Square measurement method and system based on elevation measurement, medium and program product

Abstract

A method, system, medium and program product for measuring a volume based on elevation measurement, the method comprising: determining historical topographic feature data; elevation values of all positions in the mining area in different time periods are extracted; calculating a topographic relief change rule according to the elevation value, and establishing a topographic evolution model according to the topographic relief change rule; extracting terrain boundary feature points and ponding area contour lines from the unmanned aerial vehicle inclined image data; dividing a plurality of local terrain units according to the terrain boundary feature points, and calculating an elevation difference to obtain a gradient change coefficient; determining a terrain prediction model of each local terrain unit; splicing and reconstructing the terrain prediction model of each local terrain unit to obtain underwater terrain estimation data of the ponding area; constructing a mining area terrain model according to the underwater terrain estimation data and the terrain data; and calculating the earthwork excavation volume in the mining area according to the mining area terrain model. According to the invention, the accuracy of mining volume measurement is improved.

Description

Technical Field

[0001] The present application belongs to the field of data processing methods specifically used for prediction, and in particular, relates to a volume measurement method, system, medium and program product based on elevation measurement. Background Art

[0002] In the process of mining resources, accurate measurement of mining volume is of great significance for production planning and cost accounting. Traditional mining volume measurement methods mainly rely on manual ground measurement or total station measurement. These methods not only require surveyors to enter the mining site, which poses a safety hazard, but also have low measurement efficiency and difficulty in obtaining terrain change data in the mining area in a timely manner, which easily leads to lag and inaccuracy in mining volume statistics.

[0003] In related technologies, drones equipped with high-definition cameras can be used to take multi-angle aerial photos of the mining area, collect a large amount of oblique image data, and then use photogrammetry principles and three-dimensional reconstruction technology to generate a three-dimensional point cloud model of the mining area, and then calculate the mining volume. This method avoids the safety risks of manual on-site measurement and improves measurement efficiency and the timeliness of data acquisition.

[0004] However, when there is a lot of water or mud in the mining area, the image data collected by the drone is difficult to accurately reflect the terrain undulations under the water or mud due to the reflection of the water surface and the shielding effect of the mud, resulting in large errors in the calculation results of the mining volume. This problem is particularly prominent in the rainy season or in mining areas using wet mining technology, which reduces the accuracy of mining volume measurement. Summary of the invention

[0005] The present application provides a volume measurement method, system, medium and program product based on elevation measurement, which are used to improve the accuracy of mining volume measurement.

[0006] In the first aspect, the present application provides a volume measurement method based on elevation measurement, which selects image data of non-waterlogging periods from drone oblique image data of several historical periods of the mining area, and performs three-dimensional reconstruction on the image data of non-waterlogging periods to obtain historical terrain feature data of the mining area; Extract the elevation values ​​of various locations in the mining area at different times based on historical terrain feature data; Calculate the terrain fluctuation patterns of each location based on the elevation values ​​of each location at different time periods, and establish a terrain evolution model of the mining area based on the terrain fluctuation patterns of each location; Extract terrain boundary feature points and waterlogged area contour lines from the drone oblique image data of the current period; The mining area is divided into several local terrain units according to the terrain boundary feature points, and the elevation difference between adjacent positions in each local terrain unit is calculated to obtain the slope variation coefficient of each local terrain unit; The terrain evolution model is locally optimized in each local terrain unit using the slope variation coefficient to obtain the terrain prediction model of each local terrain unit; Based on the contour of the waterlogged area, the terrain prediction models of each local terrain unit are spliced ​​and reconstructed to obtain the underwater terrain estimation data of the waterlogged area; The underwater terrain estimation data is spatially registered and fused with the terrain data of the current non-waterlogged area to construct a terrain model of the mining area; The volume of earth excavation in the mining area is calculated based on the terrain model of the mining area.

[0007] By adopting the above technical solutions, data from non-waterlogging periods are selected from historical image data for three-dimensional reconstruction, the terrain characteristics of the mining area evolving over time are obtained, and a terrain evolution model is established to predict the trend of terrain changes. The mining area is divided into local terrain units and the slope change coefficient is calculated, so that the terrain evolution model can adapt to the terrain characteristics of different regions. The prediction models of each local terrain unit are spliced ​​and reconstructed using the contour lines of the waterlogged area to achieve a reasonable estimation of the underwater terrain. Through spatial registration and data fusion, the underwater terrain estimation data is organically combined with the terrain data of the non-waterlogged area to construct a complete terrain model of the mining area, which takes into account both the temporal nature of terrain changes and the spatial differences, making the calculation results of the earthwork excavation volume more accurate and reliable, and improving the accuracy of the mining volume measurement.

[0008] In conjunction with some embodiments of the first aspect, in some embodiments, a terrain evolution model of a mining area is established according to the terrain fluctuation change law of each location, specifically including: Calculate the elevation difference sequence between adjacent time periods at each location, and perform time series decomposition on the elevation difference sequence to obtain trend terms, periodic terms, and random terms; A long-term change prediction model is established based on the trend term. The long-term change prediction model is used to characterize the continuous changes in terrain caused by mining activities. The wavelet transform is used to perform multi-scale analysis on the periodic terms, extract the periodic characteristics at different time scales, and establish a periodic change prediction model based on the periodic characteristics; Construct probability distribution models based on random terms; The long-term change prediction model, periodic change prediction model and probability distribution model are integrated to obtain the terrain evolution model of the mining area.

[0009] By adopting the above technical solution, the elevation difference sequence is decomposed into trend items, periodic items and random items, and corresponding prediction models are established respectively, realizing a comprehensive description of the terrain change characteristics. The long-term change prediction model reflects the continuous changes caused by mining activities, and the wavelet transform is used to perform multi-scale analysis on the periodic items and establish a periodic change prediction model. At the same time, the probability distribution model of the random items is constructed to describe the uncertainty changes, which can capture the deterministic change law and random disturbance characteristics at the same time, and improve the accuracy and reliability of terrain prediction.

[0010] In combination with some embodiments of the first aspect, in some embodiments, the terrain model of the mining area is obtained by training the mapping relationship between terrain data and excavation volume through a deep neural network, with historical time series data as a training set and observation data within a preset time period as a verification set. The elevation data of the non-waterlogged area and the underwater terrain estimation data at the current moment are input into the terrain model of the mining area to obtain the earthwork excavation volume. The historical time series data includes time points, elevation data of the non-waterlogged area, underwater terrain estimation data and actual excavation volume.

[0011] By adopting the above technical solution, a deep neural network is used to train the mapping relationship between terrain data and excavation volume, making full use of the implicit features and nonlinear relationships contained in historical time series data. By dividing the historical data into training sets and validation sets, the generalization ability and prediction performance of the model are guaranteed. After the model training is completed, only the elevation data of the non-waterlogged area and the underwater terrain estimation data at the current moment need to be input to obtain the earthwork excavation volume, realizing fast and accurate volume calculation and improving work efficiency.

[0012] In combination with some embodiments of the first aspect, in some embodiments, after calculating the earth excavation volume in the mining area according to the terrain model of the mining area, the method further includes: The mining area is divided into a number of rectangular grid units of equal area, and the excavation volume data and excavation depth data in each grid unit are extracted; The excavation intensity value per unit area of ​​each grid unit is calculated based on the excavation volume data, and the excavation intensity value per unit area is compared with the preset safe excavation intensity threshold to obtain the excavation intensity exceeding limit area; Based on the spatial distribution characteristics of the area where the excavation intensity exceeds the limit, the mining area is divided into high-intensity mining area, medium-intensity mining area and low-intensity mining area using a spatial clustering algorithm.

[0013] By adopting the above technical solution, the mining area is refined using a grid division method, the excavation intensity value per unit area of ​​each grid unit is calculated and compared with the safety threshold, and the area where the excavation intensity exceeds the limit is identified. The mining area is divided into different intensity levels based on the spatial clustering algorithm, forming a spatial distribution characteristic map of the mining intensity, which can improve the accuracy of locating abnormal excavation intensity areas and reveal the spatial differences in mining activities. By dividing the mining area into high, medium and low intensity levels, it provides a basis for formulating differentiated mining strategies and safety management measures, which helps to achieve the balance and safety of mining activities. The regional division results based on spatial clustering have clear physical meanings and are convenient for management and application in engineering practice.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, calculating the excavation intensity value per unit area of ​​each grid unit according to the excavation volume data specifically includes: Count the cumulative excavation volume and excavation area of ​​each grid unit within the preset time window; The cumulative excavation volume is divided by the product of the excavation area and the preset time window to obtain the initial value of the excavation intensity per unit area.

[0015] By adopting the above technical solution, the cumulative excavation volume and excavation area of ​​the grid cells within the preset time window are counted, and the initial value of the excavation intensity per unit area is calculated, thereby achieving quantitative characterization of the mining intensity, reducing the limitations of evaluating the mining intensity based only on the static excavation volume, and making the calculation results of the mining intensity more practical.

[0016] In combination with some embodiments of the first aspect, in some embodiments, after dividing the mining area into a high-intensity mining area, a medium-intensity mining area, and a low-intensity mining area, the method further includes: Collect rainfall data and groundwater level monitoring data within the mining area; Calculate the rainfall infiltration of each grid unit based on rainfall data, and analyze the water content status in combination with groundwater level monitoring data; The preset safe excavation intensity threshold of each intensity mining area is dynamically adjusted based on the water content status to obtain a revised safe excavation intensity threshold.

[0017] By adopting the above technical solutions, rainfall data and groundwater level monitoring data are collected, and the water content is analyzed to dynamically adjust the safe excavation intensity threshold, so that the determination of the threshold is more in line with the actual geological conditions. The hydrological factors are introduced into the mining intensity control system, and a correlation mechanism between mining intensity and water content is established. By calculating the rainfall infiltration and combining the groundwater level changes, the impact of the water content in the mining area on the stability of the slope can be accurately evaluated, so as to make targeted adjustments to the safe excavation intensity threshold of mining areas with different intensities. This dynamic threshold adjustment mechanism based on water content improves the scientificity and adaptability of mining intensity control, and can reduce the risk of slope instability caused by changes in water content.

[0018] In combination with some embodiments of the first aspect, in some embodiments, the preset safe excavation intensity threshold of each intensity mining area is dynamically adjusted based on the water content state, specifically including: Calculate the correlation coefficient between the cumulative rainfall infiltration and groundwater level change of each grid cell; The influence weight of rainfall infiltration on groundwater level is determined based on the correlation coefficient; Calculate the reduction coefficient of water content on slope stability according to the impact weight; The preset safe excavation intensity threshold is corrected using the reduction coefficient to obtain a corrected safe excavation intensity threshold.

[0019] By adopting the above technical solution, the mechanism of rainfall infiltration on groundwater level change is revealed through correlation analysis, and this relationship is converted into specific impact weights. The reduction coefficient of water content on slope stability is calculated based on the impact weight, which realizes the quantitative expression of the impact of hydrological factors on slope stability. The reduction coefficient is used to correct the safe excavation intensity threshold, so that the corrected threshold not only takes into account the spatial heterogeneity of mining intensity, but also reflects the dynamic change characteristics of water content, thereby achieving more accurate and reliable mining intensity control.

[0020] In the second aspect, an embodiment of the present application provides a volume measurement system based on elevation measurement, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and one or more processors call the computer instructions to enable the system to execute the method described in the first aspect and any possible implementation method of the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, comprising instructions, which, when executed on a system, causes the system to execute the method described in the first aspect and any possible implementation of the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer program product, characterized in that when the computer program product runs on a system, the system executes the method described in any possible implementation manner in the first aspect.

[0023] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. The present application provides a volume measurement method based on elevation measurement, which selects data from non-waterlogging periods from historical image data for three-dimensional reconstruction, obtains the terrain characteristics of the mining area that evolves over time, and establishes a terrain evolution model to predict the trend of terrain changes. The mining area is divided into local terrain units and the slope change coefficient is calculated, so that the terrain evolution model can adapt to the terrain characteristics of different regions. The prediction models of each local terrain unit are spliced ​​and reconstructed using the contour lines of the waterlogged area to achieve a reasonable estimate of the underwater terrain. Through spatial registration and data fusion, the underwater terrain estimation data is organically combined with the terrain data of the non-waterlogged area to construct a complete terrain model of the mining area, which takes into account both the temporal nature of terrain changes and the spatial differences, making the calculation results of the earth excavation volume more accurate and reliable, and improving the accuracy of mining volume measurement.

[0024] 2. The present application provides a volume measurement method based on elevation measurement, which uses a grid division method to refine the mining area, calculates the excavation intensity value per unit area of ​​each grid unit and compares it with the safety threshold to identify areas where the excavation intensity exceeds the limit. The mining area is divided into different intensity levels based on the spatial clustering algorithm, forming a spatial distribution characteristic map of the mining intensity, which can improve the accuracy of locating areas with abnormal excavation intensity and reveal the spatial differences in mining activities. By dividing the mining area into high, medium and low intensity levels, it provides a basis for formulating differentiated mining strategies and safety management measures, which helps to achieve the balance and safety of mining activities. The regional division results based on spatial clustering have clear physical meanings and are convenient for management and application in engineering practice.

[0025] 3. The present application provides a volume measurement method based on elevation measurement, which collects rainfall data and groundwater level monitoring data, analyzes the water content, and dynamically adjusts the safe excavation intensity threshold to make the determination of the threshold more in line with actual geological conditions. The hydrological factors are introduced into the mining intensity management and control system, and a correlation mechanism between mining intensity and water content is established. By calculating the rainfall infiltration and combining the groundwater level changes, the impact of the water content in the mining area on the stability of the slope can be accurately assessed, so as to make targeted adjustments to the safe excavation intensity threshold of mining areas with different intensities. This dynamic threshold adjustment mechanism based on water content improves the scientificity and adaptability of mining intensity management and control, and can reduce the risk of slope instability caused by changes in water content. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a flow chart of a volume measurement method based on elevation measurement in an embodiment of the present application.

[0027] Figure 2 It is a flow chart of a mining intensity control method based on excavation volume in an embodiment of the present application.

[0028] Figure 3 This is a schematic diagram of the physical device structure of a volume measurement system based on elevation measurement provided in an embodiment of the present application. DETAILED DESCRIPTION

[0029] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification and appended claims of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more listed items.

[0030] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0031] The following uses an embodiment and combines Figure 1 , a method for measuring volume based on elevation measurement in an embodiment of the present application is described: See also Figure 1 , which is a flow chart of a volume measurement method based on elevation measurement in an embodiment of the present application.

[0032] S101, selecting image data of a non-waterlogging period from the drone oblique image data of several historical periods of the mining area, and performing three-dimensional reconstruction on the image data of the non-waterlogging period to obtain historical terrain feature data of the mining area; In this step, the system selects the image data of the non-waterlogging period from the drone oblique image data of several historical periods of the mining area, and performs three-dimensional reconstruction on the image data of the non-waterlogging period to obtain the historical terrain feature data of the mining area. Among them, the drone oblique image data refers to the image data set obtained by imaging the target area from different angles using multiple sensors equipped with drones. By matching and fusing images from different angles, a three-dimensional model of the target area can be reconstructed. By analyzing the characteristics of the water body in the image data, such as color, texture, reflectivity, etc., the system can identify whether there is waterlogging in the period corresponding to the image data, and then select the image data of the non-waterlogging period for subsequent processing.

[0033] In specific implementation, the system can use an image segmentation algorithm based on deep learning to perform semantic segmentation on the oblique image data of the drone, extract the pixel mask of the water area, and determine whether there is water accumulation in this period based on the proportion of water pixels. For the image data of the non-waterlogged period selected, the system can use a 3D reconstruction algorithm based on multi-view geometry, such as PMVS, MVE, etc., to generate a point cloud model and a mesh model of the mining area through steps such as feature point matching, sparse reconstruction, and dense reconstruction, and then obtain historical terrain feature data of different periods, including terrain attribute information such as elevation, slope, and convexity.

[0034] S102, extracting elevation values ​​of various locations in the mining area at different time periods based on historical terrain feature data; In this step, the system extracts the elevation values ​​of each location in the mining area at different times based on the historical terrain feature data. The elevation value represents the vertical height of a surface point relative to a reference surface (such as sea level), and is an important parameter for describing the changes in terrain undulations. By analyzing the terrain feature data of different periods, the elevation values ​​of each location in the mining area at different historical moments can be obtained, providing data support for the subsequent establishment of a terrain evolution model.

[0035] In specific implementation, the system can use spatial interpolation algorithms to interpolate discrete point clouds or grid models onto regular grids to generate digital elevation models (DEMs) of the mining area. Common spatial interpolation algorithms include Kriging interpolation, inverse distance weighted interpolation, spline interpolation, etc. The system can select appropriate interpolation algorithms and optimize interpolation parameters to generate elevation raster data for different time periods based on data characteristics and accuracy requirements. Then, the system can extract the elevation value sequence of each location at different time periods through the coordinate index of the raster data and construct a two-dimensional space-time table.

[0036] S103, calculating the terrain fluctuation variation law of each location according to the elevation value of each location at different time periods, and establishing a terrain evolution model of the mining area according to the terrain fluctuation variation law of each location; The system calculates the terrain undulation change law of each location according to the elevation value of each location in different time periods, and establishes the terrain evolution model of the mining area according to the terrain undulation change law of each location. Specifically: the elevation difference sequence between adjacent time periods of each location is calculated, and the elevation difference sequence is decomposed into time series to obtain trend terms, periodic terms and random terms; a long-term change prediction model is established based on the trend term, and the long-term change prediction model is used to characterize the continuous changes in terrain caused by mining activities; the periodic terms are analyzed at multiple scales using wavelet transform to extract the periodic characteristics at different time scales, and a periodic change prediction model is established based on the periodic characteristics; a probability distribution model is constructed based on the random term; the long-term change prediction model, the periodic change prediction model and the probability distribution model are integrated to obtain the terrain evolution model of the mining area.

[0037] In this step, the system calculates the terrain fluctuation change law of each location based on the elevation values ​​of each location at different time periods, and establishes a terrain evolution model of the mining area based on the terrain fluctuation change law of each location. The terrain fluctuation change law reflects the change trend and periodic characteristics of the elevation value of each location over time, and is the key to describing the dynamic evolution process of the terrain. By analyzing and modeling the elevation time series data, the terrain fluctuation state at future moments can be predicted, providing a basis for the dynamic evaluation of the mining volume.

[0038] In specific implementation, the system can use time series decomposition algorithms, such as STL decomposition, EMD decomposition, etc., to decompose the elevation time series data into trend items, periodic items and random items. Among them, the trend item reflects the evolution trend of the terrain on a long-term scale, and can be fitted using methods such as polynomial regression and local weighted regression to establish a long-term change prediction model. The periodic item reflects the periodic fluctuation characteristics of the terrain on different time scales. The periodic components of different frequencies can be extracted using methods such as Fourier transform and wavelet transform, and the periodic change prediction model can be established using autoregressive models, ARIMA models, etc. The random item reflects the random disturbance and uncertainty in the terrain change, and the probability density estimation method, such as kernel density estimation, Gaussian mixture model, etc., can be used to construct a probability distribution model. Finally, the system can perform a weighted combination of the long-term change prediction model, the periodic change prediction model and the probability distribution model to obtain a comprehensive terrain evolution model for predicting the terrain undulation state at any time.

[0039] It should be noted that since mining activities have significant human intervention characteristics in changing terrain, it is necessary to fully consider the influence of factors such as mining technology and mining intensity when establishing a terrain evolution model. For example, the system can introduce external factors such as mining plans and working parameters of mining machinery and equipment to construct a conditional probability distribution model to characterize the probability characteristics of terrain changes under different mining conditions. In addition, the system can also use machine learning algorithms, such as support vector machines and random forests, to establish a nonlinear relationship mapping between terrain changes and mining processes, and learn the influence of mining activities on terrain evolution by training historical data, thereby improving the prediction accuracy and applicability of the terrain evolution model.

[0040] S104, extracting terrain boundary feature points and waterlogged area contour lines from the drone oblique image data of the current period; In this step, the system extracts terrain boundary feature points and waterlogged area contour lines from the drone oblique image data of the current period. Terrain boundary feature points refer to locations where the terrain undulations are more drastic, such as ridge lines and valley lines, and are of great reference significance in terrain analysis. The waterlogged area contour lines depict the spatial boundaries of the waterlogged range and are key information for determining the waterlogged state of the mining area. By extracting these two types of features, a detailed depiction of the terrain and waterlogged conditions in the mining area can be achieved.

[0041] In specific implementation, the system can use computer vision and image processing algorithms to perform feature extraction and edge detection on drone oblique images. For the extraction of terrain boundary feature points, the system can use algorithms based on local neighborhood analysis, such as Harris corner detection, SIFT feature point detection, etc., to find the key points of terrain changes by calculating geometric features such as gradients and curvatures in the neighborhood of pixel points. For the extraction of the contour line of the waterlogged area, the system can use segmentation algorithms based on regional growth, such as regional growth method, graph cut method, etc., by setting the seed point of the waterlogged area and expanding the area based on the similarity criterion of pixel values, to obtain a complete waterlogged area mask, and then extract the contour line of the waterlogged area through edge detection algorithms, such as Canny operator, Sobel operator, etc.

[0042] S105, dividing the mining area into a number of local terrain units according to the terrain boundary feature points, and calculating the elevation difference between adjacent positions in each local terrain unit to obtain the slope variation coefficient of each local terrain unit; In this step, the system divides the mining area into several local terrain units according to the terrain boundary feature points, and calculates the elevation difference between adjacent positions in each local terrain unit to obtain the slope variation coefficient of each local terrain unit. Local terrain units refer to areas with relatively consistent terrain undulation characteristics. By dividing the entire mining area into several local terrain units, the terrain characteristics of different areas can be more carefully portrayed. The slope variation coefficient reflects the degree of undulation within the local terrain unit and is an important indicator for evaluating terrain complexity.

[0043] In specific implementation, the system can use spatial clustering algorithms based on graph theory, such as Delaunay triangulation, Voronoi diagram, etc., to construct irregular triangulated networks (TIN) or Voronoi diagrams according to terrain boundary feature points, and divide the mining area into several non-overlapping polygonal areas as local terrain units. Then, the system can traverse the elevation grid data in each local terrain unit, calculate the height difference between adjacent grid points, and count the mean, variance, maximum value and other statistics of the height difference as the slope variation coefficient of the terrain unit. The larger the slope variation coefficient, the more drastic the elevation change inside the terrain unit and the more complex the terrain undulation.

[0044] S106, locally optimizing the terrain evolution model in each local terrain unit using the slope variation coefficient to obtain a terrain prediction model for each local terrain unit; In this step, the system uses the slope change coefficient to locally optimize the terrain evolution model in each local terrain unit to obtain the terrain prediction model of each local terrain unit. The terrain evolution model describes the overall terrain change trend of the mining area, but due to the large differences in terrain undulation characteristics of different local terrain units, it may be difficult to take into account the details of local terrain changes by directly applying the overall model for prediction. Therefore, it is necessary to optimize the terrain evolution model at the local scale to improve the accuracy and reliability of terrain prediction.

[0045] In specific implementation, the system can adopt a hierarchical optimization strategy to fine-tune and adapt the terrain evolution model on each local terrain unit. First, the system can compare the output results of the terrain evolution model, that is, the predicted elevation value with the actual elevation value, and calculate the prediction error in each local terrain unit. Then, the system can construct an error correction function to characterize the relationship between the prediction error and the slope variation coefficient, that is, the more complex the terrain undulation, the larger the prediction error may be. Finally, the system can use the error correction function to locally adjust the prediction results of the terrain evolution model to obtain the optimized prediction model of each local terrain unit. The optimized prediction model can better adapt to the undulating characteristics of different terrain units and improve the prediction accuracy of local areas.

[0046] S107, splicing and reconstructing the terrain prediction models of each local terrain unit based on the contour line of the waterlogged area to obtain underwater terrain estimation data of the waterlogged area; In this step, the system splices and reconstructs the terrain prediction models of each local terrain unit based on the contour of the waterlogged area to obtain the underwater terrain estimation data of the waterlogged area. Since the elevation data cannot be directly obtained inside the waterlogged area, it is necessary to estimate the underwater terrain by splicing the terrain prediction results of the surrounding non-waterlogged areas. The splicing and reconstruction process needs to consider the connection relationship and transition characteristics between different terrain units to ensure the continuity and rationality of the estimation results.

[0047] In specific implementation, the system can use terrain splicing algorithms based on spatial interpolation, such as Kriging interpolation and spline interpolation, etc., and use the local terrain prediction model around the waterlogged area as known sample points to perform spatial interpolation on the terrain inside the waterlogged area. First, the system can extract discrete points on the contour line of the waterlogged area as interpolation control points, and perform a buffer analysis of a certain distance from the contour line to the inside to obtain a transition area close to the contour line. Then, the system can select a certain number of virtual sample points in the transition area, and use the spatial interpolation model to estimate the terrain elevation at any position inside the waterlogged area based on the terrain prediction values ​​of the control points and virtual sample points. Finally, the system can smooth the interpolation results, eliminate mutations and discontinuities at the splicing boundaries, and obtain complete underwater terrain estimation data for the waterlogged area.

[0048] S108, performing spatial registration and data fusion on the underwater terrain estimation data and the terrain data of the current non-waterlogged area to construct a terrain model of the mining area; The system spatially registers and fuses the underwater terrain estimation data with the terrain data of the current non-waterlogging area to construct a terrain model of the mining area. The terrain model of the mining area is based on historical time series data as a training set and observation data within a preset time period as a validation set. The mapping relationship between terrain data and excavation volume is trained through a deep neural network. The current non-waterlogging area elevation data and underwater terrain estimation data are input into the mining area terrain model to obtain the earthwork excavation volume. The historical time series data includes time points, non-waterlogging area elevation data, underwater terrain estimation data and actual excavation volume.

[0049] In this step, the system spatially registers and fuses the underwater terrain estimation data with the terrain data of the current non-waterlogged area to construct a complete terrain model of the mining area. Since the underwater terrain estimation data and the terrain data of the non-waterlogged area come from different data acquisition and processing processes, there may be differences in spatial coordinate system, data format and accuracy. In order to build a unified terrain model of the mining area, it is necessary to spatially register and fuse the two parts of the data to eliminate the inconsistency between the data.

[0050] In specific implementation, the system can use registration methods based on spatial transformation, such as affine transformation, projection transformation, etc., to unify the underwater terrain estimation data and the terrain data of the non-waterlogged area into the same coordinate system. First, the system can select a certain number of control points, such as terrain feature points, permanent ground object points, etc., to match the two parts of the data. Then, the system can use least squares estimation, RANSAC and other algorithms to estimate the optimal spatial transformation parameters and convert the coordinates of the underwater terrain estimation data to the coordinate system of the terrain data of the non-waterlogged area. Finally, the system can use spatial interpolation algorithms, such as bilinear interpolation, bicubic interpolation, etc., to resample the registered underwater terrain estimation data so that it has the same spatial resolution and grid size as the terrain data of the non-waterlogged area. Through spatial registration, the deviation of the geometric position of the two parts of the data can be eliminated.

[0051] In terms of data fusion, the system can use weighted average, Markov random field and other methods to comprehensively consider the accuracy, reliability and other factors of different data sources to fuse the registered underwater terrain estimation data and non-waterlogged area terrain data. For example, the system can calculate the weight coefficient of each data source based on the accuracy evaluation indicators of different data sources, such as mean square error, correlation coefficient, etc. Then, the system can use the weighted average model to perform weighted fusion on the elevation values ​​of the overlapping areas to obtain a unified terrain surface. At the same time, the system can also introduce the Markov random field model, consider the spatial correlation between adjacent grid points, and smooth and optimize the fusion results to improve the continuity and authenticity of the terrain surface.

[0052] S109. Calculate the earth excavation volume in the mining area according to the terrain model of the mining area.

[0053] In this step, the system calculates the volume of earth excavation in the mining area based on the constructed mining area terrain model. The excavation volume refers to the volume of earth and stone actually excavated during mining activities, and is an important indicator for mine production management and cost accounting. By analyzing the elevation changes of the mining area terrain model, the spatial distribution and quantity scale of the excavated earth can be quantitatively evaluated, providing a basis for the preparation and implementation of the mining plan.

[0054] In specific implementation, the system can use the difference analysis method of the digital elevation model (DEM) to calculate the elevation change between the terrain models in the two periods before and after mining, and obtain the spatial distribution of excavated earth. First, the system can spatially match and align the terrain model data in the two periods before and after mining to ensure the consistency of the two models in coordinate system and grid size. Then, the system can perform pixel-by-pixel difference calculation on the elevation values ​​of the two models to obtain the elevation change of each grid point. Finally, the system can accumulate and sum the positive change (i.e., elevation reduction) to obtain the total volume of excavated earth. At the same time, the system can also use spatial analysis functions, such as zoning statistics and volume calculation, to quantitatively analyze the spatial distribution characteristics of excavated earth, such as calculating the excavation volume in different regions and different elevation segments, and drawing a spatial distribution map of excavation depth.

[0055] In the above embodiment, data from non-waterlogging periods are selected from historical image data for three-dimensional reconstruction, the terrain characteristics of the mining area evolving over time are obtained, and a terrain evolution model is established to predict the trend of terrain changes. The mining area is divided into local terrain units and the slope change coefficient is calculated, so that the terrain evolution model can adapt to the terrain characteristics of different regions. The prediction models of each local terrain unit are spliced ​​and reconstructed using the contour lines of the waterlogged area, thereby achieving a reasonable estimation of the underwater terrain. Through spatial registration and data fusion, the underwater terrain estimation data is organically combined with the terrain data of the non-waterlogged area to construct a complete terrain model of the mining area, which takes into account both the temporal nature of terrain changes and the spatial differences, making the calculation results of the earth excavation volume more accurate and reliable, and improving the accuracy of the mining volume measurement.

[0056] After calculating the earth excavation volume in the mining area according to the terrain model of the mining area, in order to further ensure the safety and rationality of the mining activities, the embodiment of the present application also provides a mining intensity control method based on the excavation volume. This method realizes dynamic monitoring and threshold adjustment of mining intensity by grid analysis of the mining area and combining hydrological factors such as rainfall and groundwater. Figure 2 , a method for controlling mining intensity based on excavation volume in an embodiment of the present application is described: See also Figure 2 , which is a flow chart of a mining intensity control method based on excavation volume in an embodiment of the present application.

[0057] S201, dividing the mining area into a number of rectangular grid units of equal area, and extracting excavation volume data and excavation depth data in each grid unit; This step is to grid the mining area and divide the entire area into multiple rectangular grid units of equal area. The purpose of this is to facilitate the subsequent independent analysis and calculation of each grid unit. After the division is completed, the system needs to extract the excavation volume data and excavation depth data in each grid unit, which is the basis for subsequent calculation and analysis.

[0058] The system can use GIS (Geographic Information System) technology to grid the mining area. First, the system obtains the vector boundary data of the mining area, and then divides the area into several rectangular grid units according to the preset grid size. The selection of grid size needs to comprehensively consider the calculation efficiency and accuracy requirements. After the division is completed, the system can use spatial database technology to quickly extract the excavation volume and excavation depth data in each grid unit through spatial query and statistical analysis.

[0059] S202, calculating the excavation intensity value per unit area of ​​each grid unit according to the excavation volume data, and comparing the excavation intensity value per unit area with a preset safe excavation intensity threshold to obtain an excavation intensity exceeding limit area; The system calculates the excavation intensity per unit area of ​​each grid unit based on the excavation volume data. Specifically, the cumulative excavation volume and excavation area of ​​each grid unit within the preset time window are counted; the cumulative excavation volume is divided by the product of the excavation area and the preset time window to obtain the initial value of the excavation intensity per unit area. After that, the system compares the excavation intensity per unit area with the preset safe excavation intensity threshold to obtain the excavation intensity exceeding limit area.

[0060] After obtaining the excavation volume data of each grid unit, the system needs to calculate the excavation intensity value per unit area. The excavation intensity value per unit area reflects the mining intensity of each grid unit and is an important indicator for measuring the impact of mining activities on the environment. After the calculation is completed, the system compares the excavation intensity value per unit area with the preset safe excavation intensity threshold and identifies the area where the excavation intensity exceeds the threshold, that is, the excavation intensity exceeds the limit area.

[0061] The system can calculate the excavation intensity value per unit area using the following formula: Excavation intensity value per unit area = grid unit excavation volume divided by grid unit area. Among them, grid unit excavation volume refers to the cumulative excavation volume within a grid unit within a certain period of time, and grid unit area refers to the horizontal projection area of ​​the grid unit. The preset safe excavation intensity threshold can be determined based on geological conditions, environmental requirements and other factors, or it can be obtained through empirical values ​​or reference standards. The system compares the calculated excavation intensity value per unit area with the threshold to identify the over-limit area.

[0062] When calculating the excavation intensity value per unit area, there may be an uneven distribution of excavation intensity within the grid unit, resulting in the calculation result not accurately reflecting the actual excavation intensity of the local area. To solve this problem, the system can introduce sub-grid technology to further divide the grid unit into smaller sub-grids, calculate the excavation intensity values ​​of the sub-grids separately, and then obtain the comprehensive excavation intensity value of the grid unit by weighted average or other methods. In addition, the system can also consider the excavation depth factor, and correct the excavation intensity value per unit area according to the excavation depth to obtain a more accurate excavation strength assessment result.

[0063] S203, based on the spatial distribution characteristics of the excavation intensity exceeding limit area, the mining area is divided into a high intensity mining area, a medium intensity mining area and a low intensity mining area by using a spatial clustering algorithm; After identifying the area where the excavation intensity exceeds the limit, the system needs to further analyze the spatial distribution characteristics of the area and divide the mining area into mining areas with different intensity levels. The spatial clustering algorithm is a commonly used spatial data analysis method that can cluster objects that are adjacent in space and have similar attributes to form different areas. In this step, the system uses a spatial clustering algorithm to divide the mining area into high-intensity mining areas, medium-intensity mining areas, and low-intensity mining areas according to the spatial distribution of the area where the excavation intensity exceeds the limit, providing a basis for subsequent differentiated management.

[0064] The system can use density-based spatial clustering algorithms (such as DBSCAN) or partition-based spatial clustering algorithms (such as K-means) to divide the mining area. Taking the DBSCAN algorithm as an example, the system first identifies the high-density area as the cluster center based on the distribution density of the excavation intensity exceeding the limit area, and then continuously aggregates the objects with density reachable into the corresponding cluster until all objects are assigned to the cluster or marked as noise. After clustering is completed, the system marks the cluster as a high-intensity mining area, a medium-intensity mining area, or a low-intensity mining area based on the average excavation intensity value of each cluster.

[0065] S204, collecting rainfall data and groundwater level monitoring data in the mining area; The system needs to collect rainfall data and groundwater level monitoring data in the mining area, which are important bases for analyzing water content and evaluating slope stability. Rainfall data reflects the recharge of atmospheric precipitation to surface and groundwater, and groundwater level monitoring data reflects the dynamic changes of groundwater. The system can automatically collect and transmit these data by deploying equipment such as rain gauges and water level monitoring wells.

[0066] S205, calculating the rainfall infiltration of each grid unit according to the rainfall data, and analyzing the water content state in combination with the groundwater level monitoring data; Rainfall infiltration refers to the process of rainfall infiltrating through the surface into the ground and replenishing groundwater. The amount of rainfall infiltration directly affects the change of groundwater level, which in turn affects the water content and stability of the slope. In this step, the system calculates the rainfall infiltration of each grid unit based on the collected rainfall data, and then combines the groundwater level monitoring data to comprehensively analyze the water content of the mining area.

[0067] The system can calculate rainfall infiltration using the water balance method. The basic principle is: rainfall infiltration = rainfall - surface runoff - evaporation. Among them, rainfall is directly monitored, surface runoff can be estimated by the runoff coefficient, and evaporation can be estimated by an evaporation dish or empirical formula. The system will analyze the correlation between the calculated rainfall infiltration and the groundwater level change, establish a rainfall infiltration and groundwater level response relationship model, and evaluate the water content. The water content can be characterized by indicators such as saturation and water content, or other comprehensive indicators can be defined according to actual needs.

[0068] S206. Dynamically adjust the preset safe excavation intensity threshold of each intensity mining area based on the water content status to obtain a revised safe excavation intensity threshold.

[0069] The system dynamically adjusts the preset safe excavation intensity threshold of each intensity mining area based on the water content status to obtain the revised safe excavation intensity threshold. Specifically: the correlation coefficient between the cumulative rainfall infiltration of each grid unit and the groundwater level change is calculated; the impact weight of rainfall infiltration on the groundwater level is determined based on the correlation coefficient; the reduction coefficient of the water content on the slope stability is calculated according to the impact weight; the preset safe excavation intensity threshold is corrected using the reduction coefficient to obtain the revised safe excavation intensity threshold.

[0070] The water content is an important factor affecting the stability of the slope. Changes in the water content will lead to changes in soil strength and overall stability. Therefore, it is necessary to dynamically adjust the original safe excavation intensity threshold according to the changes in the water content. In this step, the system corrects the preset safe excavation intensity thresholds for high-intensity mining areas, medium-intensity mining areas, and low-intensity mining areas based on the water content obtained in the previous step to obtain the corrected thresholds. The corrected thresholds can more accurately reflect the stability requirements of the slope and guide the intensity control of mining activities.

[0071] The system can use threshold adjustment technology based on fuzzy reasoning to establish a fuzzy relationship matrix between water content status and safe excavation intensity threshold, and then calculate the corrected threshold through fuzzy reasoning based on the real-time monitored water content status. For example, when the water content status index exceeds the membership threshold of a certain fuzzy set, the system multiplies the original threshold by the corresponding correction coefficient to obtain the corrected threshold. The correction coefficient can be obtained through expert experience or data analysis. The system can also use adaptive threshold adjustment technology to automatically learn and optimize threshold adjustment rules through real-time monitoring data and historical data to achieve dynamic update of thresholds.

[0072] When adjusting the threshold, there may be unreasonable adjustment ranges, resulting in the problem that the corrected threshold is too high or too low. A threshold that is too high will lead to excessive restrictions on mining intensity, affecting mining efficiency; a threshold that is too low will increase the risk of slope stability. In order to solve this problem, the system can introduce threshold constraint technology to set upper and lower limits for threshold adjustment to prevent the threshold adjustment from exceeding a reasonable range. At the same time, the system can also use risk assessment technology to comprehensively consider multiple factors such as water content, excavation intensity, slope structure, etc., to assess the risk of slope instability, and use risk as a constraint to optimize the threshold adjustment strategy, while ensuring slope stability and maximizing mining efficiency.

[0073] In the above embodiment, the mining area is refined by using a grid division method, the excavation intensity value per unit area of ​​each grid unit is calculated and compared with the safety threshold, and the area where the excavation intensity exceeds the limit is identified. The mining area is divided into different intensity levels based on the spatial clustering algorithm, forming a spatial distribution characteristic map of the mining intensity, which can improve the accuracy of locating abnormal excavation intensity areas and reveal the spatial differences in mining activities. By dividing the mining area into high, medium and low intensity levels, it provides a basis for formulating differentiated mining strategies and safety management measures, which helps to achieve the balance and safety of mining activities. The regional division results based on spatial clustering have clear physical meanings and are convenient for management and application in engineering practice.

[0074] The following describes the system in the embodiment of the present invention from the perspective of hardware processing. Figure 3 , which is a schematic diagram of the physical device structure of a volume measurement system based on elevation measurement provided in an embodiment of the present application.

[0075] It should be noted that Figure 3 The structure of the system shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0076] like Figure 3As shown, the system includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage part 308 to the random access memory (RAM) 303, such as executing the method in the above embodiment. In RAM 303, various programs and data required for system operation are also stored. CPU 301, ROM 302 and RAM 303 are connected to each other through a bus 304. Input / output (I / O) interface 305 is also connected to bus 304.

[0077] The following components are connected to the I / O interface 305: an input section 306 including a camera, an infrared sensor, etc.; an output section 307 including a liquid crystal display (LCD) and a speaker, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read therefrom is installed into the storage section 308 as needed.

[0078] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 309, and / or installed from a removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, various functions defined in the present invention are performed.

[0079] It should be noted that the computer-readable medium shown in the embodiment of the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable computer program. Such a propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing.

[0080] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Among them, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0081] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the system described in the above embodiment; or may exist independently without being assembled into the system. The above storage medium carries one or more computer programs, and when the above one or more computer programs are executed by a processor of a system, the system implements the method provided in the above embodiment.

[0082] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0083] As used in the above embodiments, the term "when..." may be interpreted to mean "if..." or "after..." or "in response to determining..." or "in response to detecting...", depending on the context. Similarly, the phrases "upon determining..." or "if (the stated condition or event) is detected" may be interpreted to mean "if determining..." or "in response to determining..." or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)", depending on the context.

[0084] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. 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 computer-readable storage medium. For example, the computer instructions may be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk), etc.

[0085] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

Claims

1. A method for volume measurement based on elevation measurement, characterized in that: include: Screening out image data of non-waterlogging periods from drone oblique image data of several historical periods of the mining area, and performing three-dimensional reconstruction on the image data of the non-waterlogging period to obtain historical terrain feature data of the mining area; Extracting elevation values ​​of various locations in the mining area at different time periods based on the historical terrain feature data; Calculating the terrain fluctuation variation law of each location according to the elevation values ​​of each location at different time periods, and establishing a terrain evolution model of the mining area according to the terrain fluctuation variation law of each location; Extract terrain boundary feature points and waterlogged area contour lines from the drone oblique image data of the current period; Dividing the mining area into a number of local terrain units according to the terrain boundary feature points, and calculating the elevation difference between adjacent positions in each local terrain unit to obtain the slope variation coefficient of each local terrain unit; Using the slope variation coefficient, locally optimize the terrain evolution model in each of the local terrain units to obtain a terrain prediction model for each of the local terrain units; Based on the outline of the waterlogged area, the terrain prediction models of the local terrain units are spliced ​​and reconstructed to obtain underwater terrain estimation data of the waterlogged area; The underwater terrain estimation data is spatially aligned and data-fused with the terrain data of the current non-waterlogged area to construct a terrain model of the mining area; The earth excavation volume in the mining area is calculated according to the terrain model of the mining area.

2. The method according to claim 1, characterized in that The establishing of the terrain evolution model of the mining area according to the terrain fluctuation change rules of each location specifically includes: Calculating the elevation difference sequence between adjacent time periods of each position, and performing time series decomposition on the elevation difference sequence to obtain trend terms, periodic terms and random terms; Establishing a long-term change prediction model based on the trend item, wherein the long-term change prediction model is used to characterize the continuous change of terrain caused by mining activities; Performing multi-scale analysis on the periodic term by using wavelet transform, extracting periodic features at different time scales, and establishing a periodic change prediction model based on the periodic features; constructing a probability distribution model based on the random term; The long-term change prediction model, the periodic change prediction model and the probability distribution model are integrated to obtain a terrain evolution model of the mining area.

3. The method according to claim 1, characterized in that The terrain model of the mining area is obtained by training the mapping relationship between the terrain data and the excavation volume through a deep neural network using historical time series data as a training set and observation data within a preset time period as a verification set. The non-waterlogged area elevation data and underwater terrain estimation data at the current moment are input into the terrain model of the mining area to obtain the earthwork excavation volume. The historical time series data includes a time point, the non-waterlogged area elevation data, the underwater terrain estimation data and the actual excavation volume.

4. The method according to claim 1, characterized in that: After calculating the earthwork excavation volume in the mining area according to the mining area terrain model, the method further includes: Dividing the mining area into a plurality of rectangular grid units of equal area, and extracting excavation volume data and excavation depth data in each of the grid units; Calculating the excavation intensity value per unit area of ​​each grid unit according to the excavation volume data, and comparing the excavation intensity value per unit area with a preset safe excavation intensity threshold to obtain an excavation intensity exceeding limit area; Based on the spatial distribution characteristics of the excavation intensity exceeding limit area, a spatial clustering algorithm is used to divide the mining area into a high-intensity mining area, a medium-intensity mining area and a low-intensity mining area.

5. The method according to claim 4, characterized in that The calculation of the excavation intensity value per unit area of ​​each grid unit according to the excavation volume data specifically includes: Counting the cumulative excavation volume and excavation area of ​​each grid unit within a preset time window; The accumulated excavation volume is divided by the product of the excavation area and the preset time window to obtain an initial value of the excavation intensity per unit area.

6. The method according to claim 4 or 5, characterized in that: After dividing the mining area into a high-intensity mining area, a medium-intensity mining area and a low-intensity mining area, the method further comprises: Collecting rainfall data and groundwater level monitoring data in the mining area; Calculating the rainfall infiltration of each grid unit according to the rainfall data, and analyzing the water content state in combination with the groundwater level monitoring data; The preset safe excavation intensity threshold of each intensity mining area is dynamically adjusted based on the water content state to obtain a revised safe excavation intensity threshold.

7. The method according to claim 6, characterized in that The dynamically adjusting the preset safe excavation intensity threshold of each intensity mining area based on the water content state specifically includes: Calculating the correlation coefficient between the cumulative rainfall infiltration and the groundwater level change of each grid unit; Determining the impact weight of rainfall infiltration on groundwater level based on the correlation coefficient; Calculate the reduction coefficient of the water content on the slope stability according to the influence weight; The preset safe excavation intensity threshold is corrected using the reduction coefficient to obtain a corrected safe excavation intensity threshold.

8. A volume measurement system based on elevation measurement, characterized in that: The system comprises: One or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the system to execute the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on a system, the system is caused to execute the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product is run on a system, the system is caused to execute the method according to any one of claims 1 to 7.

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