A sewer network full-scene survey method and device, electronic equipment and medium

Abstract

This invention relates to the field of drainage network monitoring technology, and discloses a method, device, electronic equipment, and medium for full-scene surveying of drainage networks. This invention acquires and integrates the geographical location data of manholes, multi-frame 3D point cloud data above and / or underwater, and their corresponding pose data. Based on coordinate transformation, it constructs a 3D point cloud model with geographic coordinates, achieving accurate spatial positioning of network nodes in a unified geographic coordinate system. By identifying pipe outlet nodes from this model and extracting their geographical location, orientation, and pipe diameter, and then automatically establishing the connection relationships between pipe outlet nodes according to spatial matching rules, an accurate network topology is generated. By integrating multi-frame point cloud and pose information for 3D reconstruction and relationship derivation, the data adaptability and analysis reliability under different manhole structures and environmental conditions are enhanced, thereby improving the overall accuracy, automation, and environmental adaptability of full-scene surveying of drainage networks.

Description

Technical Field

[0001] This invention relates to the field of drainage network monitoring technology, specifically to a method, device, electronic equipment, and medium for full-scene surveying of drainage networks. Background Technology

[0002] Drainage pipe network systems are characterized by complex structures, underground concealment, and diverse topological relationships. In these complex underground pipe network environments, the basic information of most urban drainage systems is incomplete or inconsistent with the actual situation, necessitating the acquisition of accurate data on the current state of the pipe network through pipe network surveying technology. Existing drainage pipe network surveying technologies mainly employ video inspection methods such as closed-circuit television (CCTV) and rapid video inspection (QV), but these methods have limitations: CCTV inspection requires drainage sealing operations, resulting in high costs, low efficiency, and complex inspection procedures; rapid video inspection cannot adapt to pipe environments with high water levels, exhibiting insufficient environmental adaptability. More importantly, the aforementioned video inspection technologies lack high-precision positioning capabilities, failing to provide accurate geographical coordinates of pipe network nodes. They still rely on manual segment-by-segment analysis of video images to infer pipe network connections, leading to low efficiency and a high risk of errors.

[0003] In the area of ​​3D reconstruction of inspection wells, existing equipment mainly falls into two technical categories: image-based and ultrasonic-based. Image-based equipment is only suitable for 3D reconstruction of the above-water portion, while ultrasonic-based equipment is only suitable for the underwater portion. Both types of equipment have limited application scenarios and cannot achieve 3D reconstruction of the entire inspection well scene. Furthermore, these devices lack the intelligent function of automatically identifying pipe nodes and analyzing pipe connection relationships, which has significant limitations in the application of full-scene surveying of drainage pipe networks, making it difficult to achieve ideal survey results. Summary of the Invention

[0004] This invention provides a method, device, electronic equipment, and medium for full-scene surveying of drainage pipe networks, in order to solve the problems of insufficient positioning accuracy of pipe network nodes, inaccurate establishment of connection relationships, and poor environmental adaptability in the prior art.

[0005] In a first aspect, the present invention provides a method for full-scene surveying of drainage pipe networks, comprising: acquiring survey data corresponding to each inspection well in the drainage pipe network, the survey data including the geographical location data of the inspection well, multi-frame three-dimensional point cloud data within the inspection well, and pose data corresponding to each frame of three-dimensional point cloud data, the multi-frame three-dimensional point cloud data including surface laser scanning three-dimensional point cloud data and / or underwater ultrasonic scanning three-dimensional point cloud data; based on the geographical location data of each inspection well and the pose data corresponding to each frame of three-dimensional point cloud data, transforming each frame of three-dimensional point cloud data to a preset geographic coordinate system through coordinate transformation, generating a three-dimensional point cloud model with geographic coordinates for each inspection well; identifying pipe outlet nodes from the three-dimensional point cloud model with geographic coordinates for each inspection well, and determining the geographical location, orientation, and pipe diameter of each pipe outlet node; and establishing the correspondence between each pipe outlet node according to the geographical location, orientation, and pipe diameter obtained from each inspection well through spatial matching rules, generating pipe network topology data describing the connection relationship of the pipe network.

[0006] This invention first acquires and integrates the geographical location data of inspection wells, multi-frame 3D point cloud data, and their corresponding pose data. The multi-frame 3D point cloud data includes surface laser scanning 3D point cloud data and / or underwater ultrasonic scanning 3D point cloud data, enabling the acquisition of 3D point cloud data in all scenarios and directions under various working conditions, including inspection wells with and without water. Then, based on coordinate transformation, a 3D point cloud model with geographic coordinates is constructed, achieving accurate spatial positioning of pipeline nodes in a unified geographic coordinate system, solving the problem of insufficient positioning accuracy in traditional methods. By identifying pipe outlet nodes from this model and extracting their geographical location, orientation, and pipe diameter, and then automatically establishing the connection relationships between pipe outlet nodes according to spatial matching rules, an accurate pipeline topology is generated, overcoming the shortcomings of manual judgment of connection relationships, which is prone to errors and inefficient. By integrating multi-frame point cloud and pose information for 3D reconstruction and relationship derivation, the data adaptability and analysis reliability under different inspection well structures and environmental conditions are enhanced, thereby improving the overall accuracy, automation, and environmental adaptability of the full-scene survey of drainage pipeline networks.

[0007] In one optional implementation, the step of acquiring survey data corresponding to each inspection well in the drainage network includes: placing a survey carrier integrating a positioning device, a pose measurement device, and a three-dimensional scanning device into the inspection well, wherein the three-dimensional scanning device is configured to perform circumferential rotation scanning around the axis of the survey carrier; controlling the survey carrier to move along the axial direction of the inspection well, and simultaneously triggering the positioning device, the pose measurement device, and the three-dimensional scanning device to acquire data, thereby obtaining geographical location data, multi-frame three-dimensional point cloud data, and pose data corresponding to each frame of three-dimensional point cloud data.

[0008] In this embodiment, by placing a surveying carrier integrating positioning, pose strategy, and 3D scanning functions into the inspection well, and controlling its movement along the well axis while simultaneously triggering circumferential rotation scanning and data acquisition, continuous, complete, and high-coverage 3D data acquisition of the internal structure of the inspection well is achieved during a single deployment. This operation method not only significantly improves the efficiency and consistency of data acquisition, but also effectively enhances the data adaptability and integrity in complex well environments through the coordination of the carrier's axial movement and the sensor's circumferential scanning, thus providing a reliable and rich data foundation for subsequent high-precision modeling and intelligent analysis.

[0009] In one optional embodiment, the three-dimensional scanning device includes a laser scanning module for acquiring three-dimensional point cloud data by laser scanning above water and an ultrasonic scanning module for acquiring three-dimensional point cloud data by ultrasonic scanning underwater.

[0010] In this embodiment, by employing a laser scanning module and an ultrasonic scanning module working in tandem, three-dimensional data can be acquired from both the above-water and underwater parts of the inspection well. This enables the acquisition of complete three-dimensional geometric information of the entire inspection well scene, effectively overcoming the limitations of a single type of sensor in cross-medium environments. It significantly enhances the adaptability of the survey system to complex field conditions and the completeness of data acquisition, thereby ensuring that the subsequently generated three-dimensional point cloud model can realistically and comprehensively reflect the overall structure of the inspection well, providing a reliable full-scene data foundation for pipe opening identification and pipeline topology reconstruction.

[0011] In one optional implementation, the step of generating a 3D point cloud model with geographic coordinates for each manhole by transforming the 3D point cloud data of each frame to a preset geographic coordinate system through coordinate transformation based on the geographic location data of each manhole and the pose data corresponding to each frame of 3D point cloud data includes: calculating an initial transformation matrix for transforming the 3D point cloud data of each frame from the point cloud coordinate system to the preset geographic coordinate system based on the geographic location data of each manhole and the pose data corresponding to each frame of 3D point cloud data; using the initial transformation matrix corresponding to each frame of 3D point cloud data as the initial value for the iteration of the point cloud registration algorithm, performing matching calculations and iterative optimization on the point cloud data of adjacent frames to obtain an optimized accurate transformation matrix; and transforming the 3D point cloud data of each frame to the preset geographic coordinate system according to the corresponding accurate transformation matrix to form a 3D point cloud model with geographic coordinates.

[0012] In this embodiment, an initial transformation matrix for each frame of point cloud is first calculated based on geographic location and pose data. This matrix is ​​then used as the initial value for the point cloud registration algorithm for fine-tuning. Finally, the geographic coordinates of the point cloud are unified based on the optimized and accurate transformation matrix, achieving high-precision and high-efficiency 3D model reconstruction. This method utilizes sensor data to provide a high-quality initial estimate for point cloud registration, effectively avoiding the problems of traditional registration algorithms easily getting trapped in local optima and having slow convergence speed, significantly improving the accuracy and robustness of point cloud stitching. At the same time, through staged coordinate transformation and optimization, it ensures that the final generated 3D point cloud model has both accurate absolute geographic coordinates and local geometric features, laying a highly reliable data foundation for subsequent port identification and topology analysis.

[0013] In one optional implementation, in the step of calculating the initial transformation matrix for transforming each frame of 3D point cloud data from the point cloud coordinate system to the preset geographic coordinate system based on the geographical location data of each manhole and the pose data corresponding to each frame of 3D point cloud data, the following operations are performed for each frame of 3D point cloud data: based on the pose data corresponding to the frame of 3D point cloud data and the known installation parameters of the survey vehicle, a first transformation parameter is determined from the point cloud coordinate system through the survey vehicle coordinate system to the manhole coordinate system; based on the geographical location data of the manhole where the frame of 3D point cloud data is located, a second transformation parameter is determined from the manhole coordinate system to the preset geographic coordinate system; the first transformation parameter and the second transformation parameter are combined to obtain the initial transformation matrix for transforming the frame of 3D point cloud data to the preset geographic coordinate system.

[0014] In this embodiment, the coordinate transformation process is decoupled into "determining the first transformation parameters from the point cloud coordinate system to the inspection well coordinate system based on pose data and survey carrier installation parameters" and "determining the second transformation parameters from the inspection well coordinate system to the preset geographic coordinate system based on geographic location data," and finally combined to generate an initial transformation matrix. This achieves a highly structured and clear coordinate system transformation. Through layered calculation and parameter combination, this method not only ensures that each frame of point cloud can be accurately and independently located based on its own sensor data and the absolute position of its associated well, but also significantly enhances the transparency, interpretability, and system stability of the coordinate transformation process. This provides a reliable mathematical foundation for multi-sensor fusion positioning, effectively supporting the subsequent high-precision point cloud stitching and 3D modeling.

[0015] In one optional implementation, the steps of identifying pipe nozzle nodes from the 3D point cloud model with geographic coordinates of each manhole and determining the geographical location, orientation, and pipe diameter of each pipe nozzle node include: segmenting pipe nozzle node point cloud data representing the pipe nozzle structure from the 3D point cloud model; performing spatial geometric analysis on the pipe nozzle node point cloud data to obtain the axial direction, which is used as the orientation of the pipe nozzle node; projecting the pipe nozzle node point cloud data onto a plane perpendicular to the axial direction to perform circular contour fitting, obtaining the fitted circle center and radius; and transforming the fitted circle center and radius to a preset geographic coordinate system to obtain the center coordinates and pipe diameter of the pipe nozzle node.

[0016] In this embodiment, pipe outlet point cloud data is segmented from a 3D point cloud model with geographic coordinates. Then, the axial direction is extracted based on spatial geometric analysis to determine the orientation. Finally, the center coordinates and diameter of the pipe outlet are obtained through projection fitting and coordinate transformation, achieving automated and high-precision extraction of the geometric attributes of key nodes in the pipeline network. This method significantly improves the efficiency and stability of pipe outlet parameter calculation by transforming 3D shape recognition into orientation analysis and 2D fitting problems. Furthermore, since all processing steps are based on a 3D model registered to a geographic coordinate system, the final output node coordinates, orientation, and dimensions all have a unified absolute geographic reference, providing structurally complete and positionally accurate input data for the subsequent automated and precise reconstruction of pipeline network connections.

[0017] In one optional implementation, the step of establishing a correspondence between pipe nodes based on the geographical location, orientation, and pipe diameter obtained from each inspection well, and generating pipeline topology data describing the pipeline connection relationship, includes: for each pipe node, finding associated pipe nodes that meet preset constraints within a preset search range, and establishing a connection relationship between the pipe node and the associated pipe nodes. The preset constraints include distance conditions, orientation conditions, and size conditions; and generating pipeline topology data containing pipe nodes, edges, and attributes based on the connection relationship, geographical location, orientation, and pipe diameter of each pipe node.

[0018] In this embodiment, spatial matching and association of pipe nodes are performed based on triple constraints of distance, orientation, and size. A pipe network topology containing nodes, edges, and attributes is automatically constructed based on the matching results, achieving intelligent and automatic reconstruction of drainage pipe network connections. This method effectively simulates the physical and spatial logic of actual pipe connections by introducing multi-dimensional geometric constraint rules, significantly improving the accuracy and reliability of connection relationship determination and overcoming the drawbacks of traditional methods that rely on human experience and are prone to errors. Simultaneously, the automatically generated standardized topology data can directly support pipe network analysis, simulation, and management applications, providing a clear and accurate data foundation for the digital operation and maintenance and scientific decision-making of drainage systems.

[0019] Secondly, the present invention provides a full-scene surveying device for drainage pipe networks, comprising: a data acquisition module for acquiring survey data corresponding to each inspection well in the drainage pipe network, the survey data including the geographical location data of the inspection well, multi-frame three-dimensional point cloud data within the inspection well, and pose data corresponding to each frame of three-dimensional point cloud data, the multi-frame three-dimensional point cloud data including surface laser scanning three-dimensional point cloud data and / or underwater ultrasonic scanning three-dimensional point cloud data; a point cloud conversion module for converting each frame of three-dimensional point cloud data to a preset geographic coordinate system based on the geographical location data of each inspection well and the pose data corresponding to each frame of three-dimensional point cloud data, thereby generating a three-dimensional point cloud model with geographic coordinates for each inspection well; a pipe outlet identification module for identifying pipe outlet nodes from the three-dimensional point cloud model with geographic coordinates for each inspection well, and determining the geographical location, orientation, and pipe diameter of each pipe outlet node; and a pipe network topology construction module for establishing the correspondence between each pipe outlet node according to the geographical location, orientation, and pipe diameter of each pipe outlet node obtained from each inspection well, through spatial matching rules, thereby generating pipe network topology data describing the connection relationship of the pipe network.

[0020] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the drainage network full-scene survey method described in the first aspect or any corresponding embodiment.

[0021] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions, which are used to cause a computer to execute the drainage network full-scene survey method described in the first aspect or any corresponding embodiment. Attached Figure Description

[0022] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0023] Figure 1 This is a schematic diagram of the first process of the full-scene survey method for drainage pipe network according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the second process of the full-scene survey method for drainage pipe networks according to an embodiment of the present invention; Figure 3 This is a structural block diagram of a full-scene survey device for drainage pipe networks according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0026] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0027] Currently, drainage network surveying faces challenges due to complex underground structures and a lack of basic data. Existing mainstream technologies such as CCTV and QV inspection are not only costly and have poor environmental adaptability, but also generally lack high-precision positioning capabilities, making it difficult to automatically obtain the accurate geographical coordinates and connection relationships of network nodes. Meanwhile, manhole 3D reconstruction equipment suffers from limitations such as the separation of image and ultrasonic technology routes and restricted application scenarios, failing to achieve full-scene 3D reconstruction of manholes and intelligent identification and connection analysis of pipe nodes, severely restricting the comprehensiveness, accuracy, and intelligence level of drainage network surveying. Based on this, this invention provides a method, device, electronic equipment, and medium for full-scene surveying of drainage networks to solve the problems of insufficient positioning accuracy of network nodes, inaccurate establishment of connection relationships, and poor environmental adaptability in existing technologies.

[0028] According to an embodiment of the present invention, a method for full-scene survey of drainage pipe networks is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0029] This embodiment provides a method for full-scene surveying of drainage pipe networks. Figure 1This is a flowchart of a full-scene survey method for drainage pipe networks according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps: Step S101: Survey data corresponding to each inspection well in the drainage network. The survey data includes the geographical location data of the inspection well, multi-frame three-dimensional point cloud data inside the inspection well, and pose data corresponding to each frame of three-dimensional point cloud data. The multi-frame three-dimensional point cloud data includes surface laser scanning three-dimensional point cloud data and / or underwater ultrasonic scanning three-dimensional point cloud data.

[0030] The survey data collected in this step includes three categories: Geographic location data of inspection wells: Records the absolute location of each inspection wellhead, providing a global geographic reference benchmark for the entire survey data.

[0031] Multi-frame 3D point cloud data from the manhole: This data is continuously acquired during the survey using a 3D scanning device. It records a dense set of 3D spatial points on the manhole's internal structure, including the well wall and pipe openings, forming the basis for constructing the 3D model. Specifically, the multi-frame 3D point cloud data acquired in this step includes surface laser scanning 3D point cloud data and / or underwater ultrasonic scanning 3D point cloud data. This means that surface laser scanning 3D point cloud data can be acquired using a surface laser scanning module, and underwater ultrasonic scanning 3D point cloud data can be acquired using an ultrasonic scanning module, ensuring full-scene data acquisition for various manhole conditions, including those with and without water. Surface laser scanning 3D point cloud data refers to the 3D point cloud data obtained by laser scanning the surface portion of the manhole, while underwater ultrasonic scanning 3D point cloud data refers to the 3D point cloud data obtained by ultrasonic scanning the underwater portion of the manhole.

[0032] Pose data corresponding to each frame of 3D point cloud data: Synchronously acquired by the pose measurement device, recording the real-time attitude of the 3D scanning device itself in space when each frame of point cloud data is acquired, such as roll, pitch, and yaw angle information. This is the key information for subsequent accurate stitching of multiple frames of local point clouds and conversion to a unified coordinate system.

[0033] This step, through the synchronous integration and triggering of multiple sensors, enables continuous and dynamic scanning of the internal geometry of the inspection well during the lowering of the survey vehicle along the well, ensuring that each frame of geometric data includes its precise pose information at the time of acquisition. This synchronous, multimodal data acquisition method lays a solid data foundation for subsequent high-precision, full-scene 3D reconstruction and intelligent analysis.

[0034] Step S102: Based on the geographical location data of each inspection well and the pose data corresponding to each frame of 3D point cloud data, the 3D point cloud data of each frame is transformed to a preset geographic coordinate system through coordinate transformation to generate a 3D point cloud model with geographic coordinates for each inspection well.

[0035] This step integrates the geographic location data of the inspection well and the pose data corresponding to each frame of point cloud. The collected multi-frame local 3D point cloud data is then uniformly transformed and stitched into a preset geographic coordinate system through coordinate system transformation and point cloud registration technology. This constructs a complete 3D point cloud model of the inspection well with both fine geometric structure and accurate geographic coordinates, laying a highly consistent spatial data foundation for the subsequent accurate identification and analysis of pipe nodes.

[0036] Step S103: Identify the pipe opening nodes from the 3D point cloud model with geographic coordinates of each inspection well, and determine the geographical location, orientation and pipe diameter of each pipe opening node.

[0037] This step uses an algorithm to analyze the 3D point cloud model with geographic coordinates, automatically identify and segment the point cloud representing the pipe opening structure, then extracts its axis as orientation through spatial geometric analysis, and determines its precise center coordinates and pipe diameter based on projection fitting and coordinate inverse calculation, thereby realizing the automated and high-precision extraction of the geometric attributes of the pipe opening node from the 3D model to structured parameters.

[0038] Step S104: Based on the geographical location, orientation, and pipe diameter of each pipe node obtained from each inspection well, establish the correspondence between each pipe node through spatial matching rules, and generate pipeline topology data describing the pipeline connection relationship.

[0039] This step is based on the precise geographical location, orientation, and pipe diameter of the pipe nodes identified from each manhole. By setting multiple spatial matching rules such as distance, direction, and pipe diameter tolerance, it automatically searches, judges, and establishes the connection correspondence between pipe nodes of different manholes. This generates standardized data that fully describes the geometric connection and topology of the pipeline network, ultimately achieving automated and highly reliable reconstruction from discrete node information to a systematic pipeline network topology.

[0040] The drainage network full-scene survey method provided in this embodiment acquires and integrates the geographical location data of manholes, multi-frame 3D point cloud data and their corresponding pose data, and constructs a 3D point cloud model with geographic coordinates based on coordinate transformation. This achieves accurate spatial positioning of network nodes in a unified geographic coordinate system, solving the problem of insufficient positioning accuracy in traditional methods. By identifying pipe outlet nodes from this model and extracting their geographical location, orientation, and pipe diameter, and then automatically establishing the connection relationship between pipe outlet nodes according to spatial matching rules, an accurate network topology is generated, overcoming the defects of easy error and low efficiency in manual judgment of connection relationships. By integrating multi-frame point cloud and pose information for 3D reconstruction and relationship derivation, the data adaptability and analysis reliability under different manhole structures and environmental conditions are enhanced, thereby improving the overall accuracy, automation level and environmental adaptability of the drainage network full-scene survey.

[0041] This embodiment provides a method for full-scene surveying of drainage pipe networks. Figure 2 This is a flowchart of a full-scene survey method for drainage pipe networks according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps: Step S201: Survey data corresponding to each inspection well in the drainage network. The survey data includes the geographical location data of the inspection well, multi-frame three-dimensional point cloud data inside the inspection well, and pose data corresponding to each frame of three-dimensional point cloud data. The multi-frame three-dimensional point cloud data includes surface laser scanning three-dimensional point cloud data and / or underwater ultrasonic scanning three-dimensional point cloud data.

[0042] Specifically, step S201 above includes: Step S2011: The survey carrier, which integrates a positioning device, a pose measurement device and a three-dimensional scanning device, is placed into the inspection well, wherein the three-dimensional scanning device is configured to perform circumferential rotation scanning around the axis of the survey carrier.

[0043] In one alternative implementation, the positioning device may be a network RTK positioning antenna, and the pose measurement device employs a combination of an inertial measurement unit (IMU) and a magnetometer to provide real-time pose information and absolute orientation reference.

[0044] In one optional embodiment, the three-dimensional scanning device includes a laser scanning module for acquiring three-dimensional point cloud data by laser scanning above water and an ultrasonic scanning module for acquiring three-dimensional point cloud data by ultrasonic scanning underwater.

[0045] Preferably, the surveying carrier is a telescopic pole. The aforementioned network RTK positioning antenna is installed at the top of the telescopic pole to obtain the absolute coordinates of the wellhead. An IMU and a magnetometer are installed at the bottom of the telescopic pole to provide real-time pose information and absolute direction reference. A laser scanning module and an ultrasonic scanning module are mounted on a 360° continuous rotation mechanism, respectively responsible for the 3D point cloud reconstruction of the above-water and underwater parts of the inspection well. The telescopic pole carrier is made of stainless steel or aluminum alloy and has an IP68 waterproof rating. It is manually lowered into the inspection well for vertical scanning.

[0046] According to this embodiment, it is possible to obtain three-dimensional point cloud data of the above-water portion of the inspection well by laser scanning, and also obtain three-dimensional point cloud data of the underwater portion by ultrasonic scanning. Therefore, this embodiment achieves full coverage acquisition of three-dimensional point cloud data of both the above-water and underwater portions of the inspection well through the collaborative operation of the laser scanning module and the ultrasonic scanning module. This results in excellent adaptability to various working conditions, enabling the completion of full-scene three-dimensional surveys of the inspection well under various actual working environments, such as when the well is completely dry, partially flooded, or completely full of water.

[0047] In step S2012, the survey carrier is controlled to move along the axis of the inspection well, and the positioning device, pose measurement device and three-dimensional scanning device are simultaneously triggered to collect data, obtain geographical location data, multi-frame three-dimensional point cloud data and pose data corresponding to each frame of three-dimensional point cloud data.

[0048] Specifically, during the equipment deployment phase, the surveying carrier is placed vertically at the wellhead, and the absolute coordinates (X, Y, X) of the wellhead are obtained via a network RTK positioning antenna. GPS ,Y GPS Z GPS The IMU is initialized to establish a direction reference, and the magnetometer provides a global azimuth reference. During the vertical scanning phase, the probe is lowered into the inspection well, and the laser and ultrasonic modules simultaneously perform rotational scanning. The IMU records attitude changes in real time, and the magnetometer continuously provides an absolute direction reference.

[0049] Step S202: Based on the geographical location data of each inspection well and the pose data corresponding to each frame of 3D point cloud data, the 3D point cloud data of each frame is transformed to a preset geographic coordinate system through coordinate transformation to generate a 3D point cloud model with geographic coordinates for each inspection well.

[0050] Specifically, step S202 above includes: Step S2021: Based on the geographical location data of each inspection well and the pose data corresponding to each frame of 3D point cloud data, calculate the initial transformation matrix to transform each frame of 3D point cloud data from the point cloud coordinate system to the preset geographical coordinate system through coordinate transformation.

[0051] The high frame rate point cloud stitching algorithm based on sensor fusion employs a multi-coordinate system fusion strategy, establishing four hierarchical coordinate systems: the EPSG:32650 coordinate system (global geographic coordinate system), the inspection well coordinate system (with the inspection well center as the origin), the survey carrier coordinate system (with the IMU on the telescopic rod as the reference), and the point cloud coordinate system (sensor local coordinate system). The EPSG:32650 coordinate system, i.e., the global geographic coordinate system, is used as the preset geographic coordinate system of this invention. Coordinate transformation is achieved through the following transformation matrix: T global =T rtk ×T imu ×T magnetometer ×T sensor ; Among them, T sensor This is the transformation matrix from sensor to IMU, realizing the transformation from the point cloud coordinate system to the survey vehicle coordinate system, including the sensor's installation position and attitude offset relative to the IMU; T magnetometer This is the magnetometer orientation correction matrix, used to correct the accumulated error of the IMU, provide an absolute orientation reference, and eliminate orientation drift in the carrier coordinate system; T imuThis is the attitude transformation matrix from the IMU to the well center, realizing the transformation from the survey vehicle coordinate system to the well coordinate system, including the attitude change and position offset of the IMU relative to the well center; T rtk The RTK positioning transformation matrix is ​​used to convert the manhole coordinate system to the global geographic coordinate system, transforming the point cloud data in the manhole coordinate system to the global geographic coordinate system, ensuring that each point cloud data has accurate geographic coordinate information.

[0052] For each frame of 3D point cloud data, perform the following operations: Step a: Based on the pose data corresponding to the three-dimensional point cloud data of this frame and the known installation parameters of the survey carrier, determine the first transformation parameters from the point cloud coordinate system through the survey carrier coordinate system to the inspection well coordinate system.

[0053] Step b: Based on the geographical location data of the inspection well where the three-dimensional point cloud data of this frame is located, determine the second transformation parameter from the inspection well coordinate system to the preset geographical coordinate system.

[0054] Step c: Combine the first transformation parameter and the second transformation parameter to obtain the initial transformation matrix that transforms the 3D point cloud data of the frame to the preset geographic coordinate system.

[0055] Step S2022: Using the initial transformation matrix corresponding to the 3D point cloud data of each frame as the initial value for the point cloud registration algorithm, the point cloud data of adjacent frames are matched, calculated and iteratively optimized to obtain the optimized accurate transformation matrix.

[0056] Step S2023: Transform each frame of 3D point cloud data into a preset geographic coordinate system according to the corresponding precise transformation matrix to form a 3D point cloud model with geographic coordinates.

[0057] Specifically, the above steps first constitute the initial registration stage: by calculating the T values ​​of the two frames before and after the initial registration. imu ×T magnetometer ×T sensor The transformation matrix is ​​obtained from the point cloud coordinate system to the inspection well coordinate system. Then, the initial transformation matrix is ​​calculated based on the transformation matrices at the two points. Specific steps include: magnetometer data preprocessing, using Kalman filtering to eliminate magnetic interference and providing an absolute direction reference to eliminate accumulated errors; IMU data fusion, combining gyroscope, accelerometer, and magnetometer data, using an extended Kalman filter algorithm to calculate the carrier attitude in real time, providing accurate rotation and translation matrices; and sensor fusion algorithm calculating the initial transformation matrix, fusing multi-sensor data into a unified pose estimate.

[0058] The second stage is fine registration: the initial registration based on sensor fusion serves as the initial value for KISS-ICP, and adaptive threshold matching is used to dynamically adjust the matching threshold to improve robustness. Specific algorithm steps include: point cloud preprocessing, downsampling and denoising the input point cloud; feature extraction, calculating the normal vector and curvature features of the point cloud; corresponding point search, quickly searching for nearest neighbor pairs based on a KD-tree structure; transformation matrix calculation, using the Welsch function as a robust kernel function to resist outliers and noise interference; iterative optimization, optimizing transformation parameters using the Levenberg-Marquardt algorithm; and motion compensation strategy, combining IMU data to predict carrier motion and improve registration accuracy.

[0059] The entire high frame rate real-time stitching system achieves efficient point cloud data processing through parallel computing, multi-threaded processing, and memory streaming management, including the parallel execution of data acquisition threads, preprocessing threads, registration calculation threads, and result output threads.

[0060] After completing the construction of the 3D point cloud model of one inspection well through the above process, proceed to the next inspection well and repeat the complete process until the construction of the 3D point cloud model of all inspection wells is completed.

[0061] Step S203: Identify the pipe opening nodes from the 3D point cloud model with geographic coordinates of each inspection well, and determine the geographical location, orientation and pipe diameter of each pipe opening node.

[0062] The detection and localization of the pipe opening node adopts a pipe opening identification strategy based on depth layer analysis. The location of the pipe opening is identified by depth scanning, and then the accurate detection and localization of the pipe opening is achieved based on feature point cloud extraction and geometric fitting.

[0063] Specifically, step S203 above includes: Step S2031: Extract the pipe opening node point cloud data representing the pipe opening structure from the three-dimensional point cloud model.

[0064] A depth scan is performed from the top to the bottom of the inspection well to analyze the presence of pipe opening features at each depth level. Each depth level only represents the presence of a pipe opening at that level, and further processing is performed after the depth scan is completed. A vertical projection detection method is used to perform horizontal cross-sectional contour analysis on each layer of point cloud, and candidate pipe opening regions are identified by abrupt changes in contour area, shape changes, and depth discontinuities.

[0065] Based on the depth scan results, feature point clouds belonging to the pipe opening region are extracted from the complete point cloud data, including the pipe opening boundary and internal point clouds. A multi-dimensional feature analysis method based on normal vector distribution, curvature change, and density distribution is adopted. Combined with IMU attitude data, the pipe opening boundary is determined, and an edge detection algorithm is used to accurately locate the pipe opening boundary. The authenticity of the pipe opening is verified by point cloud density analysis.

[0066] Step S2032: Perform spatial geometric analysis on the point cloud data of the pipe opening node to obtain the axial direction, which is used as the orientation of the pipe opening node.

[0067] Principal component analysis was performed on the feature point cloud of the pipe opening to calculate the eigenvalues ​​and eigenvectors of the point cloud covariance matrix. The eigenvector corresponding to the largest eigenvalue is the direction of the principal axis of the cylinder. Cov matrix =(1 / n)×Σ(P i -P mean (P) i -P mean ) T Eigenvalues,Eigenvectors=eig(Cov matrix ) Cylinder axis =Eigenvectors[:,max eigenvalue_index ] Where P i P represents the coordinates of the feature point cloud. mean Let n be the centroid of the point cloud, n be the number of points in the cloud, T denote the transpose operation, and Cov. matrix Eigenvalues, Eigenvectors = eig(Cov matrix This represents the eigenvalue decomposition of the covariance matrix, yielding eigenvalues ​​(i.e., the dispersion of the point cloud along each principal direction) and eigenvectors (i.e., the spatial orientations of these principal directions); Cylinder axis =Eigenvectors[:,max eigenvalue_index The ] indicates that the column of eigenvectors corresponding to the largest eigenvalue in the eigenvector matrix is ​​taken. This vector represents the direction in which the point cloud distribution is most dispersed, which is the direction of the main axis of the pipe, i.e., the direction of the main axis of the cylinder.

[0068] The direction vector of the cylinder's principal axis is the same as the direction vector of the nozzle, which is then transformed to the global geographic coordinate system: Direction global =R magnetometer ×R imu ×R sensor ×Cylinder axis Where R magnetometer R is the magnetometer orientation correction matrix. imu R is the IMU attitude matrix. sensor For sensor rotation matrix; Direction globalThe orientation vector of the pipe opening after conversion to the global geographic coordinate system can be used to determine the orientation of the pipe opening node.

[0069] Step S2033: Project the pipe outlet node point cloud data onto a plane perpendicular to the axial direction to fit a circular profile, and obtain the fitted circle center and radius; transform the fitted circle center and radius to a preset geographic coordinate system to obtain the center coordinates and pipe diameter of the pipe outlet node.

[0070] Projecting the feature point cloud onto a two-dimensional plane perpendicular to the principal axis of the cylinder, a projection matrix is ​​created to achieve coordinate transformation: Proj matrix =I-Cylinder axis Cylinder axis Points projected =(Points-Points mean )×Proj T matrix Where I is the identity matrix. For the outer product operation, Points represents the 3D coordinates of each point in the original point cloud coordinate set. mean The centroid coordinates of the original point cloud coordinate set; Proj matrix This is a projection matrix, used to project point cloud data in 3D space onto a cylinder perpendicular to its principal axis. axis Points on a two-dimensional plane; projected This represents the set of coordinates of two-dimensional projected points located on the plane perpendicular to the principal axis of the cylinder, obtained by subtracting the centroid from the original three-dimensional point cloud coordinates and transforming them through the projection matrix. This set is used for subsequent circular contour fitting.

[0071] The RANSAC algorithm is used to fit a circular contour on the projection plane. The center and radius are determined by the three-point method, and the interior and exterior points are identified by combining geometric constraints. Circle center Circle radius =RANSAC circle_fit (Points projected ) Among them, Points projected Represents the set of projected points, RANSAC circle_fit This is a circle fitting algorithm that outputs the coordinates of the center of the fitted circle. center and radius Circle radius RANSAC circle_fit Algorithm parameter settings: number of iterations n iterations =10, distance thresholdthreshold =σ×0.05, minimum interior point ratio min inliers_ratio =0.7, where σ is the standard deviation of the distance from the point cloud to the origin.

[0072] Based on the radius and center coordinates of the circle obtained from RANSAC fitting, the pipe diameter and the coordinates of the pipe center are calculated. The pipe diameter is Diameter = 2 × Circle. radius The coordinates of the center of the circle on the projection plane are transformed back to three-dimensional space, and then transformed to the global geographic coordinate system to obtain the coordinates of the pipe opening center: Center local =[Circle center ,0]×Proj matrix +Points mean Center global =T rtk ×T imu ×T sensor ×Center local Where T rtk T is the coordinate transformation matrix for RTK positioning. imu T is the IMU attitude coordinate transformation matrix. sensor Center is the sensor rotation coordinate transformation matrix. local The center coordinates of the pipe opening in the local point cloud coordinate system. global These are the coordinates of the pipe outlet center in the global geographic coordinate system.

[0073] Step S204: Based on the geographical location, orientation, and pipe diameter of each pipe node obtained from each inspection well, establish the correspondence between each pipe node through spatial matching rules, and generate pipeline topology data describing the pipeline connection relationship.

[0074] Specifically, step S204 above includes: Step S2041: For each pipe node, find the associated pipe nodes that meet the preset constraints within the preset search range, and establish a connection relationship between the pipe node and the associated pipe nodes. The preset constraints include distance conditions, orientation conditions, and size conditions.

[0075] Step S2042: Generate pipeline topology data containing pipe nodes, edges, and attributes based on the connection relationship, geographical location, orientation, and pipe diameter of each pipe node.

[0076] Specifically, obtain the set of all pipe outlet nodes {P1, P2, ..., P}. n Set the threshold parameter D. max =100.0m (maximum connection distance), θmax =10° (maximum angular deviation), φ max =50mm (maximum diameter deviation), then follow these steps to establish the connection between the pipe nozzle node and the associated pipe nozzle node: The first step, node search phase: Match each pipe outlet node Pi within the search range, which is within a 100-meter diameter. The distance is calculated using the formula |P i.center -P j.center | <D max , where P i.center and P j.center Represent the i-th pipe outlet node P respectively i and the j-th pipe node P j The three-dimensional center coordinates are determined. A spatial index structure (such as a KD-tree or octree) is used to accelerate the search for neighboring nodes, reducing the search time complexity from O(n²) to O(n log n).

[0077] The second step is to determine the matching conditions: the matching conditions must simultaneously satisfy both the direction condition and the diameter condition.

[0078] Directional condition determination first calculates P i and P j Direction vector of the line connecting the center coordinates of the pipe opening: V connect =(P j.center -P i.center ) / |P j.center -P i.center |

[0079] The direction matching condition is |V connect ·P i.direction |>cos(10°) and|V connect ·P j.direction |>cos(10°), where P i.direction and P j.direction Represent the i-th pipe outlet node (P) i ) and the j-th pipe node (P j The orientation vector of ).

[0080] The diameter matching condition is |P i.diameter -P j.diameter |<φ max , where P i.diameter and P j.diameter Represent the i-th pipe outlet node (P) i ) and the j-th pipe node (P j The diameter of the pipe.

[0081] The third step is the matching logic processing: The matching logic uses "AND" logic; if any condition is not met, the pairing fails. All pipe nodes within the search range are traversed for matching calculations. A matching score is calculated for each candidate node: Match score =α×Direction score +β×Diameter score .

[0082] The direction matching score is Direction score =(|V connect ·P i.direction |+|V connect ·P j.direction |) / 2; Diameter matching score score =1-|P i.diameter -P j.diameter | / φ max ; The weighting coefficients are α=0.6 and β=0.4.

[0083] Step 4: Processing the matching results: 1) No matching node: Mark as concealed pipe connection, and add concealed connection attribute annotation to the corresponding pipe connection node; 2) One matching node: Establish an edge data node, add edge data records to record connection relationships, and calculate connection parameters; 3) Multiple matching nodes: Select the node with the highest matching score to create the edge data node, and ignore other nodes.

[0084] Step 5, edge data generation: Generate edge data for successfully paired pipe node pairs, including information such as start node ID, end node ID, connection length, and slope.

[0085] 1) Connection length calculation: Length = |P j.center -P i.center |; 2) Slope calculation: Slope = arctan((P) j.center.z -P i.center.z ) / Length)×180 / π, where P j.center.z and P i.center.z Represent the j-th pipe outlet node (P) j ) and the i-th port node (P i The elevation coordinates of the center point of ().

[0086] Step 6: Verification of connectivity and optimization of topology: 1) Connectivity check: The depth-first search (DFS) algorithm is used to traverse the entire pipeline network, identify connected components, and mark disconnected pipeline segments; 2) Consistency check: Verify the rationality of the connection relationship, check for the existence of circular connections, duplicate connections and contradictory connections, and ensure that each pipe node is connected to at most one other node; 3) Integrity check: Ensure that all pipe joints have a clear connection status (connected or concealed). If there are any joints that do not belong to these two categories, it means that they have been missed and need to be reprocessed. 4) Establishing a hierarchical structure: Establish a hierarchical structure of the pipeline network (main pipeline, branch pipeline, and inlet pipeline) based on the pipeline diameter and connection relationship.

[0087] After the above steps, the following results will be obtained: The output contains a list of successfully paired pipe node pairs. Each record includes the start node ID, end node ID, connection length, slope, direction vector, connection type (main pipe, branch pipe), and connection quality score. For pipe node pairs that fail to match, the output includes a list of concealed connection nodes, containing the concealed connection node ID and coordinate information.

[0088] Complete pipeline topology data includes node data, edge data, and manhole data. Node data includes the ID, coordinates (X, Y, Z), pipe diameter, pipe opening direction, connection status, and associated manhole ID for each pipe node. Edge data includes edge ID, starting node ID, ending node ID, connection length, slope, and direction vector. Manhole data includes detailed information such as manhole ID, manhole coordinates, manhole bottom elevation, list of pipe openings within the manhole, manhole depth, and manhole diameter.

[0089] The output data also includes pipeline network hierarchy information, distinguishing between main pipeline networks, branch pipeline networks, and service pipe networks. Connectivity analysis results include a list of connected components and a list of disconnected segments. Statistical information includes key indicators such as total pipeline length, average pipe diameter, maximum pipe diameter, minimum pipe diameter, and network density.

[0090] All output data adopts a standardized format and can be directly imported into GIS systems such as ArcGIS, QGIS, and SuperMap, as well as various pipeline management platforms, to achieve standardized management and visual display of pipeline data.

[0091] In addition, regarding communication protocols, wired communication uses RS485 or CAN bus for inter-sensor communication, while wireless communication uses WiFi or 4G / 5G to communicate with the host computer. The data format uses JSON or a custom binary format.

[0092] The full-scene survey method for drainage pipe networks provided in this embodiment has the following beneficial effects: 1. Laser and acoustic wave synergistic full-scene 3D reconstruction technology: Innovatively, laser scanning is used for the above-water part and ultrasonic scanning is used for the underwater part. Combined with sensor fusion initial registration and KISS-ICP fine registration, high frame rate point cloud stitching is achieved, realizing the full-scene 3D reconstruction of inspection wells. This solves the problem of poor environmental adaptability and inability to achieve full-scene coverage of existing single-sensor technologies.

[0093] 2. High-precision positioning technology with multi-sensor fusion: It innovatively integrates positioning modules such as network RTK, IMU, and magnetometer, and achieves centimeter-level absolute positioning accuracy through sensor fusion algorithms. It establishes a multi-level coordinate system including global geographic coordinate system, survey carrier coordinate system, inspection well coordinate system, and point cloud coordinate system. Through precise coordinate transformation, it achieves seamless connection between different coordinate systems, realizes precise positioning of each point cloud in the global geographic coordinate system, and solves the problem of insufficient positioning accuracy of traditional methods.

[0094] 3. Pipe Opening Identification and Connection Establishment Algorithm: A pipe opening identification strategy based on deep layer analysis is adopted. An innovative method of detecting openings through vertical projection is combined with the RANSAC circle fitting algorithm to achieve rapid and accurate pipe opening identification. Simultaneously, through a pipe opening node pairing strategy, distance, angle, and diameter threshold parameters are set, and a matching score algorithm is used to automatically establish pipe opening connections. This supports both butt joint and concealed joint connections, achieving intelligent identification of pipe network connections and establishment of a complete topology.

[0095] As can be seen, this embodiment has significant advantages such as full-scene coverage, high-precision positioning, and intelligent analysis. First, by employing a laser and acoustic wave-based 3D reconstruction technology, it achieves full-scene 3D reconstruction of drainage network inspection wells, obtaining complete 3D point cloud data for both above-water and underwater portions, thus solving the problem of poor environmental adaptability of existing single-sensor technologies. Second, through multi-sensor fusion high-precision positioning technology, each point cloud of the inspection well can be accurately located in the global geographic coordinate system, achieving centimeter-level absolute positioning accuracy and providing precise geographic coordinate information for the network data. Finally, by employing an innovative pipe outlet identification and connection relationship establishment algorithm, it achieves intelligent identification of various node data and connection relationships in the entire study area's drainage network, including the complete topological structure of inspection well nodes, pipe outlet nodes, and pipe connection relationships, significantly improving the intelligence level of network surveying.

[0096] This embodiment also provides a full-scene surveying device for drainage pipe networks. This device is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0097] This embodiment provides a full-scene surveying device for drainage pipe networks, such as... Figure 3 As shown, it includes: The data acquisition module 301 is used to acquire the survey data corresponding to each inspection well in the drainage network. The survey data includes the geographical location data of the inspection well, the multi-frame three-dimensional point cloud data inside the inspection well, and the pose data corresponding to each frame of three-dimensional point cloud data. The multi-frame three-dimensional point cloud data includes surface laser scanning three-dimensional point cloud data and / or underwater ultrasonic scanning three-dimensional point cloud data. The point cloud conversion module 302 is used to convert the three-dimensional point cloud data of each frame to a preset geographic coordinate system based on the geographic location data of each inspection well and the pose data corresponding to each frame of three-dimensional point cloud data, and generate a three-dimensional point cloud model of each inspection well with geographic coordinates. The pipe opening identification module 303 is used to identify pipe opening nodes from the three-dimensional point cloud model with geographic coordinates of each inspection well, and to determine the geographical location, orientation and pipe diameter of each pipe opening node. The pipeline topology construction module 304 is used to establish the correspondence between each pipe node based on the geographical location, orientation and pipe diameter obtained from each inspection well, and generate pipeline topology data describing the pipeline connection relationship.

[0098] The drainage network full-scene surveying device provided in this embodiment of the invention can execute the drainage network full-scene surveying method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method. Further functional descriptions of the above modules and units are the same as in the corresponding embodiments described above, and will not be repeated here.

[0099] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0100] The following is a detailed reference. Figure 4This diagram illustrates a structural schematic suitable for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 401, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 402 or a program loaded from memory 408 into random access memory (RAM) 403. The RAM 403 also stores various programs and data required for the operation of the electronic device. The processor 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0101] Typically, the following devices can be connected to I / O interface 405: input devices 406 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 407 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 408 including, for example, magnetic tapes, hard disks, etc.; and communication devices 409. Communication device 409 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 4 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0102] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 409, or installed from a memory 408, or installed from a ROM 402. When the computer program is executed by the processor 401, it performs the functions defined in the drainage network full-scene survey method of the embodiments of the present invention.

[0103] Figure 4 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0104] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the full-scene survey method for drainage pipe networks shown in the above embodiments is implemented.

[0105] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0106] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for full-scene surveying of drainage pipe networks, characterized in that, The method includes: Acquire survey data corresponding to each inspection well in the drainage pipe network. The survey data includes the geographical location data of the inspection well, multi-frame three-dimensional point cloud data inside the inspection well, and pose data corresponding to each frame of three-dimensional point cloud data. The multi-frame three-dimensional point cloud data includes surface laser scanning three-dimensional point cloud data and / or underwater ultrasonic scanning three-dimensional point cloud data. Based on the geographic location data of each manhole and the pose data corresponding to each frame of 3D point cloud data, the 3D point cloud data of each frame is transformed to a preset geographic coordinate system through coordinate transformation, thereby generating a 3D point cloud model of each manhole with geographic coordinates. Pipe nodes are identified from the 3D point cloud model with geographic coordinates of each manhole, and the geographical location, orientation and pipe diameter of each pipe node are determined. Based on the geographical location, orientation, and pipe diameter of each pipe node obtained from each inspection well, the correspondence between each pipe node is established through spatial matching rules, generating pipeline topology data describing the pipeline connection relationship.

2. The method for full-scene surveying of drainage pipe networks according to claim 1, characterized in that, The steps for obtaining the survey data corresponding to each inspection well in the drainage pipe network include: A survey carrier integrating a positioning device, a pose measurement device, and a three-dimensional scanning device is placed inside the inspection well, wherein the three-dimensional scanning device is configured to perform circumferential rotation scanning around the axis of the survey carrier. The survey carrier is controlled to move along the axial direction of the inspection well, and the positioning device, pose measurement device and three-dimensional scanning device are simultaneously triggered to collect data, obtain the geographical location data, multi-frame three-dimensional point cloud data and pose data corresponding to each frame of three-dimensional point cloud data.

3. The method for full-scene surveying of drainage pipe networks according to claim 2, characterized in that, The three-dimensional scanning device includes a laser scanning module for acquiring the three-dimensional point cloud data of the surface laser scanning and an ultrasonic scanning module for acquiring the three-dimensional point cloud data of the underwater ultrasonic scanning.

4. The method for full-scene surveying of drainage pipe networks according to claim 1, characterized in that, The step of generating a 3D point cloud model of each inspection well with geographic coordinates by transforming the 3D point cloud data of each frame to a preset geographic coordinate system based on the geographic location data of each inspection well and the pose data corresponding to each frame of 3D point cloud data through coordinate transformation includes: Based on the geographic location data of each inspection well and the pose data corresponding to each frame of 3D point cloud data, the initial transformation matrix for transforming each frame of 3D point cloud data from the point cloud coordinate system to the preset geographic coordinate system is calculated through coordinate transformation. The initial transformation matrix corresponding to the 3D point cloud data of each frame is used as the initial value for the point cloud registration algorithm. The point cloud data of adjacent frames are matched, calculated and iteratively optimized to obtain the optimized accurate transformation matrix. Each frame of 3D point cloud data is transformed into the preset geographic coordinate system according to the corresponding precise transformation matrix to form a 3D point cloud model with geographic coordinates.

5. The method for full-scene surveying of drainage pipe networks according to claim 4, characterized in that, In the step of calculating the initial transformation matrix for transforming each frame of 3D point cloud data from the point cloud coordinate system to the preset geographic coordinate system based on the geographic location data of each inspection well and the pose data corresponding to each frame of 3D point cloud data, the following operations are performed for each frame of 3D point cloud data: Based on the pose data corresponding to the frame of 3D point cloud data and the known installation parameters of the survey carrier, the first transformation parameters from the point cloud coordinate system through the survey carrier coordinate system to the inspection well coordinate system are determined. Based on the geographic location data of the inspection well where the three-dimensional point cloud data of this frame is located, a second transformation parameter from the inspection well coordinate system to the preset geographic coordinate system is determined; The first transformation parameter and the second transformation parameter are combined to obtain the initial transformation matrix for transforming the three-dimensional point cloud data of the frame to a preset geographic coordinate system.

6. The method for full-scene surveying of drainage pipe networks according to claim 1, characterized in that, The steps of identifying pipe opening nodes from the 3D point cloud model with geographic coordinates of each inspection well, and determining the geographical location, orientation, and pipe diameter of each pipe opening node, include: The point cloud data representing the pipe opening structure is segmented from the three-dimensional point cloud model; Spatial geometric analysis is performed on the point cloud data of the pipe opening node to obtain the axial direction, which is used as the orientation of the pipe opening node; The point cloud data of the pipe outlet node is projected onto a plane perpendicular to the axial direction to perform circular contour fitting, and the center and radius of the fitted circle are obtained; the center and radius of the fitted circle are transformed into the preset geographic coordinate system to obtain the center coordinates and pipe diameter of the pipe outlet node.

7. The method for full-scene surveying of drainage pipe networks according to claim 1, characterized in that, The step of establishing the correspondence between pipe nodes based on the geographical location, orientation, and pipe diameter obtained from each inspection well, and generating pipeline topology data describing the pipeline network connection relationship through spatial matching rules, includes: For each pipe node, find associated pipe nodes that meet preset constraints within a preset search range, and establish a connection between the pipe node and the associated pipe nodes. The preset constraints include distance conditions, orientation conditions, and size conditions. Based on the connection relationships, geographical location, orientation, and pipe diameter of each pipe node, generate network topology data including pipe nodes, edges, and attributes.

8. A full-scene surveying device for drainage pipe networks, characterized in that, The device includes: The data acquisition module is used to acquire the survey data corresponding to each inspection well in the drainage network. The survey data includes the geographical location data of the inspection well, the multi-frame three-dimensional point cloud data inside the inspection well, and the pose data corresponding to each frame of three-dimensional point cloud data. The multi-frame three-dimensional point cloud data includes surface laser scanning three-dimensional point cloud data and / or underwater ultrasonic scanning three-dimensional point cloud data. The point cloud conversion module is used to convert the three-dimensional point cloud data of each frame to a preset geographic coordinate system based on the geographic location data of each manhole and the pose data corresponding to each frame of three-dimensional point cloud data, and generate a three-dimensional point cloud model of each manhole with geographic coordinates. The pipe opening identification module is used to identify pipe opening nodes from the 3D point cloud model with geographic coordinates of each inspection well, and to determine the geographical location, orientation and pipe diameter of each pipe opening node. The pipeline topology construction module is used to establish the correspondence between each pipe node based on the geographical location, orientation, and pipe diameter obtained from each inspection well, and generate pipeline topology data describing the pipeline connection relationship.

9. An electronic device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the full-scene survey method for drainage pipe networks as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the drainage network full-scene survey method according to any one of claims 1 to 7.

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