A damage diagnosis and analysis system for underground pipe networks based on SAR satellite technology

Abstract

The present invention relates to the technical field of underground pipe network damage diagnosis, and specifically to an underground pipe network damage diagnosis and analysis system based on SAR satellite technology. First, SAR imaging of the target area of ​​the urban underground pipe network is collected, and the imaging data is processed by differential interference to calculate the surface deformation and generate two-dimensional surface deformation data; then LiDAR measurement is used to perform a three-dimensional scan of the deformed area to generate three-dimensional terrain point cloud data; then the two-dimensional surface deformation data and the three-dimensional terrain point cloud data are spatially fused and analyzed to obtain the three-dimensional surface deformation data of the deformed area, and the damage diagnosis information of the underground pipe network is obtained in combination with the basic data of the underground pipe network; by combining the sensor data of the deformation area, the accuracy and efficiency of the underground pipe network damage diagnosis are improved. The present invention also calculates the deformation rate and cumulative deformation through the time series InSAR technology to achieve the prediction and early warning of potential damage, which is suitable for large-scale long-term monitoring of urban underground pipe networks.

Description

Technical Field

[0001] The invention relates to the technical field of underground pipe network damage diagnosis, and in particular to an underground pipe network damage diagnosis and analysis system based on SAR satellite technology. Background Art

[0002] Underground pipeline network refers to various pipeline network systems buried underground in urban or industrial areas, mainly including water supply, drainage, gas, heating, electricity and communications. It is an important part of urban infrastructure, supporting the daily needs of residents' lives and industrial production, and ensuring a stable supply of resources and services.

[0003] The damage of underground pipe networks affects the daily operation of cities and the lives of residents, and also brings long-term hidden dangers, increasing the difficulty of urban maintenance and management. Therefore, it is very important to diagnose damage and carry out preventive maintenance on underground pipe networks. Existing technologies monitor the leakage and damage of underground pipe networks through a variety of sensors and intelligent monitoring systems. The means used include image acquisition, sensor networks, and water flow monitoring, providing automation and information support for the management and maintenance of underground pipe network systems.

[0004] Although the existing technology has improved the detection efficiency and accuracy of underground pipe network damage to a certain extent, it still has shortcomings. When underground pipe network failure occurs, the existing technology has high requirements for the deployment of sensor equipment during the diagnosis of specific damage conditions, and the diagnosis effect is easily affected by environmental factors, resulting in the accuracy of damage diagnosis still needs to be improved. In addition, the current urban underground pipe network covers a wide range, and the existing technology is difficult to achieve large-scale long-term monitoring and analysis.

[0005] Therefore, a damage diagnosis and analysis system for underground pipeline network based on SAR satellite technology is proposed. Summary of the invention

[0006] The purpose of the present invention is to provide an underground pipe network damage diagnosis and analysis system based on SAR satellite technology. First, the synthetic aperture radar (SAR) periodic imaging of the urban underground pipe network target area is collected to obtain two-dimensional surface deformation data and determine the deformation area; then, the deformation area is scanned three-dimensionally to generate three-dimensional terrain point cloud data. Then, the two-dimensional surface deformation data and the three-dimensional terrain point cloud data are spatially fused and analyzed to obtain the three-dimensional surface deformation data of the deformation area, and the damage diagnosis information of the underground pipe network is obtained in combination with the basic data of the underground pipe network. Finally, the deformation rate and cumulative deformation are calculated to achieve the prediction and early warning of potential damage.

[0007] To achieve the above object, the present invention provides an underground pipe network damage diagnosis and analysis system based on SAR satellite technology, comprising:

[0008] Surface data acquisition unit: collects SAR periodic imaging of the target area of ​​the urban underground pipe network, and each imaging generates a surface SAR image at the corresponding moment; the surface SAR image contains phase information and amplitude information;

[0009] Two-dimensional surface analysis unit: Perform differential interferometry processing on surface SAR images at different times to remove terrain effects and noise interference, calculate the surface deformation of the target area of ​​the urban underground pipeline network, obtain two-dimensional surface deformation data and identify the deformation area;

[0010] Three-dimensional surface analysis unit: for the deformation area, a laser detection and ranging system (LiDAR) is used to measure the distance between the surface point and the device, and the three-dimensional coordinates of each surface point are obtained to form point cloud data; the point cloud data is processed into three-dimensional terrain point cloud data through a three-dimensional terrain model;

[0011] Pipeline network damage diagnosis unit: spatially fuses and analyzes the two-dimensional surface deformation data with the three-dimensional terrain point cloud data to obtain the three-dimensional surface deformation data of the deformation area, and combines the underground pipeline network basic data to obtain underground pipeline network damage information; the damage information includes damage location, damage degree and damage type;

[0012] Prediction and early warning unit: collect SAR multi-time imaging data of the target area of ​​the urban underground pipeline network, use time-series InSAR technology to analyze the evolution of surface deformation in the target area of ​​the underground pipeline network, calculate the deformation rate and cumulative deformation; set a first threshold and a second threshold, and trigger an early warning notification when the deformation rate exceeds the first threshold or the cumulative deformation exceeds the second threshold.

[0013] Furthermore, the differential interference processing step includes:

[0014] A Gaussian filter is used to remove noise interference in the underground pipe network SAR image;

[0015] use calculate Time SAR image and Time SAR image In pixels Phase Difference ;in conjugation of;

[0016] Least squares phase unwrapping is used to eliminate phase jumps and obtain terrain phase data.

[0017] Furthermore, the calculation process of the surface deformation includes:

[0018] pass Calculate the vertical displacement of each surface pixel point to obtain the two-dimensional surface deformation data; wherein, is the two-dimensional surface deformation data, is the terrain phase data, is the SAR wavelength;

[0019] The deformation area is identified through two-dimensional surface deformation data.

[0020] Furthermore, the LiDAR measurement step includes:

[0021] Use LiDAR equipment to emit laser pulses to the deformation area, measure the time difference from the emission to the return of the laser pulse, calculate the distance between the surface point and the equipment, and record the three-dimensional coordinates of the surface point;

[0022] Collecting the three-dimensional coordinates of each surface point to form point cloud data of the deformation area;

[0023] Furthermore, the acquisition process of the three-dimensional terrain point cloud data is as follows:

[0024] Remove abnormal points and noise points in point cloud data; interpolate and complete point cloud data in blank areas;

[0025] The point cloud data is connected into a local three-dimensional mesh using a triangular mesh generation algorithm; adjacent points are connected by triangulation to generate a three-dimensional network model;

[0026] The three-dimensional grid model is smoothed and optimized; the three-dimensional coordinates of each grid node are resampled to generate the three-dimensional terrain point cloud data; the three-dimensional terrain point cloud data contains the three-dimensional coordinate information of each point.

[0027] Furthermore, the process of the spatial fusion analysis is as follows:

[0028] Registering the two-dimensional surface deformation data with the three-dimensional terrain point cloud data in the same spatial coordinate system;

[0029] According to the coordinates of the two-dimensional pixel points, the two-dimensional surface deformation data and the three-dimensional terrain point cloud data are weightedly fused to obtain the three-dimensional surface deformation data of the deformation area, including deformation amplitude, deformation type and spatial distribution;

[0030] The formula of weighted fusion is:

[0031] ;

[0032] in, is the three-dimensional surface deformation data, is the two-dimensional surface deformation data, is the three-dimensional terrain point cloud data, and are weighting coefficients of the two-dimensional surface deformation data and the three-dimensional terrain point cloud data respectively.

[0033] Furthermore, the process of obtaining the underground pipe network damage information includes:

[0034] Obtaining basic data of underground pipe networks, including pipe geometry information, pipe types, pipe materials, and historical maintenance records; spatially aligning the basic data of underground pipe networks with three-dimensional surface deformation data;

[0035] Setting a third threshold value, and marking a point where the three-dimensional surface deformation data exceeds the third threshold value as a first pipe network damage point;

[0036] Performing spatial comparison on the position coordinates of the first pipe network damage point and the corresponding pipe geometry information, extracting the points where the first pipe network damage point matches the pipe structure, recording them as second pipe network damage points and obtaining the corresponding position coordinates;

[0037] According to the deformation type of the second pipe network damage point, combined with the historical maintenance record, the pipe network damage type is obtained; according to the deformation amplitude of the second pipe network damage point, combined with the pipe type and pipe material, the pipe network damage degree score is obtained;

[0038] The location coordinates of the second pipeline network damage point, the pipeline network damage type and the pipeline network damage degree score are summarized to generate the underground pipeline network damage information.

[0039] Furthermore, the calculation process of the deformation rate and the cumulative deformation includes:

[0040] Collect SAR multi-time imaging data of the urban underground pipe network area, and use time-series InSAR to obtain the vertical displacement of the surface position in the target area;

[0041] pass Calculate each surface location In time interval The deformation rate within and They are At the moment and The vertical displacement of Calculate surface position The accumulated deformation at is the total number of time points;

[0042] The surface deformation trend is analyzed based on the deformation rate and the accumulated deformation at each surface location.

[0043] Furthermore, the warning notification triggering process includes:

[0044] Setting a first threshold and a second threshold;

[0045] When the surface position When the deformation rate exceeds the first threshold or the accumulated deformation exceeds the second threshold, triggering an early warning notification and outputting early warning information;

[0046] The warning information includes the surface position coordinates where the deformation rate exceeds the first threshold or the accumulated deformation exceeds the second threshold, the deformation rate, and the accumulated deformation.

[0047] Furthermore, it also includes auxiliary analysis in combination with underground pipe network sensor data;

[0048] When the pipeline network damage type in the pipeline network damage diagnosis information of the second pipeline network damage point is multiple types of composite damage, collecting the internal sensor data of the pipeline network;

[0049] The three-dimensional surface deformation data is spatially and temporally associated with the sensor data, and the distribution of underground pipe network damage types is obtained through multi-source data fusion.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] 1. The present invention first performs SAR periodic imaging on the target area of ​​the urban underground pipeline network to generate radar echo data at different times; then, through differential interference processing and surface deformation calculation, two-dimensional surface deformation data is generated and the deformation area is identified. Then, LiDAR measurement is used to perform three-dimensional scanning to obtain three-dimensional terrain point cloud data of the deformation area. This method solves the problem that the existing technology has high requirements for sensor deployment and is prone to environmental interference by combining the wide-area monitoring capability of InSAR and the high-precision measurement of LiDAR. By capturing abnormal areas from a large range and then performing targeted high-precision scanning, the dependence on a large number of sensors is reduced, and the detection efficiency of abnormal areas in the underground pipeline network is improved.

[0052] 2. The present invention obtains three-dimensional surface deformation data by spatially fusing and analyzing two-dimensional surface deformation data with three-dimensional terrain point cloud data; then the three-dimensional surface deformation data is compared and calculated with the basic data of the underground pipe network to obtain the location and degree of damage to the pipe network. By fusing InSAR and LiDAR data, this method overcomes the limitation of the existing technology that the degree of damage to the pipe network depends on sensor data or manual detection, realizes the automatic diagnosis of the location, type and degree of damage, improves the efficiency and accuracy of the diagnosis of underground pipe network damage, and provides a decision-making basis for the scientific maintenance of the underground pipe network.

[0053] 3. The present invention collects SAR multi-time imaging data of the urban underground pipe network area, uses time-series InSAR to obtain the vertical displacement of the surface position in the pipe network area, and analyzes the evolution trend of the surface deformation by calculating the deformation rate and the cumulative deformation. The thresholds of the deformation rate and the cumulative deformation are set according to the change trend, and an early warning notification is triggered when the deformation rate or the cumulative deformation exceeds the set threshold. This method solves the problem that the existing technology is difficult to achieve large-scale long-term monitoring, realizes continuous monitoring of underground pipe networks and real-time damage warning, can timely discover potential pipe network damage risks, and provide data support for the maintenance and management of urban underground pipe networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 A schematic diagram of the process flow of an underground pipe network damage diagnosis and analysis system based on SAR satellite technology provided by an embodiment of the present invention;

[0055] Figure 2 A schematic diagram of the structure of an underground pipe network damage diagnosis and analysis system based on SAR satellite technology provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0056] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] The present invention provides an underground pipe network damage diagnosis and analysis system based on SAR satellite technology. The specific system flow chart and structure diagram refer to Figure 1 and Figure 2 .

[0058] Embodiment 1

[0059] As an embodiment of the present invention, refer to Figure 1 S10 in the embodiment is applied to a surface data acquisition unit of an underground pipe network damage diagnosis and analysis system based on SAR satellite technology. This embodiment acquires SAR periodic radar imaging of a target area of ​​an urban underground pipe network, and each imaging generates a surface SAR image at a corresponding moment; the surface SAR image includes phase information and amplitude information.

[0060] Further, referring to S20, S20 is applied to a two-dimensional surface analysis unit of an underground pipeline damage diagnosis and analysis system based on SAR satellite technology, including differential interference processing of surface SAR images at different times, removing terrain effects and noise interference, calculating the surface deformation above the underground pipeline, obtaining two-dimensional surface deformation data and identifying the deformation area.

[0061] Get time point and time point Corresponding surface SAR image and , noise filtering algorithm is used for preprocessing to reduce noise interference and improve the signal-to-noise ratio of the data.

[0062] calculate and At the surface pixel Phase difference :

[0063] ;

[0064] in, The conjugation of.

[0065] Through phase correction Eliminate terrain effects, including: Phase difference data The terrain height in is the wavelength of SAR, is the distance from the satellite orbit to the earth's surface.

[0066] Least squares phase unwrapping is used to minimize the phase difference between adjacent pixels, eliminate phase jumps, and obtain topographic phase data. :

[0067] ;

[0068] in, is the terrain phase data, for An integer multiple of , used to restore the continuous phase.

[0069] This embodiment removes the phase error caused by the difference in terrain height through differential interferometry processing, ensuring that subsequent data analysis only reflects surface changes without being interfered by terrain factors. This method improves the quality of deformation data and avoids the influence of uncorrected terrain errors on the accuracy of subsequent surface deformation detection.

[0070] pass Calculate the vertical displacement of each surface pixel point to obtain the two-dimensional surface deformation data; wherein, is the terrain phase data, The two-dimensional surface deformation data reflects the surface pixel points The amount of vertical displacement where settlement or uplift occurs.

[0071] The cluster analysis algorithm is used to divide the urban underground pipe network target area into multiple sub-areas, and the maximum vertical displacement and average vertical displacement of the sub-areas are calculated, and the sub-areas whose maximum vertical displacement and average vertical displacement exceed the set safety threshold are marked as deformation areas. The safety threshold is set according to the surface characteristics.

[0072] This embodiment calculates the vertical displacement of each surface pixel to obtain the two-dimensional surface deformation data of the target area, and then uses a clustering algorithm to capture the surface deformation area in the target area. This method can find the surface area with abnormal deformation in a large range of surface data, narrowing the scope of subsequent detection and facilitating subsequent targeted processing and analysis.

[0073] Further, referring to S30, S30 is applied to a three-dimensional surface analysis unit of an underground pipe network damage diagnosis and analysis system based on SAR satellite technology, including using LiDAR to measure the deformation area to obtain the distance between the surface point and the device, obtaining the three-dimensional coordinates of each surface point, and forming point cloud data; processing the point cloud data into three-dimensional terrain point cloud data through a three-dimensional terrain model;

[0074] The LiDAR measurement step includes: using the LiDAR device to emit a laser pulse to the deformation area, measuring the time difference between the laser pulse being emitted and being returned, and Calculate the distance between the ground point and the device, where is the speed of light, is the time difference.

[0075] The LiDAR device performs multi-point scanning by changing the emission angle of the laser pulse to cover the entire deformation area;

[0076] Collect the 3D coordinates of each surface point ;in, , is the distance between each ground point and the device, is the scanning angle, which represents the angle between the laser beam and the ground.

[0077] The three-dimensional coordinate data of all the ground points in the deformation area are integrated to generate three-dimensional point cloud data of the deformation area; the three-dimensional point cloud data reflects the height change and deformation of the ground surface in the deformation area.

[0078] This embodiment provides accurate spatial information for deformation analysis of underground pipe networks through the three-dimensional point cloud data of the deformation area in the target area of ​​LiDAR technology. The acquisition of point cloud data enhances the spatial resolution of the surface deformation area and provides basic support for subsequent three-dimensional modeling and deformation analysis.

[0079] The acquisition process of the three-dimensional terrain point cloud data is as follows:

[0080] For original 3D point cloud data , the RANSAC algorithm is used to filter out abnormal points; then the Gaussian filter is used to smooth the noise points, and then the point cloud data in the blank area is interpolated to obtain the point cloud data .

[0081] Performing Delauncy triangulation on the point cloud data to connect them into a local three-dimensional grid; connecting adjacent points with triangular facets through triangulation to generate a three-dimensional network model;

[0082] The three-dimensional grid model is smoothed and optimized; the three-dimensional coordinates of each grid node are resampled to generate the three-dimensional terrain point cloud data; the three-dimensional terrain point cloud data contains the three-dimensional coordinate information of each point.

[0083] This embodiment removes noise and fills in gaps in point cloud data to obtain high-quality point cloud data, and then generates three-dimensional terrain point cloud data through a three-dimensional grid algorithm, providing data support for the accurate analysis of complex surface deformations. In the detection and analysis of small deformations caused by damage to underground pipe networks, three-dimensional terrain point cloud data provides a data basis for the subsequent accurate positioning of damage locations and quantification of damage extents.

[0084] Further, referring to S40, S40 is applied to a pipe network damage diagnosis unit of an underground pipe network damage diagnosis and analysis system based on SAR satellite technology, including spatially fusion analysis of the two-dimensional surface deformation data and the three-dimensional terrain point cloud data, obtaining the three-dimensional surface deformation data of the deformation area, and combining the underground pipe network basic data to obtain underground pipe network damage information; the damage information includes damage location, damage degree and damage type;

[0085] Registering the two-dimensional surface deformation data with the three-dimensional terrain point cloud data in the same spatial coordinate system;

[0086] According to the coordinates of the two-dimensional pixel points, the two-dimensional surface deformation data and the three-dimensional terrain point cloud data are weightedly fused to obtain the three-dimensional surface deformation data of the deformation area, including deformation amplitude, deformation type and spatial distribution;

[0087] The formula of weighted fusion is:

[0088] ;

[0089] in, is the three-dimensional surface deformation data, is the two-dimensional surface deformation data, is the three-dimensional terrain point cloud data, and are weighted coefficients of the two-dimensional surface deformation data and the three-dimensional terrain point cloud data, respectively, satisfying .

[0090] This embodiment spatially fuses the two-dimensional surface deformation data and the three-dimensional terrain point cloud data, and associates the two-dimensional deformation information with the three-dimensional surface structure, so that the analysis of the deformation area is not limited to the plane information, but also includes the height change of the surface. This method increases the spatial resolution of the deformation area and provides more accurate three-dimensional data support for the subsequent analysis of the location and degree of damage of the underground pipeline network.

[0091] Obtain the basic data of underground pipe network, including pipe geometry information, pipe type, pipe material and historical maintenance records; transform the basic data of underground pipe network and three-dimensional surface deformation data into Perform spatial alignment; where is the point after space transformation, is the rotation matrix, is the original coordinate point, is the translation vector.

[0092] Marking points where the three-dimensional surface deformation data exceeds a threshold as first pipe network damage points;

[0093] ;

[0094] in, Indicates the damaged point of the first pipe network, is the three-dimensional surface deformation data, is the surface deformation threshold.

[0095] Performing spatial comparison on the position coordinates of the first pipe network damage point and the corresponding pipe geometry information, extracting the area where the first pipe network damage point matches the pipe structure, recording it as the second pipe network damage point and obtaining the corresponding position coordinates;

[0096] ;

[0097] in, is the damaged point of the second pipe network, is the position coordinate of the damaged point in the first pipe network, and For The corresponding pipe endpoint, The buried depth of the pipeline.

[0098] According to the deformation type of the second pipeline damage point, combined with the historical maintenance record, the pipeline damage type is obtained; the deformation type includes settlement and uplift, which is determined by the vertical displacement; the pipeline damage type includes fracture, corrosion, blockage, etc.

[0099] Obtaining a damage degree score of the pipeline network according to the deformation amplitude of the damaged point of the second pipeline network and in combination with the type and material of the pipeline;

[0100] ;

[0101] in, Score the degree of damage to the pipeline network. The three-dimensional surface deformation data of the damaged point of the second pipeline network, and They are the pipe type and material corresponding to the damage point. It is a damage severity scoring algorithm, which is defined based on the deformation amplitude and pipeline tolerance.

[0102] The location coordinates of the second pipeline network damage point, the pipeline network damage type and the pipeline network damage degree score are summarized to generate the underground pipeline network damage information.

[0103] This embodiment achieves accurate identification of the damaged location of the underground pipeline network by spatially comparing the three-dimensional surface deformation data with the geometric information of the underground pipeline network; combined with historical maintenance records and pipeline type and material, it can automatically diagnose the damage type and degree, thereby improving the accuracy and efficiency of underground pipeline network damage diagnosis; the damage information generated by this method provides decision support for the maintenance and repair of the underground pipeline network.

[0104] Further, referring to S50, S50 is applied to a prediction and early warning unit of an underground pipeline damage diagnosis and analysis system based on SAR satellite technology, including collecting SAR multi-time imaging data of the urban underground pipeline network, using time-series InSAR technology to analyze the evolution of surface deformation in the target area of ​​the underground pipeline network, and calculating the deformation rate and cumulative deformation; setting deformation rate and cumulative deformation thresholds, and triggering an early warning notification when the surface deformation exceeds the threshold.

[0105] Collect SAR multi-time imaging data of the urban underground pipe network area, and use time-series InSAR to obtain the vertical displacement of the surface position in the pipe network area;

[0106] pass Calculate each surface location In time interval The deformation rate within and They are At the moment and The vertical displacement of

[0107] pass Calculate surface position The cumulative deformation at

[0108] The surface deformation trend is analyzed based on the deformation rate and the accumulated deformation at each surface location.

[0109] This embodiment achieves continuous monitoring of surface deformation in a wide area by continuously collecting SAR imaging in the area where the underground pipeline network is located, ensuring timely capture of surface changes and providing new deformation data with high timeliness for pipeline network management decisions. The rate and cumulative deformation of the surface deformation are quantified by time-series InSAR technology to help identify potential damage trends.

[0110] Set the first threshold and the second threshold ;

[0111] For each surface location ,when or , output warning information; the warning information includes the surface position coordinates, deformation rate and cumulative deformation where the deformation rate or cumulative deformation exceeds the threshold.

[0112] This embodiment can automatically screen out potential risk areas by setting deformation rate and cumulative deformation thresholds; by setting thresholds and early warning mechanisms, it can timely detect potential damage to the underground pipeline network and issue reminders, reducing the burden of manual monitoring and improving the safety management and maintenance efficiency of the underground pipeline network.

[0113] Combined with underground pipe network sensor data for auxiliary analysis;

[0114] When the pipeline damage type in the pipeline damage diagnosis information of the second pipeline damage point is multiple types of composite, the internal sensor data of the second pipeline damage point is collected; the internal sensor data includes pressure data , Traffic data , Temperature data , stress data ;

[0115] The three-dimensional surface deformation data is spatially and temporally associated with the sensor data, and the damage type and degree of the underground pipe network are obtained through multi-source data fusion.

[0116] The time association is to synchronize the internal sensor data with the three-dimensional surface deformation data of the second pipe network damage point; the spatial association is to align the sensor data and the three-dimensional surface deformation data in space according to the geometric information of the underground pipe network;

[0117] The multi-source data fusion is:

[0118] ;

[0119] in, , , and They are the abnormal values ​​of pressure data, flow data, temperature data, and stress data; , , and They are the weights of various sensor data, which are adjusted according to the specific pipeline conditions.

[0120] According to the result of multi-source data fusion, the pre-established sensor data feature library of different damage types is used to obtain the damage type distribution of the second pipe network damage point through abnormal pattern matching. The feature library is partially exemplified in Table 1.

[0121] Table 1 Some examples of damage type sensor data feature library

[0122]

[0123] This embodiment combines internal sensor data and three-dimensional surface deformation data for spatial and temporal association, and uses multi-source data fusion and feature library pattern matching to improve the accuracy of underground pipeline network damage type determination and quantitative assessment of damage degree. Time synchronization and spatial registration ensure the accuracy of data analysis, and the use of sensor data feature library for abnormal pattern matching improves the accuracy and efficiency of automatic identification of damage types. This method not only provides comprehensive damage analysis, but also enhances the monitoring of the internal operation of the pipeline network, reduces the risk of misjudgment and missed judgment, and provides reliable support for the long-term stable operation and efficient maintenance of underground pipeline networks.

[0124] The present invention combines the wide-area monitoring capability of InSAR and the high-precision measurement of LiDAR to perform periodic imaging and three-dimensional scanning of the target area of ​​the urban underground pipeline network, generate two-dimensional surface deformation data and three-dimensional terrain point cloud data, and obtain the location and degree of damage to the pipeline network through spatial fusion analysis, and automatically diagnose the type of damage. This method solves the problems in the prior art of high requirements for sensor deployment, susceptibility to environmental interference, and reliance on manual detection of the degree of damage, and effectively improves the efficiency and accuracy of diagnosing pipeline damage. At the same time, the time-series InSAR technology is used to analyze the evolution of surface deformation, set deformation rate and cumulative deformation thresholds, provide continuous monitoring and real-time early warning capabilities, timely discover potential damage risks, and provide a decision-making basis for the scientific maintenance and management of underground pipelines.

[0125] Embodiment 2

[0126] As an embodiment of the present invention, refer to Figure 1 S10 in the figure is applied to a surface data acquisition unit of an underground pipe network damage diagnosis and analysis system based on SAR satellite technology. This embodiment describes a specific implementation method for a target area A of an urban underground pipe network.

[0127] Collect SAR periodic radar imaging of the urban underground pipeline target area A; the scope of the urban underground pipeline target area A is 1 square kilometer, the SAR satellite imaging frequency is once / 15 days, and the SAR satellite wavelength is 5.6cm (C band).

[0128] Further, referring to S20, S20 is applied to a two-dimensional surface analysis unit of an underground pipe network damage diagnosis and analysis system based on SAR satellite technology.

[0129] The surface SAR image contains phase information and amplitude information. and The phase difference of the phase difference is shown in Table 2.

[0130] Table 2 Partial data of phase calculation of pipeline network area A

[0131]

[0132] The terrain effect is eliminated by phase correction, and the least square phase unwrapping is used to minimize the phase difference between adjacent pixels, eliminate phase jumps, and obtain terrain phase data;

[0133] The two-dimensional surface deformation data is obtained by calculating the vertical displacement of each surface pixel point;

[0134] The cluster analysis algorithm is used to divide the urban underground pipeline target area A into multiple sub-areas. The maximum vertical displacement and average vertical displacement of the sub-areas are calculated, and the sub-areas whose maximum vertical displacement and average vertical displacement exceed the set safety threshold of 0.002m are marked as deformation areas.

[0135] Further, referring to S30, S30 is applied to a three-dimensional surface analysis unit of an underground pipe network damage diagnosis and analysis system based on SAR satellite technology.

[0136] The method includes using a LiDAR device to emit a laser pulse to the deformed area, measuring the time difference between the emission and return of the laser pulse, and calculating the distance between the surface point and the device.

[0137] By changing the emission angle of the laser pulse, multi-point scanning is performed to cover the entire deformation area;

[0138] The three-dimensional coordinates of each surface point are collected, and the three-dimensional coordinate data of all surface points in the deformation area are integrated to generate three-dimensional point cloud data of the deformation area.

[0139] For the original three-dimensional point cloud data, the RANSAC algorithm is used to filter out abnormal points; then the Gaussian filter is used to smooth the noise points, and then the point cloud data in the blank area is interpolated and completed to obtain the point cloud data.

[0140] Performing Delauncy triangulation on the point cloud data to connect them into a local three-dimensional grid; connecting adjacent points with triangular facets through triangulation to generate a three-dimensional network model;

[0141] The three-dimensional grid model is smoothed and optimized; the three-dimensional coordinates of each grid node are resampled to generate the three-dimensional terrain point cloud data; the three-dimensional terrain point cloud data contains the three-dimensional coordinate information of each point.

[0142] Further, referring to S40, S40 is applied to a pipeline damage diagnosis unit of an underground pipeline damage diagnosis and analysis system based on SAR satellite technology.

[0143] Registering the two-dimensional surface deformation data with the three-dimensional terrain point cloud data in the same spatial coordinate system;

[0144] According to the coordinates of the two-dimensional pixel points, the two-dimensional surface deformation data and the three-dimensional terrain point cloud data are weightedly fused to obtain the three-dimensional surface deformation data of the deformation area, including deformation amplitude, deformation type and spatial distribution;

[0145] Obtaining basic data of underground pipe network, including pipe geometry information, pipe type, pipe material and historical maintenance records; and spatially aligning the basic data of underground pipe network with three-dimensional surface deformation data.

[0146] Marking points where the three-dimensional surface deformation data exceeds a threshold as first pipe network damage points;

[0147] Performing spatial comparison on the position coordinates of the first pipe network damage point and the corresponding pipe geometry information, extracting the points where the first pipe network damage point matches the pipe structure, recording them as second pipe network damage points and obtaining the corresponding position coordinates;

[0148] According to the deformation type of the second pipeline damage point, combined with the historical maintenance record, the pipeline damage type is obtained; the deformation type includes settlement and uplift, which is determined by the vertical displacement; the pipeline damage type includes fracture, corrosion, blockage, etc.

[0149] Obtaining a damage degree score of the pipeline network according to the deformation amplitude of the damaged point of the second pipeline network and in combination with the type and material of the pipeline;

[0150] The location coordinates of the second pipeline network damage point, the pipeline network damage type and the pipeline network damage degree score are summarized to generate the underground pipeline network damage information.

[0151] Further, referring to S50, S50 is applied to a prediction and early warning unit of an underground pipe network damage diagnosis and analysis system based on SAR satellite technology.

[0152] Collect SAR multi-time imaging data of the urban underground pipe network target area A, and use time-series InSAR to obtain the vertical displacement of the surface position in the pipe network area;

[0153] Through the moment and The vertical displacement of each surface position is calculated In time interval The deformation rate within; Calculate the surface position The cumulative deformation at

[0154] The surface deformation trend is analyzed based on the deformation rate and the accumulated deformation at each surface location.

[0155] Setting a first threshold and a second threshold;

[0156] When the surface position When the deformation rate exceeds the first threshold or the accumulated deformation exceeds the second threshold, an early warning notification is triggered and early warning information is output; the early warning information includes the surface position coordinates where the deformation rate exceeds the first threshold or the accumulated deformation exceeds the second threshold, the deformation rate and the accumulated deformation.

[0157] Combined with underground pipe network sensor data for auxiliary analysis;

[0158] When the pipeline damage type in the pipeline damage diagnosis information of the second pipeline damage point is multiple types of composite, collecting internal sensor data of the second pipeline damage point; the internal sensor data includes pressure data, flow data, temperature data, and stress data;

[0159] The three-dimensional surface deformation data is spatially and temporally associated with the sensor data, and the damage type and degree of the underground pipe network are obtained through multi-source data fusion.

[0160] The time association is to synchronize the internal sensor data with the three-dimensional surface deformation data of the second pipe network damage point; the spatial association is to align the sensor data and the three-dimensional surface deformation data in space according to the geometric information of the underground pipe network;

[0161] According to the result of multi-source data fusion, the damage type of the damaged point of the second pipeline network is accurately determined by using a pre-established sensor data feature library of different damage types through abnormal pattern matching.

[0162] This embodiment achieves efficient monitoring of a large area of ​​the city's underground pipe network by combining InSAR satellite imaging and LiDAR three-dimensional scanning technology. InSAR technology has the advantage of wide-area monitoring, and can perform periodic radar imaging of the pipe network area to identify areas with abnormal surface deformation, while LiDAR is used to perform high-precision three-dimensional scanning of these abnormal areas to obtain detailed point cloud data. The present invention solves the problem in the prior art that the sensor deployment requirements are high and susceptible to environmental interference, improves the efficiency of identifying and diagnosing abnormal areas of the pipe network, and adapts to the monitoring needs of wide coverage areas.

[0163] This embodiment automatically determines the location, type and degree of damage to the pipe network by spatially fusion analysis of two-dimensional surface deformation data and three-dimensional terrain point cloud data, combined with the geometric information and historical maintenance records of the underground pipe network. This method overcomes the limitations of the existing technology that relies on sensor data and manual means to determine the degree of damage, improves diagnostic efficiency and accuracy, and provides a decision-making basis for the timely maintenance and repair of underground pipe networks.

[0164] This embodiment uses time-series InSAR technology to monitor the long-term deformation evolution of the underground pipe network area. By calculating the deformation rate and cumulative deformation, continuous monitoring and real-time early warning functions of potential damage risks are achieved. This method can timely capture the evolution trend of surface deformation in the area where the underground pipe network is located, providing data support for the long-term safe operation of the urban underground pipe network.

[0165] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An underground pipe network damage diagnosis and analysis system based on SAR satellite technology, characterized in that: include: Surface data acquisition unit: collects SAR periodic imaging of the target area of ​​the urban underground pipe network, and each imaging generates a surface SAR image at the corresponding moment; Two-dimensional surface analysis unit: performs differential interferometry processing on the surface SAR images at different times, removes terrain effects and noise interference, calculates the surface deformation of the target area, obtains two-dimensional surface deformation data and identifies the deformation area; The acquisition process of the surface deformation is to calculate the vertical displacement of each surface pixel point to obtain the two-dimensional surface deformation data; the calculation formula of the vertical displacement is: Where Δz(x,y) is the vertical displacement, Δφ terrain (x, y) is the terrain phase data after differential interferometry processing, λ is the SAR wavelength; A cluster analysis algorithm is used to divide the urban underground pipe network target area into multiple sub-areas, the maximum vertical displacement and the average vertical displacement of the sub-areas are calculated, and the sub-areas whose maximum vertical displacement and the average vertical displacement exceed the set safety threshold are marked as deformation areas; Three-dimensional surface analysis unit: for the deformation area, obtain the distance between the surface point and the device, obtain the three-dimensional coordinates of each surface point, and form point cloud data; process the point cloud data into three-dimensional terrain point cloud data through a three-dimensional terrain model; the acquisition process of the three-dimensional terrain point cloud data is as follows: Remove abnormal points and noise points in the point cloud data, and perform interpolation to complete the blank areas; The point cloud data is connected into a local three-dimensional grid using a triangular grid generation algorithm; adjacent points are connected by triangulation to generate a three-dimensional grid model; the three-dimensional grid model is smoothed and optimized; the three-dimensional coordinates of each grid node are resampled to generate the three-dimensional terrain point cloud data; Pipeline network damage diagnosis unit: spatially fuses and analyzes the two-dimensional surface deformation data with the three-dimensional terrain point cloud data to obtain the three-dimensional surface deformation data of the deformation area, and combines the underground pipeline network basic data to obtain underground pipeline network damage information; the damage information includes damage location, damage degree and damage type; The process of obtaining the underground pipe network damage information includes: Acquire basic data of underground pipe network, including pipe geometry information, pipe type, pipe material and historical maintenance records; spatially align the basic data of underground pipe network with the three-dimensional surface deformation data; Performing spatial comparison on the position coordinates of the first pipe network damage point and the corresponding pipe geometry information, extracting the points where the first pipe network damage point matches the pipe structure, recording them as the second pipe network damage point and obtaining the corresponding position coordinates; According to the deformation type of the second pipe network damage point, combined with the historical maintenance record, the pipe network damage type is obtained; according to the deformation amplitude of the second pipe network damage point, combined with the pipe type and the pipe material, the pipe network damage degree score is obtained; Summarizing the location coordinates of the second pipeline network damage point, the pipeline network damage type and the pipeline network damage degree score to generate the underground pipeline network damage information; The prediction and warning unit collects SAR multi-time imaging data of the target area, calculates the deformation rate and the cumulative deformation; sets a first threshold and a second threshold, and triggers a warning notification when the deformation rate exceeds the first threshold or the cumulative deformation exceeds the second threshold.

2. The underground pipe network damage diagnosis and analysis system based on SAR satellite technology according to claim 1 is characterized in that: The differential interference processing step comprises: Using a Gaussian filter to remove noise interference in the surface SAR image; Calculate the surface SAR image I at time t1 t1 and the surface SAR image I at time t2 t2 The phase difference Δφ(x,y) at the pixel point (x,y) is calculated as follows: in For I t2 The conjugate of (x,y); Least squares phase unwrapping is used to eliminate phase jumps and obtain terrain phase data.

3. The underground pipe network damage diagnosis and analysis system based on SAR satellite technology according to claim 1 is characterized in that: The step of acquiring the point cloud data comprises: Using a LiDAR device to emit a laser pulse to the deformation area, measuring the time difference between the laser pulse being emitted and being returned, calculating the distance between the surface point and the device, and recording the three-dimensional coordinates of the surface point; The three-dimensional coordinates of each surface point are collected to form the point cloud data.

4. The underground pipe network damage diagnosis and analysis system based on SAR satellite technology according to claim 1 is characterized in that: The process of spatial fusion analysis is as follows: Registering the two-dimensional surface deformation data with the three-dimensional terrain point cloud data in the same spatial coordinate system; According to the coordinates of the two-dimensional pixel points, the two-dimensional surface deformation data and the three-dimensional terrain point cloud data are weightedly fused to obtain the three-dimensional surface deformation data of the deformation area, including deformation amplitude, deformation type and spatial distribution; The formula of weighted fusion is: ΔS(x,y,z)=α·Δz InSAR (x,y)+β·ΔH LiDAR (x,y,z); Among them, ΔS(x, y, z) is the three-dimensional surface deformation data, Δz InSAR (x, y) is the two-dimensional surface deformation data, ΔH LiDAR (x, y, z) is the three-dimensional terrain point cloud data, and α and β are weighting coefficients of the two-dimensional surface deformation data and the three-dimensional terrain point cloud data respectively.

5. The underground pipe network damage diagnosis and analysis system based on SAR satellite technology according to claim 1 is characterized in that: The calculation process of the deformation rate and the cumulative deformation includes: Collecting SAR multi-time imaging data of the target area, and using time-series InSAR to obtain the vertical displacement of the surface position in the target area; Calculate each surface position (x * ,y * ) in the time interval Δt j The deformation rate within The calculation formula of the deformation rate is: in and They are (x * ,y * ) at time t j+1 and t j The vertical displacement of Calculate the value of (x * ,y * ) of the cumulative deformation; the calculation formula of the cumulative deformation is: The surface deformation trend of each surface location is analyzed according to the deformation rate and the accumulated deformation.

6. The underground pipe network damage diagnosis and analysis system based on SAR satellite technology according to claim 1 is characterized in that: The triggering process of the early warning notification includes: Set the first threshold and the second threshold, when the surface position (x * ,y * ) when the deformation rate exceeds the first threshold or the accumulated deformation exceeds the second threshold, an early warning notification is triggered and early warning information is output; the early warning information includes the surface position coordinates where the deformation rate exceeds the first threshold or the accumulated deformation exceeds the second threshold, the deformation rate and the accumulated deformation.

7. The underground pipe network damage diagnosis and analysis system based on SAR satellite technology according to claim 1 is characterized in that: It also includes auxiliary analysis in combination with underground pipe network sensor data; When the pipeline network damage type in the pipeline network damage diagnosis information of the second pipeline network damage point is multiple types of composite, the internal sensor data of the pipeline network is collected; The three-dimensional surface deformation data is spatially and temporally associated with the sensor data, and the distribution of underground pipe network damage types is obtained through multi-source data fusion.

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