pipeline object set. A minimum spanning tree construction module is used to group the standardized pipeline object set according to pipeline type to obtain multiple pipeline object subsets, and to construct the minimum spanning tree of each pipeline object subset based on Kruskal's algorithm and disjoint set union. The pipeline network topology construction module traverses each logical edge of the minimum spanning tree, generates a grid alignment path through a grid alignment strategy, and generates a pipeline network topology structure that conforms to engineering laying habits based on the grid alignment path. The conflict detection module identifies spatial conflicts in pipelines based on the pipeline network topology through range interference detection and path planning detection. The visualization rendering module dynamically filters pipelines based on map zoom level and multi-level filtering strategies, and performs differentiated visualization rendering of pipelines and conflicts on the pipeline network topology.

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