GIS-based pipeline connectivity visualization method

Abstract

The invention provides a GIS (Geographic Information System)-based pipeline connectivity visualization method. The method comprises the following steps of: acquiring a high-resolution image by utilizing an oblique photography technology and generating a three-dimensional model of an overground facility; designing an underground pipe network data structure, and performing standardization and symbolization processing to generate a three-dimensional pipe network model; the above-ground model and the underground model are fused through a unified coordinate system, and a two-dimensional base map and a DEM are superposed to realize integrated display; collecting pipe network data in real time by using a sensor and dynamically updating the pipe network data to a visual platform; and analyzing the connectivity of the pipe network nodes by adopting a self-adaptive strategy based on layer priority and father node priority, and performing visual display on a GIS (Geographic Information System) platform. The intelligent level of pipeline management can be effectively improved, manual monitoring errors and cost are reduced, and a data-driven decision basis is provided for pipe network maintenance and management.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of geographic information systems (GIS) and three-dimensional modeling, in particular to a GIS-based pipe connectivity visualization method for realizing high-precision digital modeling of aboveground and underground facilities and visualization of pipe state to intuitively show the overall structure of the pipe network and its operating state, thereby providing strong support for pipe management and maintenance decision-making. BACKGROUND

[0002] As an important part of urban infrastructure, urban pipe networks connect the economic, living and transportation lifelines of the city, and bear multiple functions such as information transmission, energy transportation and resource processing, providing key protection for the construction, coordination and sustainable development of the city. The progress of modern science and technology makes it possible to innovate pipe network management, and the digital and intelligent management mode of pipe networks will be an important part of the sustainable development of future cities. With the continuous development and application of three-dimensional geographic information systems (3DGIS) technology, the efficiency of pipe network management has been significantly improved, but the problems that follow cannot be ignored.

[0003] Currently, three-dimensional pipe network models generally have low productivity and reusability, and there are problems such as uncertainty of pipe model spatial posture. These deficiencies directly affect the management efficiency of pipe networks, and it is urgent to improve the production and application efficiency of models to solve the difficulties in spatial cognition and workload of pipe networks in the past. In addition, existing pipe network management often focuses too much on underground space and ignores the mutual influence between aboveground and underground, resulting in low correlation between the two. This situation not only restricts the comprehensiveness of pipe network management, but also affects scientific decision-making of urban infrastructure. At the same time, current pipe network spatial analysis methods cannot adapt to the expanding pipe network infrastructure system, and cannot effectively integrate real-time data for spatial analysis, thereby reducing the timeliness of data utilization. SUMMARY

[0004] The purpose of the present application is to provide a GIS-based pipe connectivity visualization method, which realizes clear display and efficient reuse of pipe network models by batch modeling of pipe network symbolization and constructing high-precision aboveground and underground integrated pipe network three-dimensional models. At the same time, real-time data is integrated for spatial analysis to realize dynamic monitoring and management of pipe networks.

[0005] The purpose of the present application is achieved by the following technical solutions: The GIS-based pipe connectivity visualization method comprises the following steps: Step S1: Use oblique photography technology to collect and generate a three-dimensional model of aboveground facilities, and perform single-body processing on image data to realize independent display of aboveground facilities, thereby optimizing facility management and data visualization; Step S2: Design the data structure of the underground pipe network, perform standardization and symbolization processing, and generate an accurate three-dimensional pipe network model through pose adjustment to lay the foundation for subsequent display and analysis; Step S3: Fuse and display the aboveground and underground models through a unified coordinate system, and superimpose a two-dimensional base map and a digital elevation model (DEM) to realize three-dimensional display of aboveground and underground facilities and enhance the coherence of spatial display; Step S4: Collect real-time data through sensors and integrate them into the platform to dynamically update the pipe network model, realize real-time monitoring and emergency response functions of the pipe network, and ensure the timeliness and accuracy of the data; Step S5: Collect real-time data of the pipe network, dynamically analyze the connectivity of the pipe network, and combine with the GIS platform to display the state of the pipe network and the connectivity of the pipeline to realize spatial analysis and intelligent management of the pipe network.

[0006] Further, the step S1 specifically includes: Step S101: Capture high-resolution images of ground facilities through multi-angle shooting by a drone; Step S102: Preprocess the collected image data to ensure the accuracy and consistency of the data; Step S103: Generate high-precision dense point clouds through feature extraction, depth estimation, focal length correction, multi-view depth map fusion, and coordinate optimization; Step S104: Construct a three-dimensional model of the ground and perform individualization processing on independent facilities.

[0007] Further, the step S2 specifically includes: Step S201: Determine key data requirements, construct an object model of nodes and connection relationships, and realize dynamic association; Step S202: Through data cleaning, format unification, and symbol design, establish a symbol library to ensure data standardization and consistency in three-dimensional display; Step S203: Collect pipeline coordinates and calculate rotation parameters to adjust the pose of the pipe point model to ensure consistency with the actual pipe network direction in the three-dimensional environment; Step S204: Import and match symbolized three-dimensional model data, complete model modeling, and perform visualization and effect optimization.

[0008] Further, the step S3 specifically includes: Step S301: Align and fuse the aboveground and underground models in a unified spatial coordinate system through coordinate registration and multi-level homogeneous coordinate transformation to ensure data consistency and spatial coherence; Step S302: Realize superimposed display of the two-dimensional base map, DEM, and three-dimensional model to ensure complete display effect of multi-dimensional data; Step S303: An integrated three-dimensional model of the above-ground and underground space is constructed through improved nonlinear coordinate transformation, dynamic transparency control and enhanced posture adjustment technology.

[0009] Further, the step S4 specifically comprises: Step S401: Intelligent adaptive sensors are installed at key nodes, real-time pressure and flow rate data are collected by using self-optimizing Internet of Things technology, and are intelligently transmitted to the system; Step S402: Real-time data is efficiently integrated into the three-dimensional pipe network model through hierarchical correction and dynamic smoothing update, ensuring data synchronization and accuracy; Step S403: Based on intelligent multi-dimensional visualization technology, pipe data is displayed in a three-dimensional scene, realizing multi-angle monitoring and intelligent early warning response.

[0010] Further, the step S5 specifically comprises: Step S501: The connectivity of the pipe network nodes is analyzed by selecting a pipe connectivity test method based on layer priority and parent node priority adaptive strategy, which calculates the priority according to the pressure, flow rate, historical fault frequency and adjacent node state of each node, so that high-priority nodes are processed first; Step S502: The traversal process of the nodes is dynamically displayed on the GIS platform, and the connectivity breakpoints are marked in real time to facilitate user monitoring, wherein the nodes that have been traversed are marked in green, and the nodes that have not been traversed remain in the original color; nodes with high weight are marked in dark color, and nodes with low weight are marked in light color; if an unconnected node or a breakpoint is found, the system marks the node with red flashing on the platform, and displays the detailed information of the node such as pressure, flow rate and state in the side bar; Further, in the step S501, the specific steps of the mixed BFS layer priority top-down and parent node priority bottom-up traversal algorithm are as follows: (1) The traversal direction is dynamically adjusted according to the number of nodes in the front set, and top-down or bottom-up traversal is selected to optimize traversal efficiency and reduce invalid node checking; (2) When the number of nodes in the front set is less than the threshold , layer priority top-down traversal is performed; (3) When the number of nodes in the front set reaches or exceeds the threshold , parent node priority bottom-up traversal is performed, starting from the bottom node to preferentially find the parent node, reducing redundant neighbor checking.

[0011] The beneficial effects of the present application include: (1) The application collects multi-angle, high-resolution image data through oblique photography technology, and combines an improved three-dimensional modeling method to model ground facilities and underground pipe networks in detail, effectively solving the problem that traditional two-dimensional drawings cannot accurately describe the layout of pipe networks in three-dimensional space. In the data collection stage, the system pre-processes and adjusts the attitude of the image data, and ensures the accuracy of model details through individualization processing. The finally generated three-dimensional pipe network model has high precision and spatial consistency, can truly restore the layout of ground and underground pipe networks, and ensures the authenticity and accuracy of the pipe network data, providing a reliable data basis for subsequent visualization display and scientific analysis; (2) The application fully considers the mutual dependency of ground facilities and underground pipe networks, and proposes a construction method of an integrated model of ground and underground facilities. The system uses a unified coordinate system to fuse the three-dimensional model of ground facilities and the underground pipe network model, and superimposes a two-dimensional base map and a digital elevation model (DEM) to realize unified spatial expression of ground and underground pipe network data. This method ensures accurate display of the spatial relationship and hierarchical logic of ground and underground facilities, supports three-dimensional display and multi-dimensional interactive management, and provides important technical support for comprehensively mastering the overall operation status of pipe network facilities; (3) The application designs a symbol library containing pipe nodes, connection relationships, pipe material attributes, etc. to realize standardized management of different types of pipe network data. The symbol library uses a unified data format and symbol definition to ensure the standardization and consistency of different pipe network type symbols, and improves the display effect of the three-dimensional model. The application of the standardized symbol library not only makes the model display highly consistent, but also facilitates quick calling and reuse in different application scenarios, significantly improving the maintainability and reusability of the model, and providing standardized support for subsequent add, delete, modify and query operations; (4) The application integrates high-precision sensors to support real-time collection and dynamic updating of key operating parameters such as pressure and flow rate, and drives the dynamic updating of the pipe network model through real-time data feedback from the sensors. Combined with hierarchical correction and dynamic smoothing update mechanism, the system can quickly respond to changes in pipe network status, ensuring the timeliness and accuracy of the data. On this basis, the system can efficiently update the analysis results according to real-time data, realize real-time monitoring, and provide flexible and efficient dynamic analysis capabilities for pipe network management; (5) On the GIS platform, the application supports visualization display of the real-time operating status of the pipe network, including multi-dimensional data such as pressure, flow rate, flow direction, etc. Users can clearly and intuitively monitor the operating status of the pipe network through the platform. In addition, the application designs an adaptive strategy selection pipe connectivity test method based on layer priority and parent node priority, which significantly reduces redundant access, improves traversal efficiency and resource utilization, and enhances the accuracy and stability of the system visualization display.

[0012] Additional advantages, objects, and features of the application will be disclosed in the following detailed description. These advantages and features will become clear to those skilled in the art from the following detailed description, the appended claims, and the drawings. BRIEF DESCRIPTION OF DRAWINGS

[0013] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings, in which: Figure 1 A flowchart of a GIS-based pipeline connectivity visualization method of the present application; Figure 2 A flowchart of a three-dimensional model construction process of above-ground facilities of the present application; Figure 3 A flowchart of a three-dimensional model and symbol library construction process of underground pipe networks of the present application; Figure 4 A schematic diagram of three-dimensional scene model classification of the present application; Figure 5 A schematic diagram of above-ground and underground model integration display of the present application; Figure 6 A schematic diagram of real-time data acquisition and dynamic monitoring of the present application; Figure 7 A schematic diagram of pipeline connectivity analysis and intelligent early warning of the present application; Figure 8 A three-dimensional pipe network symbol library example diagram of the present application; Figure 9 A three-dimensional above-ground and underground integrated pipe network system visualization example diagram of the present application. DETAILED DESCRIPTION

[0014] The preferred embodiments of the present application will be described in detail below. It should be clear that the preferred embodiments are only for illustrating the present application, and are not intended to limit the protection scope of the present application.

[0015] In view of the deficiencies of the traditional pipe network management system in three-dimensional modeling and dynamic data analysis, the present application makes full use of three-dimensional geographic information system (GIS) modeling technology and real-time data analysis algorithm to realize fine modeling and multi-level data analysis of above-ground facilities and underground pipe network. Among them, the three-dimensional modeling technology is the basis for realizing accurate spatial representation. Through high-resolution multi-angle image data and combined with improved three-dimensional reconstruction algorithm for data processing, the pipe network is modeled in multiple levels, effectively solving the problem that the traditional two-dimensional drawing cannot accurately display the three-dimensional structure of the pipe network; at the same time, by using the above-ground and underground collaborative modeling technology, the spatial relationship between the above-ground buildings, roads and underground pipe network can be integrated displayed in the model, providing more intuitive three-dimensional visualization effect; in addition, through the standardized design of the symbol library, the standardized management of multiple types of data such as pipe node, connection relationship and pipe material attribute is realized, which not only improves the consistency of model display, but also facilitates the rapid calling and reuse in different application scenarios, improving the maintainability and reusability of the model.

[0016] The real-time data analysis algorithm is the core of the system dynamic update and monitoring capability. Through efficient real-time update and monitoring mechanism, the system can automatically capture the sensor data of the node pressure, flow rate, etc., dynamically update the state of the pipe network, timely identify potential risk points, and improve the response efficiency of the pipe network management; in addition, the present application realizes multiple data analysis functions, including dynamically calculating the pipe network connectivity, analyzing the flow direction and flow rate, accurately positioning the leakage point and simulating the diffusion path and buffer zone, and realizing data visualization display combined with GIS three-dimensional platform, which not only improves the real-time and precision of the system, but also ensures the response speed of the system, realizes intuitive display of the pipe network connectivity and real-time state in the three-dimensional scene, and significantly enhances the understanding and processing capability of the complex pipe network structure, providing strong dynamic analysis support for the pipe network management.

[0017] As shown in Figure 1 , a pipe network connectivity visualization method based on GIS of the present application comprises the following steps: Step S1: using oblique photography technology to collect and generate a three-dimensional model of above-ground facilities, performing single-body processing on the image data to realize independent display of the above-ground facilities, and optimizing facility management and data visualization; Step S2: designing the data structure of underground pipe network, performing standardized and symbolized processing, and generating an accurate three-dimensional pipe network model through pose adjustment to lay a foundation for subsequent display and analysis; Step S3: fusing and displaying the above-ground and underground models through a unified coordinate system, and superimposing a two-dimensional base map and a digital elevation model (DEM) to realize three-dimensional display of above-ground and underground facilities and enhance the coherence of spatial display; Step S4: Collect real-time data through sensors and integrate into the platform, dynamically update the pipe network model, realize real-time monitoring and emergency response functions of the pipe network, and ensure the timeliness and accuracy of the data; Step S5: Collect real-time data of the pipe network, dynamically analyze the connectivity of the pipe network, and combine with the GIS platform to display the state of the pipe network and the connectivity of the pipe network, realize spatial analysis and intelligent management of the pipe network.

[0018] As Figures 2 to 7 shown, the specific steps of the above method will be further described below through a specific embodiment.

[0019] Step S1 includes the following steps: Step S101, shoot the ground facilities from multiple angles by the unmanned aerial vehicle, collect high-resolution images, the specific operation is: (1) Based on the topographic features of the target area and the accuracy requirements of three-dimensional modeling, use flight control software to set the flight path, flight height, flight speed and camera field of view of the unmanned aerial vehicle, to ensure the coverage and overlap rate of the image; Among them, the city area with high building density is selected for shooting, a city area with an area of 1500m x 1000m is selected for shooting, and a snake-shaped path is planned for the unmanned aerial vehicle route to cover the entire target area; Set 6 image control points as reference points, whose coordinates (2000 national geodetic coordinate system and 1985 national elevation datum) are respectively: {(4251123.435, 5832154.224, 52.88); (4251187.942, 5832168.312, 51.659); (4251159.182, 5832287.757, 50.052); (4251191.001, 5832276.453, 52.652); (4251163.234, 5832294.951, 49.244); (4251157.572, 5832288.231, 50.092)}; The flight height is set to 65.5 meters to ensure a high enough resolution and to avoid buildings; In this embodiment, the unmanned aerial vehicle is equipped with a camera with a focal length of 25mm, and its field of view (FoV) is about 85 degrees; The forward overlap rate of the image is set to 85%, and the lateral overlap rate is set to 75% to ensure the continuity of the image and the integrity of the three-dimensional modeling, and to provide high-quality image data for subsequent three-dimensional modeling; Among them, according to the forward overlap rate requirement, the shooting interval d of the unmanned aerial vehicle is calculated, and the calculation formula is: , substitute the flight height = 65.5 meters, the forward overlap rate = 85%, FoV = , the shooting interval : According to the lateral overlap requirement, the flight line interval s is calculated, and the calculation formula is , the flight height is substituted =65.5 meters, the lateral overlap is , the FoV is , and the flight line interval s is obtained ; In actual operation, the unmanned aerial vehicle is set according to the above parameters, maintains a flight height of 65.5 meters, and takes an image every 18.01 meters, and each flight line interval is 30.02 meters, so as to ensure that the image coverage and overlap meet the modeling requirements; in the simulation scene, the flight control software generates a flight line scheme, and the images of each flight line are seamlessly connected, and the image coverage of the whole region reaches 100%; (2) Collect high-resolution images according to the planned route, and the unmanned aerial vehicle flies along the serpentine path and takes an image every 18.01 meters, for example: the spatial position of image 1 is (100.53 meters, 200.89 meters, 49.92 meters), and the camera attitude is pitch angle 0.28°, roll angle -0.12°, and yaw angle 0.18°; the spatial position of image 2 is (119.27 meters, 203.55 meters, 50.17 meters), and the camera attitude is pitch angle 0.83°, roll angle -0.09°, and yaw angle 5.03°; the multi-view image data collected includes the spatial position (longitude, latitude and altitude) of the image and the attitude information (pitch angle, roll angle and yaw angle) of the camera; for example, the collection point coordinates of image 1 are (100.53, 200.89, 49.92), the camera attitude is pitch angle 0.28°, roll angle -0.12°, and yaw angle 0.18°; the collection point coordinates of image 2 are (119.27, 203.55, 50.17), the camera attitude is pitch angle 0.83°, roll angle -0.09°, and yaw angle 5.03°; the image data collected in sequence is stored and recorded in the flight log in real time, and the specific parameters of each image include the shooting time, spatial position, flight line number and image number, so as to ensure the integrity and traceability of the data; After the collection is completed, all image data will be input into the image processing software for subsequent image stitching and three-dimensional modeling. The final output data is a multi-view image set with complete spatial reference, which ensures complete regional coverage and meets the requirement of overlap, and provides high-precision data support for three-dimensional modeling; In step S102, the collected image data is preprocessed to ensure the accuracy and consistency of the data, and the specific process is as follows: (1) The clarity of the collected image is scored by using the structural similarity (SSIM) index to ensure that the image is clear and free of blur, and the calculation formula of SSIM is: wherein, and respectively represent the mean value of images i and j; and respectively represent the variance of images i and j; represent the covariance of images i and j; and is a constant, and the value is and , wherein L is the dynamic range of the image (255 in this example); the image definition score threshold is set to 100, and the image below the threshold value will be determined to be blurred; for the collected images, the SSIM score of image 1 is 105.3, which is higher than the set threshold 100, and thus is determined to be clear; the SSIM score of image 2 is 98.5, which is lower than the threshold 100, and needs to be re-collected to ensure the quality; (2) After the definition check is completed, adaptive orthographic correction is performed to eliminate the image tilt distortion caused by the terrain height difference, wherein the adaptive orthographic correction formula is: In this example, the reference point is set to , the object height h = 52.88 m, the distance Z = 65.5 m, and the adaptive parameter β = 0.2, and the slope angle For image 1, the original projection coordinates are (100.53, 200.89), and the corrected coordinates obtained by substituting the adaptive orthographic correction formula are (100.55, 200.93), successfully eliminating the distortion caused by the terrain and height difference; finally, after the definition check and orthographic correction of all images are completed, high-quality orthographic image data is generated, providing a basis guarantee for subsequent three-dimensional modeling and analysis.

[0020] Step S103, generate high-precision dense point cloud by feature extraction, depth estimation, focal length correction, multi-view depth map fusion and coordinate optimization; (1) In step S103, the specific operation of the improved SIFT algorithm is as follows: First, the local contrast of the image is quickly regionally filtered, and a contrast threshold setting condition is used to screen the region with significant features and extract feature points; in image 1, the gradient calculation of point A is , the result is 4.5, which is greater than the set contrast threshold 3.0, so the point is extracted as a feature point; Subsequently, the scale parameter is set, and a multi-scale image is generated by an adaptive bilateral filter. The construction of the multi-scale image adopts the formula , wherein the filter parameter Set to 1.6, the calculated filtering result is 3.2; Next, set the scale multiple , generate Gaussian blur difference images at different scales. The detection of salient feature points uses the formula ; for example, the difference value of a certain feature point is 1.8, which significantly satisfies the set threshold, so the point is recorded as a salient feature point. Subsequently, the gradient amplitude value of the feature point is calculated , the result is 5.3, and the direction is assigned based on the formula , the calculated direction angle is 45°.

[0021] Finally, principal component analysis (PCA) is used to reduce the dimensionality of the extracted feature point descriptors, compressing 128-dimensional vectors to 64-dimensional to reduce storage requirements while retaining the main information. The final output is a set of feature points containing feature point positions, directions, gradient amplitudes, and reduced descriptors. In this example, feature point A (x=150, y=300) has a gradient amplitude of 5.3, a direction angle of 45°, and a descriptor that is a 64-dimensional vector. The entire process significantly improves the efficiency of feature point detection and the storage efficiency of descriptors, providing higher computational and storage performance support for subsequent image matching and feature recognition.

[0022] (2) Use KD-tree to store the feature descriptors of the image, find the preliminary feature matching pairs through nearest neighbor search, and use the improved KD-RANSAC algorithm to filter the matching pairs. The improved KD-RANSAC filtering calculation formula is: Set the distance threshold to , substitute the matching distance , satisfy , and confirm the matching is valid. (3) First, weight the average of the depth values of each feature point in different view images to obtain accurate depth information, complete depth estimation, and the dynamic depth estimation formula is: Substitute the three-view image weight , the corresponding depth value , and calculate ; Subsequently, according to the estimated depth value Z, correct the focal length to reduce errors, and the adaptive focal length correction formula is: Substitute the initial focal length , the correction factor , and the reference depth , and calculate After that, the obtained depth and the corrected focal length , the three-dimensional coordinates of the feature points are calculated, and the three-dimensional coordinate update calculation formula is: The pixel coordinates of the feature points , the camera principal point coordinates and the corrected focal length are substituted into the formula, and the three-dimensional coordinates of the feature points are calculated; through this operation, the conversion from two-dimensional pixel coordinates to three-dimensional space coordinates is completed, and the focal length correction effectively reduces the error and improves the accuracy of coordinate calculation.

[0023] (4) The preliminary depth map is generated by multi-view collaborative depth calculation, and the dynamic correction algorithm is used for smoothing processing of the depth map, and the specific operation is: First, for each view image, the depth information of other views is weighted and averaged to obtain the preliminary depth value of each pixel, and the multi-view collaborative depth calculation formula is: The depth values of the pixel points in three different views are , , and the corresponding weights are , , , so the preliminary depth value is obtained; Then, the depth information in the neighborhood is used to correct the preliminary generated depth map to improve the smoothness of the depth map, and the depth dynamic map correction formula is: The preliminary depth value of the pixel point is substituted, the depth values contained in the neighborhood range are , and the smoothing factor , so the corrected depth value is ; Finally, through the steps of multi-view collaborative calculation and depth correction, a smooth and accurate depth map is generated, which ensures that the depth information is more reliable and delicate in the subsequent three-dimensional reconstruction; (5) After generating the depth map of all views, dense point cloud data needs to be generated by depth map fusion, and the specific operation is: First, the depth maps of all views are fused with quality weighting; for the depth map data of each view, the three-dimensional coordinates of each view are fused by a view-weighted fusion formula to generate preliminary dense point cloud data, wherein the view-weighted fusion formula is: In this example, the quality weights of the three-view images are , , , and the three-dimensional coordinates converted from the depth maps are , , Therefore, the fused dense point cloud coordinates are obtained ; After that, in order to improve the smoothness of the dense point cloud, the local least squares method is used to minimize the residual of the local area of the point cloud to determine the optimized point cloud coordinates, wherein the three-dimensional coordinate optimization correction formula is: In this example, the dense point cloud coordinates are , and the field point set contains coordinates , , and the optimized dense point cloud coordinates are obtained by minimization ; (6) The details of the point cloud are increased by multi-scale fusion, and then noise detection and elimination are performed to improve the quality of the dense point cloud, so that the output data has the characteristics of high precision and low noise, and is suitable for three-dimensional modeling and visualization applications, and the specific steps are as follows: First, in order to increase the details of the dense point cloud, the details are accumulated by multi-scale depth map fusion, wherein the multi-scale fusion formula is: In this example, the three-dimensional coordinates of the three scale layers are , , , the fusion weights are , , , and the final fused dense point cloud coordinates are ; After that, the low-density points in the point cloud are detected, which are regarded as noise and eliminated, wherein the noise detection and elimination formula is: In this embodiment, the density threshold is set to 0.1, and when the density value of a point in the dense point cloud is lower than 0.1, the point is regarded as noise and eliminated, otherwise it is retained; After multi-view collaborative calculation, three-dimensional coordinate optimization, multi-scale fusion and noise removal processing, a high-quality dense point cloud is finally generated. Step S104, ground three-dimensional model construction is performed and independent facilities are singularized. The specific steps of the present example are as follows: (1) TIN (Triangulated Irregular Network) grid is constructed based on dense point cloud data. The TIN network weight is determined according to the point density, and the TIN grid size is adaptively adjusted according to the TIN network weight, so as to optimize the calculation efficiency. The adaptive TIN network weight formula is as follows: In the present example, the Euclidean distance between point i and point j is , the regional point cloud density is , the offset is , the density parameter δ = 0.6, the weight between point i and point j is calculated as , and the TIN grid size is optimized according to the weight to generate an optimized triangular net. (2) Texture mapping is performed on the TIN grid. The texture is allocated with priority weight. High-quality images are preferentially allocated to the grid area to ensure that the surface texture of the generated model has high precision and consistency. The texture mapping priority weight formula is as follows: Substitute the image focal length f = 25 mm, the angle , and the distance d = 5 m to obtain the texture mapping priority weight . (3) Vector surface covering and dynamic singularization are performed on the basis of the TIN grid. The specific steps are as follows: First, vector surfaces are generated based on point cloud data. High-density areas (such as buildings and roads) are automatically detected and combined with local curvature to generate vector surface covering suitable for complex features. The vector surface covering is based on density and curvature information to calculate the regional density. The weighted regional density calculation formula combining density and curvature is as follows: Substitute the number of points in the region , the area of the region , the local curvature , and the maximum curvature value to obtain the weighted regional density value . Then, the vector surface is generated according to the density, and dynamic singularization is performed on it. The matching distance between points and surfaces is calculated through the direction constraint formula. The matching distance formula based on direction constraint is as follows: substitute the model surface point coordinates , vector surface point coordinates , direction weight coefficient , the included angle between the normal vectors of the point and the surface , θ is the included angle between the normal vectors of the point and the surface, , the matching distance between the point and the surface is obtained , the individualization processing is completed according to the matching distance; (4) A differential triggering mechanism is realized to detect the change of the model position or attribute, and when the change exceeds a threshold value, the update of the model is triggered to maintain the timeliness of the model, wherein the differential triggering update mechanism is: In the implementation method, , if the change of the model is , the update mechanism is triggered; (5) The generated model is subjected to precision evaluation, wherein the weighted average error calculation formula based on the direction and the density is: substitute the three check points , the total errors thereof are , the weights thereof are , the direction angles thereof are Therefore, the weighted average error of the model is , which meets the model precision requirement.

[0024] The step S2 specifically comprises the following steps: Step S201, determine the key data requirement, construct the object model of the node and the connection relationship, and realize dynamic association, and the specific operation steps are: (1) Analyze the characteristics of the pipeline network, determine the key attributes that need to be recorded, including ID, node type (such as pipeline, valve, pump, etc.), category, diameter, material, three-dimensional coordinate and other basic information, and the connection relationship between nodes, wherein the node type is divided into three types of line, point and pump, the category can be divided into seven categories of water supply, drainage, gas, power, communication, heat and industry according to the “Technical Specification for Urban Underground Pipeline Detection”, and the material is divided into three categories of steel, cement and polyvinyl chloride (PVC); (2) Create a "pipe node" object model, including the following attributes: pipe ID, type, category, diameter, material, three-dimensional coordinates; among them, the pipe node object line1={001, "line", "Gas Pip", 300mm, "steel", (10.68, 15.2, 22.36)}, the pipe node object point1={101, "point", "Gas Pip", 300mm, "steel", (22.34, 34.8, 23.21)}, the attributes respectively represent the ID, type, category, diameter, material and position coordinates of the pipe node; Create a "connection relationship" object model to represent the connection relationship between pipe nodes, including attributes: starting node ID, ending node ID, connection type (such as direct connection, connection through valve, etc.), node connection type has adjoin, intersect, correlation, separation relationship; among them, node object R1={01, 11, "intersect"}, the attributes respectively represent the starting node ID, ending node ID and connection type of the link relationship object; (3) Use the database to associate the pipe node line1, point1 objects and connection relationship object R1 and use the graph database Neo4j for storage, and continue to reference other nodes through the connection relationship object, so that it can establish a many-to-many connection relationship; At the same time, provide interfaces for CRUD (CRUD) operations to realize dynamic management of the pipe network structure; Step S202, through data cleaning, format unification and symbol design, establish a symbol library to ensure data standardization and consistency in three-dimensional display, the specific operation steps are: (1) First, scan the original pipe network data comprehensively; identify and delete duplicate records, for example, pipe node A and node B coordinates are (12.5, 15.3, 8.2), ID is 101, at this time only one node is retained; for invalid data (such as null or abnormal value), such as the diameter of node C is empty or the material attribute of node D is abnormal and shows "UNKNOWN", mark these data and delete them; then, for missing pipe network data, use multi-level spatio-temporal similarity analysis method for filling, among them, the filling value calculation formula is: Substitute the three nodes closest to the node to be filled in: Node A , Node B , Node C , the Euclidean distance between nodes5.33, 7.81, 9.23, respectively, the to-be-filled node coordinate values are , thereby ensuring the integrity and consistency of the pipe network data; (2) Then, format standardization processing is performed on all pipe attribute data; the pipe diameter is uniformly converted to millimeter units, for example, the original diameter of 30 cm is converted to 300 mm; the material attribute is mapped to standard classifications, such as “Steel”, “Cement”, and “PVC”; at the same time, the coordinate system is unified to the national geodetic coordinate system (WGS84), for example, the original coordinates (116.405, 39.904) are converted to (5000.12, 2000.36, 32.45), to ensure that all data use the same coordinate reference, avoiding spatial misplacement problems; (3) Then, symbols conforming to industry standards are designed according to pipe types. Specific symbol shapes, colors, and sizes are defined for each pipe type. For example, a water supply pipe node is designed as a blue circle with a diameter of 10px; a gas pipe node is designed as a red square with a side length of 12px; a heat pipe node is designed as an orange triangle with a side length of 8px. These symbol templates are stored through JSON files, for example, the template of the gas pipe node is {“type”: “gas”, “shape”: “square”, “color”: “red (RGB: 255, 0, 0)”, “size”: “12px”}; (4) Finally, the designed symbols are stored according to pipe types, constructing a clear hierarchical structure; Through this complete process, all symbols have the consistency of industry standards, and users can quickly retrieve and call the required symbols according to pipe types, significantly improving the efficiency of data management; Step S203, collect pipeline coordinates and calculate rotation parameters, adjust the pose of the pipe point model to ensure consistency with the actual pipe network direction in the three-dimensional environment, and the specific operation steps are as follows: (1) Obtain the three-dimensional coordinates of the starting point and the ending point of each pipeline through the pipe network database, for example, the starting point coordinates of the pipeline ID 101 are (10.50, 20.30, 5.20), and the ending point coordinates are (15.72, 22.85, 5.50). Check and standardize the original data to ensure that it uses a consistent coordinate system and unit. If abnormal values or null values are found, they need to be marked or interpolated.

[0025] (2) Substitute the starting point (10.50, 20.30, 5.20) and the ending point (15.72, 22.85, 5.50) of the pipeline; calculate the horizontal rotation angle according to the three-dimensional rotation angle formula and the vertical rotation angle wherein: (3) In the three-dimensional modeling environment, load the start and end point models into the scene; according to the rotation angle and , adjust the direction of the model; after adjustment, verify the matching degree of the model with the actual pipeline direction, including: ensuring the accuracy of the start and end point coordinate positions, checking whether the pipe point and pipeline connection meet the topological relationship; Step S204, import and match the symbolized three-dimensional model data, perform visualization and effect optimization, and the specific operation steps are: (1) Import the adjusted pose of the pipe point model and pipeline data into the three-dimensional modeling environment, and based on the three-dimensional symbols, build a complete three-dimensional pipe network model, which provides a basis for subsequent visualization and analysis. The following is the specific process: Import the adjusted pipe point and pipeline model, including three-dimensional coordinates, material, type, direction, etc. For example, the parameters of the pipe point model Point1 are {ID: 001, Type: "Valve", Material: "Steel", Coord: (10.50, 20.30, 5.20), Orientation: (30.15°, 15.42°, 0°)}; the parameters of the pipeline model Line1 are {ID: 101, Type: "Gas Pipe", Material: "PVC", Start:(10.50, 20.30, 5.20), End: (15.72, 22.85, 5.50), Diameter: 300mm}; After importing, load the symbol files Valve.3ds and Pipe.3ds, the symbols are made based on CSG (Constructive Solid Geometry) and B-Rep (Boundary Representation) methods, and after importing, the symbols need to be checked for integrity to ensure the correctness of the size, color and map, such as the diameter of the valve symbol and the material and pipeline symbol information; (2) Based on the CSG method, the pipe point model is completed by combining basic geometric bodies. Taking the valve as an example, it is composed of a cylinder with a diameter of 300 mm and a hemisphere, and the adjustment direction is (30.15°, 15.42°, 0°) to meet the actual arrangement. Based on the B-Rep method, the starting point (10.50, 20.30, 5.20) and the ending point (15.72, 22.85, 5.50) of Line1 are defined as boundary points to generate a closed surface model and refine it into a three-level structure of points, lines and surfaces, and establish a topological relationship with Point1, the type is Adjoin; (3) After modeling, the symbolic model is given color to distinguish the type of pipe network, for example, the gas pipe network is red; at the same time, the connection smoothness is checked to ensure the smooth transition of pipe point and pipeline, the model proportion and map details are optimized to ensure the consistency of display under different viewing angles. The optimized three-dimensional model is saved as Gas_Pipeline_Model.3ds, and the storage path is / SymbolLibrary / Gas; (4) Finally, the model integrity and display effect are checked from different perspectives to ensure that all pipe network elements accurately reflect their actual shape and spatial relationship, and can be clearly displayed; the optimized three-dimensional model file is saved in the symbol library to support subsequent visualization analysis and pipe network management; The step S3 specifically comprises the following steps: Step S301: Through coordinate registration and multi-level homogeneous coordinate transformation, the aboveground and underground models are aligned and fused in a unified spatial coordinate system to ensure data consistency and spatial continuity. The specific operation steps are as follows: (1) Firstly, the adjusted ground and subsurface models are imported into the 3D modeling environment, which contain accurate 3D coordinates, material types, orientation angles and structure information. The parameters of the ground building model Surface_Model are {ID: 201, Type: "Building", Material: "Concrete", Coord: (100.50, 220.30, 50.20), Orientation: (0°, 0°, 0°)}, and the parameters of the subsurface pipeline model Subsurface_Model are {ID: 301, Type: "Pipeline", Material: "Steel", Start: (100.50, 220.30, -5.20), End: (110.72, 230.85, -5.50), Diameter: 300mm}. These data are represented by vectors, defining the starting point, ending point and orientation angle of the ground and subsurface models, which form the basis for the fusion process. Then, the spatial alignment of the ground and subsurface models is achieved through dynamic registration technology. In this process, registration weights are introduced to enable the alignment accuracy of the models in X, Y and Z directions to be self-adaptively adjusted to meet different spatial requirements. The dynamic coordinate registration formula is: ; (2) Next, the preliminarily aligned models are subjected to multi-level homogeneous coordinate transformation to adjust the rotation, scaling and translation of the aligned models. Nonlinear rotation parameters , adaptive scaling coefficients and transparency control matrix O(α=0.5) are introduced to complete the accurate adjustment and optimization of the models. The multi-level homogeneous coordinate transformation formula is: (3) Finally, the fused ground and subsurface models are combined into a complete 3D model, and the storage formula is where the fusion model storage formula is Combined with the above data calculation, the complete 3D model after fusion contains the geometric features, material properties and transparency information of the ground and subsurface. The model is stored as / GIS_Fusion / Complete_Model.gis. The transparency of the fused complete three-dimensional model is adjusted to 50%, the scale is consistent, the posture is adjusted accurately, and the model is finally fused in a unified three-dimensional coordinate system. Through this process, the complete three-dimensional model not only has high-precision geometric shape, but also can provide clear and consistent spatial display effect under different viewing angles. Step S302: Implementing the superimposed display of the two-dimensional base map, DEM and three-dimensional model to ensure the complete display effect of multi-dimensional data, the specific operation steps are: (1) First, import the two-dimensional base map ( ), digital elevation model (DEM, ) and three-dimensional fusion model ( ) into the three-dimensional modeling environment; these data contain rich spatial information: the two-dimensional base map data contains terrain texture, building distribution and other features, DEM data provides elevation information for accurate definition of terrain, and the three-dimensional fusion model contains integrated data of aboveground and underground geometric features and material properties; among them, the two-dimensional base map data contains fields {land number, road boundary, river direction}, DEM data provides fields {grid number, elevation value}, and three-dimensional fusion model data includes {model ID, material type, spatial coordinates}, which provides the basis for subsequent superimposed display; (2) Then, use three-dimensional superimposed display technology to superimpose the three-dimensional pipe network model and the aboveground building model with the base map and DEM; introduce the level control parameter to adjust the transparency and display priority of the two-dimensional base map, DEM and three-dimensional fusion model, wherein the three-dimensional visualization superimposition formula is; to realize the reasonable connection of the three, so that the aboveground and underground models can be displayed in the real terrain environment. For example, when superimposed, the display priority of the two-dimensional base map is the highest, which is used to provide texture background, and the DEM data provides elevation reference, and the three-dimensional model presents specific geometric details and material properties; (3) Display the superimposed model in the three-dimensional view, use dynamic transparency control , through transparency control, users can observe the structure of aboveground and underground models under different transparency, which is convenient for understanding the hierarchical relationship of three-dimensional space, and introduce the dynamic transparency control formula; wherein the initial value of transparency (full transparency), and decreases with time by , reaches at ; through transparency adjustment, users can gradually transition from two-dimensional base map to three-dimensional structure, and clearly observe the hierarchical relationship of aboveground and underground models; Step S303: An integrated three-dimensional model of aboveground and underground space is constructed by improved nonlinear coordinate transformation, dynamic transparency control and enhanced pose adjustment technology, and the specific operation steps are as follows: (1) First, a nonlinear fusion formula is used to realize the fusion of aboveground model and underground model in a unified three-dimensional coordinate system. The input data includes the fields of aboveground model: {ID, Type, Material, Coord, Orientation}, which are used to describe the unique identification, model type, material property, spatial coordinates and direction angle of the aboveground model; the fields of underground model: {ID, Type, Material, Start, End, Diameter}, which are used to describe the unique identification, model type, material property, start and end three-dimensional coordinates and pipe diameter of the underground pipe network; and fusion parameters represent the weight of the aboveground model. The nonlinear fusion formula is as follows: Through substitution and calculation, the fusion results of aboveground model and underground model are obtained. ; (2) Subsequently, an improved nonlinear registration formula is applied to dynamically adjust the pose and scale of the fused model through rotation matrix , scaling matrix and translation matrix . The input parameters include rotation angle function , which describes the rotation angles in X, Y and Z directions as 15°, 10° and 5°, respectively; scaling coefficient , which describes the scaling ratios in X, Y and Z directions as 1.1, 1.0 and 0.9, respectively; and translation vector , which describes the translation amounts in X, Y and Z directions as 5, 3 and 2, respectively. The improved nonlinear registration formula is as follows: Through the joint action of rotation, scaling and translation matrices, the registered three-dimensional model is obtained, which contains the coordinate, scale and pose information after precise adjustment: (3) Finally, the display effect is optimized through a dynamic transparency control formula. The input data includes time t and view angle related parameters: azimuth angle ϕ and pitch angle ψ, which are used to describe the dynamic adjustment of transparency with view angle. The transparency control function is as follows: Through setting the initial time and a view parameter , the transparency is calculated ; Finally, the transparency is applied to , the display model is calculated, wherein the dynamic transparency display model calculation formula is The optimized display model is obtained , the field contains the three-dimensional model coordinates, pose and transparency information finally used for display; The step S4 specifically comprises the following steps: Step S401: install intelligent adaptive sensors at key nodes, collect pressure, flow rate and other data in real time using self-optimizing Internet of Things technology, and intelligently transmit to the system, the specific operation steps are as follows: (1) Install intelligent adaptive sensors at key nodes (such as corners, intersection points or positions where flow rate changes significantly) of the pipeline network. These sensors have the ability to collect key data such as pressure (P) and flow rate (v) in real time, and dynamically adjust the sampling frequency through the built-in edge computing function to optimize data transmission and resource usage. The data structure of the sensor includes a time stamp (ttt), a unique node identification number (N), a current pressure value (P), a current flow rate value (v), and a pressure change rate (ΔP / Δt) and a flow rate change rate (Δv / Δt); in this case, the sensor with node number 101 is located at coordinates (150.0, 300.5, -10.0), and its currently collected pressure value is 250 kPa, the flow rate is 3.8 m / s, and the corresponding change rates are 1.2 kPa / s and 0.25 m / s 2 ; (2) The collected data is intelligently transmitted through the optimized Internet of Things protocol, and the data packet format is as follows: In this example, the data packet collected by the device with sensor number 101 at 14:00:00 on December 17, 2024 is: {2024−12−1714:00:00,101,250,3.8,1.2,0.25}; (3) After the system receives the data, it is stored according to the change trend and importance to support real-time monitoring and historical data analysis. When storing, the pressure value and flow rate value are processed by exponential decay, and the storage format is as follows: In this example, the exponential decay factor is and , the stored pressure value and flow rate value are and ; Therefore, the data record finally stored by the node 101 is: ; Step S402: Efficiently integrate real-time data into the three-dimensional pipe network model through hierarchical correction and dynamic smoothing update, ensure data synchronization and accuracy, the specific operation steps are: (1) Integrate the real-time data collected by the key nodes into the three-dimensional pipe network model, the input data of simulation includes time t, node unique identification number N, pressure value P(t), flow rate value v(t), and its change rate ΔP / Δt and Δv / Δt; Node state describes the pressure, flow rate and its dynamic change characteristics of node i at time t, wherein the calculation formula of node state is: Therefore, the state data of node 101 is obtained ; (2) Then, map the node state data to the corresponding node of the three-dimensional model, and correct the abnormal data by nonlinear weighted correction to eliminate the influence of noise and abnormal fluctuation, wherein the nonlinear weighted average formula is: In this example, the weight correction coefficient α=0.8, representing the weight of the normal data at the previous time, the normal pressure data of node 101 at the previous time of t=12:00 is 249kPa, and the current observation data is 250kPa, then the corrected pressure data is ; At the same time, the correction value of flow rate is updated from 3.7m / s to 3.72m / s; (3) The system uses interpolation smoothing formula to calculate the smoothed node value among multiple levels of nodes to ensure the continuity of data, wherein the interpolation smoothing formula is: In this example, the pressure data of the node is Therefore, the smoothing value is Through the smoothing interpolation operation, the node data of the three-dimensional model can maintain continuity and consistency in space; (4) The system automatically adjusts the data update frequency according to the change trend of pressure and flow rate, wherein the adaptive update frequency calculation formula is: In this example, when the pressure of node 101 is And The update interval is calculated as Δt=f(1.2,0.25)=5s, which ensures that the system can collect data and update the three-dimensional pipe network model at a dynamic frequency; Ultimately, the output is an optimized and highly integrated 3D pipeline network model, which includes the corrected and smoothed node states. Pressure and flow velocity are dynamically updated in real time to reflect the pipeline network operation, thereby ensuring data synchronization and model accuracy. Step S403: Display pipeline data in a 3D scene based on intelligent multi-dimensional visualization technology to achieve multi-angle monitoring and intelligent early warning response. The specific operation steps are as follows: (1) The system first loads real-time node data for pressure (P) and flow velocity (v), and simultaneously calculates their rate of change (P / v). and In a 3D scene, to highlight areas of dramatic change, the system uses the following transparency calculation formula: In this example, the pressure at node 202 is 280 kPa, the flow velocity is 4.2 m / s, and the rates of change are 2.5 kPa / s and 0.3 m / s, respectively. 2 Transparency is calculated as Therefore, this node will be displayed as semi-transparent in 3D visualization to emphasize areas of significant pressure and flow rate changes; (2) The system dynamically adjusts the transparency and focus based on the user's observation perspective and the importance priority of the nodes, prioritizing the display of key nodes. The formula for adaptive transparency and focus control is as follows: In this example, the user's viewing distance is 20m, the node priority score is 8, and the transparency is calculated. Adjusted to Clarity of nodes prioritized for display Control as This means that important nodes are displayed more prominently; (3) The system automatically identifies abnormal changes and triggers early warnings based on the rate of change of pressure and flow rate. The intelligent early warning triggering mechanism is as follows: In this example, the data change rate of node 303 is... , Because the pressure change rate exceeds the threshold, the system triggers an early warning, which is displayed as a red highlighted node, and at the same time pops up the prompt message "Node 303 is abnormal, pressure fluctuation needs to be checked immediately"; The final output of this step is a 3D visualization display: areas with significant changes in transparency and color are highlighted, key nodes dynamically adjust focus according to the user's perspective and priority, and abnormal nodes are highlighted in red and trigger early warning prompts, providing intuitive and real-time monitoring and risk response functions for pipeline network operation status.

[0026] The step S5 specifically comprises the following steps: Step S501: analyzing the connectivity of the pipe network nodes by mixing the BFS layer priority top-down and parent node priority bottom-up traversal algorithm, which calculates the priority according to the pressure, flow rate, historical failure frequency and adjacent node state of each node, so that high-priority nodes are processed first, and the specific operation steps are as follows: (1) Before implementing the mixed BFS layer priority top-down and parent node priority bottom-up traversal algorithm, the basic data of the nodes need to be collected and verified first, and the initial weight of the nodes is calculated to ensure that the key nodes can be processed first, and the operation steps are as follows: Firstly, the pressure value (P), flow rate value (v), historical failure frequency (F) and adjacent node information (P) of each node are extracted from the pipe network database. Assuming that the pressure P(A) of node A is 120 kPa, the flow rate v(A) is 2.5 m / s, the historical failure frequency F(A) is 3, and the adjacent node weight sum is If it is found that the data of a certain node is missing during data extraction, the system will immediately mark the node as "data abnormal" state and generate a notification to remind the check; Then, the integrity of each node is verified to ensure that all fields have been filled without omission; if it is found that the data of a certain node is missing during data extraction, the system will immediately mark the node as "data abnormal" state and generate a notification to remind the check; If the node data is complete, the initial weight calculation is continued, wherein the initial weight calculation formula is: In this example, the weight coefficients are , , , , , , the period of the periodic adjustment factor is , the time point is , and the weight of node A is calculated as ; Finally, the weight is added to the priority queue Q, and then all nodes are arranged in descending order of weight to form an initial priority queue; By this method, the weight and priority of the node are calculated, and the high-priority nodes are processed first in the subsequent traversal process, laying a foundation for the algorithm execution (2) In the mixed BFS layer priority top-down and parent node priority bottom-up traversal algorithm, the operation steps for constructing the node priority queue are as follows: Firstly, the node with the highest weight in the priority queue Q is taken out Assume that the initial order of the priority queue is , where has the highest weight and is ; the connectivity of the node is traversed using a hybrid BFS algorithm, which simultaneously performs bidirectional search from top to bottom in layer priority and from bottom to top in parent node priority to quickly identify the node set adjacent to ; Thereafter, for each adjacent node, the weight is dynamically updated according to the traversal state of the adjacent node , and the update formula is: In this example, the initial weight of the node is , the weight of the adjacent node is , the weight of the adjacent node and are substituted, the stress of the node is 110 kPa, the flow rate is 3.0 m / s, the historical failure frequency is , the adjustment factors , , , , and the weight coefficients are , , , respectively, and the weight update of the node is calculated to be ; After updating, the node is re-added to the priority queue Q and reordered in descending order of weight, and the priority queue becomes ; Thereafter, the node with the highest new weight is taken out of the queue, and the above operations are repeated until all nodes are traversed or the user stops the operation; By dynamically updating the weight of each node and reordering, it is ensured that the node taken out each time is the node with the highest current priority. The traversal process continues until the priority queue is empty or the user interrupts the operation; finally, the connectivity of the nodes is comprehensively analyzed, and the priority state of the nodes is dynamically adjusted; In this embodiment, in step S501, the specific implementation steps of the hybrid BFS layer priority top-down and parent node priority bottom-up traversal algorithm are as follows: (1) The system first obtains the current front set from the priority queue Q, and the calculation formula is: In this example, the priority queue contains 100 nodes, of which the nodes not yet visited are Therefore contains 50 nodes; (2) traversal strategy decision is made, if the number of nodes in the frontier set is lower than the threshold , the layer priority top-down traversal is executed, and the nodes are expanded layer by layer; otherwise, the parent node priority bottom-up traversal is switched to, and the redundant node check is skipped; wherein, the frontier size threshold formula is: In this example, the scale factor , the total number of nodes , the reference constant , the threshold value is obtained by substituting the data Since > , the parent node priority bottom-up traversal is selected; (3) when the number of nodes in the current frontier set reaches or exceeds the threshold , the parent node priority bottom-up traversal is executed, and the parent nodes are preferentially found from the bottom layer nodes, thereby reducing the redundant neighbor check, and the specific steps are as follows: First, according to each node in the current frontier set, the parent node set of the node is obtained; the unvisited parent node is added to the new frontier set , and the screening formula is: In this example, the current frontier set , and the node relationship is , ; under the condition of not being visited, the new frontier set is obtained by merging; Subsequently, the parent nodes with high priority are preferentially processed, when , it is necessary to check whether it has not been visited, if the condition is met, then is added to the next round of traversal set. At the same time, the neighbor back-prediction optimization is used, that is, the access state of the neighbor nodes is used to judge whether some parent nodes have been visited, so as to avoid repeated calculation; in this example, , while has been visited when is processed, then the repeated check of can be skipped here; For the parent node set of multiple frontier nodes, batch processing is adopted to reduce the memory and calculation burden. In this example, for , batch processing is performed respectively and , the merging result is ; whenever an unvisited parent node is found, it is added to the frontier set while the priority queue is updated and the node is marked as visited to avoid repeated traversal; In this example, the current input data is: the initial frontier set , the parent node relationship of the nodes is , and all parent nodes have not been visited. In the first round of operations, the parent node set is found to be ; after screening ; at this time, the frontier set is updated to , and the visited nodes are marked as Visited={N3,N4} = ; Continue this process for the parent node set of the updated frontier set , perform the same batch processing and screening operation, until the frontier set is empty or all target nodes are visited; by optimizing the parent node set layer by layer, and combining neighbor backtracking and batch processing method, this algorithm significantly improves the traversal efficiency while reducing redundant calculation; (4) when the number of nodes in the current frontier set is less than the threshold , perform a layer-priority top-down traversal, the specific steps are: First, determine the important nodes of each layer according to the dynamic layering strategy and adjust the expansion depth; the importance of the node is determined by the initial weight , the node with large weight is preferentially traversed, thereby reducing the redundant access of deep layer nodes; in this example, the current frontier set , , therefore is preferentially processed; for , perform neighbor checking to obtain its unvisited neighbor set , the neighbors of , among which is unvisited, then is added to the new frontier set and marked as visited. Secondly, perform neighbor checking to check whether the neighbor nodes of each node are unvisited; When processing high-frequency access nodes, for , enable an adaptive frontier cache mechanism to cache its neighbor set so that it can be directly read from the cache during subsequent access, avoiding repeated calculation; after completing the neighbor checking, update the current frontier set to ; for the newly added frontier nodes and , update their weights, wherein the weight update formula is: In this example, the weight adjustment coefficient , The dynamic weight increment G is determined by the pressure and flow rate change rate, and the calculation formula is: In this example, the parameters are set to , The dynamic weight increment is calculated; then the weight increment and new weight of are calculated, and is obtained; for , the initial weight , the weight increment , and are calculated. In addition, the neighbor nodes of are checked, and the neighbors of are ; where has not been visited, add to the new frontier set, and update the weight of to calculate ; After sorting all node weights, the priority queue order is ; the updated frontier set is ; Through the above operation process, the new frontier set is , and the weight update is ; the dynamic hierarchical strategy effectively reduces the redundant access of deep nodes, the adaptive frontier cache improves the access efficiency, the weight update mechanism optimizes the node priority distribution, and finally realizes the efficient layer priority traversal strategy.

[0027] Step S502: dynamically display the traversal process of the nodes on the GIS platform and mark the connectivity breakpoints in real time, and the specific steps are as follows: When implementing the dynamic display of the node traversal process, the system first connects the GIS platform and loads the node distribution diagram and initial state data of the pipe network. Based on the traversal priority queue Q, the system extracts nodes in order from high to low according to the weight order and performs traversal operations. For example, the node with the highest weight is extracted from the queue, and its initial state is: pressure P = 120 kPa, flow rate v = 2.5 m / s, state "normal", weight ; on the GIS platform, this node is marked as dark green, indicating that it is a traversed node and has a high weight; Next, the system analyzes the adjacent nodes connected to according to the hybrid BFS traversal algorithm. If it is detected that the adjacent node The connectivity of the node is normal, the system updates its weight to , and marks it as light green on the GIS platform, indicating that the node has been traversed and has a low weight; at the same time, for untraversed nodes such as , keep its initial color (gray) to distinguish the unprocessed state; During the traversal process, if the system detects a breakpoint node (i.e., a node not connected to the main network), such as node , the system will mark it with a red flashing color and display the detailed information of the node in the sidebar, including the pressure P = 90 kPa, the flow rate v = 0.0 m / s, and the state "disconnected", and at the same time, the system will automatically record the breakpoint information for subsequent repair and analysis; After each traversal is completed, the system dynamically updates the state and color marking of the nodes, and adjusts the display level of the GIS platform in real time to ensure that high-weight nodes and breakpoints are more prominent; users can view the detailed information of the nodes in real time in the sidebar of the GIS platform, including their pressure, flow rate, historical failure frequency, and weight data, to facilitate further decision-making.

[0028] At the end of the traversal, the connectivity state of all nodes forms an intuitive distribution diagram on the GIS platform, and the high-weight area and problem nodes are immediately apparent, finally supporting the connectivity analysis and optimization of the pipe network.

[0029] In this embodiment, the pipe network system data is fully preprocessed, Figure 2 a flowchart for constructing a three-dimensional model of above-ground facilities; Figure 3 a flowchart for constructing a three-dimensional model of underground pipe network and a symbol library; Figure 4 a schematic diagram of three-dimensional scene model classification; Figure 5 a flowchart for integrated display of above-ground and underground models; Figure 6 a real-time data acquisition and dynamic monitoring process; Figure 7 a flowchart for pipe network connectivity analysis and intelligent early warning; Figure 8 a three-dimensional pipe network symbol library example diagram; Figure 9 a three-dimensional above-ground and underground integrated pipe network system visualization example diagram; the present application models in detail the above-ground facilities and constructs a standardized pipe network symbol library, realizes integrated and accurate modeling of above-ground and underground pipe network systems, and real-time display of the running state and connectivity of the pipe network.

[0030] The principles and implementation methods of the present application are described in the specific embodiments; the above embodiment descriptions are only used to help understand the method and core idea of the present application; at the same time, for those skilled in the art, according to the idea of the present application, the specific implementation methods and application scope will be changed; in view of the above, the content of the specification should not be understood as a limitation of the present application.

Claims

1. A GIS-based method for visualizing pipeline connectivity, characterized in that: Includes the following steps: Step S1: Use oblique photogrammetry to acquire and generate 3D models of above-ground facilities, and process the image data into individual units to enable independent display of above-ground facilities, optimize facility management and data visualization. Step S2: Design the data structure of the underground pipeline network, perform standardization and symbolization processing, and generate an accurate three-dimensional pipeline network model through attitude adjustment; Step S3: The above-ground and underground models are integrated and displayed using a unified coordinate system, and a two-dimensional base map and digital elevation model are overlaid to achieve a three-dimensional display of above-ground and underground facilities; Step S4: Collect real-time data through sensors and integrate it into the platform to dynamically update the pipeline network model, realize real-time monitoring and emergency response functions of the pipeline network, and ensure the timeliness and accuracy of the data; Step S5: Collect real-time data of the pipeline network, dynamically analyze the network connectivity, and combine the network status and pipeline connectivity with the GIS platform to realize network spatial analysis and intelligent management.

2. The GIS-based pipeline connectivity visualization method according to claim 1, characterized in that: Step S1 specifically includes: Step S101: Capture high-resolution images of ground facilities by taking multi-angle photographs using a drone. The specific operation steps are as follows: Flight control software is used to plan routes based on terrain features and target accuracy, determining flight path length, flight altitude, flight speed, and camera field of view parameters. Operate the drone according to the planned route to acquire high-resolution images from multiple perspectives, record the spatial position and attitude of each image, and provide basic data for subsequent 3D modeling; Step S102: Preprocess the acquired image data to ensure data accuracy and consistency. The specific steps are as follows: Check the image data for sharpness, ensuring there is no blurring or ghosting. Verify the image coverage and overlap rate, ensuring the acquired images meet the standards for forward and lateral overlap rates. The formula for calculating overlap rate is: Adaptive orthorectification adjusts the imagery to a geographic coordinate system, eliminating tilt distortion caused by terrain and elevation differences. Adaptive orthorectification adds adaptive parameters. The orthorectification amount of ground features is automatically adjusted according to changes in slope. The adaptive orthorectification formula is as follows: in, and These are the coordinates after orthorectification; and These are the two-dimensional coordinates of the original projection of the image; The height or relative height of ground features; and Two-dimensional coordinates of a reference point or benchmark; The vertical distance between the object or ground feature and the camera, i.e., the object distance; These are parameters for adaptive orthorectification, used to control the effect of slope on image correction; The slope angle represents the angle of inclination of the ground surface relative to the horizontal plane. Step S103: Generate a high-precision dense point cloud through feature extraction, depth estimation, focal length correction, multi-view depth map fusion, and coordinate optimization; Step S104: Construct a three-dimensional model of the ground and process the independent facilities as individual units.

3. The GIS-based pipeline connectivity visualization method according to claim 2, characterized in that: The specific steps of step S103 include: (1) Feature extraction is performed using the improved SIFT algorithm to efficiently extract image feature points and generate descriptors, ensuring the scale and selection invariance of image feature points. The specific operation steps are as follows: First, fast region filtering is performed based on the local contrast of the image, extracting feature points only in regions that meet certain criteria to reduce the number of irrelevant points. The fast region filtering formula is as follows: in, For images Local contrast, The gradient of the image, The contrast threshold; Subsequently, adaptive bilateral filtering is used to generate a multi-level scale space, preserving edge details and reducing computational cost. The calculation formula for constructing the multi-level fast scale space is as follows: in, It is a scale The blurred image below It is an adaptive bilateral filter. For the input image, For a fuzzy scale, ; Subsequently, a difference Gaussian image is generated, and convolutional differences between multiple scales are calculated to detect significant feature points at different scales. The formula for calculating the multi-scale convolutional difference is as follows: in, It is a difference Gaussian image. and These are Gaussian blurred images at different scales, where k is a multiple of the scale. Different scales for the convolution kernel; Subsequently, gradient magnitude and direction are assigned, and weighting coefficients are used to reduce the influence of noise on the feature point orientation, ensuring orientation stability. The calculation formula for region-weighted orientation assignment is as follows: in For gradient magnitude, For the gradient direction, These are the weighting coefficients for different regions within the neighborhood; Finally, principal component analysis is used to reduce the dimensionality of the descriptors, preserving key information and reducing storage requirements. The calculation formula for the adaptive compressed descriptor is as follows: Among them, For the original 128-dimensional SIFT descriptor, This is the compressed descriptor. Principal component analysis; (2) Perform feature matching operation, use KD tree to store feature descriptors, find matching pairs through nearest neighbor search, and combine neighborhood density and dynamic distance threshold for accurate matching. The improved KD-RANSAC screening calculation formula is as follows: in, For target feature points Matching point Euclidean distance, As the baseline threshold, and These are the current and baseline scales, respectively. For feature points The neighborhood density; (3) Obtain depth information of feature points through multi-view images and generate a preliminary sparse point cloud. The specific operation steps are as follows: First, a weighted average of the depth values ​​of each feature point under different viewpoints is calculated to obtain accurate depth information. The dynamic depth estimation formula is as follows: in, The weighted depth values ​​of the calculated feature points; The number of images from different perspectives; Perspective weights are determined by the sharpness or overlap of the images. Let be the depth value measured from the i-th viewpoint; Then, based on the calculated depth The camera's focal length is corrected to reduce coordinate deviations caused by focal length errors. The adaptive focal length correction formula is as follows: in, The corrected focal length is used for three-dimensional coordinate calculations; This is the original camera focal length; This is the focal length correction factor, used to adjust the effect of depth changes on the focal length; The reference depth is a fixed value, which is usually chosen as the reference depth. The depth of the current feature point; Then, using the obtained depth and the corrected focal length Calculate the three-dimensional coordinates of each feature point, where the formula for updating the three-dimensional coordinates is: in, and The three-dimensional coordinates of the feature point; The depth value is obtained from dynamic depth estimation; and These are the two-dimensional coordinates of the feature points in the image; and The position of the camera's principal point is the coordinate of the center of the optical axis on the image plane; This is the corrected focal length, used to ensure the accuracy of three-dimensional coordinate calculations; (4) After the sparse point cloud is generated, a depth map for each viewpoint is generated to record the depth information of each pixel. (5) After generating depth maps from all perspectives, dense point cloud data is generated by fusion of the depth maps; (6) After obtaining complete dense point cloud data, further optimize the dense point cloud.

4. The GIS-based pipeline connectivity visualization method according to claim 3, characterized in that: After the sparse point cloud is generated, a depth map is further generated for each viewpoint to record the depth information of each pixel. The specific steps are as follows: First, when generating the depth map for each image, depth information from other viewpoints is used to calculate the depth through a weighted average method to reduce single-viewpoint errors. The multi-view collaborative depth calculation formula is as follows: in, The similarity weights for viewpoints are calculated based on image angles and distances. and In the i-th and j-th images respectively, pixels The collaborative depth value; Subsequently, after generating the initial depth map, it is dynamically corrected to reduce noise and improve the smoothness of the depth map. The dynamic depth map correction formula is as follows: in, These are the corrected depth map values; This is the original depth map value; This is a smoothing factor used to adjust the smoothing effect in the neighborhood; For pixels The neighborhood range includes the set of surrounding pixels; The number of pixels in the neighborhood; After generating depth maps from all perspectives, dense point cloud data is generated by fusing the depth maps. The specific operation is as follows: First, when fusing depth maps from different viewpoints, viewpoint weighting is used to enhance the weight of viewpoints with higher image quality in the point cloud. The viewpoint weighting fusion formula is as follows: in, The final fusion generates the 3D coordinates of the dense point cloud; The number of images; For the first The quality weight of each image is determined based on the image's resolution and sharpness; For from the first The 3D coordinates generated from the depth map of an image, based on the image's rotation matrix. Translation vector Obtained through conversion; Subsequently, local least squares was used to optimize the 3D coordinates on the fused dense point cloud data to improve the smoothness of the point cloud. The 3D coordinate optimization correction formula is as follows: in, The optimized 3D coordinates are used to improve the smoothness of the point cloud; These are the three-dimensional coordinate values ​​to be optimized. The set of neighboring points of the current point; For other three-dimensional coordinate points in the neighborhood; Euclidean distance is used to measure the distance difference between the optimization point and its neighboring points; After obtaining complete dense point cloud data, the specific steps for further optimizing the dense point cloud are as follows: First, multi-scale fusion is employed to accumulate detailed information at different scales layer by layer to achieve higher point cloud detail representation. The multi-scale fusion formula is as follows: in, The coordinates of the final dense point cloud after multi-scale fusion; The number of scale layers; Let be the fusion weight of the s-th layer, representing the influence of this scale in the final fusion; The 3D coordinates of the s-th layer depth map are obtained through the image rotation matrix. Translation vector Obtained through conversion; Next, noise detection is performed on the dense point cloud to remove points with low confidence, thereby improving the overall data quality. The noise detection and removal formulas are as follows: in, This is the point cloud density value, used to measure the reliability of the current point; This is the density threshold; points below this value are considered noise points and are discarded. For the three-dimensional coordinate points to be detected, their density is used to determine whether to retain them.

5. The GIS-based pipeline connectivity visualization method according to claim 2, characterized in that: Step S104 specifically includes: (1) Construct a 3D model. Based on dense point cloud data, construct a TIN mesh. By adaptively adjusting the TIN mesh size, generate small meshes in dense regions and large meshes in sparse regions to optimize computational efficiency. The adaptive TIN network weight formula is as follows: in, The weights between points i and j are used to control the construction of the TIN mesh; Let be the Euclidean distance between points i and j, used to measure the distance between points; This represents the point cloud density for the current region, describing the distribution of the point cloud within that region. This is the density threshold used to control the level of mesh detail. (2) Perform texture mapping on the generated TIN mesh, and assign textures using priority weights to prioritize images with vertical viewing angles and high focal lengths in texture mapping. The priority weight formula for texture mapping is as follows: Where W is the priority weight of texture mapping; The focal length of the image represents its resolution. The angle between the image viewpoint and the normal of the triangular mesh surface is used to measure the degree of matching between the image angle and the model surface. This is the distance from the image center to the triangular mesh face, used to determine the distance relationship between the image and the model; (3) Perform vector surface coverage and dynamic single-unitization processing. The specific steps are as follows: First, vector surfaces are generated based on point cloud data. High-density areas are automatically detected and combined with local curvature to generate vector surface coverage suitable for complex terrain features. The weighted density formula combining density and curvature is as follows: in, These are the weighted region density values, used to generate the vector surface; The number of points within the region; This refers to the area of ​​the region; Local curvature indicates the degree of bending of the model surface; To achieve the maximum curvature value, ensure that the curvature effect is normalized; Subsequently, directional constraints are introduced during the model individualization process. This ensures that when matching points and surfaces, not only distance but also the consistency of the normal vector direction is considered, thereby improving the accuracy of the individualization process. The matching distance formula for the directional constraints is as follows: in, The matching distance between points and surfaces; The coordinates of points on the model surface; α represents the coordinates of a point on a vector surface; α is the direction weighting coefficient, used to balance the weights of distance and direction consistency; θ is the angle between the point normal vector and the surface normal vector. Ensure that the normal vectors are in the same direction; (4) Implement a differential triggering mechanism to detect changes in model position or attributes. When the change exceeds a threshold, an update is triggered. The differential triggering update formula is: in, , , These represent the changes in the model's position along the X, Y, and Z directions, respectively. To update the trigger threshold, control the update frequency; (5) Conduct model accuracy assessment. By assigning higher orientation and density weights to important regions, a more accurate preliminary accuracy assessment of the overall model is performed. The formula for calculating the weighted average error based on orientation and density is as follows: in, This is the weighted average error; Assign higher weights to critical regions for checkpoint i; The total error at the i-th checkpoint; The direction angle of the checkpoint is used to assess the impact of direction on the error.

6. The GIS-based pipeline connectivity visualization method according to claim 1, characterized in that: Step S2 specifically includes: Step S201: Determine key data requirements, construct an object model of node and connection relationships, and implement dynamic association; Step S202: Establish a symbol library through data cleaning, format unification, and symbol design to ensure data standardization and consistency in 3D display; Step S203: Collect pipeline coordinates and calculate rotation parameters, adjust the orientation of the pipe point model to ensure that the orientation is consistent with the actual pipeline network in the 3D environment. The specific operation steps are as follows: (1) Prepare data by collecting the coordinates of the start and end points of each pipeline segment to ensure that the data format meets the calculation requirements and to provide the necessary basic data for the calculation of rotation parameters; (2) Rotation parameter calculation is performed to obtain the orientation angle of the pipe node, so as to ensure that the orientation of the model in the three-dimensional environment is consistent with the actual pipeline. In order to make the rotation parameter calculation formula more adaptable to complex scenes in the three-dimensional environment, the two-dimensional angle is extended to the three-dimensional angle and the height information is incorporated to more accurately represent the pipe orientation. The three-dimensional rotation angle calculation formula is as follows: in, The horizontal rotation angle is the offset angle of the pipeline in the XY plane, used to determine the horizontal offset of the pipeline. The vertical rotation angle is the angle of inclination of the pipeline relative to the horizontal plane, used to determine the offset of the pipeline in the vertical direction. , , , , and These are the X, Y, and Z coordinates of the pipeline's start and end points, respectively. (3) Adjust the attitude. Based on the calculated rotation parameters, adjust the orientation of each pipe point model to ensure that it is consistent with the actual pipeline orientation in the three-dimensional environment and that the attitude adjustment conforms to the actual pipeline layout. Step S204: Import and match the symbolized 3D model data to complete model building, and then perform visualization and effect optimization. The specific steps are as follows: (1) Import the adjusted pipe point model and pipeline data into the 3D modeling environment for modeling; (2) Match the three-dimensional model with the actual pipe points and pipelines according to the definitions in the symbol library to ensure that each symbol correctly represents its corresponding pipeline element; (3) Visualize all pipeline data, use different colors to distinguish different types of pipelines to enhance the visualization effect of the pipeline network, and check the display effect of the model in the three-dimensional environment to ensure that all pipeline elements can accurately reflect their actual shape and spatial relationship. (4) Conduct a final check on the visualization effect to ensure that the model is clearly visible from different perspectives, and make adjustments and optimizations.

7. The GIS-based pipeline connectivity visualization method according to claim 1, characterized in that: Step S3 specifically includes: Step S301: Through coordinate registration and multi-level homogeneous coordinate transformation, align and merge the above-ground and underground models in a unified spatial coordinate system to ensure data consistency and spatial coherence. The specific operation steps are as follows: (1) Using dynamic registration technology to connect the ground model and underground models Accurate alignment in three-dimensional space; by introducing registration weights. and This allows the model to flexibly adjust alignment accuracy in the X, Y, and Z directions, thereby adaptively adjusting alignment requirements in each direction and achieving accurate display in a unified coordinate system. The dynamic coordinate registration formula is as follows: in, To ensure that the aligned above-ground and underground models are positioned consistently in a unified coordinate system; and These are registration matrices along the X, Y, and Z directions, used for independent alignment of the above-ground and underground models, respectively. and To register weights, the alignment accuracy of the above-ground and underground models in different directions is controlled; and These are the coordinate matrices for the above-ground and underground models, representing the model data for buildings and pipelines, respectively. (2) After the initial registration, the improved homogeneous coordinate transformation matrix is ​​used. Rotation, scaling, and transparency control are implemented. Nonlinear rotation parameters are introduced to control the rotation angle, and adaptive scaling factors ensure consistent model proportions across different directions, guaranteeing accurate scaling and pose adjustment of the model in 3D space. The multi-level homogeneous coordinate transformation formula is as follows: in, An improved multi-level homogeneous coordinate transformation matrix controls the model's rotation, scaling, translation, and transparency adjustment to ensure alignment accuracy. It is a nonlinear rotation matrix, with a rotation angle of... Precise adjustment of attitude in three-dimensional space; To ensure a consistent scale between above-ground and underground models, an adaptive scaling factor is used. This is a transparency control matrix; adjusting transparency enhances the fusion effect of 3D models. (3) Combine the above-ground and underground models into a complete three-dimensional model through an improved multi-level homogeneous coordinate transformation. And it is stored in the GIS platform, where the fusion model storage formula is: in, The merged complete 3D model includes overall data from both above and below ground. For improved multi-level homogeneous coordinate transformation matrices; and This represents the coordinate matrix of the above-ground and underground models; Step S302: Implement the overlay display of the 2D base map, DEM, and 3D model to ensure the complete display effect of multidimensional data. The specific operation steps are as follows: (1) Importing a two-dimensional base map and DEM data This provides the foundation for terrain and location information for 3D models, ensuring that above-ground and underground models can be displayed in realistic terrain environments; (2) Employing 3D overlay display technology, the 2D base map, DEM, and 3D model are simultaneously displayed in the same view; hierarchical control parameters are introduced. This allows the transparency and display priority of each data layer to be dynamically adjusted, ensuring a natural connection and clear display of each data layer in three-dimensional space. The three-dimensional visualization overlay formula is as follows: in, These are hierarchical control parameters used to adjust the transparency and display priority of the base map, DEM, and 3D model, ensuring a natural connection between different data layers. (3) Display the overlay model in the 3D view and use dynamic transparency control. To meet different display needs, the dynamic transparency control formula is: in, The transparency is dynamically controlled by parameters, and the transparency can be adjusted according to needs to meet diverse display requirements; Step S303: Construct an integrated 3D model of above-ground and underground spaces using improved nonlinear coordinate transformation, dynamic transparency control, and enhanced attitude adjustment techniques. The specific steps are as follows: (1) Using nonlinear fusion technology, the ground model is integrated. With underground model Blended in a unified 3D coordinate system; through smooth fusion parameters This achieves a smooth transition at model boundaries, ensuring the naturalness of the fusion process. The formula for nonlinear data fusion is as follows: in, To smoothly integrate parameters, the integration ratio of above-ground and underground models is controlled and automatically adjusted according to the model proximity to achieve a smooth boundary transition. The above-ground and underground models are merged; (2) Introduce an improved nonlinear coordinate registration formula For the fused model Detailed alignment is achieved using the rotation matrix R, translation matrix T, and scaling factor. and nonlinear control function To achieve dynamic adjustment in different directions, the nonlinear coordinate registration formula is as follows: in, It is a non-linear control function that fine-tunes alignment accuracy based on rotation angle; The adaptive scaling factor ensures consistent model proportions. To apply the above-ground and underground models after nonlinear registration; (3) Introduce view-related transparency control in 3D display. Transparency over time and azimuth angle Pitch angle Dynamic adjustment allows users to clearly identify above-ground and underground structures from different viewing angles. The dynamic viewing angle transparency control formula is as follows: in, This is a dynamic transparency control function for the viewing angle, based on time. and perspective , Adjust transparency; This is a 3D display model under dynamic transparency control.

8. The GIS-based pipeline connectivity visualization method according to claim 1, characterized in that: Step S4 specifically includes: Step S401: Install intelligent adaptive sensors at key nodes to collect pressure and flow rate data in real time using self-optimizing IoT technology and intelligently transmit the data to the system. The specific operation steps are as follows: (1) Install intelligent adaptive sensors at key nodes of the pipeline network to support automatic adjustment of sampling frequency and optimize resource utilization with edge computing; the real-time data collected by the sensors includes pressure P and flow velocity V, reflecting the real-time status of the pipeline network operation; (2) The sensor transmits the collected data to the system via the Internet of Things, wherein the self-optimized transmission data format is: in, A data packet, containing the set of data currently being transmitted; For data collection time; This is the node ID, used to uniquely identify a specific location in the pipeline network; This is a pressure value, reflecting the fluid pressure at the pipeline node; This is the flow velocity value, reflecting the speed of the fluid at the pipe network node; The pressure change rate is the pressure change per unit time, representing the dynamic trend of pressure. The rate of change of flow velocity is the change in flow velocity per unit time, representing the dynamic trend of flow velocity. (3) After receiving the data, the system stores it hierarchically according to the trend and importance to support real-time monitoring and historical data analysis. The data storage format is as follows: in, Data records stored in the database; and As an exponential decay factor, it controls the decrease of pressure and flow rate with distance or time, respectively, to avoid the influence of remote nodes or historical data on the current analysis; Step S402: Through hierarchical correction and dynamic smoothing updates, real-time data is efficiently integrated into the 3D pipeline network model to ensure data synchronization and accuracy. The specific operation steps are as follows: (1) Integrate the data of key nodes into the three-dimensional pipeline network model, correct abnormal data, and perform interpolation smoothing between multi-level nodes, wherein the node status is: in, The state of node i at time t includes pressure, flow velocity and its rate of change; and The pressure and flow rate values ​​are given at time t. and Let be the rate of change of pressure and flow velocity, respectively, representing the dynamic changes of pressure and flow velocity at the node over time; (2) To reduce the impact of outliers on the model, a nonlinear weighted average formula is used for outlier correction. The nonlinear weighted average formula is as follows: in, These are the corrected data values; This is a correction factor, ranging from 0.5 to 0.9, used to control the smoothness. and This includes the normal data from the previous moment and the current observation data; (3) Perform interpolation smoothing between multi-level nodes to ensure data continuity. The formula is as follows: in, These are the smoothed data values; The number of nodes for smoothing; To compress the observation data for each interpolation node; (4) The system automatically adjusts the data update frequency according to the changing trends of pressure and flow rate. The formula for calculating the adaptive update frequency is: in, This refers to the update frequency, i.e., the model refresh interval. This is an adaptive function used to calculate the frequency of data updates; and The absolute rate of change of pressure and flow velocity represents the rate of change. The system adjusts the update interval according to its magnitude; the larger the fluctuation, the higher the update frequency. Step S403: Display pipeline data in a 3D scene based on intelligent multi-dimensional visualization technology to achieve multi-angle monitoring and intelligent early warning response. The specific operation steps are as follows: (1) In the three-dimensional scene, the system adjusts the transparency and color according to the rate of change of node pressure and flow velocity to highlight the fluctuating area. The multi-dimensional visualization transparency calculation formula is as follows: in, Adjust the node transparency to highlight areas of dramatic change. It is a multi-dimensional visualization function that calculates transparency based on pressure, flow rate and their rate of change, making areas with large changes more obvious; (2) The system dynamically adjusts the transparency and focus based on the user's perspective and node priority to make important nodes more clearly visible. The formula for perspective-adaptive transparency and focus control is as follows: in, Dynamic transparency, adaptively adjusted based on the user's viewing distance; This is a transparency function used to adjust transparency based on distance. This represents the distance from the user's viewing angle; a smaller value indicates that the user is closer. This is a small offset to avoid the abnormal situation where the line of sight is zero; This provides focus control, allowing users to concentrate their attention on key elements; This is the focus function, used to control the degree of focus on nodes according to their priority. This represents the node priority; a higher value indicates a more important node. (3) The system automatically identifies abnormal changes and triggers early warnings based on the rate of change of pressure and flow rate. The intelligent early warning triggering mechanism is as follows: in, and This is the warning threshold. When the rate of change of pressure or flow rate exceeds this value, the system triggers an alarm to prompt an inspection.

9. The GIS-based pipeline connectivity visualization method according to claim 1, characterized in that: Step S5 specifically includes: Step S501: Analyze the connectivity of pipeline nodes by selecting a pipeline connectivity testing method based on an adaptive strategy of layer priority and parent node priority. This method calculates the priority of each node based on its pressure, flow velocity, historical fault frequency, and the status of neighboring nodes, allowing high-priority nodes to be processed first. The specific operation steps are as follows: (1) Collect and verify the basic data of the nodes, calculate the initial weights, and ensure that key nodes can be processed first. The operation steps are as follows: First, the system extracts the pressure P, flow rate v, historical failure frequency F, and neighboring node information for each node from the database. If the extraction fails, the system will mark the node and generate an error report for subsequent inspection. Next, the data integrity of each node is checked to ensure that there are no missing fields. If a node is missing data, the node is marked as "data abnormal" and a notification is generated. Subsequently, the initial weights are calculated, and the formula for calculating the initial weights is as follows: in, The weight of node N is used to determine node priority; , , , These are weighting coefficients, which respectively control the influence of pressure, flow rate, failure frequency, and neighboring nodes on the weighting. The pressure value of node N is processed in logarithmic form to avoid the excessive influence of the pressure value; The flow velocity value at node N is expressed as a square root. The value should be a small positive number to prevent calculation problems when the flow rate is zero. The historical failure frequency of node N is used to measure the reliability of the node. Let N be the sum of the weights of the neighboring nodes of node N, representing the relationship between node N and its neighboring nodes; As a periodic adjustment factor, where Control the adjustment range, The system monitoring cycle ensures dynamic adjustment of weights; Finally, initialize the priority queue and assign weights to all nodes. Add to priority queue Q and sort in descending order of weight; (2) Use a hybrid BFS algorithm that prioritizes top-down and parent-node-priority bottom-up traversal to traverse nodes with high weights in the priority queue, and dynamically update node priorities based on the data. The operation steps are as follows: First, retrieve the node N with the highest current weight from the priority queue and perform a connectivity traversal using a hybrid BFS algorithm that prioritizes top-down and parent-node-priority bottom-up traversal, and update the weights of neighboring nodes; if the priority queue is empty, end the traversal process. Subsequently, the priority queue Q is reordered according to the updated weights to ensure that the node retrieved each time is the highest priority node; Finally, repeat the above steps until all nodes have been traversed or the user stops operating. Step S502: Dynamically display the node traversal process on the GIS platform and mark connectivity breakpoints in real time for easy user monitoring. Traversed nodes are marked in green, while untraversed nodes retain their original color. Nodes with high weight are marked in dark colors, while nodes with low weight are marked in light colors. If an unconnected node or breakpoint is found, the system marks the node with a flashing red light on the platform and displays detailed information about the node in the sidebar.

10. A GIS-based pipeline connectivity visualization method according to claim 9, characterized in that: In step S501, the specific steps of the hybrid BFS layer-first top-down and parent node-first bottom-up traversal algorithm are as follows: (1) Dynamically adjust the traversal direction based on the number of nodes in the front set, choosing either top-down or bottom-up traversal to optimize traversal efficiency and reduce the checking of invalid nodes. The specific steps are as follows: First, the front set is calculated. The front set is the set of nodes traversed at the current level, containing nodes that have not yet been visited but have been selected as the next nodes to be visited. The formula for calculating the front set is: in, The set of the frontiers of the current layer; This is the set of sorted nodes in the priority queue; Subsequently, a front size check is performed to determine whether the current number of front set nodes is lower than the system-set threshold. This threshold is used to determine when to switch traversal strategies, where the formula for the front edge size threshold is: in, The threshold value of the leading edge determines the switching of the traversal method; This is a scaling factor used to control the relationship between the frontier threshold and the total number of nodes; This represents the total number of nodes; It serves as a baseline constant, used to balance the dynamic changes in the threshold. Finally, if the number of nodes in the frontier set is lower than the threshold The execution layer prioritizes traversing from top to bottom, expanding nodes layer by layer; otherwise, it switches to traversing the parent node from bottom to top, skipping redundant node checks. (2) The number of nodes in the current edge set is less than the threshold. When traversing, the execution layer prioritizes top-down traversal, and the specific steps are as follows: First, a dynamic layering strategy is applied to determine the expansion depth based on the importance of nodes in each layer, prioritizing the expansion of important nodes in shallower layers, thereby reducing redundant checks on deeper nodes. Secondly, perform a neighbor check to check whether each node's neighbor nodes have not been visited; Furthermore, an adaptive front-end caching mechanism is applied to cache the neighbor information of frequently accessed front-end nodes so that they can be directly read during subsequent accesses, avoiding redundant calculations. Then, the front set is updated by adding unvisited neighbor nodes to the front set and marking them as "visited"; Then, the frontier weight self-balancing strategy is applied to perform self-balancing based on the node weight distribution in the frontier set, prioritizing the processing of key nodes with higher weights. Subsequently, for each node N in the frontier set, update the weights of its unvisited neighbor nodes and reorder the priority queue Q, where the neighbor node weight update formula is: in, and The weights of node N before and after the update; and The adjustment coefficient is used to control the magnitude of weight updates; For dynamic weight increments based on pressure and flow rate changes; and For the rate of change of pressure and flow rate; Finally, the priority queue Q is reordered according to the updated weights to ensure that the node retrieved each time is the highest priority node; (3) The number of nodes in the current set reaches or exceeds the threshold. When performing a traversal, the parent node is traversed from bottom to top, starting from the bottom-level nodes to prioritize finding the parent node, reducing redundant neighbor checks. The specific steps are as follows: First, a hierarchical search of parent nodes is performed, prioritizing the examination of high-priority parent nodes to reduce the access volume of irrelevant nodes. The formula for searching parent nodes level by level is as follows: in, This is the updated frontier set, containing only the newly added parent nodes in the current traversal layer; The parent node of node NNN; The current frontier set represents the set of nodes that have been traversed in the current layer; Returns the set of parent nodes of node N; This indicates a condition that the parent node has not been visited, used to filter visited nodes and avoid redundant traversal; Furthermore, neighbor reverse inference optimization is applied, which infers whether the parent node has been visited by other paths by inferring the access status of neighbor nodes, thereby avoiding duplicate checks; Finally, multi-level parent node batch processing is performed to batch process the parent nodes of multiple front nodes to reduce the memory burden in a single access. Each time a valid parent node is found, the parent node is added to the front set and the parent nodes in the priority queue are updated. If the parent node has already been accessed, the node is skipped.

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