Underground pipe network modeling method and system based on feature position calibration

Abstract

The invention provides an underground pipe network modeling method and system based on feature position calibration, and the method comprises the steps: a pipe network three-dimensional modeling step: constructing a pipe network basic model according to a pipe network plane design drawing; a flight path planning step of planning the flight path of the unmanned aerial vehicle according to the feature position points; a detection data acquisition step: acquiring a scene image and pipe network data; a pipe point data analysis step: acquiring actual coordinates of the feature position points according to the scene image, analyzing road surface adjustment data at a reference object around the pipe point, and analyzing pipe network data at the pipe point through an anomaly detection strategy to obtain a pipe point state; a pipe network model updating step: updating the feature position points and the burying depth of each pipe point in the pipe network basic model according to the actual coordinates and the road surface adjustment data, and updating the state of each pipe point in the pipe network basic model according to the state of each pipe point to obtain a pipe network accurate model; the method has the advantages that the accuracy and practicability of the underground pipe network three-dimensional model are improved, and the real-time updating of the pipe network state is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of underground pipe network modeling, and more particularly to an underground pipe network modeling method and system based on feature position calibration. Background Art

[0002] In the process of urban construction and development, the municipal underground pipeline network, as the "lifeline" of the city, undertakes important functions such as water supply, drainage, gas, electricity, and communications. Its safe and stable operation is crucial to the normal operation of the city. However, there are many problems in the traditional management of municipal underground pipeline networks. On the one hand, the existing underground pipeline network data is mostly stored in the form of two-dimensional drawings and text records. The information is scattered and not intuitive, making it difficult to fully and accurately display the spatial location, direction, connection relationship, and relationship between the pipeline network and the surrounding environment.

[0003] As cities continue to expand and underground pipe networks become increasingly complex, traditional two-dimensional management methods are inefficient in pipe network planning, design, daily maintenance, troubleshooting, and repair. For example, when planning and designing pipe networks, it is difficult for designers to effectively analyze and optimize spatial conflicts between different types of pipe networks based on two-dimensional data; in daily maintenance, it is difficult for inspectors to quickly locate potential problem points in the pipe network; when troubleshooting and repairing, maintenance personnel need to spend a lot of time and energy to sort out pipe network information, which prolongs the troubleshooting time.

[0004] With the rapid development of computer technology, surveying and mapping technology, and geographic information system (GIS) technology, 3D modeling technology is being used more and more widely in the municipal field. 3D modeling technology can integrate various information of underground pipe networks, build intuitive and realistic 3D models, and realize visual management of underground pipe networks. Through 3D models, managers can observe the distribution of pipe networks from different angles and levels, quickly obtain detailed information on pipe networks, and provide strong support for municipal engineering construction, pipe network planning and design, daily maintenance, and troubleshooting. However, the current 3D modeling is only based on the original plane drawings. Modeling is performed directly according to the data size, and the actual scene is prone to change, which makes it difficult for the constructed model to accurately and intuitively match the actual scene, and it is impossible to provide accurate pipe network layout information to inspection personnel and later municipal planning. Summary of the invention

[0005] In view of the deficiencies in the prior art, the object of the present invention is to provide an underground pipe network modeling method and system based on feature position calibration.

[0006] To achieve the above object, the present invention provides the following technical solutions: A method for modeling an underground pipe network based on feature position calibration, comprising: The three-dimensional modeling step of the pipeline network is to retrieve the scene model in the record, and to construct a basic model of the pipeline network in the scene model according to the plane design drawing of the pipeline network; The flight path planning step includes selecting characteristic position points in the pipe network plane design diagram and planning the flight path of the UAV according to the characteristic position points; The detection data collection step is to obtain scene images and pipe network data collected by the equipment on the drone; a pipe point data analysis step, obtaining the actual coordinates of the feature position point according to the scene image, selecting a pipeline detection point between two adjacent feature position points in the scene image as the pipe point, selecting a reference object around the pipe point in the scene image to analyze the road surface adjustment data, and analyzing the pipe network data at the pipe point through an abnormality detection strategy to obtain the pipe point status, wherein the pipe point status includes the normal state of the pipeline, the deformation state of the pipeline, and the settlement state of the pipeline; The pipeline network model updating step is to update the coordinates of the characteristic position points in the pipeline network basic model with the actual coordinates, and to update the buried depth of each pipeline point in the pipeline network basic model with the pavement adjustment data, and to update the status of each pipeline point in the pipeline network basic model according to the status of the pipeline point and the pipeline network data of the pipeline point to obtain an accurate model of the pipeline network.

[0007] Furthermore, the control point data analysis step includes a road surface analysis strategy, and the road surface analysis strategy includes a reference object analysis sub-step and a road surface analysis sub-step. The reference object analysis sub-step measures the actual exposed height value of the reference object according to the scene image, compares the actual exposed height value with the recorded exposed height value of the reference object in the scene model, and if the two do not match, performs a road surface analysis sub-step; The pavement analysis sub-step retrieves the construction records of the reference object in the municipal system. If the retrieval is successful, it analyzes whether the pavement construction data is consistent with the exposed change of the reference object. If they are consistent, the pavement construction data is used as the pavement adjustment data for updating the buried depth of the pipe point. Otherwise, the exposed change of the reference object is combined with the pavement construction data to analyze whether the changed area covers the pipe point. If so, the calculated pipe point change is used as the pavement adjustment data for updating the buried depth of the pipe point.

[0008] Furthermore, the pavement analysis sub-step includes a control point coverage analysis sub-strategy, which analyzes the reference object change slope based on the scene image, calculates the reference object change area radius based on the reference object exposure change amount and the reference object change slope, and then calculates the straight-line distance between the reference object and the control point, and compares it with the reference object change area radius. If the reference object change area radius is greater than the straight-line distance, then calculates the change amount at the control point in the reference object change area.

[0009] Furthermore, the pipeline network data is a detection map, which includes the actual buried depth of the pipeline, pipeline contour points and stress strain, and the anomaly detection strategy includes a pipeline anomaly judgment sub-step and a pipeline state calculation sub-step. The pipeline abnormality judgment sub-step is to judge whether the theoretical buried depth of the pipeline in the pipeline network basic model under the modification of the road surface adjustment data is consistent according to the actual buried depth of the pipeline. If it is consistent, the normal state of the pipeline is output, otherwise, the pipeline state calculation step is performed; The pipeline state calculation sub-step calculates the pipeline centerline offset and ovality according to the actual pre-buried depth of the pipeline and the pipeline contour points, respectively, and then calculates the pipeline centerline offset, ovality, stress strain and pipe wall thickness through the pipeline deformation formula to obtain the pipeline deformation rate, compares the pipeline deformation rate with a preset deformation threshold, and outputs the pipeline state according to the comparison result.

[0010] Furthermore, the pipeline deformation calculation formula is configured as: , , in, is the pipe centerline offset, is the ovality of the pipe surface, is the pipe stress strain, is the pipe wall thickness, is the pipe deformation rate, are the first constant, the second constant, the third constant, the fourth constant and the fifth constant respectively, Deformation threshold.

[0011] Furthermore, the pipe point data analysis step includes a pipe point selection strategy, which includes taking the marking point and the pipeline detection point selected between the two adjacent feature position points as the pipe point when there are marking points such as pipeline intersection and pipeline alignment points between the two adjacent feature position points.

[0012] Furthermore, in the pipe network model updating step, the actual coordinates are longitude and latitude coordinates, and the actual relative position is determined according to the longitude and latitude coordinates of two adjacent characteristic position points, and the theoretical relative position of the same two characteristic position points in the pipe network basic model is updated according to the actual relative position.

[0013] Furthermore, in the pipeline network model updating step, when the theoretical relative position deviations of every two adjacent characteristic position points are the same, the coordinate deviation value of any characteristic position point is calculated, and each characteristic position point is translated in the pipeline network basic model based on the coordinate deviation value.

[0014] Furthermore, in the pipeline network model updating step, when the theoretical relative position deviation of some characteristic position points therein occurs, these characteristic position points are demarcated as local offset areas, the boundary points of the local offset areas are used as control points, the control points are displaced and adjusted according to the coordinate deviation values, and the mesh in the area is deformed by a mesh deformation algorithm in the pipeline network basic model.

[0015] An underground pipe network modeling system based on feature position calibration, comprising: The three-dimensional construction module of the pipe network retrieves the scene model in the record and constructs the basic model of the pipe network in the scene model according to the plane design drawing of the pipe network; A flight path planning module selects characteristic location points in the pipe network plan design diagram and plans the flight path of the UAV according to the characteristic location points; The detection data acquisition module obtains scene images and pipe network data collected by the equipment on the drone; A pipe point data analysis module, which obtains the actual coordinates of the feature position point according to the scene image, selects a pipeline detection point between two adjacent feature position points in the scene image as the pipe point, selects reference objects around the pipe point in the scene image to analyze the road surface adjustment data, and analyzes the pipe network data at the pipe point through an abnormality detection strategy to obtain the pipe point status, which includes the normal state of the pipeline, the deformation state of the pipeline, and the settlement state of the pipeline; The pipe network model updating module updates the coordinates of the characteristic position points in the pipe network basic model with the actual coordinates, and updates the buried depth of each pipe point in the pipe network basic model with the road surface adjustment data. According to the status of the pipe point and the pipe network data of the pipe point, the status of each pipe point in the pipe network basic model is updated to obtain an accurate model of the pipe network.

[0016] The beneficial effects of the present invention are as follows: through the steps of three-dimensional basic modeling of the pipeline network, flight path planning of the UAV, detection data collection, pipe point data analysis and pipeline network model updating, the actual scene data is collected by the UAV, combined with the feature position calibration and anomaly detection strategy, the accurate construction and real-time updating of the three-dimensional model of the pipeline network are realized, and the problem that the traditional modeling method is difficult to accurately reflect the actual scene changes is solved. The accuracy and practicality of the three-dimensional model of the underground pipeline network are improved, and the real-time updating of the pipeline network status and the intelligent identification of abnormal situations are realized. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is the overall flow chart of the present invention; Figure 2 It is a flow chart of road surface adjustment and reference object coverage area analysis in the present invention; Figure 3 It is a pipe network model diagram in the present invention. DETAILED DESCRIPTION

[0018] The present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. The same parts are represented by the same reference numerals. It should be noted that the words "front", "rear", "left", "right", "upper" and "lower" used in the following description refer to directions in the accompanying drawings, and the words "bottom surface" and "top surface", "inner" and "outer" refer to directions toward or away from the geometric center of a specific component, respectively.

[0019] The current 3D modeling is only based on the original plane drawing and is directly modeled according to the data size. However, the actual scene is prone to change, which makes it difficult for the constructed model to accurately and intuitively match the actual scene, and it is impossible to provide accurate pipeline layout information to the inspection personnel and the later municipal planning. Therefore, the present invention designs this underground pipeline modeling method based on feature position calibration, such as Figure 1-2 As shown, including: The steps of three-dimensional modeling of pipeline network are as follows: first, from the existing record database, according to specific search conditions, accurately retrieve the scene model corresponding to the current pipeline network design area. This scene model is obtained and stored through the previous geographic information collection, satellite remote sensing image processing or other professional surveying and mapping methods. It contains the basic geographic information of the area such as topography, building distribution, road direction, etc. On the basis of the successfully retrieved scene model, the basic model of the pipeline network is gradually constructed according to the pipeline network plan design drawing. The pipeline network plan design drawing details the direction, diameter, node location and connection relationship between different pipelines of various pipeline networks (such as water supply pipeline network, drainage pipeline network, gas pipeline network, etc.). According to these design information, in the geographic space of the scene model, professional three-dimensional modeling software is used to define the spatial position, geometric shape and topological relationship of each pipeline network, and the two-dimensional plan design drawing is converted into a three-dimensional basic model of the pipeline network.

[0020] As the basic data model of the entire pipeline network inspection and update process, the pipeline network basic model provides accurate spatial position reference and geometric shape information for subsequent flight path planning, data collection, model update and other steps. It ensures that subsequent work can be carried out closely around the actual design of the pipeline network and ensures the accuracy and consistency of the entire inspection process.

[0021] The flight path planning step is to carefully identify and select representative feature location points on the pipeline network plan design drawing. These feature location points usually include the starting point, end point, turning point, pipe diameter change point, node and connection points with other important facilities (such as pumping stations, valve wells, etc.) of the pipeline network. Based on the selected feature location points, use the existing path planning algorithm and UAV flight control software to plan a reasonable UAV flight path.

[0022] By planning the flight path of the drone based on characteristic location points, it can be ensured that the drone accurately covers all key locations and areas in the pipeline network during the flight. This enables the subsequently collected data to fully reflect the key information of the pipeline network and avoid blind spots in data collection or missing important characteristic points.

[0023] In the detection data collection step, when the UAV flies according to the pre-planned flight path, various professional equipment carried by the UAV starts working. Among them, the optical camera or other imaging equipment is responsible for collecting scene images around the pipeline network. These images record the actual situation of the environment in which the pipeline network is located, including the topography of the ground, the condition of buildings, the surrounding vegetation coverage and other information, which provides an intuitive visual basis for the subsequent analysis of the relationship between the pipeline network and the surrounding environment and the actual location of the pipe point. At the same time, the pipeline detection equipment carried (such as underground pipeline detectors, lidar, etc.) collects the data of the pipeline network itself. The pipeline network data is a detection map, which includes key parameter information such as the actual buried depth of the pipeline, pipeline contour points, and stress strain.

[0024] The pipe point data analysis step is to obtain the actual coordinates of the feature position points according to the scene image. In the scene image, according to the distance between two adjacent feature position points and the direction of the pipeline, several pipeline detection points are evenly selected as pipe points according to certain rules. Then, objects with obvious characteristics and relatively stable (such as street lamp poles, fire hydrants, etc.) around the pipe points are carefully selected in the scene image as reference objects. By analyzing the relationship between the position changes of these reference objects and the pipe points in the image, combined with topographic data and historical image data, it is determined whether the road surface at the location of the pipe point has been adjusted (such as construction changes, settlement, uplift, etc.), and the corresponding road surface adjustment data, such as road surface settlement, uplift height, etc., are calculated. The pipeline network data at the pipe point is analyzed through anomaly detection strategies to obtain the pipe point status, which includes the normal state of the pipeline, the deformation state of the pipeline, and the settlement state of the pipeline.

[0025] By accurately obtaining the actual coordinates of feature location points and analyzing the status of pipe points, the actual location of each pipe point in the pipeline network can be accurately determined in geographic space, and its operating status can be accurately evaluated. Combined with pavement adjustment data and pipeline network data, it can be determined whether problems such as pipeline settlement or deformation may occur, thereby further updating the pipeline network model to provide an accurate pipeline network model.

[0026] The pipeline network model updating step is to update the coordinates of the characteristic position points in the pipeline network basic model with the actual coordinates, and to update the buried depth of each pipeline point in the pipeline network basic model with the road surface adjustment data. If the road surface around a certain pipeline point has settled, the buried depth value of the pipeline point in the pipeline network model is correspondingly reduced according to the amount of settlement; conversely, if the road surface is bulging, the buried depth value of the pipeline point is increased. According to the pipeline point status and the pipeline network data of the pipeline point, the status of each pipeline point in the pipeline network basic model is updated to obtain an accurate model of the pipeline network. For pipeline points in a normal pipeline state, the corresponding attribute parameters in the model are kept unchanged; for pipeline points in a pipeline deformation state, the relevant geometric parameters such as the pipe diameter are adjusted in the model to intuitively display the deformation of the pipeline; for pipeline points in a pipeline settlement state, in addition to updating the buried depth, corresponding identification or color distinction can also be added to the model (such as Figure 3 As shown in the figure, the pipeline points in different states can be clearly identified in the model, and finally an accurate model of the pipeline network that can accurately reflect the actual state of the pipeline network can be obtained.

[0027] By comprehensively updating the coordinates, burial depth and status of the basic model of the pipeline network, the resulting precise model of the pipeline network can highly accurately reflect the true status of the pipeline network in the actual geographical environment. Whether it is the spatial position of the pipeline network, the burial depth of the pipe points, or the operating status of the pipe points, they are all closely aligned with the actual situation. As time goes by, the status of the pipeline network will continue to change. By continuously updating the pipeline network model, it is possible to record the status changes of the pipeline network in different periods, analyze the aging trend of the pipeline network, the law of fault development, etc., which is helpful to formulate reasonable pipeline maintenance and update plans and optimize pipeline network operations.

[0028] like Figure 2 As shown, the pipe point data analysis step includes a road surface analysis strategy, and the road surface analysis strategy includes a reference object analysis sub-step and a road surface analysis sub-step. In the reference object analysis sub-step, in the acquired scene image, objects with obvious features and relatively stable are selected around the pipe point, such as street lamp poles, fire hydrants, etc., as reference objects. Professional image measurement technology is used to accurately measure the actual exposed height value of the reference object through known image proportional relationships and related measurement algorithms. At the same time, the recorded exposed height value corresponding to the reference object is retrieved from the pre-built scene model, and the actual measured height value is compared with the recorded value in the scene model. If the two values ​​match, it means that the road surface around the reference object may not have changed significantly during the time period; if the two do not match, it means that the road surface may have changes such as settlement and uplift, and further road surface analysis sub-steps are required; In the pavement analysis sub-step, when it is determined that further analysis of the pavement condition is required, the construction record of the location of the reference object is retrieved by interacting with the municipal system database. The municipal system database stores detailed information on various construction activities in the city, including construction time, location, construction content, etc. If the construction record is successfully retrieved, the relationship between the pavement construction data and the reference object exposure change is analyzed next. The pavement construction data may include information such as construction type (such as road renovation, underground facility laying, etc.), construction depth, and construction scope. The difference between the actual measured exposure height value and the scene model record value is calculated as the reference object exposure change. If the pavement construction data is The impact of construction on the road height reflected is consistent with the exposure change of the reference object. For example, if the construction record shows that a certain depth of road backfilling was carried out, and the exposed height of the reference object just reduced the corresponding value, then it can be relatively certain that the road change was caused by this construction. At this time, the road construction data is used as the road adjustment data for updating the buried depth of the pipe point. On the contrary, if the two are inconsistent, it means that there is a possibility of underground settlement or uplift. It is necessary to further analyze whether the changed area covers the pipe point. The exposed change of the reference object is combined with the road construction data. Through the spatial analysis method, the approximate range of the road change is determined, and it is judged whether the range covers the pipe point of concern. If the changed area covers the pipe point, it is necessary to calculate the change of the pipe point under this change as the road adjustment data for updating the buried depth of the pipe point.

[0029] The pavement analysis strategy can avoid the limitations of making judgments based on a single data or simple comparison. It comprehensively considers the actual measurement data, scenario model records and municipal construction information, greatly improving the accuracy of judging pavement changes. By updating the buried depth of pipe points based on pavement adjustment data obtained through scientific analysis, it can more accurately simulate the actual status of the underground pipeline network, providing reliable basic data for the maintenance, inspection and planning of new projects of the pipeline network.

[0030] like Figure 2 As shown in FIG. 1 , the road surface analysis sub-step includes a pipe point coverage analysis sub-strategy, which analyzes the reference object change slope according to the scene image (measures the vertical height difference and horizontal distance between different positions of the bottom of the reference object and the surrounding ground feature points, and uses mathematical methods such as trigonometric functions to calculate the slope value of the area). Based on the obtained reference object exposure change amount (i.e., the difference between the actual measured exposure height value and the scene model record value) and the calculated reference object change slope, the relevant mathematical model is used to calculate the reference object change area radius, and then the straight-line distance between the reference object and the pipe point is calculated and compared with the reference object change area radius. If the reference object change area radius is greater than the straight-line distance, the change amount at the pipe point in the reference object change area is calculated; assuming that the projection distance between the pipe point and the reference object in the horizontal direction is , according to the changing slope and the reference exposure change , can be obtained by formula Calculate the change at the pipe point .

[0031] By analyzing the slope of the reference object change, calculating the radius of the reference object change area, and comparing it with the straight-line distance between the reference object and the pipe point, the impact range of the road surface change on the pipe point can be accurately defined. This method is based on scientific measurement and calculation, and is more accurate and reliable. In a complex urban pipe network environment, road surface changes in different areas may vary. Through this precise analysis method, it is possible to clearly determine whether each pipe point is affected by road surface changes, providing accurate basic data for subsequent pipe point status analysis and model updates, which is crucial for updating the buried depth of pipe points in the pipe network model, ensuring that the model can truly reflect the actual status of the pipe network underground. Accurate pipe point change data helps pipe network managers to more accurately evaluate the operation of the pipe network.

[0032] The anomaly detection strategy includes pipeline anomaly judgment sub-step and pipeline status calculation sub-step. The pipeline abnormality judgment sub-step judges whether the theoretical buried depth of the pipeline in the pipeline network basic model is consistent with the modified road surface adjustment data according to the actual buried depth of the pipeline (the theoretical buried depth of the pipeline is the design depth in the pipeline network basic model plus the road surface adjustment data). If they are consistent, the normal state of the pipeline is output, otherwise, the pipeline state calculation step is performed; The pipeline status calculation sub-step calculates the pipeline centerline offset and ovality according to the actual pre-buried depth of the pipeline and the pipeline contour points. The pipeline centerline offset is obtained by comparing the actual centerline position of the pipeline (obtained by fitting the contour points) with the centerline position in the original design or normal state, and using coordinate calculation and other methods to obtain the offset distance value. The ovality is determined by the distribution of the pipeline contour points through a specific mathematical algorithm (such as calculating the proportional relationship between the major axis and the minor axis, etc.) to determine the degree to which the pipeline cross-sectional shape deviates from the circle. The pipeline centerline offset, ovality, stress strain and pipe wall thickness are calculated through the pipeline deformation formula to obtain the pipeline deformation rate. The pipeline deformation rate is compared with the preset deformation threshold, and the pipeline status is output according to the comparison result. In the actual operation of the pipeline network, the pipeline may be affected by multiple factors such as ground subsidence, uneven soil pressure, and changes in internal fluid pressure. This multi-parameter comprehensive calculation method can more accurately evaluate the abnormality of the pipeline, which plays a true and accurate role in the display of the three-dimensional model of the pipeline network.

[0033] The pipeline deformation calculation formula is configured as: , , in, The centerline offset of the pipeline measures whether the pipeline deviates from its original position. is the ovality of the pipe surface, is the pipe stress strain, is the pipe wall thickness, is the pipe deformation rate, They are the first constant, the second constant, the third constant, the fourth constant and the fifth constant, which are used to adjust the weights and proportions of each item in the deformation rate calculation. Deformation threshold.

[0034] The pipe point data analysis step includes the pipe point selection strategy. When it is found that there are marking points such as pipeline intersection points and pipeline alignment points between two adjacent characteristic position points, these marking points will be included in the pipe point selection range due to their special significance in the pipe network system. The pipe intersection point is the location where different pipes intersect with each other. The connection and sealing of the pipes and the mutual influence between different pipes here are relatively complex, and it is a key part of the pipe network operation status monitoring; the pipe alignment point is related to the accuracy and continuity of the pipeline laying, and is used to judge the rationality of the overall layout of the pipe network and whether the pipeline has shifted. In addition to incorporating these marking points, a certain number of pipeline detection points must be selected between two adjacent characteristic position points according to the principle of uniform distribution. The purpose is to fully cover the key parts and provide reasonable multi-point data for subsequent pipeline updates.

[0035] In the pipe point data analysis step, the actual coordinates are the longitude and latitude coordinates. The high-precision navigation satellite system receiver carried by the UAV is used to obtain the longitude and latitude coordinate information of the feature points. At the same time, combined with the inertial measurement unit data on the UAV, the acquired longitude and latitude coordinates are subjected to attitude compensation and dynamic correction to improve the accuracy of the coordinates. The actual relative position is determined according to the longitude and latitude coordinates of the two adjacent feature position points, and the theoretical relative position of the same two feature position points in the pipe network basic model is updated according to the actual relative position. There are two situations that need to be updated separately. One is the overall offset, which can be updated by overall translation, and the other is partial deviation, which can be updated by local offset.

[0036] Specifically, the overall offset method is: in the pipeline model update step, when the theoretical relative position deviations of every two adjacent characteristic position points are the same, the coordinate deviation value of any characteristic position point is calculated, and each characteristic position point is translated based on the coordinate deviation value in the pipeline basic model.

[0037] Specifically, the local offset method is as follows: in the pipeline model updating step, when the theoretical relative position deviation of some characteristic position points is found, these characteristic position points are defined as local offset areas, and the boundary points of the local offset areas are used as control points. The control points are adjusted according to the coordinate deviation values, and the mesh in the area is deformed by the mesh deformation algorithm in the pipeline network basic model.

[0038] The above are only preferred embodiments of the present invention. The protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A method for underground pipe network modeling based on feature position calibration, characterized in that: include: The three-dimensional modeling step of the pipeline network is to retrieve the scene model in the record, and to construct a basic model of the pipeline network in the scene model according to the plane design drawing of the pipeline network; The flight path planning step includes selecting characteristic position points in the pipe network plane design diagram and planning the flight path of the UAV according to the characteristic position points; The detection data collection step is to obtain scene images and pipe network data collected by the equipment on the drone; a pipe point data analysis step, obtaining the actual coordinates of the feature position point according to the scene image, selecting a pipeline detection point between two adjacent feature position points in the scene image as the pipe point, selecting a reference object around the pipe point in the scene image to analyze the road surface adjustment data, and analyzing the pipe network data at the pipe point through an abnormality detection strategy to obtain the pipe point status, wherein the pipe point status includes the normal state of the pipeline, the deformation state of the pipeline, and the settlement state of the pipeline; The pipeline network model updating step is to update the coordinates of the characteristic position points in the pipeline network basic model with the actual coordinates, and to update the buried depth of each pipeline point in the pipeline network basic model with the pavement adjustment data, and to update the status of each pipeline point in the pipeline network basic model according to the status of the pipeline point and the pipeline network data of the pipeline point to obtain an accurate model of the pipeline network.

2. The underground pipe network modeling method based on feature position calibration according to claim 1, characterized in that: The control point data analysis step includes a road surface analysis strategy, and the road surface analysis strategy includes a reference object analysis sub-step and a road surface analysis sub-step. The reference object analysis sub-step measures the actual exposed height value of the reference object according to the scene image, compares the actual exposed height value with the recorded exposed height value of the reference object in the scene model, and if the two do not match, performs a road surface analysis sub-step; The pavement analysis sub-step retrieves the construction records of the reference object in the municipal system. If the retrieval is successful, it analyzes whether the pavement construction data is consistent with the exposed change of the reference object. If they are consistent, the pavement construction data is used as the pavement adjustment data for updating the buried depth of the pipe point. Otherwise, the exposed change of the reference object is combined with the pavement construction data to analyze whether the changed area covers the pipe point. If so, the calculated pipe point change is used as the pavement adjustment data for updating the buried depth of the pipe point.

3. The underground pipe network modeling method based on feature position calibration according to claim 2 is characterized in that: The road surface analysis sub-step includes a control point coverage analysis sub-strategy, which analyzes the reference object change slope based on the scene image, calculates the reference object change area radius based on the reference object exposure change amount and the reference object change slope, and then calculates the straight-line distance between the reference object and the control point and compares it with the reference object change area radius. If the reference object change area radius is greater than the straight-line distance, calculates the change amount at the control point in the reference object change area.

4. The underground pipe network modeling method based on feature position calibration according to claim 1 or 3, characterized in that: The pipeline network data is a detection map, which includes the actual buried depth of the pipeline, pipeline contour points and stress strain. The anomaly detection strategy includes a pipeline anomaly judgment sub-step and a pipeline state calculation sub-step. The pipeline abnormality judgment sub-step is to judge whether the theoretical buried depth of the pipeline in the pipeline network basic model under the modification of the road surface adjustment data is consistent according to the actual buried depth of the pipeline. If it is consistent, the normal state of the pipeline is output, otherwise, the pipeline state calculation step is performed; The pipeline state calculation sub-step calculates the pipeline centerline offset and ovality according to the actual pre-buried depth of the pipeline and the pipeline contour points, respectively, and then calculates the pipeline centerline offset, ovality, stress strain and pipe wall thickness through the pipeline deformation formula to obtain the pipeline deformation rate, compares the pipeline deformation rate with a preset deformation threshold, and outputs the pipeline state according to the comparison result.

5. The underground pipe network modeling method based on feature position calibration according to claim 4 is characterized in that: The pipeline deformation calculation formula is configured as: ， ， in, is the pipe centerline offset, is the ovality of the pipe surface, is the pipe stress strain, is the pipe wall thickness, is the pipe deformation rate, are the first constant, the second constant, the third constant, the fourth constant and the fifth constant respectively, Deformation threshold.

6. The underground pipe network modeling method based on feature position calibration according to claim 1, characterized in that: The pipe point data analysis step includes a pipe point selection strategy, which includes taking the marked point and the pipeline detection point selected between the two adjacent feature position points as the pipe point when there are marking points such as pipeline intersection and pipeline alignment points between the two adjacent feature position points.

7. The underground pipe network modeling method based on feature position calibration according to claim 6 is characterized in that: In the pipe network model updating step, the actual coordinates are longitude and latitude coordinates, and the actual relative position is determined according to the longitude and latitude coordinates of two adjacent characteristic position points, and the theoretical relative position of the same two characteristic position points in the pipe network basic model is updated according to the actual relative position.

8. The underground pipe network modeling method based on feature position calibration according to claim 7 is characterized in that: In the pipeline network model updating step, when the theoretical relative position deviations of every two adjacent characteristic position points are the same, the coordinate deviation value of any characteristic position point is calculated, and each characteristic position point is translated in the pipeline network basic model based on the coordinate deviation value.

9. The underground pipe network modeling method based on feature position calibration according to claim 8, characterized in that: In the pipeline network model updating step, when the theoretical relative position deviation of some characteristic position points is detected, the characteristic position points are demarcated as local offset areas, the boundary points of the local offset areas are used as control points, the control points are displaced and adjusted according to the coordinate deviation values, and the mesh in the area is deformed by a mesh deformation algorithm in the pipeline network basic model.

10. An underground pipe network modeling system based on feature position calibration, characterized in that: include The three-dimensional construction module of the pipe network retrieves the scene model in the record and constructs the basic model of the pipe network in the scene model according to the plane design drawing of the pipe network; A flight path planning module selects characteristic position points in the pipe network plan design diagram and plans the flight path of the UAV according to the characteristic position points; The detection data acquisition module obtains scene images and pipe network data collected by the equipment on the drone; A pipe point data analysis module, which obtains the actual coordinates of the feature position point according to the scene image, selects a pipeline detection point between two adjacent feature position points in the scene image as the pipe point, selects reference objects around the pipe point in the scene image to analyze the road surface adjustment data, and analyzes the pipe network data at the pipe point through an abnormality detection strategy to obtain the pipe point status, which includes the normal state of the pipeline, the deformation state of the pipeline, and the settlement state of the pipeline; The pipe network model updating module updates the coordinates of the characteristic position points in the pipe network basic model with the actual coordinates, and updates the buried depth of each pipe point in the pipe network basic model with the road surface adjustment data. According to the status of the pipe point and the pipe network data of the pipe point, the status of each pipe point in the pipe network basic model is updated to obtain an accurate model of the pipe network.

Images