Intelligent monitoring method and system for waste dump slope and related device

Abstract

The invention discloses an intelligent monitoring method and system for a waste dump slope and a related device, and relates to the field of area monitoring, and the method comprises the steps: obtaining a plurality of satellite images of the slope in an observation period through a satellite radar, and dividing the satellite images into measurement pairs; extracting pixel phase difference data of the target measurement pair, and converting the pixel phase difference data into initial deformation data; removing deformation interference data of the initial deformation data, and adjusting a phase value to obtain earth surface deformation data; calculating the accumulated deformation of the measuring points in the sight line direction of the satellite radar according to the earth surface deformation data of the measuring points in the target measuring pair, and obtaining the deformation field of the slope according to the accumulated deformation of the plurality of measuring points; and identifying a potential deformation area of the slope according to the deformation field of the slope, and evaluating the stability. Deformation monitoring is achieved through the satellite radar, large-scale image data are divided into measurement pairs, the calculation complexity during data processing is effectively reduced, and high-quality data are provided for deformation analysis. Therefore, slope deformation monitoring can be effectively carried out.

Description

Technical Field

[0001] This application relates to the field of regional monitoring technology, and in particular to an intelligent monitoring method, system and related device for spoil heap slopes. Background Technology

[0002] Landslides on spoil heaps (slopes formed by accumulated mining waste) are a common geological problem in open-pit mining. Because spoil heap landslides can have adverse effects on the mining area, the surrounding ecological environment, infrastructure, and the safety of residents, it is necessary to monitor the deformation of spoil heaps in order to provide early warning of potential landslide risks.

[0003] Existing slope monitoring technologies, such as manual measurement, GPS monitoring, or total stations (a type of high-precision optical measuring instrument), all suffer from complex processing methods. Therefore, how to effectively monitor slope deformation is a pressing technical problem that needs to be solved. Summary of the Invention

[0004] In view of the above problems, this application provides an intelligent monitoring method, system, and related devices for spoil heap slopes to achieve effective deformation monitoring of slopes. The specific solution is as follows:

[0005] The first aspect of this application provides an intelligent monitoring method for spoil heap slopes, the monitoring method for spoil heap slopes comprising:

[0006] Multiple satellite images of the slope within the observation period are acquired by satellite radar. The multiple satellite images are divided into multiple measurement pairs. Each measurement pair includes two satellite images. The imaging time interval between the two satellite images and the spatial distance between the satellites during imaging are both within a set threshold range.

[0007] Extract pixel phase difference data from multiple target measurement pairs, convert the pixel phase difference data into initial deformation data of the slope, obtain initial deformation data for each target measurement pair, and the coherence coefficient of the two satellite images of the target measurement pair is greater than a threshold.

[0008] Deformation interference data of the initial deformation data of multiple target measurement pairs is removed, and the phase value of the initial deformation data after removing the deformation interference data is adjusted to obtain the surface deformation data of multiple target measurement pairs.

[0009] The cumulative deformation of the slope is calculated by using surface deformation data from multiple measuring points in the target measurement pair along the line of sight of the satellite radar, and the deformation field of the slope is obtained by filling in the cumulative deformation data of the multiple measuring points.

[0010] Based on the deformation field of the slope, potential deformation zones of the slope are identified, and the stability of the potential deformation zones is evaluated.

[0011] In one possible implementation, the coherence coefficient of the two satellite images of the target measurement pair is: the coherence coefficient of the two satellite images at high coherence points. The process of selecting the target measurement pair includes:

[0012] Multiple candidate measurement points are obtained by selecting measurement points with image amplitude values ​​greater than a threshold from multiple measurement points in each satellite image;

[0013] If the spectral characteristics of the candidate measurement points are consistent across multiple satellite images, then the candidate measurement points are identified as highly coherent points, and multiple highly coherent points are obtained.

[0014] The measurement pair containing at least one identical high coherence point is used as the target measurement pair.

[0015] In one possible implementation, extracting pixel phase difference data from multiple target measurement pairs and converting the pixel phase difference data into initial deformation data of the slope includes:

[0016] Spatially align the two satellite images for each target measurement pair so that there is a correspondence between the pixels of the two satellite images;

[0017] Calculate the complex conjugate product of corresponding pixels in two satellite images, and use the phase part of the complex conjugate product as the pixel phase difference of corresponding pixels to obtain pixel phase difference data of multiple target measurement pairs.

[0018] The pixel phase difference data is padded and formatted to obtain the initial deformation data.

[0019] In one possible implementation, the removal of deformation interference data from the initial deformation data of multiple target measurement pairs includes:

[0020] The three-dimensional topographic data of the slope is obtained, and a theoretical phase corresponding to the topographic undulation of the slope is simulated and generated based on the three-dimensional topographic data and the orbital parameters of the satellite radar.

[0021] The theoretical phase and the noise phase are subtracted from the mixed phase of the initial deformation data to obtain the initial deformation data after removing the deformation interference data.

[0022] In one possible implementation, adjusting the phase value of the initial deformation data after removing the deformation interference data includes:

[0023] Phase unwrapping is performed on the initial deformation data after removing the deformation interference data, and the phase values ​​with limited range are adjusted to unlimited, continuous true phase values.

[0024] In one possible implementation, the surface deformation data for each target measurement pair includes the phase difference of the same measurement point in the two satellite images of each target measurement pair;

[0025] The calculation of the cumulative deformation at multiple measuring points along the line-of-sight direction of the satellite radar, based on surface deformation data from multiple measuring points at the target measurement center, includes:

[0026] The phase difference at the same measurement point of each target measurement pair is converted into a displacement in the line-of-sight direction of the satellite radar;

[0027] The total displacement of the same measuring point of multiple target measurement pairs is obtained by summing the displacements of the same measuring point of multiple target measurement pairs.

[0028] Obtain the imaging time interval between the two satellite images in each target measurement pair, and calculate the time sum of multiple target measurement pairs;

[0029] Calculate the average deformation rate of the same measuring point based on the sum of the time and the sum of the displacement at the same measuring point.

[0030] The product of the average deformation rate and the time length of the observation period is taken as the cumulative deformation at the same measuring point.

[0031] The cumulative deformation at multiple measuring points is obtained.

[0032] In one possible implementation, obtaining the deformation field of the slope based on the cumulative deformation of multiple measuring points includes:

[0033] The cumulative deformation of multiple measuring points is preprocessed. After data preprocessing, spatial interpolation is performed on the cumulative deformation of multiple measuring points to form a deformation distribution map of the slope, thereby obtaining the deformation field of the slope.

[0034] In one possible implementation, identifying potential deformation zones of the slope based on the slope's deformation field and evaluating the stability of the potential deformation zones includes:

[0035] The potential deformation areas of the slope are identified based on the distribution pattern of deformation in the deformation field.

[0036] Obtain the deformation amount of the potential deformation region and calculate the deformation rate. Determine the stability of the potential deformation region based on the magnitude of the deformation amount and the magnitude of the deformation rate.

[0037] A second aspect of this application provides an intelligent monitoring system for spoil heap slopes, wherein the intelligent monitoring method for spoil heap slopes includes:

[0038] The acquisition unit is used to acquire multiple satellite images of the slope within the observation period via satellite radar, divide the multiple satellite images into multiple measurement pairs, each measurement pair includes two satellite images, and the imaging time interval between the two satellite images and the spatial distance between the satellites during imaging are both within a set threshold range.

[0039] An extraction unit is used to extract pixel phase difference data of multiple target measurement pairs, convert the pixel phase difference data into the initial deformation data of the slope, obtain the initial deformation data of each target measurement pair, and the coherence coefficient of the two satellite images of the target measurement pair is greater than a threshold.

[0040] A separation unit is used to remove deformation interference data from the initial deformation data of multiple target measurement pairs, adjust the phase value of the initial deformation data after removing the deformation interference data, and obtain the surface deformation data of multiple target measurement pairs.

[0041] The calculation unit is used to calculate the cumulative deformation of multiple measuring points in the line-of-sight direction of the satellite radar by using the surface deformation data of multiple measuring points in the target measurement pair, and to fill in the deformation field of the slope according to the cumulative deformation of multiple measuring points.

[0042] The identification unit is used to identify potential deformation areas of the slope based on the deformation field of the slope, and to evaluate the stability of the potential deformation areas.

[0043] A third aspect of this application provides an electronic device, comprising at least one processor and a memory connected to the processor, wherein:

[0044] The memory is used to store computer programs;

[0045] The processor is used to execute the computer program so that the electronic device can implement the intelligent monitoring method for spoil heap slopes as described in the first aspect or any implementation thereof.

[0046] Based on the above technical solution, this application provides an intelligent monitoring method, system, and related device for spoil heap slopes. The method acquires multiple satellite images of the slope within an observation period using satellite radar, divides these images into multiple measurement pairs, extracts pixel phase difference data from target measurement pairs where the coherence coefficient of two satellite images is greater than a threshold, converts the pixel phase difference data into initial deformation data of the slope, obtains initial deformation data for each target measurement pair, removes deformation interference data from the initial deformation data of multiple target measurement pairs, and adjusts the phase value of the initial deformation data after removing the deformation interference data to obtain surface deformation data for multiple target measurement pairs. The cumulative deformation of multiple measuring points in the line-of-sight direction of the satellite radar is calculated using the surface deformation data of multiple measuring points in each target measurement pair, and the deformation field of the slope is obtained by filling in the cumulative deformation of multiple measuring points. The potential deformation areas of the slope are identified based on the deformation field, and the stability of the potential deformation areas is evaluated. This method utilizes satellite radar for deformation monitoring. When processing satellite image data, it divides large-scale image data into multiple measurement pairs, effectively reducing computational complexity. Furthermore, this method uses highly coherent target measurement pairs for deformation analysis. High coherence indicates a high proportion of effective deformation signal and low noise in the pixel phase difference data of the image, providing high-quality data for deformation analysis. It can also identify potential deformation areas of the slope and assess the stability of these areas. Therefore, this method is effective for slope deformation monitoring. Attached Figure Description

[0047] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the originals and elements are not necessarily drawn to scale.

[0048] Figure 1 A flowchart illustrating an intelligent monitoring method for spoil heap slopes provided in this application embodiment;

[0049] Figure 2 A schematic diagram of the structure of an intelligent monitoring system for spoil heap slopes provided in this application embodiment;

[0050] Figure 3 This application provides a hardware structure block diagram of an electronic device. Detailed Implementation

[0051] The embodiments of this application are described below with reference to the accompanying drawings. The terminology used in the implementation section of this application is for explaining specific embodiments only and is not intended to limit the scope of this application.

[0052] The embodiments of this application will now be described with reference to the accompanying drawings. Those skilled in the art will recognize that, with technological advancements and the emergence of new scenarios, the technical solutions provided in the embodiments of this application are equally applicable to similar technical problems.

[0053] The terms “comprising” and “having”, and any variations thereof, in the specification and accompanying drawings of this application are intended to cover non-exclusive inclusion, such that a process, method, system, product, or apparatus that comprises a series of elements is not necessarily limited to those elements, but may include other elements not expressly listed or inherent to such process, method, product, or apparatus.

[0054] While existing slope monitoring technologies offer decent accuracy and reliability, they struggle with large-scale, high-precision real-time monitoring and involve complex data processing. To address these issues, this application provides an intelligent monitoring method for spoil heap slopes. The intelligent monitoring method for spoil heap slopes according to this application is described in detail below with reference to the accompanying drawings.

[0055] Reference Figure 1 , Figure 1 A flowchart illustrating an intelligent monitoring method for spoil heap slopes provided in this application embodiment is shown below. Figure 1 As shown in the embodiment of this application, an intelligent monitoring method for spoil heap slopes may include steps S10 to S14, which are described in detail below.

[0056] S10. Acquire multiple satellite images of the slope within the observation period using satellite radar, and divide the multiple satellite images into multiple measurement pairs.

[0057] In this context, "satellite radar" refers to remote sensing equipment mounted on an artificial satellite, capable of transmitting signals to the Earth's surface and receiving reflected echoes to observe the surface. "Observation period" refers to the total time spent monitoring the slope, representing the complete time span from the initial acquisition of a satellite image to the final sequential acquisition of satellite images. "Satellite image" refers to a visual image formed after the satellite radar captures information reflected from the Earth's surface and processes it. In this embodiment, each satellite image is an image containing the slope at a different time, and each satellite image includes multiple measuring points, which are marker points with clear geographical coordinates on the surface of the slope area. Their locations can be manually selected or automatically determined by an algorithm.

[0058] In this embodiment, after acquiring multiple satellite images of the slope, preprocessing is required. This preprocessing may include radiometric correction, atmospheric correction, image cropping, and baseline connectivity mapping (including temporal baselines (time difference between images) and spatial baselines (orbital geometric distance)). The multiple satellite images are then divided into multiple measurement pairs. Each measurement pair may include two satellite images, and the imaging time interval between the two images and the spatial distance between the satellites at the time of imaging are both within a set threshold range. In this embodiment, the set threshold range can be pre-set manually, and this set threshold range is a small range to divide the multiple satellite images into multiple small baseline measurement pairs. Specifically, the set threshold range for the imaging time interval can be from 12 days to 84 days, and the set threshold range for the spatial distance between the satellites at the time of imaging can be from 1.6 meters to 168.9 meters.

[0059] In this embodiment, to accurately extract surface deformation data, multiple target measurement pairs are selected from a set of measurement pairs for subsequent deformation data extraction. A target measurement pair can be defined as a measurement pair containing at least one identical high coherence point. A high coherence point can be a measurement point with strong image amplitude, strong signal consistency, and minimal fluctuation in multiple satellite images; it serves as a stable data anchor point in the satellite images, accurately preserving the true information of the surface. Therefore, this embodiment selects high coherence points and then selects target measurement pairs based on these high coherence points. The specific process is as follows:

[0060] Multiple candidate measurement points are selected from multiple measurement points in each satellite image if the image amplitude value is greater than a threshold. If the spectral characteristics of the candidate measurement points are consistent in multiple satellite images, the candidate measurement points are determined as high coherence points, and multiple high coherence points are obtained. Measurement pairs containing at least one identical high coherence point are taken as target measurement pairs.

[0061] In satellite imagery, the image amplitude represents the intensity of the signal reflected back to the satellite radar. Spectral characteristics refer to the patterns and properties of electromagnetic wave reflection, absorption, emission, and scattering exhibited by objects on the Earth's surface (such as vegetation, buildings, and rocks) within the electromagnetic spectrum detectable by radar satellites; these characteristics include grayscale values ​​and reflectivity in different bands. Stable spectral characteristics mean that the spectral characteristics of the same measurement point remain consistent across multiple satellite images, without significant variations.

[0062] Therefore, in this embodiment, measurement points with large image amplitude and stable spectral characteristics are selected as high coherence points. The larger the image amplitude value, the more stable the scattering characteristics, the stronger the scattering ability, and the less interference from factors such as vegetation and atmosphere. This allows for more stable reflection of satellite radar signals, ensuring that reliable signals can be continuously obtained during long-term monitoring. This is beneficial for accurately acquiring information on surface deformation. Spectral information can reflect the changes in the signal over time. Stable spectral characteristics indicate smaller signal fluctuations and better time-series correlation. Images less susceptible to random interference can more accurately reflect the true deformation of the surface.

[0063] The coherence of satellite images can refer to a physical quantity that quantifies the consistency and stability of two satellite images of the same region under the same conditions, and can be represented by a coherence coefficient. In this embodiment, the coherence coefficient of the two satellite images of the target measurement pair can be the coherence coefficient of the two satellite images at the high coherence point, and the coherence coefficient of the two satellite images at the high coherence point is greater than a threshold (which can be set manually), indicating that the signals of the two satellite images of the target measurement pair in the pixel region where the high coherence point is located are almost identical and there is no significant difference.

[0064] S11. Extract pixel phase difference data of multiple target measurement pairs, convert the pixel phase difference data into initial deformation data of the slope, obtain the initial deformation data of each target measurement pair, and the coherence coefficient of the two satellite images of the target measurement pair is greater than the threshold.

[0065] Pixel phase difference data refers to the phase difference between the radar echo signals of corresponding pixels in two satellite images of a target measurement pair. Specifically, the process for extracting pixel phase difference data from multiple target measurement pairs in this embodiment is as follows:

[0066] Spatially align the two satellite images of each target measurement pair so that there is a correspondence between the pixels of the two satellite images; calculate the complex conjugate product of the corresponding pixels in the two satellite images, and use the phase part of the complex conjugate product as the pixel phase difference of the corresponding pixels to obtain the pixel phase difference data of multiple target measurement pairs.

[0067] Each pixel in the satellite image is stored as a complex number, containing amplitude and phase information. Taking a pair of corresponding pixels in two satellite images of a target measurement pair as an example, the two pixels belong to two different satellite images and are located in corresponding positions. Both pixels are complex numbers. The conjugate complex number of one pixel is calculated, and the complex number corresponding to the other pixel is multiplied by the conjugate complex number. The phase part of the product is extracted to obtain the pixel phase difference of this pair of corresponding pixels. The pixel phase difference of other corresponding pixels is calculated in the same way to obtain the pixel phase difference data of two satellite images in the target measurement pair. In this way, pixel phase difference data of two satellite images in multiple target measurement pairs can be obtained.

[0068] In this embodiment, after acquiring pixel phase difference data from two satellite images of multiple target measurement pairs, the pixel phase difference data is augmented and format-normalized (for example), and then converted into interferometric data to obtain the initial deformation data of this embodiment. Data augmentation refers to supplementing the pixel phase difference data with amplitude information and geographic metadata. After format normalization, complete interferometric data (initial deformation data) usable for analysis can be generated. Geographic metadata can refer to data related to the actual surface location, such as latitude and longitude, projection information, etc., facilitating the association between pixel phase difference data and surface location.

[0069] S12. Remove deformation interference data from the initial deformation data of multiple target measurement pairs, adjust the phase value of the initial deformation data after removing the deformation interference data, and obtain the surface deformation data of multiple target measurement pairs.

[0070] Deformation interference data can refer to data that interferes with the detection of surface deformation, such as topographic phase and noise phase. The specific process for removing deformation interference data related to topographic and noise phases from the initial deformation data is as follows:

[0071] The system acquires three-dimensional topographic data of the slope and simulates and generates a theoretical phase corresponding to the topographic undulation of the slope based on the three-dimensional topographic data and the orbital parameters of the satellite radar. The theoretical phase is subtracted from the mixed phase of the initial deformation data, and then the noise phase is subtracted to remove the topographic phase and noise phase from the initial deformation data.

[0072] The three-dimensional topographic data of the slope can refer to the basic data describing the topography of the slope. Specifically, in this embodiment, the three-dimensional topographic data of the slope is the DEM data of the slope. DEM (Digital Elevation Model) data is the core basic topographic data. It is a three-dimensional model that expresses the elevation of the earth's surface using a digitized grid. DEM data can be obtained through existing surveying data or satellite remote sensing data.

[0073] Specifically, this embodiment can generate the phase difference (terrain phase, or theoretical phase in this embodiment) between the two satellite images in each target measurement pair due to terrain undulations based on DEM data and satellite imaging geometric parameters (such as orbit and viewing angle). The terrain phase of multiple target measurement pairs is removed from the interferometric data containing multiple target measurement pairs, and then the phase caused by radar satellite orbital errors is removed to obtain the initial deformation data after removing the terrain phase. This embodiment can use various filtering algorithms, such as the Goldstein filter (a nonlinear adaptive phase filtering algorithm), to suppress or reduce noise in the differential interferometric data, thereby removing the noisy phase and obtaining the initial deformation data (differential interferometric data) after removing deformation interference data in this embodiment. Of course, in another optional embodiment, the terrain phase can also be removed using differential interferometry, time series stacking, etc.

[0074] Since the phase of the initial deformation data is a periodic physical quantity, its value range is limited to a fixed range. In this embodiment, the phase value of the initial deformation data is limited to (-π, π]. Phase values ​​exceeding this fixed range can be folded into this fixed range to form a wrapped phase. Therefore, after removing the deformation interference data of the initial deformation data, this embodiment needs to perform phase unwrapping on the initial deformation data to adjust the phase values ​​with limited ranges into unrestricted, continuous true phase values.

[0075] Specifically, this embodiment can achieve phase untangling of the initial deformation data using the minimum cost flow method. The minimum cost flow method is an optimization algorithm that transforms the phase untangling problem into a network flow problem by constructing a cost flow network. It finds the optimal phase untangling path by identifying the minimum cost flow, thus achieving phase untangling. Specifically, the phase difference between each pixel and its neighboring pixels in the initial deformation data is obtained. A cost flow network is constructed, where each node corresponds to a pixel, and edges connect nodes. The phase difference is used as the edge value (cost). The lowest-cost correction scheme is found, and the original tangled phase is corrected according to this scheme to obtain the true phase.

[0076] After phase unwrapping, this embodiment can further separate some deformation interference data in the differential interferometric data using high coherence points to obtain surface deformation data for multiple target measurement pairs. Specifically, this embodiment can perform regression analysis on the high coherence points of multiple target measurement pairs, utilizing the signal stability of the high coherence points and the characteristic differences of each phase to iteratively separate elevation residuals, atmospheric phase, residual phase, and deformation phase. This gradually removes non-target phases from the mixed phases in the differential interferometric data, retaining the deformation phase. The obtained deformation phases of the multiple target measurement pairs constitute the surface deformation data for these multiple target measurement pairs in this embodiment. Elevation residuals can refer to the phase caused by errors in the digital elevation model and deviations in terrain undulation correction when removing the terrain phase; atmospheric phases can refer to the phase caused by ionospheric and tropospheric delays (such as water vapor and temperature changes); residual phases can refer to the remaining errors; and deformation phases can refer to the phase changes corresponding to surface displacement caused by factors such as subsidence.

[0077] Specifically, this embodiment acquires highly coherent points included in multiple target measurement pairs. Since the elevation residual is a static phase related to the terrain, this embodiment uses the elevation residual of the highly coherent points as the independent variable to establish a regression model. The model is then fitted using the least squares method to obtain the phase component corresponding to the elevation residual, and this phase component is separated. Because the atmospheric phase is a temporally random signal, this embodiment can utilize spatial continuity to achieve separation. A filtered regression model is established using the phase mean of the highly coherent point and its adjacent highly coherent points as constraints. The atmospheric phase is then fitted using spatiotemporal filtering to fit the changes in atmospheric phase over time and space, and the atmospheric phase is separated by separating the fitted results. Since the residual phase is random noise without temporal or spatial regularity, this embodiment separates the residual phase by fitting the deformation phase. This embodiment uses time as the independent variable to establish a smoothing model of the deformation phase (e.g., linear or nonlinear deformation), fits this smoothing model using the least squares method, and the remaining fitting residual is the residual phase, which is then separated. Through the separation of the non-target phases described above three times, the deformation phase of the high-coherence point with relatively high quality is finally obtained, resulting in surface deformation data for multiple target measurement pairs. Of course, this embodiment can also repeatedly repeat the above iterative separation process to further improve the quality of the surface deformation data.

[0078] S13. Calculate the cumulative deformation of multiple measuring points in the line-of-sight direction of the satellite radar by using the surface deformation data of multiple measuring points in the target measurement, and fill in the deformation field of the slope based on the cumulative deformation of multiple measuring points.

[0079] S14. Identify potential deformation zones of the slope based on the slope deformation field and assess the stability of the potential deformation zones.

[0080] In this embodiment, the line of sight (LOS) of the satellite radar refers to the direction of the line connecting the radar satellite and the target point on the ground (the measurement point in this example). Therefore, the surface deformation data in this example consists of the displacement components of multiple measurement points on the ground along this line of sight during the target measurement alignment. The cumulative deformation of the measurement point refers to the total displacement of the measurement point along the line of sight of the satellite radar within a specific time period. Since the signal of highly coherent points is stable, this example mainly calculates the cumulative deformation of highly coherent points. The specific process for obtaining the cumulative deformation of each measurement point is as follows:

[0081] The surface deformation data for each target measurement pair includes the phase difference of the same measurement point in the two satellite images of each target measurement pair; the phase difference of the same measurement point in each target measurement pair is converted into a displacement in the line-of-sight direction of the satellite radar; the displacements of the same measurement point in multiple target measurement pairs are summed to obtain the total displacement of the same measurement point in multiple target measurement pairs; the imaging time interval of the two satellite images in each target measurement pair is obtained, and the time sum of multiple target measurement pairs is calculated; based on the time sum and the total displacement of the same measurement point, the average deformation rate of the same measurement point is calculated; the product of the average deformation rate and the time length of the observation period is taken as the cumulative deformation of the same measurement point. Following the aforementioned process, this embodiment can obtain the cumulative deformation of multiple measurement points in multiple target measurement pairs respectively.

[0082] In this embodiment, after obtaining the cumulative deformation of multiple highly coherent points with stable signals, the cumulative deformation of these points is used as data anchors. Mathematical modeling and other methods are then used to extrapolate the cumulative deformation of other measuring points, thereby obtaining the deformation distribution covering the entire slope area. Specifically, this embodiment can perform data preprocessing on the cumulative deformation of multiple measuring points (e.g., removing outliers). After data preprocessing, spatial interpolation is performed on the cumulative deformation of multiple measuring points to form a deformation distribution map of the slope, thus obtaining the slope's deformation field.

[0083] Specifically, in this embodiment, when performing spatial interpolation, a continuous mathematical function can be constructed for the three-dimensional scene. This mathematical function can include all measurement points. The parameters and calculation form of the mathematical function are determined by the cumulative deformation of highly coherent points. The cumulative deformation of other measurement points is predicted based on the completed mathematical function.

[0084] After obtaining the deformation field of the slope in this embodiment, potential deformation areas of the slope can be identified, and the stability of these potential deformation areas can be evaluated. Specifically, this embodiment can identify potential deformation areas of the slope based on the distribution pattern of deformation in the deformation field; obtain the deformation amount of the potential deformation area and calculate the deformation rate; and determine the stability of the potential deformation area based on the magnitude of the deformation amount and the magnitude of the deformation rate. For example, by judging the magnitude of the deformation amount and the magnitude of the deformation rate relative to the corresponding threshold, different deformation levels corresponding to each area can be determined. When the deformation level of an area is higher than the risk level, the area can be considered a potential deformation area.

[0085] This application provides an intelligent monitoring method for spoil heap slopes. The method acquires multiple satellite images of the slope within an observation period using satellite radar, divides these images into multiple measurement pairs, extracts pixel phase difference data from target measurement pairs where the coherence coefficient of two satellite images is greater than a threshold, converts the pixel phase difference data into initial deformation data of the slope, obtains initial deformation data for each target measurement pair, removes deformation interference data from the initial deformation data of multiple target measurement pairs, and adjusts the phase value of the initial deformation data after removing the deformation interference data to obtain surface deformation data for multiple target measurement pairs. The method calculates the cumulative deformation of multiple measuring points along the line of sight of the satellite radar using the surface deformation data of multiple measuring points in each target measurement pair, and fills the slope deformation field based on the cumulative deformation of multiple measuring points. Finally, the method identifies potential deformation areas of the slope based on the slope deformation field and evaluates the stability of these potential deformation areas. This method utilizes satellite radar for deformation monitoring. When processing satellite image data, it divides large-scale image data into multiple measurement pairs, effectively reducing computational complexity. Furthermore, this method uses highly coherent target measurement pairs for deformation analysis. High coherence indicates a high proportion of effective deformation signal and low noise in the pixel phase difference data of the image, providing high-quality data for deformation analysis. It can also identify potential deformation areas of the slope and assess the stability of these areas. Therefore, this method is effective for slope deformation monitoring.

[0086] The above describes an intelligent monitoring method for spoil heap slopes provided by embodiments of this application. The following will describe a system that applies the above-described intelligent monitoring method for spoil heap slopes.

[0087] Please see Figure 2 , Figure 2 This is a schematic diagram of the structure of an intelligent monitoring system for spoil heap slopes provided in an embodiment of this application. Figure 2 As shown, the intelligent monitoring system for the spoil heap slope may include:

[0088] The acquisition unit 100 is used to acquire multiple satellite images of the slope within the observation period through satellite radar, divide the multiple satellite images into multiple measurement pairs, each measurement pair includes two satellite images, and the imaging time interval between the two satellite images and the spatial distance between the satellites during imaging are within a set threshold range.

[0089] Extraction unit 110 is used to extract pixel phase difference data of multiple target measurement pairs, convert the pixel phase difference data into initial deformation data of the slope, obtain the initial deformation data of each target measurement pair, and the coherence coefficient of the two satellite images of the target measurement pair is greater than the threshold.

[0090] The separation unit 120 is used to remove deformation interference data from the initial deformation data of multiple target measurement pairs, adjust the phase value of the initial deformation data after removing the deformation interference data, and obtain the surface deformation data of multiple target measurement pairs.

[0091] The calculation unit 130 is used to calculate the cumulative deformation of multiple measuring points in the line-of-sight direction of the satellite radar by means of the surface deformation data of multiple measuring points in the target measurement, and to fill in the deformation field of the slope based on the cumulative deformation of multiple measuring points.

[0092] The identification unit 140 is used to identify potential deformation zones of the slope based on the slope's deformation field and to assess the stability of the potential deformation zones.

[0093] In one possible implementation, the coherence coefficient of the two satellite images of the target measurement pair is: the coherence coefficient of the two satellite images at high coherence points. The intelligent monitoring system for the spoil heap slope may also include a screening unit for the target measurement pair, which may be specifically configured as follows:

[0094] Multiple candidate measurement points are selected from multiple measurement points in each satellite image if the image amplitude value is greater than a threshold. If the spectral characteristics of the candidate measurement points are consistent in multiple satellite images, the candidate measurement points are determined as high coherence points, and multiple high coherence points are obtained. Measurement pairs containing at least one identical high coherence point are taken as target measurement pairs.

[0095] In one possible implementation, the extraction unit 110 extracts pixel phase difference data from multiple target measurement pairs and converts the pixel phase difference data into initial deformation data of the slope. This can be specifically configured as follows:

[0096] Spatially align the two satellite images of each target measurement pair to ensure a correspondence between pixels in the two images; calculate the complex conjugate product of the corresponding pixels in the two images, and use the phase part of the complex conjugate product as the pixel phase difference of the corresponding pixels to obtain pixel phase difference data for multiple target measurement pairs; perform data completion and format normalization on the pixel phase difference data to obtain initial deformation data.

[0097] In one possible implementation, the deformation interference data in the separation unit 120, which removes initial deformation data from multiple target measurement pairs, can be specifically configured as follows:

[0098] Acquire three-dimensional topographic data of the slope, and simulate and generate a theoretical phase corresponding to the topographic undulation of the slope based on the three-dimensional topographic data and the orbital parameters of the satellite radar. Subtract the theoretical phase and noise phase from the mixed phase of the initial deformation data to obtain the initial deformation data after removing deformation interference data.

[0099] In one possible implementation, the phase value of the initial deformation data after removing deformation interference data in the separation unit 120 can be specifically configured as follows:

[0100] Phase unwrapping is performed on the initial deformation data after removing deformation interference data, and the phase values ​​with limited range are adjusted to unlimited, continuous true phase values.

[0101] In one possible implementation, the surface deformation data for each target measurement pair includes the phase difference of the same measurement point in the two satellite images of each target measurement pair;

[0102] The calculation unit 130 calculates the cumulative deformation of multiple measuring points along the line-of-sight direction of the satellite radar by using surface deformation data from multiple measuring points aligned with the target measurement. This can be specifically configured as follows:

[0103] The phase difference of the same measurement point in each target measurement pair is converted into a displacement in the line-of-sight direction of the satellite radar; the displacements of the same measurement point in multiple target measurement pairs are summed to obtain the total displacement of the same measurement point in multiple target measurement pairs; the imaging time interval between the two satellite images in each target measurement pair is obtained, and the time sum of multiple target measurement pairs is calculated; based on the time sum and the total displacement of the same measurement point, the average deformation rate of the same measurement point is calculated; the product of the average deformation rate and the time length of the observation period is taken as the cumulative deformation of the same measurement point; the cumulative deformation of multiple measurement points is obtained.

[0104] In one possible implementation, the deformation field of the slope is obtained by filling in the calculation unit 130 based on the cumulative deformation of multiple measuring points, which can be specifically configured as follows:

[0105] The cumulative deformation of multiple measuring points is preprocessed. After the data preprocessing, the cumulative deformation of multiple measuring points is spatially interpolated to form a deformation distribution map of the slope and obtain the deformation field of the slope.

[0106] In one possible implementation, the identification unit 140 identifies potential deformation zones of the slope based on the slope's deformation field and evaluates the stability of these potential deformation zones. Specifically, this can be configured as follows:

[0107] Identify potential deformation zones of the slope based on the distribution pattern of deformation in the deformation field; obtain the deformation amount of the potential deformation zone and calculate the deformation rate; determine the stability of the potential deformation zone based on the magnitude of the deformation amount and the magnitude of the deformation rate.

[0108] This application also provides an electronic device in its embodiments. (See reference...) Figure 3 The diagram illustrates a structural schematic suitable for implementing the electronic device in the embodiments of this application. The electronic device in the embodiments of this application may include, but is not limited to, fixed terminals such as mobile phones, laptops, PDAs (personal digital assistants), PADs (tablet computers), desktop computers, etc. Figure 3 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0109] like Figure 3 As shown, the electronic device may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303. When the electronic device is powered on, the RAM 303 also stores various programs and data required for the operation of the electronic device. The processing unit 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output interface (I / O interface) 305 is also connected to the bus 304.

[0110] Typically, the following devices can be connected to I / O interface 305: input devices 306 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 307 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 308 including, for example, memory cards, hard drives, etc.; and communication devices 309. Communication device 309 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 3 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown. More or fewer devices may be implemented or have instead.

[0111] This application also provides a computer program product including computer-readable instructions, which, when executed on an electronic device, cause the electronic device to implement any of the intelligent monitoring methods for spoil heap slopes provided in this application.

[0112] This application also provides a computer-readable storage medium carrying one or more computer programs. When the one or more computer programs are executed by an electronic device, the electronic device can implement any of the intelligent monitoring methods for spoil heap slopes provided in this application.

[0113] It should also be noted that the system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and 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. In addition, in the system embodiment drawings provided in this application, the connection relationship between modules indicates that they have a communication connection, which can be implemented as one or more communication buses or signal lines.

[0114] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware, or it can be implemented by special-purpose hardware including application-specific integrated circuits, special-purpose CPUs, special-purpose memory, special-purpose components, etc. Generally, any function performed by a computer program can be easily implemented by corresponding hardware, and the specific hardware structure used to implement the same function can also be diverse, such as analog circuits, digital circuits, or special-purpose circuits. However, for this application, software program implementation is more often the preferred implementation method. Based on this understanding, the technical solution of this application, 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 is stored in a readable storage medium, such as a computer floppy disk, USB flash drive, mobile hard disk, ROM, RAM, magnetic disk, or optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, training equipment, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0115] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product.

[0116] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, training device, or data center to another website, computer, training device, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a training device or data center that integrates one or more available media. The available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives (SSDs)).

[0117] The various embodiments in this specification are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the apparatus embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0118] It is understood that before using the technical solutions disclosed in the various embodiments of this disclosure, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in this disclosure in an appropriate manner in accordance with relevant laws and regulations, and user authorization should be obtained.

[0119] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for intelligent monitoring of spoil heap slopes, characterized in that, The monitoring methods for the slopes of the spoil heap include: Multiple satellite images of the slope within the observation period are acquired by satellite radar. The multiple satellite images are divided into multiple measurement pairs. Each measurement pair includes two satellite images. The imaging time interval between the two satellite images and the spatial distance between the satellites during imaging are both within a set threshold range. Extract pixel phase difference data from multiple target measurement pairs, convert the pixel phase difference data into initial deformation data of the slope, obtain initial deformation data for each target measurement pair, and the coherence coefficient of the two satellite images of the target measurement pair is greater than a threshold. Deformation interference data of the initial deformation data of multiple target measurement pairs is removed, and the phase value of the initial deformation data after removing the deformation interference data is adjusted to obtain the surface deformation data of multiple target measurement pairs. The cumulative deformation of the slope is calculated by using surface deformation data from multiple measuring points in the target measurement pair along the line of sight of the satellite radar, and the deformation field of the slope is obtained by filling in the cumulative deformation data of the multiple measuring points. Identify potential deformation zones of the slope based on the slope's deformation field, and assess the stability of the potential deformation zones.

2. The intelligent monitoring method for spoil heap slopes according to claim 1, characterized in that, The coherence coefficient of the two satellite images of the target measurement pair is: the coherence coefficient of the two satellite images at high coherence points. The process of selecting target measurement pairs includes: Multiple candidate measurement points are obtained by selecting measurement points with image amplitude values ​​greater than a threshold from multiple measurement points in each satellite image; If the spectral characteristics of the candidate measurement points are consistent across multiple satellite images, then the candidate measurement points are identified as highly coherent points, and multiple highly coherent points are obtained. The measurement pair containing at least one identical high coherence point is used as the target measurement pair.

3. The intelligent monitoring method for spoil heap slopes according to claim 1, characterized in that, The step of extracting pixel phase difference data from multiple target measurement pairs and converting the pixel phase difference data into initial deformation data of the slope includes: Spatially align the two satellite images for each target measurement pair so that there is a correspondence between the pixels of the two satellite images; Calculate the complex conjugate product of corresponding pixels in two satellite images, and use the phase part of the complex conjugate product as the pixel phase difference of corresponding pixels to obtain pixel phase difference data of multiple target measurement pairs. The pixel phase difference data is padded and formatted to obtain the initial deformation data.

4. The intelligent monitoring method for spoil heap slopes according to claim 1, characterized in that, The deformation interference data from which the initial deformation data of multiple target measurement pairs are removed includes: The three-dimensional topographic data of the slope is obtained, and a theoretical phase corresponding to the topographic undulation of the slope is simulated and generated based on the three-dimensional topographic data and the orbital parameters of the satellite radar. The theoretical phase and the noise phase are subtracted from the mixed phase of the initial deformation data to obtain the initial deformation data after removing the deformation interference data.

5. The intelligent monitoring method for spoil heap slopes according to claim 1, characterized in that, The adjustment of the phase value of the initial deformation data after removing the deformation interference data includes: Phase unwrapping is performed on the initial deformation data after removing the deformation interference data, and the phase values ​​with limited range are adjusted to unlimited, continuous true phase values.

6. The intelligent monitoring method for spoil heap slopes according to claim 1, characterized in that, The surface deformation data for each target measurement pair includes the phase difference of the same measurement point in the two satellite images of each target measurement pair; The calculation of the cumulative deformation at multiple measuring points along the line-of-sight direction of the satellite radar, based on surface deformation data from multiple measuring points at the target measurement center, includes: The phase difference at the same measurement point of each target measurement pair is converted into a displacement in the line-of-sight direction of the satellite radar; The displacement of the same measuring point of multiple target measurement pairs is summed to obtain the total displacement of the same measuring point of multiple target measurement pairs; Obtain the imaging time interval between the two satellite images in each target measurement pair, and calculate the time sum of multiple target measurement pairs; Calculate the average deformation rate of the same measuring point based on the sum of the time and the sum of the displacement at the same measuring point. The product of the average deformation rate and the duration of the observation period is taken as the cumulative deformation at the same measuring point. The cumulative deformation at multiple measuring points is obtained.

7. The intelligent monitoring method for spoil heap slopes according to claim 1, characterized in that, The process of obtaining the deformation field of the slope by filling in the cumulative deformation of multiple measuring points includes: The cumulative deformation of multiple measuring points is preprocessed. After data preprocessing, spatial interpolation is performed on the cumulative deformation of multiple measuring points to form a deformation distribution map of the slope, thereby obtaining the deformation field of the slope.

8. The intelligent monitoring method for spoil heap slopes according to claim 1, characterized in that, The step of identifying potential deformation zones of the slope based on the slope's deformation field and assessing the stability of the potential deformation zones includes: The potential deformation areas of the slope are identified based on the distribution pattern of deformation in the deformation field. Obtain the deformation amount of the potential deformation region and calculate the deformation rate. Determine the stability of the potential deformation region based on the magnitude of the deformation amount and the magnitude of the deformation rate.

9. An intelligent monitoring system for spoil heap slopes, characterized in that, The intelligent monitoring method for the slope of the spoil heap includes: The acquisition unit is used to acquire multiple satellite images of the slope within the observation period via satellite radar, divide the multiple satellite images into multiple measurement pairs, each measurement pair includes two satellite images, and the imaging time interval between the two satellite images and the spatial distance between the satellites during imaging are both within a set threshold range. An extraction unit is used to extract pixel phase difference data of multiple target measurement pairs, convert the pixel phase difference data into the initial deformation data of the slope, obtain the initial deformation data of each target measurement pair, and the coherence coefficient of the two satellite images of the target measurement pair is greater than a threshold. A separation unit is used to remove deformation interference data from the initial deformation data of multiple target measurement pairs, adjust the phase value of the initial deformation data after removing the deformation interference data, and obtain the surface deformation data of multiple target measurement pairs. The calculation unit is used to calculate the cumulative deformation of multiple measuring points in the line-of-sight direction of the satellite radar by using the surface deformation data of multiple measuring points in the target measurement pair, and to fill in the deformation field of the slope according to the cumulative deformation of multiple measuring points. The identification unit is used to identify potential deformation areas of the slope based on the deformation field of the slope, and to evaluate the stability of the potential deformation areas.

10. An electronic device, characterized in that, It includes at least one processor and a memory connected to the processor, wherein: The memory is used to store computer programs; The processor is used to execute the computer program to enable the electronic device to implement the intelligent monitoring method for spoil heap slopes as described in any one of claims 1 to 8.

Images