A method and device for fusing construction of a surface subsidence basin
Patent Information
- Application Number
- CN202610960910.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-06-30
- Publication Date
- 2026-10-09
AI Technical Summary
融合过程对实测数据的依赖性较弱，且仅以InSAR和UAV数据自身为融合基础，缺乏高精度地面实测数据对二者适用区域的定量界定依据，导致融合边界的主观性较强、衔接区域的平滑处理不够精细
[0020]本申请提供一种地表沉陷盆地融合构建方法，采集InSAR监测数据和UAV监测数据，利用地表移动观测站获取高精度水准的测量数据作为基准，将目标工作面的沉陷盆划分为外盆地、内盆地及衔接过渡区，然后，针对衔接过渡区，采用加权融合策略进行数据融合与平滑处理；最后，将各区域数据进行拼接，构建沉陷盆地模型。可以克服InSAR与UAV监测数据融合时，适用区域划分主观性强、衔接区域处理粗糙，导致融合盆地精度低、连续性差的缺陷，实现InSAR监测数据与UAV监测数据的客观、精细化融合，提升沉陷盆地的构建精度与连续性。
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Abstract
Description
Technical Field
[0001] This application relates to the field of subsidence monitoring technology, and in particular to a method and apparatus for constructing integrated surface subsidence basins. Background Technology
[0002] Underground coal mining causes movement and deformation of overlying strata and the surface, leading to a series of geological disasters and environmental problems such as damage to buildings, destruction of aquifers, and land degradation. Accurate monitoring of the spatial distribution and evolution of surface subsidence basins in mining areas is a crucial foundation for coal mine safety production, geological disaster early warning, and ecological restoration of mining areas.
[0003] Currently, monitoring methods for surface subsidence in mining areas are mainly divided into two categories: traditional ground surveying and modern remote sensing surveying. Traditional methods include leveling, total station surveying, and Global Navigation Satellite System (GNSS) observation. Although the monitoring accuracy can reach the millimeter level, they suffer from problems such as long observation cycles, high labor intensity, and high costs. Moreover, they obtain discrete point data, making it difficult to acquire continuous surface deformation information. Among modern remote sensing methods, Interferometric Synthetic Aperture Radar (InSAR) can acquire deformation information over a large area with high spatial resolution, offering advantages such as all-weather, all-time operation, and low cost. However, it is prone to decoherence in areas with large gradient deformation in mining areas, leading to phase unwrapping errors and making it difficult to accurately extract large-scale deformation information. Unmanned Aerial Vehicle (UAV) photogrammetry technology has the advantages of high mobility and high efficiency, and can acquire large gradient deformation information in the center of the subsidence basin. However, its monitoring accuracy is relatively low for sub-centimeter-level minute deformations at the edge of the subsidence basin.
[0004] There have been some attempts at InSAR and UAV fusion in existing technologies. The fusion process is less dependent on the measured data and is based solely on the InSAR and UAV data themselves. It lacks high-precision ground measured data to quantitatively define the applicable areas of the two, resulting in a strong subjectivity of the fusion boundary and insufficient smoothing of the transition area.
[0005] Therefore, there is an urgent need for a method to integrate and construct surface subsidence basins, so as to achieve objective and refined integration of InSAR monitoring data and UAV monitoring data, and improve the accuracy and continuity of subsidence basin construction. Summary of the Invention
[0006] This application provides a method and apparatus for constructing a surface subsidence basin, which can achieve objective and refined fusion of InSAR monitoring data and UAV monitoring data, thereby improving the accuracy and continuity of the subsidence basin construction.
[0007] In a first aspect, this application provides a method for constructing a surface subsidence basin, the method comprising: Collect InSAR monitoring data, UAV monitoring data, and measurement data from surface mobile observation stations at the target working face; Based on the InSAR monitoring data, the UAV monitoring data, and the measurement data from the surface mobile observation station, the subsidence basin of the target working face is divided into an outer basin, an inner basin, and a transitional zone. The InSAR monitoring data and UAV monitoring data within the transition zone are weighted and fused to obtain fused data; The InSAR monitoring data of the outer basin, the UAV monitoring data of the inner basin, and the fused data of the connecting transition zone are stitched together to construct a subsidence basin model.
[0008] Optionally, based on the InSAR monitoring data, the UAV monitoring data, and the measurement data from the surface motion observation station, the subsidence basin of the target working face is divided into an outer basin, an inner basin, and a transitional zone, including: Based on the measurement data from the surface mobile observation station and the InSAR monitoring data, the maximum deformation threshold for reliable InSAR monitoring is determined; Based on the measurement data from the surface mobile observation station and the UAV monitoring data, determine the minimum deformation threshold for reliable monitoring of the UAV; Based on the maximum deformation threshold of the InSAR reliable monitoring and the minimum deformation threshold of the UAV reliable monitoring, the subsidence basin of the target working face is divided into an outer basin, an inner basin, and a transition zone.
[0009] Optionally, based on the measurement data from the surface mobile observation station and the InSAR monitoring data, the maximum deformation threshold for reliable InSAR monitoring is determined, including: Calculate the root mean square error between InSAR monitoring data from multiple monitoring points and measurement data from a surface mobile observation station; The maximum deformation value whose root mean square error is less than the first preset accuracy threshold is determined as the maximum deformation threshold for reliable InSAR monitoring.
[0010] Optionally, based on the measurement data from the surface motion observation station and the UAV monitoring data, a minimum deformation threshold for reliable UAV monitoring is determined, including: Calculate the root mean square error between UAV monitoring data from multiple monitoring points and measurement data from surface motion observation stations; The minimum deformation value for which the root mean square error is less than the second preset accuracy threshold is determined as the minimum deformation threshold for reliable UAV monitoring.
[0011] Optionally, based on the maximum deformation threshold reliably monitored by InSAR and the minimum deformation threshold reliably monitored by UAV, the subsidence basin of the target working face is divided into an outer basin, an inner basin, and a transitional zone, including: The area in the InSAR monitoring data where the subsidence is no greater than the maximum deformation threshold of the InSAR reliable monitoring is defined as the outer basin; the outer basin is the reliable monitoring range of the InSAR monitoring data. The area in the UAV monitoring data where the subsidence is not less than the minimum deformation threshold for reliable UAV monitoring is defined as the inner basin; the inner basin is the reliable monitoring range of the UAV monitoring data. The InSAR monitoring data is extracted to the subsidence boundary of the outer basin, and the UAV monitoring data is extracted to the subsidence boundary of the inner basin, forming a transition zone.
[0012] Optionally, the method further includes: if there is an overlap between the reliable monitoring range of the InSAR monitoring data and the reliable monitoring range of the UAV monitoring data, the overlapping area is included in the transition zone.
[0013] Optionally, the method further includes: if there is missing InSAR monitoring data or UAV monitoring data in the transition zone, a spatial interpolation method is used to fill in the missing data.
[0014] Optionally, the InSAR monitoring data and UAV monitoring data within the transition zone are weighted and fused to obtain fused data, including: For any target point within the transition zone, the first InSAR weight and the first UAV weight are determined based on the target point's deformation level estimate, the maximum deformation threshold for reliable InSAR monitoring, and the minimum deformation threshold for reliable UAV monitoring. Based on the first InSAR weight and the first UAV weight of the target point, the InSAR sinking value and the UAV sinking value of the target point are weighted and summed to obtain the fused sinking value of the target point.
[0015] Optionally, the InSAR monitoring data and UAV monitoring data within the transition zone are weighted and fused to obtain fused data, including: Based on the root mean square error of InSAR monitoring data in the outer basin and the root mean square error of UAV monitoring data in the inner basin, the second InSAR weight and the second UAV weight of each target point in the transition zone are determined. Based on the second InSAR weight and the second UAV weight of the target point, the InSAR sinking value and the UAV sinking value of the target point are weighted and summed to obtain the fused sinking value of the target point.
[0016] Secondly, this application also provides a surface subsidence basin integration and construction device, the device comprising: The data acquisition module is used to collect InSAR monitoring data, UAV monitoring data, and measurement data from surface mobile observation stations at the target working face; The region division module is used to divide the subsidence basin of the target working face into an outer basin, an inner basin, and a transition zone based on InSAR monitoring data, UAV monitoring data, and measurement data from surface mobile observation stations. The data fusion module is used to perform weighted fusion processing on the InSAR monitoring data and UAV monitoring data in the transition zone to obtain fused data; The model building module is used to stitch together InSAR monitoring data from the outer basin, UAV monitoring data from the inner basin, and fused data from the transition zone to construct a subsidence basin model.
[0017] Thirdly, this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the surface subsidence basin fusion construction method as described above.
[0018] Fourthly, this application also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the surface subsidence basin fusion construction method as described above.
[0019] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the surface subsidence basin fusion construction method as described above.
[0020] This application provides a method for constructing a surface subsidence basin by fusing InSAR and UAV monitoring data. High-precision leveling data obtained from surface mobile observation stations is used as a benchmark to divide the subsidence basin of the target working face into an outer basin, an inner basin, and a transitional zone. Then, for the transitional zone, a weighted fusion strategy is used for data fusion and smoothing. Finally, the data from each region are stitched together to construct a subsidence basin model. This method overcomes the shortcomings of InSAR and UAV monitoring data fusion, such as the strong subjectivity of applicable area division and coarse processing of transitional zones, which leads to low accuracy and poor continuity of the fused basin. It achieves objective and refined fusion of InSAR and UAV monitoring data, improving the accuracy and continuity of the subsidence basin construction. Attached Figure Description
[0021] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0022] Figure 1 A flowchart illustrating a method for constructing a surface subsidence basin, as provided in this application embodiment. Figure 1 ; Figure 2 A flowchart illustrating a method for constructing a surface subsidence basin, as provided in this application embodiment. Figure 2 ; Figure 3 A schematic diagram illustrating the regional division of a surface subsidence basin provided in this application embodiment; Figure 4 A schematic diagram of a three-dimensional visualization model of a subsidence basin provided in an embodiment of this application; Figure 5 A schematic diagram of a method and apparatus for constructing a surface subsidence basin provided in this application embodiment; Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation
[0023] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0024] All actions involving the acquisition of signal information or data in this application are carried out in accordance with the relevant data protection laws and policies of the country where the application is located, and with the authorization of the owner of the relevant device.
[0025] In the embodiments of this application, "multiple" refers to two or more. Terms such as "first" and "second" are used only for descriptive purposes and should not be construed as indicating or implying relative importance or order.
[0026] The surface subsidence basin fusion construction method provided in this application can be applied to remote monitoring in, but is not limited to, the following fields: subsidence monitoring of various metal / non-metal mining operations, high-speed railway and highway subgrade monitoring, and landslide and slope stability monitoring.
[0027] Figure 1 and Figure 2 This is a flowchart illustrating a method for constructing a surface subsidence basin, as provided in an embodiment of this application. The following is a summary of the process. Figure 1 and Figure 2 This application introduces the method for constructing surface subsidence basins, such as... Figure 1 As shown, the method includes the following steps: Step 110: Collect InSAR monitoring data, UAV monitoring data, and measurement data from the surface mobile observation station at the target working face.
[0028] The acquisition process includes collecting Interferometric Synthetic Aperture Radar (InSAR) monitoring data, Unmanned Aerial Vehicle (UAV) monitoring data, and measurement data from mobile surface observation stations at the target working face. InSAR monitoring data refers to time-series radar imagery acquired through spaceborne or airborne synthetic aperture radar systems, including Differential Interferometric Synthetic Aperture Radar (D-InSAR) monitoring data and / or Small Baseline Subset InSAR (SBAS-InSAR) monitoring data. UAV monitoring data refers to aerial imagery acquired by UAVs equipped with optical cameras or multispectral sensors. Measurement data from mobile surface observation stations refers to high-precision leveling data obtained using traditional geodetic surveying methods by deploying mobile surface observation stations in the main cross-section area of the working face. The spacing between the measurement points of the mobile surface observation stations is determined according to the measurement procedures for the monitoring environment based on the mining depth. While surface mobile observation stations provide the most accurate, technologically mature, and reliable measurement data, the acquired data is discrete point data that cannot cover the entire area. Furthermore, acquiring this data is labor-intensive, costly, and involves long observation cycles, and is limited by terrain and access conditions. Therefore, this application uses high-precision leveling data collected by surface mobile observation stations as the reference data for this application. The observation accuracy of the surface mobile observation station measurement data meets the requirements for second-order leveling.
[0029] Step 120: Based on InSAR monitoring data, UAV monitoring data, and measurement data from surface moving observation stations, the subsidence basin of the target working face is divided into an outer basin, an inner basin, and a transitional zone.
[0030] InSAR excels at monitoring minute deformations at basin edges, while UAV excels at monitoring large-scale deformations at the basin center. Based on practical monitoring data, InSAR achieves millimeter-level accuracy when deformation is below 100mm, but its monitoring capabilities exceed those of large deformation areas. UAV monitoring data shows larger errors at the basin edges, but accuracy improves significantly after subsidence reaches 600mm. Therefore, the subsidence basin of the target working face is divided into an outer basin, an inner basin, and a transitional zone, such as... Figure 3 As shown, the outer basin 1 is the reliable monitoring range of InSAR monitoring data, the inner basin 2 is the reliable monitoring range of UAV monitoring data, and the transition zone 3 requires further data fusion processing.
[0031] High-precision leveling measurement data from surface mobile observation stations can be used to verify the accuracy of InSAR and UAV monitoring data at different deformation levels, thereby determining the reliable monitoring thresholds for InSAR and UAV monitoring data.
[0032] For example, the reliable monitoring threshold for InSAR monitoring data can be set to 80 mm, and the reliable monitoring threshold for UAV monitoring data can be set to 600 mm. Then, based on these two reliable monitoring thresholds, the subsidence basin of the target working face can be divided into an outer basin, an inner basin, and a transitional zone. Outer basin: a region of minor deformation, subsidence as measured by InSAR The region is monitored using InSAR data, which has the advantages of high accuracy and high spatial resolution in this area.
[0033] Inner basin: a region of significant deformation, with substantial subsidence of the UAV. The area is monitored using UAV data, which allows UAVs to acquire high-precision, large-scale deformation information within this region.
[0034] Transition zone: This is the transition area between the two types of monitoring data. In this area, the InSAR monitoring data has become decoherent, and the accuracy of the UAV monitoring data has not yet reached its optimal level. Therefore, weighted fusion processing of the two types of monitoring data is required.
[0035] In one possible implementation, the reliable monitoring thresholds for InSAR monitoring data and UAV monitoring data can be determined based on the root mean square error of the monitoring data and the measurement data from the surface mobile observation station. Step 120, based on the InSAR monitoring data, UAV monitoring data, and the measurement data from the surface mobile observation station, divides the subsidence basin of the target working face into an outer basin, an inner basin, and a transition zone, specifically including the following steps: Step 121: Based on the measurement data from the surface moving observation station and the InSAR monitoring data, determine the maximum deformation threshold for reliable InSAR monitoring.
[0036] Specifically, the root mean square error (RMSE) between InSAR monitoring data from multiple monitoring points and measurement data from a surface mobile observation station can be calculated. The maximum deformation value whose RMSE is less than a first preset accuracy threshold is determined as the maximum deformation threshold for reliable InSAR monitoring. The first preset accuracy threshold is set based on practical experience.
[0037] In practice, it is necessary to group monitoring points of different deformation levels and calculate the RMSE of InSAR monitoring data. The formula for calculating RMSE is as follows: in, The root mean square error is the difference between the InSAR monitoring data from this group of monitoring points and the measurement data from the surface mobile observation station. Number of monitoring points; For the first InSAR monitoring data from each monitoring point; For the first Measurement data from surface motion observation stations at each monitoring point.
[0038] When grouped When the deformation level is less than a preset accuracy threshold (e.g., 30mm), the deformation level of the group is determined to be within the reliable monitoring range of InSAR; when the group's... When the preset accuracy threshold is exceeded, the InSAR monitoring data of that group is determined to be decoherent, and its monitoring results are unreliable. Then, a critical group with an RMSE less than the preset accuracy threshold is found, and the InSAR monitoring data of the monitoring point with the largest deformation in that group is taken as the maximum deformation threshold for reliable InSAR monitoring.
[0039] For example, suppose the RMSE is calculated by grouping monitoring points with different deformation levels, and the preset accuracy threshold is 30mm. For 10 monitoring points with deformation levels in the range of 0 to 20mm, the calculated RMSE is 5mm, and the result is considered reliable; for 10 monitoring points with deformation levels in the range of 20 to 40mm, the RMSE is 12mm, and the result is considered reliable; for 10 monitoring points with deformation levels in the range of 40 to 60mm, the RMSE is 18mm, and the result is considered reliable; for 10 monitoring points with deformation levels in the range of 60 to 80mm, the RMSE is 25mm, and the result is considered reliable; for 10 monitoring points with deformation levels in the range of 80 to 100mm, the RMSE is 28mm, and the result is considered reliable; for 10 monitoring points with deformation levels in the range of 100 to 120mm, the RMSE is 35mm, which exceeds the accuracy threshold of 30mm, and the result is considered unreliable; monitoring points with deformation levels above 120mm are also considered unreliable. Therefore, the data of the monitoring point with the largest deformation among 10 monitoring points with deformation levels in the range of 80 to 100 mm can be used as the maximum deformation threshold for reliable InSAR monitoring, for example, 98 mm, or the endpoint value of 100 mm of the group can be taken as the maximum deformation threshold for reliable InSAR monitoring.
[0040] Step 122: Based on the measurement data from the surface moving observation station and the UAV monitoring data, determine the minimum deformation threshold for reliable UAV monitoring; Specifically, the root mean square error (RMSE) between UAV monitoring data from multiple monitoring points and measurement data from surface mobile observation stations can be calculated. The minimum deformation value for which the RMSE is less than a second preset accuracy threshold is determined as the minimum deformation threshold for reliable UAV monitoring. The second preset accuracy threshold is set based on practical experience.
[0041] In practice, it is necessary to group monitoring points of different deformation levels and calculate the RMSE of UAV monitoring data. The formula for calculating RMSE is as follows: in, The root mean square error is the difference between the UAV monitoring data from this group of monitoring points and the measurement data from the surface motion observation station. Number of monitoring points; For the first UAV monitoring data from each monitoring point; For the first Measurement data from surface motion observation stations at each monitoring point.
[0042] When grouped When the deformation level is less than a preset accuracy threshold (e.g., 30mm), the deformation level of the group is determined to be within the reliable monitoring range of the UAV; when the group's... When the preset accuracy threshold is exceeded, the UAV monitoring data of that group is determined to be decoherent, and its monitoring results are unreliable. Then, a critical group with an RMSE less than the preset accuracy threshold is found, and the UAV monitoring data of the monitoring point with the smallest deformation in that group is taken as the minimum deformation threshold for reliable UAV monitoring.
[0043] Step 123: Based on the maximum deformation threshold of InSAR reliable monitoring and the minimum deformation threshold of UAV reliable monitoring, the subsidence basin of the target working face is divided into an outer basin, an inner basin, and a transition zone.
[0044] Specifically, areas in the InSAR monitoring data where the subsidence is no greater than the maximum deformation threshold for reliable InSAR monitoring are designated as outer basins, and areas in the UAV monitoring data where the subsidence is no less than the minimum deformation threshold for reliable UAV monitoring are designated as inner basins. The subsidence boundaries of the InSAR monitoring data in the outer basins and the UAV monitoring data in the inner basins are extracted to form a transition zone. It is important to note that if there is an overlap between the reliable monitoring ranges of the InSAR and UAV monitoring data, this overlapping area is included in the transition zone. If there are missing InSAR or UAV monitoring data within the transition zone, spatial interpolation methods are used to fill in the missing data to address local data gaps and ensure the integrity of the merged basin. For example, Ordinary Kriging (OK) or Inverse Distance Weighting (IDW) interpolation methods can be used to fill in the missing data.
[0045] Based on the above scheme, the subsidence basin of the target working face is divided into three regions according to the magnitude of subsidence: Outer basin: a region of minor deformation, subsidence as measured by InSAR The region is monitored using InSAR data, which has the advantages of high accuracy and high spatial resolution in this area.
[0046] Inner basin: a region of significant deformation, with substantial subsidence of the UAV. The area is monitored using UAV data, which allows UAVs to acquire high-precision, large-scale deformation information within this region.
[0047] Transition zone: This is the transition area between the two types of monitoring data. In this area, the InSAR monitoring data has become decoherent, and the accuracy of the UAV monitoring data has not yet reached its optimal level. Therefore, weighted fusion processing of the two types of monitoring data is required.
[0048] Based on high-precision ground-based measured data from surface mobile observation stations, the applicable areas of InSAR and UAV monitoring data are quantitatively defined. By using the measurement data from surface mobile observation stations, the monitoring accuracy thresholds of InSAR and UAV at different deformation levels are calibrated. By dividing the subsidence basin into zones based on objective data, the subjective arbitrariness of the fusion boundary delineation can be avoided, and the fusion zoning has a clear physical basis and reproducibility.
[0049] Step 130: Perform weighted fusion processing on the InSAR monitoring data and UAV monitoring data in the transition zone to obtain fused data.
[0050] For each grid point P within the transition zone, its subsidence value is obtained from the InSAR monitoring results. and the subsidence value in UAV monitoring results The fusion sinking value is calculated using a fusion strategy based on the inverse distance weighting method or the error weighting method. The process involves performing differential interferometry, phase unwrapping, and atmospheric correction on the acquired InSAR time-series images, and then calculating the deformation of each pixel along the radar line-of-sight direction. Using multi-period UAV aerial imagery, digital surface models for each period were reconstructed using a motion-based structure reconstruction algorithm. The deformation values obtained after registering these multi-period digital surface models were then used as... .
[0051] In one possible implementation, based on the inverse distance weighting method at the deformation level, InSAR monitoring data and UAV monitoring data in the transition zone are weighted and fused to obtain fused data. Specifically, this includes the following steps: Step a1: For any target point within the transition zone, determine the first InSAR weight and the first UAV weight based on the target point's deformation level estimate, the maximum deformation threshold for reliable InSAR monitoring, and the minimum deformation threshold for reliable UAV monitoring.
[0052] The first InSAR weight and the first UAV weight of the target point can be determined according to the relative position of the deformation level within the transition interval. Specifically, the first InSAR weight of the target point can be determined first based on the estimated deformation level of the target point, the maximum deformation threshold for reliable InSAR monitoring, and the minimum deformation threshold for reliable UAV monitoring. Then, the first UAV weight is determined based on the first InSAR weight, as shown in the following formula: in, The first InSAR weight; The first UAV weight; The minimum deformation threshold for reliable UAV monitoring; The deformation level estimate of the target point can be obtained in advance by regional interpolation; The maximum deformation threshold for reliable InSAR monitoring.
[0053] Alternatively, the first UAV weight of the target point can be determined first based on the estimated deformation level of the target point, the maximum deformation threshold of reliable InSAR monitoring, and the minimum deformation threshold of reliable UAV monitoring, and then the first InSAR weight can be determined based on the first UAV weight. This application does not make specific limitations on this.
[0054] Step a2: Based on the first InSAR weight and the first UAV weight of the target point, perform a weighted summation of the InSAR sinking value and the UAV sinking value of the target point to obtain the fused sinking value of the target point.
[0055] in, The fusion sinking value of the target point; The first InSAR weight; The InSAR sinking value of the target point; The first UAV weight; The UAV sinking value at the target point.
[0056] The closer the deformation level The outer boundary, the greater the weight of the InSAR sinking value; the closer to the outer boundary, the greater the weight of the sinking value. The greater the weight of the UAV sinking value within the inner boundary, the better. The deformation-level inverse distance weighting method can achieve a continuous transition from InSAR weights to UAV weights, ensuring the smoothness of the data in the transition area.
[0057] In one possible implementation, a weighted fusion process is performed on InSAR monitoring data and UAV monitoring data within the transition zone based on a priori weighting method using root mean square error to obtain fused data. This process specifically includes the following steps: Step b1: Based on the root mean square error of InSAR monitoring data in the outer basin and the root mean square error of UAV monitoring data in the inner basin, determine the second InSAR weight and the second UAV weight of each target point in the transition zone.
[0058] The RMSE of InSAR and UAV monitoring data in their respective reliable monitoring areas is calculated separately, and this is used as a priori accuracy index to determine the weights. The specific formula is as follows: in, This is the second InSAR weight; This is the second UAV weight; The root mean square error of InSAR monitoring data in the outer basin; This represents the root mean square error of UAV monitoring data within the inner basin.
[0059] Step b2: Based on the second InSAR weight and the second UAV weight of the target point, perform a weighted summation of the InSAR sinking value and the UAV sinking value of the target point to obtain the fused sinking value of the target point.
[0060] in, The fusion sinking value of the target point; This is the second InSAR weight; The InSAR sinking value of the target point; This is the second UAV weight; The UAV sinking value at the target point.
[0061] Error weighting method uses RMSE prior accuracy for quantification fusion, which can ensure that high-precision data receives higher weight.
[0062] After fusion, the boundaries between the transition zone and the inner and outer basins are subjected to overall smoothing filtering to ensure the continuity and smoothness of the entire fused basin area and eliminate local abrupt changes caused by data source switching.
[0063] Step 140: Combine the InSAR monitoring data of the outer basin, the UAV monitoring data of the inner basin, and the fused data of the transition zone to construct a subsidence basin model.
[0064] InSAR monitoring data from the outer basin, UAV monitoring data from the inner basin, and weighted fused data from the transition zone are stitched together to construct a complete Digital Elevation Model (DEM) of the subsidence basin. Outputs may include: a spatial distribution map of the subsidence basin (including contour lines), boundary layers for each zone, and a 3D visualization model of the subsidence basin. For example... Figure 4 This is a schematic diagram of a three-dimensional visualization model of a subsidence basin provided in this application. It provides more reliable area data support for the inversion of predicted subsidence parameters during mining operations. Traditional methods rely solely on discrete point data from the main cross-section for parameter inversion, resulting in limited data volume. The fused subsidence basin model constructed in this application can invert more comprehensive and reliable predicted parameters, providing a scientific basis for subsequent subsidence prediction and damage assessment of the working face.
[0065] The accuracy of integrated monitoring of subsidence basins is significantly better than that of single monitoring methods. Field results show that integrated monitoring of subsidence basins outperforms single monitoring methods in both mean absolute error and root mean square error: compared to UAV monitoring, the mean absolute error is reduced by more than 50%; compared to InSAR monitoring, the root mean square error is reduced by more than 90%, and the Nash efficiency coefficient can reach over 0.99.
[0066] The following describes a surface subsidence basin fusion construction device provided in this application. The surface subsidence basin fusion construction device described below and the surface subsidence basin fusion construction method described above can be referred to in correspondence.
[0067] Figure 5 A schematic diagram of a surface subsidence basin fusion construction device provided in this application embodiment is shown below. Figure 5 As shown, the device 500 includes: The data acquisition module 510 is used to acquire InSAR monitoring data, UAV monitoring data and measurement data from the surface mobile observation station at the target working face; The region division module 520 is used to divide the subsidence basin of the target working face into an outer basin, an inner basin, and a transition zone based on InSAR monitoring data, UAV monitoring data, and measurement data from surface mobile observation stations. Data fusion module 530 is used to perform weighted fusion processing on InSAR monitoring data and UAV monitoring data in the transition zone to obtain fused data; The model building module 540 is used to stitch together InSAR monitoring data from the outer basin, UAV monitoring data from the inner basin, and fused data from the transition zone to build a subsidence basin model.
[0068] It should be noted that the surface subsidence basin fusion construction device provided in this application embodiment can realize all the method steps implemented in the above-mentioned surface subsidence basin fusion construction method embodiment, and can achieve the same technical effect. Here, the parts that are the same as those in the method embodiment and the beneficial effects will not be described in detail.
[0069] Figure 6 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 6 As shown, the electronic device may include a processor 610, a communications interface 620, a memory 630, and a communication bus 640. The processor 610, communications interface 620, and memory 630 communicate with each other via the communication bus 640. The processor 610 can call logical instructions from the memory 630 to execute the aforementioned method for constructing fusion of surface subsidence basins.
[0070] Furthermore, the logical instructions in the aforementioned memory 630 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0071] On the other hand, this application also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute the above-described method for constructing fusion of surface subsidence basins.
[0072] In another aspect, this application also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described method for constructing fusion of surface subsidence basins.
[0073] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.
[0074] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.
[0075] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.
Claims
1. A method for constructing a surface subsidence basin, characterized in that, The method includes: Collect synthetic aperture radar interferometric InSAR monitoring data, UAV monitoring data, and measurement data from surface mobile observation stations at the target working face; Based on the InSAR monitoring data, the UAV monitoring data, and the measurement data from the surface mobile observation station, the subsidence basin of the target working face is divided into an outer basin, an inner basin, and a transitional zone. The InSAR monitoring data and UAV monitoring data within the transition zone are weighted and fused to obtain fused data; The InSAR monitoring data of the outer basin, the UAV monitoring data of the inner basin, and the fused data of the connecting transition zone are stitched together to construct a subsidence basin model.
2. The method according to claim 1, characterized in that, Based on the InSAR monitoring data, the UAV monitoring data, and the measurement data from the surface mobile observation station, the subsidence basin of the target working face is divided into an outer basin, an inner basin, and a transitional zone, including: Based on the measurement data from the surface mobile observation station and the InSAR monitoring data, the maximum deformation threshold for reliable InSAR monitoring is determined; Based on the measurement data from the surface mobile observation station and the UAV monitoring data, determine the minimum deformation threshold for reliable monitoring of the UAV; Based on the maximum deformation threshold of the InSAR reliable monitoring and the minimum deformation threshold of the UAV reliable monitoring, the subsidence basin of the target working face is divided into an outer basin, an inner basin, and a transition zone.
3. The method according to claim 2, characterized in that, Based on the measurement data from the surface mobile observation station and the InSAR monitoring data, the maximum deformation threshold for reliable InSAR monitoring is determined, including: Calculate the root mean square error between InSAR monitoring data from multiple monitoring points and measurement data from a surface mobile observation station; The maximum deformation value whose root mean square error is less than the first preset accuracy threshold is determined as the maximum deformation threshold for reliable InSAR monitoring.
4. The method according to claim 1, characterized in that, Based on the measurement data from the surface mobile observation station and the UAV monitoring data, the minimum deformation threshold for reliable UAV monitoring is determined, including: Calculate the root mean square error between UAV monitoring data from multiple monitoring points and measurement data from surface motion observation stations; The minimum deformation value for which the root mean square error is less than the second preset accuracy threshold is determined as the minimum deformation threshold for reliable UAV monitoring.
5. The method according to claim 2, characterized in that, Based on the maximum deformation threshold reliably monitored by InSAR and the minimum deformation threshold reliably monitored by UAV, the subsidence basin of the target working face is divided into an outer basin, an inner basin, and a transitional zone, including: The area in the InSAR monitoring data where the subsidence is no greater than the maximum deformation threshold of the InSAR reliable monitoring is defined as the outer basin; the outer basin is the reliable monitoring range of the InSAR monitoring data. The area in the UAV monitoring data where the subsidence is not less than the minimum deformation threshold for reliable UAV monitoring is defined as the inner basin; the inner basin is the reliable monitoring range of the UAV monitoring data. The InSAR monitoring data is extracted to the subsidence boundary of the outer basin, and the UAV monitoring data is extracted to the subsidence boundary of the inner basin, forming a transition zone.
6. The method according to claim 5, characterized in that, The method further includes: If there is an overlap between the reliable monitoring range of the InSAR monitoring data and the reliable monitoring range of the UAV monitoring data, the overlapping area is included in the transition zone.
7. The method according to claim 5, characterized in that, The method further includes: If InSAR or UAV monitoring data is missing in the transition zone, spatial interpolation is used to fill in the missing data.
8. The method according to claim 1, characterized in that, The InSAR monitoring data and UAV monitoring data within the transition zone are weighted and fused to obtain fused data, including: For any target point within the transition zone, the first InSAR weight and the first UAV weight are determined based on the target point's deformation level estimate, the maximum deformation threshold for reliable InSAR monitoring, and the minimum deformation threshold for reliable UAV monitoring. Based on the first InSAR weight and the first UAV weight of the target point, the InSAR sinking value and the UAV sinking value of the target point are weighted and summed to obtain the fused sinking value of the target point.
9. The method according to claim 1, characterized in that, The InSAR monitoring data and UAV monitoring data within the transition zone are weighted and fused to obtain fused data, including: Based on the root mean square error of InSAR monitoring data in the outer basin and the root mean square error of UAV monitoring data in the inner basin, the second InSAR weight and the second UAV weight of each target point in the transition zone are determined. Based on the second InSAR weight and the second UAV weight of the target point, the InSAR sinking value and the UAV sinking value of the target point are weighted and summed to obtain the fused sinking value of the target point.
10. A device for constructing a surface subsidence basin, characterized in that, The device includes: The data acquisition module is used to collect InSAR monitoring data, UAV monitoring data, and measurement data from surface mobile observation stations at the target working face; The region division module is used to divide the subsidence basin of the target working face into an outer basin, an inner basin, and a transition zone based on InSAR monitoring data, UAV monitoring data, and measurement data from surface mobile observation stations. The data fusion module is used to perform weighted fusion processing on the InSAR monitoring data and UAV monitoring data in the transition zone to obtain fused data; The model building module is used to stitch together InSAR monitoring data from the outer basin, UAV monitoring data from the inner basin, and fused data from the transition zone to construct a subsidence basin model.