Method, device and system for monitoring geological disasters along pipeline, computer equipment and medium

Abstract

The invention discloses a method, a device and a system for monitoring geological disasters along a pipeline, computer equipment and a medium. The method comprises the following steps: acquiring master-slave SAR image data output by a synthetic aperture radar, and performing phase interference processing and / or offset tracking processing on the master-slave SAR image data to output a first geological deformation area; acquiring master-slave remote sensing image data of satellite optical remote sensing; carrying out image difference processing on the second geological deformation area to output a second geological deformation area; outputting a geological disaster deformation hidden danger area according to the first geological deformation area and the second geological deformation area; in the geological disaster deformation hidden danger area, acquiring and processing master and slave geological disaster image data photogrammetric by the unmanned aerial vehicle so as to output geological disaster deformation data; or acquiring and processing master and slave LiDAR image data measured by the Li-DAR laser radar so as to output geological disaster deformation data; and based on the geological disaster deformation data, obtaining geological disaster point deformation data output by the earth surface deformation monitoring network. According to the scheme, multi-dimensional sensing of pipeline disasters is achieved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of disaster measurement technology, and in particular to a method, device, system, computer equipment and medium for monitoring geological disasters along pipelines. Background Technology

[0002] Optical remote sensing is a technology developed from remote sensing. It typically refers to imaging and observing targets in the visible, near-infrared, and short-wave infrared electromagnetic spectrum to acquire and analyze the optical characteristics of the observed objects. Optical remote sensing technology mainly includes satellite-based remote sensing, while spaceborne platform-based optical remote sensing technology primarily involves two processes: data acquisition and processing. Data acquisition and processing mainly include geometric correction, image fusion, reflectance and reflectance inversion, etc., aiming to correct geometric deviations, improve spatial resolution, and convert digitally quantized (DN) images into images with physically meaningful ground feature parameters. Spaceborne platform-based optical remote sensing technology, due to its high timeliness, macroscopic scope, and rich information, can be applied to various technical fields.

[0003] In the field of geological disaster monitoring along long-distance pipelines on mountain roads and various pipelines in urban underground pipe networks, optical remote sensing technology based on satellite platforms can also be used to investigate, analyze and assess major natural disasters. However, currently, geological disaster monitoring along pipelines only uses optical remote sensing technology as a single technical means. Since satellite remote sensing is only suitable for observing targets with a large area, it will lose its ability to identify small geological disaster hazards discovered through manual investigation, especially disaster points with a very small planar projection area, resulting in insufficient accuracy in hazard identification. Summary of the Invention

[0004] This invention provides a method, device, system, computer equipment, and medium for monitoring geological hazards along pipelines, so as to achieve multi-dimensional perception of hidden and slowly changing geological hazards along long-distance pipelines, thereby improving the accuracy of geological hazard monitoring along pipelines.

[0005] To achieve the above objectives, in a first aspect, embodiments of the present invention provide a method for monitoring geological hazards along pipelines, the monitoring method comprising:

[0006] Acquire master-slave SAR image data output by synthetic aperture radar and master-slave remote sensing image data from satellite optical remote sensing;

[0007] Phase interferometry and / or offset tracking processing are performed on the master-slave SAR image data to output a first geological deformation region; image difference processing is performed on the master-slave remote sensing image data to output a second geological deformation region.

[0008] Based on the first geological deformation region and the second geological deformation region, output the geological hazard deformation potential area;

[0009] Within the geological hazard deformation risk area, and where there is no vegetation within the geological hazard deformation risk area, master and slave geological hazard image data are acquired by UAV photogrammetry, and the master and slave geological hazard image data are processed to output geological hazard deformation data;

[0010] Alternatively, within the geological hazard deformation risk area, and where there is vegetation within the geological hazard deformation risk area, master-slave LiDAR image data measured by Li-DAR lidar is acquired, and the master-slave LiDAR image data is processed to output geological hazard deformation data.

[0011] Based on the geological disaster deformation data, obtain the geological disaster point deformation data output by the surface deformation monitoring network.

[0012] Optionally, phase interferometry processing is performed on the master-slave SAR image data to output a first geological deformation region; including:

[0013] The master-slave SAR image data is focused to form master-slave single-view complex data; wherein, the master-slave single-view complex data includes master-slave phase data;

[0014] Surface phase data and flatland phase data are removed from the master-slave single-view complex data based on an external reference digital elevation model;

[0015] The master-slave single-view complex data after removal is subjected to complex conjugate multiplication to output interference phase data, and the interference phase data is subjected to phase unwrapping and orbit refining.

[0016] The interference phase data after phase unwrapping and orbit refining are subjected to deformation transformation processing to output the first geological deformation region.

[0017] Optionally, offset tracking processing is performed on the master-slave SAR image data to output the first geological deformation region; including:

[0018] The master-slave SAR image data are initially registered based on orbital parameters, and then sub-pixel level registration is performed on the initially registered master-slave SAR image data.

[0019] Based on pixel blocks of a certain window size, intensity cross-correlation calculation is performed on the master-slave SAR image data after sub-pixel level registration to output the initial geological deformation area;

[0020] Based on external reference satellite orbit data, satellite orbit offsets in the initial geological deformation region are removed; and ionospheric interference offsets in the initial geological deformation region are removed.

[0021] The initial geological deformation region after removal is subjected to directional and distance deformation to output the first geological deformation region;

[0022] The master-slave SAR image data undergoes phase interferometry and offset tracking processing to output the first geological deformation region, including:

[0023] The first geological deformation region is output by superimposing the first geological deformation region output by phase interferometry processing of the master-slave SAR image data and the first geological deformation region output by offset tracking processing of the master-slave SAR image data.

[0024] Optionally, image interpolation processing is performed on the master-slave remote sensing image data to output a second geological deformation region, including:

[0025] Geometric and radiometric corrections are performed on the master-slave remote sensing image data;

[0026] Gray-scale features are extracted from the master-slave remote sensing image data after geometric and radiometric correction.

[0027] Determine the difference in grayscale values ​​of each pixel in the master-slave remote sensing image data after grayscale feature extraction;

[0028] The second geological deformation region is output based on the difference in grayscale values ​​of each pixel and a preset grayscale threshold.

[0029] Optionally, the master-slave geological hazard image data is processed to output geological hazard deformation data, including:

[0030] The master-slave geological disaster image data is transformed and output as master-slave 3D point cloud data, master-slave digital surface model data, and master-slave digital orthophoto map;

[0031] The master and slave 3D point cloud data are initially registered and ICP registered; the registered 3D point cloud data are then processed by cloud distance analysis to output 3D deformation data of geological disasters.

[0032] Geological hazard surface deformation data are determined based on the master-slave digital surface model data and the master-slave digital orthophoto map; wherein, the geological hazard deformation data includes the geological hazard three-dimensional deformation data and the geological hazard surface deformation data.

[0033] Optionally, the master-slave LiDAR image data is processed to output geological hazard deformation data, including:

[0034] The master-slave LiDAR image data is converted to output master-slave 3D point cloud data, master-slave digital surface model data, and master-slave digital orthophoto map.

[0035] The master-slave 3D point cloud data is preprocessed;

[0036] The preprocessed master-slave 3D point cloud data is then subjected to point cloud filtering and classification processing.

[0037] Based on the master-slave three-dimensional point cloud data after point cloud filtering and classification, cloud distance analysis is performed to output three-dimensional deformation data of geological disasters.

[0038] Geological hazard surface deformation data are determined based on the master-slave digital surface model data and the master-slave digital orthophoto map; wherein, the geological hazard deformation data includes the geological hazard three-dimensional deformation data and the geological hazard surface deformation data.

[0039] Secondly, embodiments of the present invention also provide a pipeline geological disaster monitoring device, the device comprising:

[0040] The acquisition module is used to acquire master-slave SAR image data output by synthetic aperture radar and master-slave remote sensing image data from satellite optical remote sensing.

[0041] The first output module is used to perform phase interferometry processing and / or offset tracking processing on the master-slave SAR image data to output a first geological deformation region; and to perform image difference processing on the master-slave remote sensing image data to output a second geological deformation region.

[0042] The second output module is used to output the geological disaster deformation hazard area based on the first geological deformation area and the second geological deformation area.

[0043] The third output module is used to acquire master-slave geological disaster image data from UAV photogrammetry when there is no vegetation in the geological disaster deformation hazard area, and to process the master-slave geological disaster image data to output geological disaster deformation data; or, when there is vegetation in the geological disaster deformation hazard area, to acquire master-slave LiDAR image data from LiDAR measurement, and to process the master-slave LiDAR image data to output geological disaster deformation data.

[0044] The fourth output module is used to obtain the deformation data of geological disaster points output by the surface deformation monitoring network based on the geological disaster deformation data.

[0045] Thirdly, embodiments of the present invention also provide a pipeline geological disaster monitoring system, which includes: a space-based monitoring platform, an aerial monitoring platform, a three-dimensional surface deformation monitoring network, and a control module for performing the pipeline geological disaster monitoring method described in the first aspect above;

[0046] The space-based monitoring platform includes synthetic aperture radar and satellite optical remote sensing; the airborne monitoring platform includes UAV photogrammetry equipment and Li-DAR lidar; the three-dimensional surface deformation monitoring network includes multiple GNSS displacement monitoring devices and a rainfall detection module; the location of the GNSS displacement monitoring devices is determined by the geological disaster deformation data monitored and output by the airborne monitoring platform.

[0047] Each of the GNSS displacement monitoring devices is communicatively connected; each of the GNSS displacement monitoring devices, the synthetic aperture radar, the satellite optical remote sensing, the UAV photogrammetry equipment, and the Li-DAR lidar are all communicatively connected to the control module.

[0048] Fourthly, embodiments of the present invention also provide a computer device, the computer device comprising:

[0049] At least one processor; and

[0050] A memory communicatively connected to the at least one processor; wherein,

[0051] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the pipeline geological hazard monitoring method described in the first aspect.

[0052] Fifthly, embodiments of the present invention also provide a computer-readable storage medium storing computer instructions, which are used to cause a processor to execute the pipeline geological disaster monitoring method described in the first aspect.

[0053] In this embodiment of the invention, master-slave SAR image data output by synthetic aperture radar and master-slave remote sensing image data from satellite optical remote sensing are acquired; phase interferometry and / or offset tracking processing are performed on the master-slave SAR image data to output a first geological deformation region; image difference processing is performed on the master-slave remote sensing image data to output a second geological deformation region; geological hazard deformation potential areas are output based on the first and second geological deformation regions; when there is no vegetation in the geological hazard deformation potential area, master-slave geological hazard image data from UAV photogrammetry is acquired, and the master-slave geological hazard image data is processed. The system outputs geological hazard deformation data; or, within areas with potential geological hazard deformation and vegetation, it acquires master-slave LiDAR image data measured by Li-DAR lidar and processes the master-slave LiDAR image data to output geological hazard deformation data; based on the geological hazard deformation data, it acquires geological hazard point deformation data output by the surface deformation monitoring network. In this way, through potential geological hazard deformation areas, geological hazard deformation data, and geological hazard point deformation data, it achieves multi-level perception of hidden and slowly changing geological hazards along long-distance pipelines, thereby improving the accuracy of geological hazard monitoring along pipelines.

[0054] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0055] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0056] Figure 1 This is a schematic flowchart of a method for monitoring geological hazards along a pipeline, provided in an embodiment of the present invention.

[0057] Figure 2 This is a schematic flowchart of another method for monitoring geological hazards along a pipeline provided by an embodiment of the present invention;

[0058] Figure 3 This is a schematic diagram of a pipeline geological disaster monitoring device provided in an embodiment of the present invention;

[0059] Figure 4 This is a schematic diagram of a pipeline geological disaster monitoring system provided in an embodiment of the present invention;

[0060] Figure 5 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0061] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0062] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0063] Figure 1 This is a schematic flowchart of a pipeline geological hazard monitoring method provided by an embodiment of the present invention. This embodiment is applicable to the measurement of concealed and slowly changing geological hazards (such as landslides, collapses, debris flows, and water damage) along long-distance pipelines. The method can be executed by a pipeline geological hazard monitoring device. Figure 1 As shown, the method specifically includes the following steps:

[0064] S110: Acquire master-slave SAR image data output by synthetic aperture radar and master-slave remote sensing image data from satellite optical remote sensing.

[0065] The master and slave SAR image data output by the synthetic aperture radar (SAR) consist of the master SAR image and the slave SAR image (first phase) of the same area at the geological disaster site, acquired using spaceborne SAR. Specifically, the master SAR image can be image data from the Sentel-A or Sentel-B satellite in either ascending or descending orbit; the slave SAR image can also be image data from the Sentel-A or Sentel-B satellite in either ascending or descending orbit. The first phase of the SAR image refers to the time baseline between the master and slave SAR images; this time baseline can be determined based on the actual geological disaster monitoring situation.

[0066] In addition, the main SAR image and the secondary SAR image can be acquired at a certain frequency; this embodiment does not limit the specific value of the frequency; for example, the frequency is: 2 months / each SAR image; the phase information of the ground objects can be mainly obtained from each SAR image, and the phase information of the ground objects represents the distance information between the spaceborne synthetic aperture radar SAR and the target object.

[0067] The master-slave remote sensing image data of satellite optical remote sensing refers to the master remote sensing image and the slave remote sensing image of the same area at the geological disaster site, which are collected by satellite optical remote sensing. The first phase of the first phase of the remote sensing image is the time baseline between the master remote sensing image and the slave remote sensing image. This time baseline can be determined according to the actual geological disaster monitoring situation. The gray value of each pixel can be mainly extracted from each remote sensing image.

[0068] S120. Perform phase interferometry and / or offset tracking processing on the master-slave SAR image data to output the first geological deformation region; and perform image difference processing on the master-slave remote sensing image data to output the second geological deformation region.

[0069] Specifically, phase interferometry and / or offset tracking processing are performed on master-slave SAR image data to output the first geological deformation region. In scenarios with dense vegetation cover and rapid large deformation (such as sudden landslides), offset tracking processing can be used to output the first geological deformation region because phase interferometry processing of master-slave SAR image data will lose coherence. In other scenarios, excluding dense vegetation cover and rapid large deformation (such as sudden landslides), phase interferometry processing of master-slave SAR image data will not lose coherence. In this embodiment, phase interferometry processing can be used alone to output the first geological deformation region. Alternatively, phase interferometry and offset tracking processing can be used simultaneously, thereby superimposing and comparing the geological deformation regions output by phase interferometry and offset tracking processing to output the first geological deformation region. The first geological deformation region is a large-area, wide-ranging geological deformation delineation area, where the probability of geological hazards is relatively high. Therefore, the location of geological hazards can be roughly determined based on the first geological deformation region.

[0070] Image interpolation processing is performed on master-slave remote sensing image data to output the second geological deformation region. This involves using master-slave remote sensing image data of the same area at a geological disaster site, acquired under dense vegetation cover and rapid large deformation (such as sudden landslides), as well as other scenarios. Image interpolation processing is then performed on the master-slave remote sensing image data to output the second geological deformation region. The probability of geological disasters existing within the second geological deformation region is also relatively high; therefore, the location of a geological disaster can be roughly determined based on the second geological deformation region.

[0071] S130. Output the geological disaster deformation hazard area based on the first geological deformation area and the second geological deformation area.

[0072] It is understandable that, since satellite optical remote sensing and spaceborne synthetic aperture radar (SAR) are different monitoring methods, the output first and second geological deformation areas may be the same or different. This embodiment can ultimately output a geological hazard deformation risk area based on the first and second geological deformation areas; the geological hazard deformation risk area is an area that has already shown obvious signs of deformation, whose stability has been compromised, and which may experience catastrophic events (such as landslides, collapses, debris flows, and ground subsidence) under certain future conditions (such as heavy rainfall, earthquakes, and human engineering activities).

[0073] S140. In areas with potential geological hazard deformation and where there is no vegetation, acquire master-slave geological hazard image data from UAV photogrammetry, and process the master-slave geological hazard image data to output geological hazard deformation data; or, in areas with potential geological hazard deformation and where there is vegetation, acquire master-slave LiDAR image data from LiDAR measurement, and process the master-slave LiDAR image data to output geological hazard deformation data.

[0074] In areas with dense vegetation, drone monitoring cannot penetrate the vegetation to obtain the true deformation of the ground surface. In this embodiment, within the geological hazard deformation risk area, and when there is no vegetation in the geological hazard deformation risk area, master and slave geological hazard image data can be obtained by drone photogrammetry. The master and slave geological hazard image data are complete and accurate master geological hazard images and complete and accurate slave master geological hazard images of the same area at the geological hazard location, collected by drone low-altitude photogrammetry from different angles such as vertical and inclined. By processing the master and slave geological hazard image data, geological hazard deformation data can be accurately output. Geological hazard deformation data can include three-dimensional geological hazard deformation data and surface geological hazard deformation data (such as surface cracks, subsidence troughs, faults, landslide walls, vertical displacement of the surface, volume change, calculation of the profile before and after the change, and surface geological hazard deformation height, length, area, angle, and slope).

[0075] In densely vegetated areas, LiDAR measurement technology can be used to compensate for deficiencies. Specifically, in areas with potential geological hazard deformation and where vegetation exists, master-slave LiDAR image data can be acquired. This master-slave LiDAR image data consists of the master and slave LiDAR images of the same area at the geological hazard site measured by LiDAR. The master-slave LiDAR image data is processed to accurately output geological hazard deformation data. This geological hazard deformation data can also include three-dimensional geological hazard deformation data and surface geological hazard deformation data (such as surface cracks, subsidence troughs, faults, landslide walls, vertical surface displacement, volume changes, calculation of profiles before and after changes, and surface geological hazard deformation height, length, area, angle, and slope). Thus, this embodiment outputs geological hazard deformation data, achieving high-precision "surface" deformation data from areas with potential geological hazard deformation, accurately locating the morphology and deformation characteristics of the hazard body.

[0076] S150. Based on geological disaster deformation data, obtain the deformation data of geological disaster points output by the surface deformation monitoring network.

[0077] The surface deformation monitoring network can be specifically deployed based on geological disaster deformation data (three-dimensional geological disaster deformation data and surface geological disaster deformation data). The surface deformation monitoring network can include multiple GNSS displacement monitoring devices. The deployment type of the surface deformation monitoring network can be cross-shaped, grid-shaped, triangular (or radial), arbitrary, or alignment-shaped. In this way, the geological disaster point deformation data output by the surface deformation monitoring network can be obtained. This output of geological disaster point deformation data realizes the acquisition of high-precision key "point" deformation data from geological disaster deformation data, accurately grasps the development stage of pipeline geological disasters, and thus provides a criterion for pipeline geological disaster early warning.

[0078] This invention enables multi-level perception of hidden and slowly changing geological hazards along long-distance pipelines by using geological hazard deformation potential areas, geological hazard deformation data, and geological hazard point deformation data. It also enables coarse-to-fine deformation data analysis, thereby improving the accuracy of geological hazard monitoring along pipelines and overcoming the problem of low accuracy in geological hazard monitoring using single remote sensing technology. This provides reliable data for early warning of geological hazards along pipelines.

[0079] Optionally, based on the above embodiments, each step can be further refined. Figure 2 This is a flowchart illustrating another method for monitoring geological hazards along pipelines provided in an embodiment of the present invention; as shown below. Figure 2 As shown, the monitoring method specifically includes the following steps:

[0080] S210: Acquire master-slave SAR image data output by synthetic aperture radar and master-slave remote sensing image data from satellite optical remote sensing.

[0081] S220. Perform phase interferometry and / or offset tracking processing on the master-slave SAR image data to output the first geological deformation region; perform image difference processing on the master-slave remote sensing image data to output the second geological deformation region.

[0082] Specifically, phase interferometry processing is performed on master-slave SAR image data to output the first geological deformation area; this includes: 1) focusing the master-slave SAR image data to form master-slave single-view complex data (SLC data); wherein, master-slave single-view complex data includes master-slave phase data; 2) removing surface phase data and flat land phase data from the master-slave single-view complex data based on an external reference digital elevation model (DEM); specifically, absolute master-slave phase data = topographic phase data + flat land phase data + deformation phase data + atmospheric delay phase data + noise phase data; utilizing the second track difference The method of separation, based on the external reference digital elevation model (DEM), can remove surface phase data and flat-ground phase data from the master-slave single-view complex data; while the influence of atmospheric delay phase and noise phase can be ignored; 3) The removed master-slave single-view complex data is subjected to complex conjugate multiplication to output interferometric phase data, and the interferometric phase data is subjected to phase unwrapping and orbit refinement processing; the orbit refinement processing can eliminate the influence of orbit error; 4) Deformation transformation processing is performed on the interferometric phase data after phase unwrapping and orbit refinement processing to output the first geological deformation region. Specifically, there is the following mathematical relationship between the interferometric phase data and the deformation information (first geological deformation region) of the ground target point along the radar line of sight (LOS direction):

[0083] in, λ represents the LOS-oriented deformation information of the required ground target point (first geological deformation region); λ is the radar wavelength. This is interferometric phase data.

[0084] Specifically, offset tracking processing is performed on master-slave SAR image data to output the first geological deformation region; this includes: 1) initial registration of master-slave SAR image data based on orbital parameters (which can eliminate the deviation between master-slave SAR image data based on orbital parameters), and sub-pixel-level registration of the initially registered master-slave SAR image data; 2) intensity cross-correlation calculation of the sub-pixel-level registered master-slave SAR image data based on pixel blocks of a certain window size to output the initial geological deformation region; 3) removing satellite orbit offsets in the initial geological deformation region based on external reference satellite orbital data (which can eliminate large-scale pixel position deviations caused by satellite viewpoint terrain effects); and removing ionospheric interference offsets in the initial geological deformation region; 4) processing the removed initial geological deformation region... Perform directional deformation and distance displacement deformation To output the first geological deformation region; specifically:

[0085]

[0086]

[0087] Understandably, in scenarios with dense vegetation cover and rapid large deformation (such as sudden sliding caused by landslides), since the master-slave SAR image data will lose coherence when undergoing phase interferometry processing, the above-mentioned offset tracking processing can be used to output the first geological deformation area.

[0088] In other scenarios, besides dense vegetation cover and rapid large deformation (such as sudden sliding caused by landslides), since the phase interferometry processing of master-slave SAR image data does not lose coherence, this embodiment can use phase interferometry processing alone to output the first geological deformation region; or it can use phase interferometry processing and offset tracking processing simultaneously, so that the first geological deformation region output by phase interferometry processing of master-slave SAR image data and the first geological deformation region output by offset tracking processing of master-slave SAR image data are superimposed to output the first geological deformation region.

[0089] Specifically, image difference processing is performed on master-slave remote sensing image data to output the second geological deformation region, including: 1) Performing geometric and radiometric corrections on the master-slave remote sensing image data; geometric correction ensures that the two remote sensing image data are strictly aligned, guaranteeing that the same pixel (x, y) corresponds to the same location on the ground. Radiometric correction eliminates "spurious changes" caused by factors such as solar altitude angle and atmospheric conditions. The goal of geometric and radiometric corrections is to ensure that the pixel grayscale values ​​of subsequent master-slave remote sensing image data only represent the true reflectance of the ground objects, thus achieving comparability. 2) Extracting grayscale features from the geometrically and radiometrically corrected master-slave remote sensing image data; 3) Determining the difference in grayscale values ​​of each pixel in the master-slave remote sensing image data after grayscale feature extraction; 4) Outputting the second geological deformation region based on the difference in grayscale values ​​of each pixel and a preset grayscale threshold; specifically:

[0090]

[0091] Where k is the number of bands, i and j represent pixel coordinates, T is the preset grayscale threshold, and D is the grayscale threshold. ij D represents the difference in grayscale values ​​of each pixel. ij =|f ij (t2)-f ij (t1)|, f represents feature information (generally grayscale information of the image), D ij The larger the value, the more likely the pixel is to change. Since deformation caused by pipeline geological disasters (such as landslides and collapses) will cause changes in spectral characteristics such as surface cover, color or texture, this significant change in gray value becomes an important basis for identifying the second geological deformation area. This method is simple and fast and can quickly extract the change characteristics of pipeline geological disasters.

[0092] S230. Overlay and compare the first and second geological deformation areas to output the geological disaster deformation hazard areas.

[0093] The first geological deformation area, output by the spaceborne synthetic aperture radar, is a large-scale, macroscopic "general survey" task. This first geological deformation area is a large, potentially risky zone. The second geological deformation area, output by satellite optical remote sensing image data, is overlaid and compared with the first geological deformation area. This process supplements and refines the information of the first geological deformation area, enabling more accurate screening and identification of specific geological hazards adjacent to the pipeline. This completes the judgment from screening geological deformation areas to identifying target points in geological hazard deformation hazard areas.

[0094] S240. In areas with potential geological hazard deformation, and where there is no vegetation, acquire master-slave geological hazard image data from UAV photogrammetry, and process the master-slave geological hazard image data to output geological hazard deformation data; or, in areas with potential geological hazard deformation, and where there is vegetation, acquire master-slave LiDAR image data from LiDAR measurement, and process the master-slave LiDAR image data to output geological hazard deformation data.

[0095] Specifically, point cloud processing is performed on master-slave geological hazard image data to output geological hazard deformation data, including: 1) converting master-slave geological hazard image data to output master-slave 3D point cloud data, master-slave digital surface model data (DSM), and master-slave digital orthophoto map (DOM); 2) performing initial registration and ICP registration on the master-slave 3D point cloud data; 3) performing cloud distance analysis processing (e.g., M3C2 method) on the registered 3D point cloud data to output geological hazard 3D deformation data; 4) determining geological hazard surface deformation data based on the master-slave digital surface model data (DSM) and master-slave digital orthophoto map (DOM); whereby geological hazard deformation data includes geological hazard 3D deformation data and geological hazard surface deformation data. Specifically, differential calculation based on the master-slave digital surface model data (DSM) can calculate the vertical displacement, volume change, and profile information before and after the change of the surface. Geological information such as the height, length, area, angle, and slope of surface deformation caused by geological hazards can also be directly obtained from the master-slave digital surface model data (DSM). In other words, geological hazard surface deformation data can include: vertical displacement of the surface, volume change, profile information before and after the change, and geometric information such as the height, length, area, angle, and slope of surface deformation caused by geological hazards.

[0096] Compared to spaceborne synthetic aperture radar and satellite optical remote sensing, which are space-based "general survey" methods with a lower monitoring frequency and are mainly used for delineating large-scale deformation areas, UAVs are aerial "detailed survey" methods with a higher monitoring frequency. After the target area is locked by space-based monitoring, UAVs can conduct mobile, high-precision, and high-resolution investigations of geological disaster deformation data within areas with potential geological hazards, effectively supplementing the shortcomings of space-based technology in terms of spatial resolution.

[0097] Considering the presence of vegetation in areas prone to geological hazard deformation, where UAV photogrammetry cannot penetrate the vegetation to obtain geological hazard deformation data, LiDAR can be used for measurement, acquiring master-slave LiDAR image data. The master-slave LiDAR image data is then processed to output geological hazard deformation data, including: 1) converting the master-slave LiDAR image data to output master-slave 3D point cloud data, master-slave digital surface model data (DSM), and master-slave digital orthophoto map (DOM); 2) preprocessing the master-slave 3D point cloud data; specifically, preprocessing may include pathline calculation, point cloud data calculation, and flight strip calculation of the master-slave 3D point cloud data. Adjustment, noise and outlier removal; 3) Point cloud filtering and classification processing is performed on the preprocessed master-slave 3D point cloud data; the progressive densification triangular mesh filtering algorithm can be used for point cloud filtering and classification processing; the purpose of point cloud filtering and classification processing on the preprocessed master-slave 3D point cloud data is to remove vegetation; 4) Cloud distance analysis processing is performed on the master-slave 3D point cloud data after point cloud filtering and classification processing to output geological hazard 3D deformation data; 5) Geological hazard surface deformation data is determined based on master-slave digital surface model data (DSM) and master-slave digital orthophoto map (DOM); among which, geological hazard deformation data includes geological hazard 3D deformation data and geological hazard surface deformation data. Specifically, differential processing based on master-slave digital surface model data (DSM) can calculate the vertical displacement, volume change, and profile information before and after the change of the surface. Geological information such as the height, length, area, angle, and slope of surface deformation caused by geological hazards can also be directly obtained from the master-slave digital surface model data (DSM). In other words, geological hazard surface deformation data can include: vertical displacement of the surface, volume change, profile information before and after the change, and geometric information such as the height, length, area, angle, and slope of surface deformation caused by geological hazards.

[0098] S250. Based on geological disaster deformation data, obtain deformation data of geological disaster points output by the surface deformation monitoring network.

[0099] Among them, by using deformation data of geological disaster points, high-precision key "point" deformation data can be obtained from geological disaster deformation data, so as to accurately grasp the development stage of pipeline geological disasters and thus provide a criterion for early warning of pipeline geological disasters.

[0100] This invention specifically illustrates how to output geological hazard deformation risk areas, geological hazard deformation data, and geological hazard point deformation data. In this way, by using geological hazard deformation risk areas, geological hazard deformation data, and geological hazard point deformation data, multi-level perception of hidden and slowly changing geological hazards along long-distance pipelines is achieved, and deformation data analysis from coarse to fine is performed. This improves the accuracy of geological hazard monitoring along pipelines, overcomes the problem of low accuracy in geological hazard monitoring using single remote sensing technology, and provides reliable data for early warning of geological hazards along pipelines.

[0101] This invention also provides a schematic diagram of a pipeline geological disaster monitoring device. This pipeline geological disaster monitoring device can execute the pipeline geological disaster monitoring method provided in any embodiment of this invention, and has the corresponding functional modules and beneficial effects of the method. Figure 3 This is a schematic diagram of the structure of a pipeline geological disaster monitoring device provided in an embodiment of the present invention; as shown below. Figure 3 As shown, the device includes:

[0102] The acquisition module 10 is used to acquire master-slave SAR image data output by synthetic aperture radar and master-slave remote sensing image data from satellite optical remote sensing.

[0103] The first output module 20 is used to perform phase interferometry processing and / or offset tracking processing on master-slave SAR image data to output a first geological deformation region; and to perform image difference processing on master-slave remote sensing image data to output a second geological deformation region.

[0104] The second output module 30 is used to output the geological disaster deformation hazard area based on the first geological deformation area and the second geological deformation area;

[0105] The third output module 40 is used to acquire master-slave geological disaster image data from UAV photogrammetry when there is no vegetation in the geological disaster deformation hazard area, and to process the master-slave geological disaster image data to output geological disaster deformation data; or, in the geological disaster deformation hazard area where there is vegetation, to acquire master-slave LiDAR image data from Li-DAR lidar measurement, and to process the master-slave LiDAR image data to output geological disaster deformation data.

[0106] The fourth output module 50 is used to obtain the deformation data of geological disaster points output by the surface deformation monitoring network based on the geological disaster deformation data.

[0107] Based on the same inventive concept, this invention also provides a pipeline geological disaster monitoring system. Figure 4 This is a schematic diagram of the structure of a pipeline geological disaster monitoring system provided in an embodiment of the present invention; as shown below. Figure 4 As shown, the system includes: a space-based monitoring platform 01, an aerial monitoring platform 02, a three-dimensional surface deformation monitoring network 03, and a control module 04 that executes the pipeline geological disaster monitoring method described in the above embodiment;

[0108] The space-based monitoring platform 01 includes a synthetic aperture radar 100 and a satellite optical remote sensing 200; the airborne monitoring platform 02 includes an unmanned aerial vehicle (UAV) photogrammetry device 300 and a Li-DAR lidar 400; the three-dimensional surface deformation monitoring network 03 includes multiple GNSS displacement monitoring devices 500 and a rainfall detection module 600; the location of the GNSS displacement monitoring devices 500 is determined by the geological disaster deformation data monitored and output by the airborne monitoring platform 02.

[0109] Each GNSS displacement monitoring device 500 is connected to the control module 04. Each GNSS displacement monitoring device 500, synthetic aperture radar 100, satellite optical remote sensing 200, UAV photogrammetry equipment 300 and Li-DAR lidar 400 are all connected to the control module 04.

[0110] Specifically, the location of the GNSS displacement monitoring equipment 500 is determined by the geological disaster deformation data output by the aerial monitoring platform 02; a three-dimensional surface deformation monitoring network 03 is formed by deploying multiple GNSS displacement monitoring equipment 500s; the three-dimensional surface deformation monitoring network 03 includes monitoring lines and monitoring points on the monitoring lines. The monitoring lines within the three-dimensional surface deformation monitoring network, which are composed of various GNSS displacement monitoring equipment 500s and rainfall detection modules 600, should meet the requirements of covering the deformation amount and direction (displacement vector) in the geological disaster deformation data and passing through different geomorphic features within the geological disaster deformation data; the monitoring points within the three-dimensional surface deformation monitoring network are any GNSS displacement monitoring equipment 500 and rainfall detection modules 600 on the monitoring lines, which can be determined according to the monitoring lines and the different geomorphic features they pass through;

[0111] Each GNSS displacement monitoring device 500 is connected via communication and can transmit satellite data acquired from the base station to the control module 04 for data processing through wireless IoT technology, realizing remote, high-precision, automated monitoring of deformation data at geological disaster sites. Specifically, based on geological disaster deformation data, the GNSS displacement monitoring device 500 can obtain the displacement-time curve of the deformation data at the geological disaster site. By analyzing the slope (tangent angle) and velocity changes of the displacement-time curve, the development stage of pipeline geological disasters can be determined, thus providing a criterion for pipeline geological disaster early warning. This also enables multi-level perception of concealed and slowly changing geological disasters along long-distance pipelines, and from coarse to fine deformation data analysis, thereby improving the accuracy of geological disaster monitoring and identification along pipelines.

[0112] This invention also provides a computer device. Figure 5 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. The computer device is intended to represent various forms of digital computers, such as embedded computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The computer device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0113] like Figure 5 As shown, computer device 011 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 and a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer programs stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of computer device 100. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0114] Multiple components in computer device 011 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of monitors, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows computer device 100 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0115] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, central processing unit (CPU), graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as a method for monitoring geological hazards along a pipeline.

[0116] In some embodiments, a pipeline geological hazard monitoring method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on computer device 011 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the pipeline geological hazard monitoring method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform a pipeline geological hazard monitoring method by any other suitable means (e.g., by means of firmware).

[0117] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0118] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0119] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0120] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer device having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0121] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0122] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0123] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0124] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

[0125] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. A method of monitoring geological hazards along a pipeline, characterized in that, The method comprises the following steps: acquiring master-slave SAR image data of synthetic aperture radar output and acquiring master-slave remote sensing image data of satellite optical remote sensing; performing phase interference processing and / or offset tracking processing on the master-slave SAR image data to output a first geological deformation area; performing image difference processing on the master-slave remote sensing image data to output a second geological deformation area; outputting a geological disaster deformation hidden danger area according to the first geological deformation area and the second geological deformation area; in the geological disaster deformation hidden danger area, and when there is no vegetation in the geological disaster deformation hidden danger area, acquiring master-slave geological disaster image data of unmanned aerial vehicle photogrammetry, and performing processing on the master-slave geological disaster image data to output geological disaster deformation data; or, in the geological disaster deformation hidden danger area, and when there is vegetation in the geological disaster deformation hidden danger area, acquiring master-slave LiDAR image data of Li-DAR laser radar measurement, and performing processing on the master-slave LiDAR image data to output geological disaster deformation data; based on the geological disaster deformation data, acquiring geological disaster point deformation data output by a ground deformation monitoring network.

2. The method of claim 1, wherein, performing phase interference processing on the master-slave SAR image data to output a first geological deformation area; the method comprises the following steps: performing focusing processing on the master-slave SAR image data to form master-slave single-view complex data; wherein the master-slave single-view complex data comprises master-slave phase data; removing ground phase data and flat ground phase data in the master-slave single-view complex data based on an external reference digital elevation model; performing complex conjugate multiplication processing on the removed master-slave single-view complex data to output interference phase data, and performing phase unwrapping processing and orbit refining processing on the interference phase data; performing deformation conversion processing on the interference phase data after phase unwrapping processing and orbit refining processing to output a first geological deformation area.

3. The method of claim 2, wherein, performing offset tracking processing on the master-slave SAR image data to output a first geological deformation area; the method comprises the following steps: performing initial registration on the master-slave SAR image data based on orbit parameters, and performing sub-pixel level registration on the master-slave SAR image data after initial registration; performing intensity cross-correlation calculation on the master-slave SAR image data after sub-pixel level registration based on a pixel block of a certain window size to output an initial geological deformation area; removing satellite orbit offset in the initial geological deformation area and removing ionospheric interference offset in the initial geological deformation area based on external reference satellite orbit data; performing direction bit and distance bit deformation on the removed initial geological deformation area to output a first geological deformation area; performing phase interference processing and offset tracking processing on the master-slave SAR image data to output a first geological deformation area, which comprises the following steps: superimposing the first geological deformation area output by performing phase interference processing on the master-slave SAR image data and the first geological deformation area output by performing offset tracking processing on the master-slave SAR image data to output a first geological deformation area.

4. The method of claim 1, wherein, performing image difference processing on the master-slave remote sensing image data to output a second geological deformation area, which comprises the following steps: Geometric correction and radiation correction are performed on the master-slave remote sensing image data. Gray value feature extraction is performed on the master-slave remote sensing image data after geometric correction and radiation correction. The pixel gray value difference of the master-slave remote sensing image data after gray value feature extraction is determined. A second geological deformation area is output according to the pixel gray value difference and a preset gray threshold.

5. The method of claim 1, wherein, The master-slave geological disaster image data is processed to output geological disaster deformation data, including: The master-slave geological disaster image data is converted to output master-slave three-dimensional point cloud data, master-slave digital surface model data, and master-slave digital orthographic image map; Initial registration and ICP registration are performed on the master-slave three-dimensional point cloud data; cloud distance analysis processing is performed on the registered three-dimensional point cloud data to output geological disaster three-dimensional deformation data; Geological disaster ground deformation data is determined according to the master-slave digital surface model data and the master-slave digital orthographic image map; wherein the geological disaster deformation data includes the geological disaster three-dimensional deformation data and the geological disaster ground deformation data.

6. The method of claim 1, wherein, The master-slave LiDAR image data is processed to output geological disaster deformation data, including: The master-slave LiDAR image data is converted to output master-slave three-dimensional point cloud data, master-slave digital surface model data, and master-slave digital orthographic image map; The master-slave three-dimensional point cloud data is preprocessed; Point cloud filtering classification processing is performed on the preprocessed master-slave three-dimensional point cloud data; Cloud distance analysis processing is performed on the master-slave three-dimensional point cloud data after point cloud filtering classification processing to output geological disaster three-dimensional deformation data; Geological disaster ground deformation data is determined according to the master-slave digital surface model data and the master-slave digital orthographic image map; wherein the geological disaster deformation data includes the geological disaster three-dimensional deformation data and the geological disaster ground deformation data.

7. A device for monitoring geological disasters along a pipeline, characterized in that, It includes: An acquisition module is configured to acquire master-slave SAR image data output by a synthetic aperture radar and master-slave remote sensing image data acquired by satellite optical remote sensing; A first output module is configured to perform phase interference processing and / or offset tracking processing on the master-slave SAR image data to output a first geological deformation area; Image difference processing is performed on the master-slave remote sensing image data to output a second geological deformation area; A second output module is configured to output a geological disaster deformation hidden danger area according to the first geological deformation area and the second geological deformation area; A third output module is configured to, when there is no vegetation in the geological disaster deformation hidden danger area, acquire master-slave geological disaster image data of unmanned aerial vehicle photogrammetry and process the master-slave geological disaster image data to output geological disaster deformation data; or, when there is vegetation in the geological disaster deformation hidden danger area, acquire master-slave LiDAR image data of LiDAR laser radar measurement and process the master-slave LiDAR image data to output geological disaster deformation data; A fourth output module is configured to acquire geological disaster point deformation data output by a ground deformation monitoring network based on the geological disaster deformation data.

8. A geohazard monitoring system along a pipeline, comprising: The application relates to a pipeline along line geological disaster monitoring method and a computer device thereof. The space-based monitoring platform comprises a synthetic aperture radar and satellite optical remote sensing; the aerial monitoring platform comprises unmanned aerial vehicle photogrammetry equipment and Li-DAR laser radar; the ground surface deformation three-dimensional monitoring network comprises a plurality of GNSS displacement monitoring devices and rainfall detection modules; the positions of the GNSS displacement monitoring devices are determined by geological disaster deformation data monitored and output by the aerial monitoring platform; The GNSS displacement monitoring devices are in communication connection; the GNSS displacement monitoring devices, the synthetic aperture radar, the satellite optical remote sensing, the unmanned aerial vehicle photogrammetry equipment and the Li-DAR laser radar are all in communication connection with the control module. The computer device comprises:

9. A computer device, comprising: at least one processor; and a memory in communication connection with the at least one processor; wherein the memory stores a computer program which can be executed by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the pipeline along line geological disaster monitoring method in any one of claims 1-6. The computer readable storage medium stores computer instructions for enabling the processor to execute the pipeline along line geological disaster monitoring method in any one of claims 1-6.

10. A computer-readable storage medium, characterized in that, ​

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