Ground surface settlement and crack monitoring method, device and equipment for high and cold mining area and storage medium

Abstract

The embodiment of the invention provides a ground surface settlement and crack monitoring method, device and equipment for a high and cold mining area and a storage medium. The method comprises the following steps: acquiring original time sequence image data, point cloud data, multi-stage elevation data ground surface radar image data and an online map of a research area; determining a first planar settlement amount based on the original time sequence image data; determining a second planar settlement amount based on the point cloud data; carrying out fusion treatment on the first planar settlement volume and the second planar settlement volume to obtain an initial planar overall settlement volume; comparing the multi-stage elevation data to obtain a point settlement amount; performing data verification on the initial planar overall settlement volume based on the point settlement volume and preset settlement volume data to obtain a target planar overall settlement volume; performing combined processing on the target planar overall settlement volume and an online map to obtain a two-dimensional settlement image; extracting surface morphological features from the surface radar image data; and based on the surface morphological characteristics and the two-dimensional settlement image, determining a geological disaster-prone risk area.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of surface morphology monitoring, and relate to but are not limited to a method, device, equipment and storage medium for monitoring surface subsidence and cracks in a high-cold mining area. Background Art

[0002] In recent years, coal still occupies a dominant position in my country's energy structure and is also an important basic industry of the national economy. After coal mining, the rock mass around the goaf becomes unstable and moves, which will induce geological problems such as surface subsidence, landslides and water disasters. Nowadays, with the continuous deepening of mining depth, these problems are becoming increasingly prominent. In order to effectively implement ecological restoration in mining areas, multi-dimensional data fusion monitoring is necessary.

[0003] In alpine mining areas, especially those in high-altitude areas such as the Qilian Mountains, the topography is particularly unique, with alpine swamps and alpine meadow vegetation widely distributed. Most of these vegetation are short and dense, which can easily block ground points, posing a considerable challenge to monitoring. When using drone technology for monitoring, despite precise coordinate alignment, the data obtained is mainly a digital surface model (DSM). However, DSM data is often mixed with point cloud information of vegetation, which undoubtedly interferes with the precise monitoring of ground point elevation. For the monitoring of surface subsidence in alpine mining areas, it is crucial to accurately obtain the elevation of ground points.

[0004] In terms of data processing, InSAR (Synthetic Aperture Radar Interferometry) technology also faces the problem of temporal and spatial incoherence in high-altitude mining areas. The presence of vegetation such as cultivated land, grassland and woodland will significantly reduce the coherence of data processing, resulting in a sharp decrease in the number of high-coherence points, which in turn causes data gaps in some areas, bringing great inconvenience to monitoring work.

[0005] What is even more difficult is that due to the dense vegetation in the high-altitude mining areas, the surface cracks are often severely covered. Modern technical means such as drone technology, 3D laser scanning technology and high-resolution digital photos are unable to detect and identify the development of cracks inside the rock mass at a meter-scale in the field. If manual measurement technology is used, it is difficult to deeply explore the impact of relevant rock properties on the development of cracks, which undoubtedly adds more difficulty and uncertainty to the monitoring and early warning of surface subsidence in mining areas. Summary of the invention

[0006] The embodiments of the present application provide a method, device, equipment and storage medium for monitoring surface subsidence and cracks in a high-altitude mining area.

[0007] The technical solution of the embodiment of the present application is implemented as follows:

[0008] In a first aspect, an embodiment of the present application provides a method for monitoring ground subsidence and cracks in a high-cold mining area, the method comprising:

[0009] Obtain original time series image data, point cloud data, multi-period elevation data, surface radar image data and online maps of the study area;

[0010] Determine a first planar settlement amount based on the original time series image data; determine a second planar settlement amount based on the point cloud data;

[0011] The first planar sedimentation amount and the second planar sedimentation amount are merged to obtain an initial planar overall sedimentation amount;

[0012] Comparing the multiple phases of elevation data to obtain point-like settlement;

[0013] Based on the point-shaped settlement amount and the preset settlement amount data, the initial planar overall settlement amount is data verified to obtain the target planar overall settlement amount;

[0014] Combining the target planar overall settlement amount with the online map to obtain a two-dimensional settlement image;

[0015] Extracting surface morphological features from the surface radar image data;

[0016] Based on the surface morphological features and the two-dimensional settlement image, the risk areas prone to geological disasters are determined.

[0017] In a second aspect, an embodiment of the present application provides a surface subsidence and crack monitoring device in a high-cold mining area, the device comprising:

[0018] An acquisition module is used to acquire original time series image data, point cloud data, multi-period elevation data, surface radar image data and online maps of the study area; a determination module is used to determine a first planar settlement based on the original time series image data; and a second planar settlement based on the point cloud data; a fusion module is used to fuse the first planar settlement and the second planar settlement to obtain an initial planar overall settlement; a comparison module is used to compare the multi-period elevation data to obtain a point settlement; a verification module is used to verify the initial planar overall settlement based on the point settlement and preset settlement data to obtain a target planar overall settlement; a combination module is used to combine the target planar overall settlement with the online map to obtain a two-dimensional settlement image; an extraction module is used to extract surface morphological features from the surface radar image data; the determination module is also used to determine a risk area prone to geological disasters based on the surface morphological features and the two-dimensional settlement image.

[0019] In some embodiments, the determination module is also used to pair the original time series image data to obtain a constructed small baseline set; filter the small baseline set to obtain a filtered differential interference map; phase unwrap the filtered differential interference map to obtain surface deformation characteristics; perform multiple inversion processes on the surface deformation information to obtain surface deformation results; and geocode the surface deformation results to obtain the first planar settlement amount.

[0020] In some embodiments, the determination module is also used to perform noise reduction filtering on the point cloud data based on a preset adaptive local filtering threshold method to obtain filtered point cloud data; and compare multiple periods of DEM data formed based on the filtered point cloud data to obtain the second planar settlement amount.

[0021] In some embodiments, the determination module is also used to obtain initial ground points from the point cloud data based on a preset cloth simulation algorithm; perform interpolation and fitting processing on the initial ground points in sequence to obtain a rough terrain surface; calculate the average value of the horizontal slope change between each point in the rough terrain surface and multiple nearest points to obtain the local slope change rate of the point cloud; based on the local slope change rate of the point cloud, calculate the local filtering threshold of each point to obtain filtered point cloud data.

[0022] In some embodiments, the combining module is further used to align the coordinates of the target planar overall settlement amount with the online map, and use the online map as a base to attach the settlement amount to the image to obtain the two-dimensional settlement image.

[0023] In some embodiments, the determination module is also used to set multiple geological hazard indicators that reflect the degree of ground subsidence disaster; the multiple geological hazard indicators are dimensionlessly processed to obtain quantitative data of geological hazard indicators; based on a preset entropy weight method, weight information is assigned to the quantitative data of geological hazard indicators, and based on a preset weighted summation algorithm, the subsidence hazard of the study area is evaluated to obtain the subsidence hazard results; the subsidence hazard results are graded to obtain the geological disaster-prone risk area.

[0024] In a third aspect, an embodiment of the present application provides a surface subsidence and crack monitoring device for a high-altitude mining area, comprising: a memory for storing executable instructions; and a processor for executing the executable instructions stored in the memory to implement the above-mentioned surface subsidence and crack monitoring method for a high-altitude mining area.

[0025] In a fourth aspect, an embodiment of the present application provides a computer program product or a computer program, which includes executable instructions, and the executable instructions are stored in a computer-readable storage medium; wherein, the processor for surface subsidence and crack monitoring in high-altitude mining areas reads the executable instructions from the computer-readable storage medium, and when executing the executable instructions, the above-mentioned surface subsidence and crack monitoring method in high-altitude mining areas is implemented.

[0026] In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium storing executable instructions for causing a processor to execute the executable instructions to implement the above-mentioned method for monitoring surface subsidence and cracks in high-cold mining areas.

[0027] The surface settlement and crack monitoring method, device, equipment and storage medium for high-cold mining areas provided in the embodiments of the present application obtain the original time series image data, point cloud data, multi-period elevation data and surface radar image data of the study area; determine the first planar settlement based on the original time series image data; determine the second planar settlement based on the point cloud data; fuse the first planar settlement and the second planar settlement to obtain an initial planar overall settlement; compare the multi-period elevation data to obtain the point settlement; verify the initial planar overall settlement based on the point settlement and preset settlement data to obtain the target planar overall settlement; combine the target planar overall settlement with the online map to obtain a two-dimensional settlement image; extract surface morphological features from the surface radar image data; and determine the risk area prone to geological disasters based on the surface morphological features and the two-dimensional settlement image. In this way, the present application realizes the multi-level three-dimensional monitoring of surface subsidence and cracks by integrating air, space and land, and uses the point cloud data formed by the UAV laser radar scanning to make up for the missing low-coherence point data in the InSAR data; for the influence of the alpine swamp and alpine meadow vegetation in the alpine mining area, a point cloud filtering algorithm with an adaptive local filtering threshold is used to filter out noise points and separate ground points from non-ground points; for the problem of limited ability of UAVs to receive RTK signals in alpine mining areas, the static data of the base station is used to assist in solving the UAV data to improve the accuracy of the solution; for the geographical characteristics of dense vegetation in the alpine mining area, ground penetrating radar is used to realize geophysical non-destructive testing, that is, the development of internal cracks in the rock mass at the meter-scale in the field is obtained, and the influence of related rock properties can be explored; the four technologies of InSAR, UAV aerial survey, RTK elevation measurement and ground penetrating radar are combined to realize the fusion of data at different levels, which improves the precision and accuracy of predicting risk areas prone to geological disasters. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a structural schematic diagram of a surface subsidence and crack monitoring system for a high-cold mining area provided in an embodiment of the present application;

[0029] Figure 2 It is a flow chart of a method for monitoring ground subsidence and cracks in a high-cold mining area provided in an embodiment of the present application;

[0030] Figure 3 It is a structural schematic diagram of the air-space-ground surface subsidence and crack monitoring technology adapted to the geographical characteristics of high-cold mining areas provided by the embodiment of the present application;

[0031] Figure 4 It is a flow chart of the air-ground-integrated mine settlement monitoring method provided in the embodiment of the present application;

[0032] Figure 5 It is a flow chart of a point cloud filtering algorithm with an adaptive local filtering threshold provided in an embodiment of the present application;

[0033] Figure 6 It is a schematic diagram of the composition structure of the surface settlement and crack monitoring device for high-cold mining areas provided in an embodiment of the present application;

[0034] Figure 7 It is a schematic diagram of the composition structure of the surface subsidence and crack monitoring equipment in the high-cold mining area provided in the embodiment of the present application. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application.

[0036] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict. Unless otherwise defined, all technical and scientific terms used in the embodiments of the present application have the same meaning as those commonly understood by those skilled in the art to which the embodiments of the present application belong. The terms used in the embodiments of the present application are only for the purpose of describing the embodiments of the present application and are not intended to limit the present application.

[0037] The following describes an exemplary application of the surface settlement and crack monitoring equipment of the high-cold mining area of ​​the embodiment of the present application. The surface settlement and crack monitoring equipment of the high-cold mining area provided by the embodiment of the present application can be implemented as a terminal or a server. In one implementation, the surface settlement and crack monitoring equipment of the high-cold mining area provided by the embodiment of the present application can be implemented as various types of terminals such as laptops, tablet computers, desktop computers, mobile devices, etc.; in another implementation, the surface settlement and crack monitoring equipment of the high-cold mining area provided by the embodiment of the present application can also be implemented as a server, wherein the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and big data and artificial intelligence platforms. The terminal and the server can be directly or indirectly connected by wired or wireless communication, which is not limited in the embodiment of the present application. Below, the exemplary application of the surface settlement and crack monitoring equipment of the high-cold mining area when it is implemented as a server will be described.

[0038] join Figure 1 , Figure 1 It is a structural schematic diagram of the surface settlement and crack monitoring system for high-cold mining areas provided in the embodiment of the present application. In order to realize the monitoring of surface settlement and cracks in high-cold mining areas, the embodiment of the present application can provide a surface settlement and crack monitoring platform for high-cold mining areas, and the surface settlement and crack monitoring platform for high-cold mining areas can be implemented as a surface settlement and crack monitoring application for high-cold mining areas. The surface settlement and crack monitoring system 10 for high-cold mining areas provided in the embodiment of the present application includes a terminal 110, a network 120 and a server 130, wherein the server 130 is a server for the surface settlement and crack monitoring application for high-cold mining areas. The server 130 can constitute the surface settlement and crack monitoring equipment for high-cold mining areas in the embodiment of the present application. The terminal 110 is connected to the server 130 via the network 120, and the network 120 can be a wide area network or a local area network, or a combination of the two.

[0039] In some embodiments, please refer to Figure 1When monitoring the surface settlement and cracks in the high-cold mining area, the terminal 110 sends the original time series image data, point cloud data, multi-period elevation data, surface radar image data and online map of the study area to the server 130 through the network 120. The server 130 determines the first planar settlement based on the original time series image data; determines the second planar settlement based on the point cloud data; fuses the first planar settlement with the second planar settlement to obtain the initial planar overall settlement; compares the multi-period elevation data to obtain the point settlement; verifies the initial planar overall settlement based on the point settlement and the preset settlement data to obtain the target planar overall settlement; combines the target planar overall settlement with the online map to obtain a two-dimensional settlement image; extracts the surface morphological features from the surface radar image data; and finally, determines the risk area prone to geological disasters based on the surface morphological features and the two-dimensional settlement image. After obtaining the geological disaster prone risk area, the server 130 sends the geological disaster prone risk area to the terminal through the network 120, thereby realizing the monitoring of surface subsidence and cracks in the high-cold mining area.

[0040] The surface settlement and crack monitoring method for high-cold mining areas provided in the embodiment of the present application can also be implemented based on a cloud platform and through cloud technology. For example, the server 130 can be a cloud server. The two-dimensional settlement image of the study area and the risk area prone to geological disasters are determined by the cloud server.

[0041] It should be noted here that cloud technology refers to a hosting technology that unifies hardware, software, network and other resources in a wide area network or local area network to achieve data computing, storage, processing and sharing. Cloud technology is a general term for network technology, information technology, integration technology, management platform technology, application technology, etc. based on the cloud computing business model. It can form a resource pool that can be used on demand and is flexible and convenient. Cloud computing technology will become an important support. The backend services of the technical network system require a large amount of computing and storage resources, such as video websites, picture websites and more portal websites. With the high development and application of the Internet industry, in the future, each item may have its own identification mark, and all need to be transmitted to the backend system for logical processing. Data of different levels will be processed separately. All kinds of industry data require strong system backing support, which can only be achieved through cloud computing.

[0042] The present application embodiment provides a method for monitoring ground subsidence and cracks in a high-cold mining area. Figure 2 , Figure 2 This is a schematic diagram of the process of surface subsidence and crack monitoring in high-cold mining areas provided by the embodiment of the present application Figure 1 , will combine Figure 2 The steps shown are explained.

[0043] Step S210, obtaining original time series image data, point cloud data, multi-period elevation data, surface radar image data and online maps of the study area.

[0044] In some embodiments, the study area refers to an area where the rock mass around the goaf is unstable after coal mining.

[0045] In some embodiments, the original time series image data is obtained through sky monitoring technology, that is, obtained based on satellite optical images. The original time series image data refers to a set of remote sensing image data of the same high-cold mining area study area obtained at different time points. These data are arranged in chronological order and can reflect the geological changes of the high-cold mining area study area over time.

[0046] In some embodiments, the point cloud data is collected based on sky monitoring technology, that is, obtained through airborne lidar scanning.

[0047] In some embodiments, multi-period elevation data refers to data obtained through surface monitoring technology, that is, the crack location is analyzed based on images obtained by drone photography, points are evenly laid out in the direction of crack development, and elevation measurement is performed using real-time kinematic (RTK).

[0048] In some embodiments, before acquiring the surface radar image data, during the forward process, the transmitting antenna in the ground penetrating radar host continuously emits high-frequency pulse electromagnetic waves to the surface cracks. When the electromagnetic waves encounter cracks with changes in dielectric constant and conductivity, the electromagnetic waves will be reflected back. The reflected electromagnetic waves will be received by the receiving antenna in the radar host and reflected on the host screen, thereby realizing the detection, identification and positioning of different rock types.

[0049] In some embodiments, the surface radar image data is acquired through underground monitoring technology, that is, acquired based on ground penetrating radar scanning.

[0050] In some embodiments, before acquiring the original time series image data, point cloud data, multi-period elevation data and surface radar image data of the study area, the server can perform atmospheric correction and geometric correction processing on the original time series image data, point cloud data, multi-period elevation data and surface radar image data of the high-cold mining area to improve the accuracy of the data. Here, atmospheric correction means that the total radiation brightness of the ground target finally measured by the sensor is not a reflection of the true reflectivity of the surface, which includes the radiation error caused by atmospheric absorption, especially scattering. Atmospheric correction is the process of eliminating these radiation errors caused by atmospheric influences and inverting the true surface reflectivity of the ground object. Geometric correction refers to the use of a series of mathematical models to correct and eliminate the deformation caused by factors such as deformation of photographic materials, distortion of objective lenses, atmospheric refraction, curvature of the earth, rotation of the earth, and terrain undulations during remote sensing imaging, resulting in the geometric position, shape, size, orientation and other characteristics of the objects on the original image being inconsistent with the expression requirements in the reference system.

[0051] Step S220, determining a first planar settlement amount based on the original time series image data; and determining a second planar settlement amount based on the point cloud data.

[0052] In some embodiments, the first planar settlement amount refers to the settlement amount calculated for a planar study area in a high-cold mining area based on SBAR-InSAR technology.

[0053] In some embodiments, the second surface settlement refers to a supplementary surface settlement calculated for the surface study area of ​​the high-cold mining area based on the comparison of multi-period point cloud data. Here, the first surface settlement and the second surface settlement are expressed as the vertical drop distance or height change of the surface of the area relative to a reference plane.

[0054] Step S230: fusing the first planar settlement amount and the second planar settlement amount to obtain an initial planar overall settlement amount.

[0055] Step S240, comparing the multiple phases of elevation data to obtain point settlement.

[0056] Step S250: Based on the point-like settlement amount and the preset settlement amount data, the initial planar overall settlement amount is data verified to obtain a target planar overall settlement amount.

[0057] Step S260: combining the target planar overall settlement amount with the online map to obtain a two-dimensional settlement image.

[0058] Step S270: extracting surface morphological features from the surface radar image data.

[0059] Step S280, determining the risk area for geological disasters based on the surface morphological features and the two-dimensional settlement image.

[0060] UAV aerial survey technology: (1) In view of the geographical characteristics of the high-cold mining area, the airborne LiDAR technology is used to obtain a three-dimensional laser scanning surface model, and a specific noise reduction filter method is used to remove the low vegetation in the high-cold mining area, retaining only the ground points. In this way, the true elevation value of the ground point can be obtained. (2) In view of the problem that InSAR data is blank in some areas, the use of UAV aerial survey technology can make up for the blank elevation values ​​in these areas. (3) In view of the limited ability of UAVs to receive RTK signals in high-cold mining areas, the GPS base station is used to obtain static data, and the combined solution method with the POS data collected by the UAV lidar is used to improve the coordinate accuracy.

[0061] Ground penetrating radar technology: (1) In view of the dense vegetation in the high-altitude mining area, ground penetrating radar realizes non-destructive geophysical detection and effectively detects the distribution of macroscopic cracks inside the rock mass. (2) The detection speed is fast, the detection process is continuous, and the operation is convenient and flexible. When scanning, you only need to keep moving in the detection direction. When the radar is moving, the host screen can display the image data.

[0062] The surface settlement and crack monitoring method, device, equipment and storage medium for high-cold mining areas provided in the embodiments of the present application obtain the original time series image data, point cloud data, multi-period elevation data and surface radar image data of the study area; determine the first planar settlement based on the original time series image data; determine the second planar settlement based on the point cloud data; fuse the first planar settlement and the second planar settlement to obtain an initial planar overall settlement; compare the multi-period elevation data to obtain the point settlement; verify the initial planar overall settlement based on the point settlement and preset settlement data to obtain the target planar overall settlement; combine the target planar overall settlement with the online map to obtain a two-dimensional settlement image; extract surface morphological features from the surface radar image data; and determine the risk area prone to geological disasters based on the surface morphological features and the two-dimensional settlement image. In this way, the present application realizes the multi-level three-dimensional monitoring of surface subsidence and cracks by integrating air, space and land, and uses the point cloud data formed by the UAV laser radar scanning to make up for the missing low-coherence point data in the InSAR data; for the influence of the alpine swamp and alpine meadow vegetation in the alpine mining area, a point cloud filtering algorithm with an adaptive local filtering threshold is used to filter out noise points and separate ground points from non-ground points; for the problem of limited ability of UAVs to receive RTK signals in alpine mining areas, the static data of the base station is used to assist in solving the UAV data to improve the accuracy of the solution; for the geographical characteristics of dense vegetation in the alpine mining area, ground penetrating radar is used to realize geophysical non-destructive testing, that is, the development of internal cracks in the rock mass at the meter-scale in the field is obtained, and the influence of related rock properties can be explored; the four technologies of InSAR, UAV aerial survey, RTK elevation measurement and ground penetrating radar are combined to realize the fusion of data at different levels, which improves the precision and accuracy of predicting risk areas prone to geological disasters.

[0063] In some embodiments, the above step S220 can also be implemented through steps S2211 to S2215:

[0064] Step S2211, pairing the original time series image data to obtain a constructed small baseline set.

[0065] Step S2212: filter the small baseline set to obtain a filtered differential interference pattern.

[0066] Step S2213, performing phase unwrapping on the filtered differential interference pattern to obtain a time series deformation feature.

[0067] Step S2214, performing multiple inversion processes on the time series deformation features to obtain time series deformation results.

[0068] Step S2215: geocoding the time series deformation results to obtain the first planar settlement.

[0069] In some embodiments, step S220 may be implemented by steps S2221 to S2222:

[0070] Step S2221: Based on a preset adaptive local filtering threshold method, the point cloud data is subjected to noise reduction filtering to obtain filtered point cloud data.

[0071] Step S2222: Compare the multi-period DEM data formed based on the filtered point cloud data to obtain the second planar settlement amount.

[0072] In some embodiments, the above-mentioned step S2221 can be implemented in the following manner: based on a preset cloth simulation algorithm, obtaining an initial ground point from the point cloud data; performing interpolation processing and fitting processing on the initial ground point in turn to obtain a rough terrain surface; calculating the average value of the horizontal slope change between each point in the rough terrain surface and multiple nearest points to obtain the local slope change rate of the point cloud; based on the local slope change rate of the point cloud, calculating the local filtering threshold of each point to obtain filtered point cloud data.

[0073] In some embodiments, the above step S260 is implemented in the following manner: aligning the coordinates of the target planar overall settlement amount with the online map, and using the online map as a base, attaching the settlement amount to the image to obtain the two-dimensional settlement image.

[0074] In some embodiments, the above step S280 can be implemented by following the steps S281 to S284:

[0075] Step S281, setting a plurality of geological hazard indicators reflecting the degree of ground subsidence disaster.

[0076] Step S282, dimensionlessly processing the plurality of geological hazard indicators to obtain quantitative data of the geological hazard indicators.

[0077] Step S283, based on the preset entropy weight method, assign weight information to the quantitative data of the geological hazard indicators, and based on the preset weighted summation algorithm, evaluate the subsidence hazard of the study area to obtain the subsidence hazard result.

[0078] Step S284, classifying the settlement hazard results into different levels to obtain the geological disaster prone risk areas.

[0079] The following is an explanation of an exemplary application of the embodiments of the present application in a practical application scenario.

[0080] See also Figure 3The present invention discloses a schematic diagram of the structure of an air-space-ground surface subsidence and crack monitoring technology that adapts to the geographical characteristics of high-cold mining areas. The method mainly obtains surface overall data, point verification data and crack data, mainly to reflect the five geological hazard indicators of settlement, crack depth, location, shape and size, and deformation trend.

[0081] The raw data of space monitoring technology mainly comes from satellite optical images 1. InSAR technology 2 is used to perform time series analysis on the images to obtain time series deformation variables, which are stored in the preliminary surface settlement database 3. Due to the influence of the geographical characteristics of the high-altitude mining area, it is easy to cause time and space incoherence, which makes it impossible to monitor effective data in some areas. Sky monitoring technology is more autonomous, and three-dimensional laser scanning can easily obtain surface point cloud data, without the problem of time and space incoherence. Therefore, sky monitoring technology is used as a supplement. This technology mainly uses airborne laser radar scanning 4 to obtain point cloud data in the study area, which is the surface elevation. In high-altitude areas, the altitude is usually high, and the ability of drones to receive RTK signals is limited. Therefore, it is necessary to establish a static base station to receive the Global Positioning System (GPS) signal and align the laser radar with the static base station signal. After that, point cloud data processing is performed 5. By comparing the point cloud data at two time points before and after, the change in surface settlement can be obtained, and the settlement is stored in the settlement database missing due to geographical reasons 6. By combining the data obtained from space monitoring technology with the data from sky monitoring technology, a surface precipitation database 7 can be obtained.

[0082] The surface monitoring technology mainly uses RTK8 as a measurement tool. By measuring the elevation data of multiple points, the multi-period elevation data is compared to obtain the settlement of multiple points. The settlement is saved in the point settlement verification database 9. The point verification data is mainly the surface overall data to verify the accuracy of settlement monitoring. The surface overall data with good verification effect and the online map can be used to produce a two-dimensional settlement image 10. The two-dimensional settlement image 10 can reflect the settlement amount, location and deformation trend.

[0083] Ground penetrating radar can realize non-destructive testing of geophysical prospecting and effectively detect the distribution of macro cracks inside the rock mass. Due to the dense vegetation in the high-cold mining area, the surface cracks are seriously covered. Therefore, the underground monitoring technology mainly uses ground penetrating radar to scan the crack area in the study area 11, and then obtain the crack depth, morphology and damage characteristics. The obtained information is stored in the crack morphology database 12. The database 12 is combined with the two-dimensional settlement image 10 to uniformly quantify and grade the geological hazard indicators. According to the entropy method, the results of the quantitative processing of each settlement factor are calculated separately, and the corresponding weight information is assigned. Then, the weighted sum algorithm is used to evaluate the settlement hazard in the study area. After superposition analysis, the risk zoning of geological disasters can be obtained 13.

[0084] The embodiment of the present application provides an air-ground-integrated mine pit subsidence monitoring method, which is executed by a server. Figure 4 is a flow chart of the air-ground integrated mine settlement monitoring method provided in the embodiment of the present application, such as Figure 4 As shown, it will be combined Figure 4 The flowchart is shown for explanation.

[0085] Step 1: Obtain the longitude and latitude of the study area to be explored.

[0086] Step 2: Obtain original time series images containing surface deformation information.

[0087] Step 3: InSAR time series analysis method uses SBAS-InSAR to pair the original time series images of the study area and construct a small baseline set. Then use filtering to obtain the filtered differential interferogram, and then perform phase unwrapping to calculate the correct deformation information. After two inversions, the ground deformation results are obtained. The inverted deformation results are converted from the radar coordinate system to the geodetic coordinate system. The SBAS inversion results are geocoded, and the coded images are processed to obtain the surface settlement amount.

[0088] Step 4: Process the encoded tiff image in ARCGIS software to obtain the surface settlement amount.

[0089] Step 5: In high-altitude cold areas, the altitude is usually high, and the drone's ability to receive RTK signals is limited. Therefore, a static base station is established to receive GPS signals to improve the coordinate accuracy of the drone, and then the laser radar is turned on and aligned with the static base station and waits for a few minutes. When the static base station and the drone-mounted laser radar receive signals at the same time, use the planned route to fly and scan. After the flight, do not turn off the static base station and align it with the drone-mounted laser radar for a few more minutes.

[0090] Step 6: Since most alpine mining areas are covered with alpine swamps and alpine meadows, noise points are usually detected in the point cloud data, which affects the monitoring of ground points. This step is mainly used to remove noise points and separate ground points from non-ground points. Figure 5 This step uses the point cloud filtering algorithm with adaptive local filtering threshold to perform noise reduction filtering on the point cloud data:

[0091] (1) Use the classic cloth simulation filter (CSF) algorithm to obtain the initial ground points of the point cloud data.

[0092] (2) The initial ground points obtained in the previous step are interpolated and fitted. The point cloud data obtained by interpolation and fitting is the rough terrain surface.

[0093] (3) Calculate the average value of the horizontal slope change between each point in the point cloud data and its n nearest points. The average value obtained at this time is the local slope change rate of the point cloud.

[0094] (4) Calculate the local filtering threshold of each point based on the local slope change rate of each point. At this point, the point cloud data can be filtered to complete the classification task of ground points and non-ground points.

[0095] Step 7: Deformation comparison of point cloud data. Compare the DEMs formed by multiple periods of point cloud data.

[0096] Step 8: Subtract the DEM data from multiple periods and export the comparison data, which is the supplementary data for the surface settlement.

[0097] Step 9: The preliminary surface settlement data obtained by the sky monitoring technology and the supplementary surface settlement data obtained by the sky monitoring technology are combined to obtain the overall surface data, that is, to achieve large-scale settlement monitoring around the mine pit.

[0098] Step 10: Lay out leveling points, use the images obtained from drone photography to analyze the location of the cracks, evenly lay out points in the direction of crack development, and use RTK to measure the elevation. Record the coordinates of each point and the corresponding elevation value. Regularly measure the elevation of the leveling points to obtain the amount of settlement.

[0099] Step 11: Subtract the elevation values ​​obtained in several periods to obtain the settlement deformation amount during the measurement period.

[0100] Step 12: Use the point-like settlement amount to find the corresponding position of the coordinate point in the surface settlement amount, and compare and verify the settlement amounts of the two.

[0101] Step 13: Assemble the ground penetrating radar, carefully install the battery, host, transmitter and receiver, and move forward along the direction of the deployed points. Always pay attention to the radar image data during the scanning process.

[0102] Step 14: Read image data. During the forward movement, the transmitting antenna in the ground penetrating radar host continuously emits high-frequency pulse electromagnetic waves to the surface cracks. When the electromagnetic waves encounter cracks with changed dielectric constant and conductivity, they will be reflected back. The reflected electromagnetic waves will be received by the receiving antenna in the radar host and reflected on the host screen, thereby realizing the detection, identification and positioning of different lithologies.

[0103] Step 15: Analyze the GPR radar images and record the crack morphology and damage characteristics.

[0104] Step 16: Use ARCGIS software to align the online map with the coordinates of the overall surface settlement, use the online map as the base, and attach the settlement to the image to obtain a settlement image.

[0105] Step 17: The settlement location, the trend of settlement, the cumulative settlement, the crack depth and the crack morphology can reflect the severity of the ground settlement disaster. Use the "Extract Multi-Values ​​to Points" in ARCGIS to extract the values ​​of other indicators using the cumulative settlement. The five geological hazard indicators are dimensionlessly processed and converted into quantitative data. The entropy weight method is used to assign corresponding weight information, and the weighted sum algorithm is used to evaluate the settlement hazard in the study area. The hazard is divided into five levels: very low, low, medium, relatively high, and high, so as to make a risk-prone zoning map in ARCGIS software.

[0106] In view of the unique geographical and geomorphological features of the alpine mining area, this application adopts corresponding technical means to obtain complete data on settlement and crack development around the mine pit; in view of the problem that InSAR technology is easy to lose coherence in alpine swamp and alpine meadow vegetation environments, drones equipped with three-dimensional laser scanning technology are used to compensate for the data of the lost coherence area; in view of the disadvantage that the alpine swamp and alpine meadow vegetation in the alpine mining area affect the monitoring effect, a point cloud filtering algorithm is used to distinguish between ground points and non-ground points; in view of the problem that the ability of drones to receive RTK signals in alpine mining areas is limited, base station static data is used to assist in solving drone data and obtain more accurate results; in view of the obvious crack areas in the alpine mining area, RTK is used to measure the elevation, and ground penetrating radar is used to monitor the crack depth and overburden damage characteristics of the alpine mining area, which can comprehensively analyze the surface subsidence and crack problems in the alpine mining area, and finally the entropy weight method is used to divide the risk areas prone to geological disasters, in order to provide a solution for surface subsidence monitoring in alpine mining areas.

[0107] Figure 6 : is a schematic diagram of the composition structure of the surface settlement and crack monitoring device for high-cold mining areas provided in the embodiment of the present application, such as Figure 6As shown, the surface settlement and crack monitoring device 600 in the high-cold mining area includes: an acquisition module 601, which is used to acquire the original time series image data, point cloud data, multi-period elevation data, surface radar image data and online map of the study area; a determination module 602, which is used to determine the first planar settlement amount based on the original time series image data; and determine the second planar settlement amount based on the point cloud data; a fusion module 603, which is used to fuse the first planar settlement amount and the second planar settlement amount to obtain an initial planar overall settlement amount; a comparison module 604, which is used to compare the multi-period elevation data, the first planar settlement amount and the second planar settlement amount. The elevation data is compared to obtain the point settlement; the verification module 605 is used to verify the initial surface overall settlement based on the point settlement and the preset settlement data to obtain the target surface overall settlement; the combination module 606 is used to combine the target surface overall settlement with the online map to obtain a two-dimensional settlement image; the extraction module 607 is used to extract surface morphological features from the surface radar image data; the determination module 602 is also used to determine the risk area prone to geological disasters based on the surface morphological features and the two-dimensional settlement image.

[0108] In some embodiments, the determination module is also used to pair the original time series image data to obtain a constructed small baseline set; filter the small baseline set to obtain a filtered differential interference map; phase unwrap the filtered differential interference map to obtain surface deformation characteristics; perform multiple inversion processes on the surface deformation information to obtain surface deformation results; and geocode the surface deformation results to obtain the first planar settlement amount.

[0109] In some embodiments, the determination module is also used to perform noise reduction filtering on the point cloud data based on a preset adaptive local filtering threshold method to obtain filtered point cloud data; and compare multiple periods of DEM data formed based on the filtered point cloud data to obtain the second planar settlement amount.

[0110] In some embodiments, the determination module is also used to obtain initial ground points from the point cloud data based on a preset cloth simulation algorithm; perform interpolation and fitting processing on the initial ground points in sequence to obtain a rough terrain surface; calculate the average value of the horizontal slope change between each point in the rough terrain surface and multiple nearest points to obtain the local slope change rate of the point cloud; based on the local slope change rate of the point cloud, calculate the local filtering threshold of each point to obtain filtered point cloud data.

[0111] In some embodiments, the combining module is further used to align the coordinates of the target planar overall settlement amount with the online map, and use the online map as a base to attach the settlement amount to the image to obtain the two-dimensional settlement image.

[0112] In some embodiments, the determination module is also used to set multiple geological hazard indicators that reflect the degree of ground subsidence disaster; the multiple geological hazard indicators are dimensionlessly processed to obtain quantitative data of geological hazard indicators; based on a preset entropy weight method, weight information is assigned to the quantitative data of geological hazard indicators, and based on a preset weighted summation algorithm, the subsidence hazard of the study area is evaluated to obtain the subsidence hazard results; the subsidence hazard results are graded to obtain the geological disaster-prone risk area.

[0113] It should be noted that the description of the device of the embodiment of the present application is similar to the description of the above method embodiment, and has similar beneficial effects as the method embodiment, so it is not repeated. For technical details not disclosed in the embodiment of the device, please refer to the description of the method embodiment of the present application for understanding.

[0114] It should be noted that in the embodiments of the present application, if the above-mentioned surface subsidence and crack monitoring method in the high-cold mining area is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application can be essentially or partly reflected in the form of a software product, which is stored in a storage medium and includes several instructions for a terminal to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a U disk, a mobile hard disk, a read-only memory (ROM), a magnetic disk or an optical disk. In this way, the embodiments of the present application are not limited to any specific combination of hardware and software.

[0115] The present application provides a surface subsidence and crack monitoring device in a high-cold mining area. Figure 7 Schematic diagram of the structure of the surface subsidence and crack monitoring equipment for high-cold mining areas provided in the embodiment of the present application, such as Figure 7 As shown, the surface subsidence and crack monitoring device 700 for high-cold mining areas at least includes: a processor 701 and a computer-readable storage medium 702 configured to store executable instructions, wherein the processor 701 generally controls the overall operation of the surface subsidence and crack monitoring device for high-cold mining areas. The computer-readable storage medium 702 is configured to store instructions and applications executable by the processor 701, and can also cache data to be processed or processed by each module in the processor 701 and the surface subsidence and crack monitoring device 700 for high-cold mining areas, which can be implemented through flash memory (FLASH) or random access memory (Random Access Memory, RAM).

[0116] The present application embodiment provides a storage medium storing executable instructions, wherein the executable instructions are stored. When the executable instructions are executed by a processor, the processor will execute the method provided by the present application embodiment, for example, Figure 2 The method shown.

[0117] In some embodiments, the storage medium can be a computer-readable storage medium, for example, a ferroelectric random access memory (FRAM), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a flash memory, a magnetic surface memory, an optical disk, or a compact disk read-only memory (CD-ROM), etc.; it can also be various devices including one or any combination of the above memories.

[0118] In some embodiments, executable instructions may be in the form of a program, software, software module, script or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a stand-alone program or as a module, component, subroutine or other unit suitable for use in a computing environment.

[0119] As an example, executable instructions may, but need not correspond to, a file in a file system, may be stored as part of a file storing other programs or data, for example, in one or more scripts in a HyperText Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files storing one or more modules, subroutines, or code portions). As an example, executable instructions may be deployed to be executed on one electronic device, or on multiple electronic devices located at one location, or on multiple electronic devices distributed at multiple locations and interconnected by a communication network.

[0120] The above is only an embodiment of the present application and is not intended to limit the protection scope of the present application. Any modifications, equivalent substitutions and improvements made within the spirit and scope of the present application are included in the protection scope of the present application.

[0121] It should be understood that "one embodiment" or "an embodiment" mentioned throughout the specification means that specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in various embodiments of the present application, the size of the sequence number of the above-mentioned processes does not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application. The above-mentioned sequence numbers of the embodiments of the present application are only for description and do not represent the advantages and disadvantages of the embodiments.

[0122] It should be noted that, in this article, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method or device. In the absence of further restrictions, an element defined by the statement "comprises one..." does not exclude the presence of other identical elements in the process, method, article or device including the element. In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic, for example, the division of the units is only a logical function division, and there may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed.

[0123] The above is only an implementation method of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for monitoring ground subsidence and cracks in a high-cold mining area, characterized in that: The method comprises: Obtain original time series image data, point cloud data, multi-period elevation data, surface radar image data and online maps of the study area; Determine a first planar settlement amount based on the original time series image data; determine a second planar settlement amount based on the point cloud data; The first planar sedimentation amount and the second planar sedimentation amount are merged to obtain an initial planar overall sedimentation amount; Comparing the multiple phases of elevation data to obtain point-like settlement; Based on the point-shaped settlement amount and the preset settlement amount data, the initial planar overall settlement amount is data verified to obtain the target planar overall settlement amount; Combining the target planar overall settlement amount with the online map to obtain a two-dimensional settlement image; Extracting surface morphological features from the surface radar image data; Based on the surface morphological features and the two-dimensional settlement image, the risk areas prone to geological disasters are determined.

2. The method according to claim 1, characterized in that The determining of a first planar settlement amount based on the original time series image data comprises: Pairing the original time series image data to obtain a constructed small baseline set; Performing filtering on the small baseline set to obtain a filtered differential interference pattern; performing phase unwrapping on the filtered differential interference pattern to obtain a time series deformation feature; Performing multiple inversion processes on the time series deformation characteristics to obtain time series deformation results; The time series deformation result is subjected to geocoding processing to obtain the first planar settlement amount.

3. The method according to claim 1, characterized in that The determining of a second planar settlement amount based on the point cloud data includes: Based on a preset adaptive local filtering threshold method, performing noise reduction filtering on the point cloud data to obtain filtered point cloud data; The multi-period DEM data formed based on the filtered point cloud data are compared to obtain the second planar settlement amount.

4. The method according to claim 3, characterized in that The method of performing noise reduction filtering on the point cloud data based on a preset adaptive local filtering threshold method to obtain filtered point cloud data includes: Based on a preset cloth simulation algorithm, obtaining an initial ground point from the point cloud data; Performing interpolation processing and fitting processing on the initial ground points in sequence to obtain a rough terrain surface; Calculate the average value of the slope change in the horizontal direction between each point and multiple nearest points in the rough terrain surface to obtain the local slope change rate of the point cloud; Based on the local slope change rate of the point cloud, a local filtering threshold of each point is calculated to obtain filtered point cloud data.

5. The method according to claim 1, characterized in that The step of combining the target planar overall settlement amount with the online map to obtain a two-dimensional settlement image includes: The target planar overall settlement amount is aligned with the online map, and the online map is used as a base, and the settlement amount is attached to the image to obtain the two-dimensional settlement image.

6. The method according to claim 1, characterized in that The step of determining the geological disaster prone risk area based on the surface morphological features and the two-dimensional settlement image includes: Set up multiple geological hazard indicators that reflect the extent of ground subsidence disasters; Perform dimensionless processing on the plurality of geological hazard indicators to obtain quantitative data of the geological hazard indicators; Based on the preset entropy weight method, weight information is assigned to the quantitative data of the geological hazard indicators, and based on the preset weighted summation algorithm, the subsidence hazard of the study area is evaluated to obtain the subsidence hazard result; The subsidence hazard results are classified into different levels to obtain the geological disaster prone risk areas.

7. A surface settlement and crack monitoring device for high-cold mining areas, characterized in that: The device comprises: The acquisition module is used to obtain the original time series image data, point cloud data, multi-period elevation data and surface radar image data of the study area; A determination module, configured to determine a first planar settlement amount based on the original time series image data; and determine a second planar settlement amount based on the point cloud data; A fusion module, used for fusing the first planar settlement amount and the second planar settlement amount to obtain an initial planar overall settlement amount; A comparison module is used to compare the multiple phases of elevation data to obtain point settlement; A verification module, used for performing data verification on the initial planar overall settlement based on the point settlement and preset settlement data to obtain a target planar overall settlement; A combining module is used to combine the target planar overall settlement amount with the online map to obtain a two-dimensional settlement image; An extraction module, used for extracting surface morphological features from the surface radar image data; The determination module is also used to determine the risk area prone to geological disasters based on the surface morphological characteristics and the two-dimensional settlement image.

8. A surface subsidence and crack monitoring device for high-cold mining areas, characterized in that: include: A memory for storing executable instructions; The processor is used to implement the surface subsidence and crack monitoring method in the high-cold mining area as described in any one of claims 1 to 6 when executing the executable instructions stored in the memory.

9. A computer-readable storage medium storing executable instructions for causing a processor to execute the executable instructions to implement the method for monitoring surface subsidence and cracks in a high-cold mining area as described in any one of claims 1 to 6.

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