A method, system, device and medium for monitoring annual earthwork changes of an open-pit mine based on an analog-digital elevation model
By using differential operations and multi-dimensional rule set screening in a simulated digital elevation model, combined with linear fitting and clustering, the problems of high frequency and accuracy in monitoring annual earthwork changes in open-pit mines were solved, achieving low-cost and efficient monitoring results.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- INST OF MINERAL RESOURCES CHINESE ACAD OF GEOLOGICAL SCI
- Filing Date
- 2026-03-17
- Publication Date
- 2026-06-09
Smart Images
Figure CN122176342A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of remote sensing monitoring, and in particular to a method, system, equipment and medium for monitoring interannual earthwork and rock changes in open-pit mines based on a simulated digital elevation model. Background Technology
[0002] The interannual volume variation of open-pit mining is a core indicator for measuring resource consumption and ecological disturbance, and accurate monitoring is crucial for mine management. Traditional monitoring relies on manual measurement, LiDAR, or stereo image pairs, which suffers from high costs and difficulty in achieving high-frequency continuous monitoring.
[0003] In recent years, artificial intelligence-generated simulated DEM (Pseudo DEM) technology has emerged, which can invert the surface elevation using only two-dimensional RGB images, greatly reducing monitoring costs and providing a new path for high-frequency volume monitoring in open-pit mines.
[0004] However, the current monitoring methods based on Pseudo DEM still have significant shortcomings: there is a lack of systematic volume estimation and error correction mechanisms based on time series DEM (Digital Elevation Model) differences; there are no scientific screening rules for slope, vegetation cover and uncertainty constraints, resulting in large estimation errors; and the aggregation statistics at the plot and mining area levels are incomplete, failing to truly reflect the extent of surface disturbance.
[0005] In summary, existing methods are insufficient to meet the needs of accurate and efficient monitoring in open-pit mines, making the development of relevant improvement technologies of great significance. Summary of the Invention
[0006] The purpose of this application is to provide a method, system, equipment and medium for monitoring interannual earthwork changes in open-pit mines based on a simulated digital elevation model. This method can meet the needs of accurate and efficient monitoring in open-pit mines, realize the end-to-end process from two-dimensional remote sensing images to monitoring volume changes in open-pit mines, and has the advantages of low cost, high frequency and quantification.
[0007] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a method for monitoring interannual earthwork and rockfall variations in open-pit mines based on a simulated digital elevation model, including: Remote sensing images of the same open-pit mine at different times are acquired, and corresponding simulated digital elevation models are generated through three-dimensional inversion using artificial intelligence neural networks or stereo image pairs; the simulated digital elevation model sequence contains pixel-level elevation values and pixel-level variance; Differential calculations are performed on the simulated digital elevation models of adjacent time phases to obtain an elevation change raster. The elevation change raster is filtered based on a multi-dimensional rule set to generate candidate change areas; the multi-dimensional rule set includes: vegetation index filtering rules, slope filtering rules, uncertainty filtering rules, and continuity filtering rules. For each candidate patch within the candidate change region, a multi-temporal elevation change sequence is extracted; and the multi-temporal elevation change sequence is linearly fitted, and the filtered candidate patches are obtained based on the goodness of fit. The selected candidate patches are clustered into mining area units based on spatial proximity. The volume changes of all pixels within each mining area unit are accumulated to calculate the annual volume change of earthwork in open-pit mines, thereby realizing the monitoring of annual earthwork changes in open-pit mines.
[0008] Secondly, this application provides a monitoring system for interannual earthwork variation in open-pit mines based on a simulated digital elevation model. The system is used to execute the aforementioned monitoring method for interannual earthwork variation in open-pit mines based on a simulated digital elevation model. The system includes: The simulated digital elevation model generation module is used to acquire remote sensing images of the same open-pit mine at different time phases, and generate corresponding simulated digital elevation models through artificial intelligence neural networks or stereo image pairs in 3D inversion; the simulated digital elevation model sequence contains pixel-level elevation values and pixel-level variance. The differential operation module is used to perform differential operations on the analog digital elevation model of adjacent time phases to obtain the elevation change raster. The candidate change region generation module is used to filter the elevation change raster based on a multi-dimensional rule set to generate candidate change regions; the multi-dimensional rule set includes: vegetation index filtering rules, slope filtering rules, uncertainty filtering rules, and continuity filtering rules; The candidate patch determination module after screening is used to extract a multi-temporal elevation change sequence for each candidate patch within the candidate change region; and to perform linear fitting on the multi-temporal elevation change sequence to obtain the screened candidate patches based on the goodness of fit. The volume change calculation module is used to cluster the selected candidate patches into mining area units based on spatial proximity, accumulate the volume changes of all pixels in each mining area unit, calculate the annual volume change of earthwork in open-pit mines, and realize the monitoring of annual earthwork changes in open-pit mines.
[0009] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described method for monitoring interannual earthwork changes in open-pit mines based on a simulated digital elevation model.
[0010] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method for monitoring interannual earthwork changes in open-pit mines based on a simulated digital elevation model.
[0011] According to the specific embodiments provided in this application, this application has the following technical effects: (1) A standardized volume estimation system based on time series simulation DEM difference was constructed to realize the systematic quantification of the annual earthwork changes in open-pit mines, and to solve the problem of the lack of a unified standard for estimation caused by the lack of such a system in existing methods; (2) The first screening of the elevation change grid is carried out by using a multi-dimensional rule set containing vegetation index, slope, uncertainty and continuity. The second screening is combined with the linear fitting of multi-temporal elevation change sequence to form a dual error correction and effective area identification mechanism, which greatly reduces the error of earthwork estimation and significantly improves the monitoring accuracy. (3) By clustering the selected candidate patches into mining area units through spatial proximity, a hierarchical aggregation statistical model of “candidate patch-mining area unit” is constructed, which improves the statistical system of plot and mining area level and can truly reflect the scope of surface disturbance and the overall picture of earthwork changes in open mine. (4) Relying on remote sensing images combined with artificial intelligence neural networks to simulate DEM, it continues the low-cost advantage, and the whole process algorithm is automated. It can adapt to the rapid processing of remote sensing images of different time phases, and can realize high-frequency continuous monitoring of open mines, taking into account both low cost and high efficiency of monitoring. (5) Form a standardized and automated monitoring scheme for the entire process of “image acquisition - DEM inversion - differential operation - multi-dimensional screening - sequence fitting - clustering and aggregation - volume calculation”, reduce manual intervention, improve the ease of operation of monitoring changes in open-pit mine earthwork, provide a standardized basis for the quantitative assessment of resource consumption and ecological disturbance in the mining area, and improve the scientific nature of mining area management. Attached Figure Description
[0012] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0013] Figure 1 This is a flowchart illustrating a method for monitoring interannual earthwork changes in open-pit mines based on a simulated digital elevation model, provided as an embodiment of this application. Detailed Implementation
[0014] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0015] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.
[0016] In one exemplary embodiment, such as Figure 1 As shown, a method for monitoring the interannual earthwork variation in open-pit mines based on a simulated digital elevation model is provided. This method is executed by computer equipment, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is described using a server as an example, and includes the following steps S1 to S5.
[0017] S1: Acquire remote sensing images of the same open-pit mine at different times, and generate corresponding simulated digital elevation models through existing artificial intelligence neural networks or stereo image pairs in 3D inversion.
[0018] The simulated digital elevation model in this application was achieved through prior 3D inversion using artificial intelligence neural networks or stereo image pairs. Subsequently, all DEMs were spatially registered to the same UTM coordinate system, and annual offset errors were eliminated using bidirectional matching and affine correction. In addition to elevation information, the generated DEMs also record pixel-level variance. , to represent the uncertainty in the topographic estimation at that point.
[0019] S2: Perform differential calculations on the analog digital elevation models of adjacent time phases to obtain an elevation change raster.
[0020] Differences are calculated between DEMs of adjacent time phases to obtain an elevation change raster. The differenced results (i.e., the elevation change raster) are then morphologically filtered to eliminate local noise points, and the layer variance is recorded simultaneously.
[0021] In the formula, Represents an elevation change grid. The analog digital elevation model representing the t+1 phase. The analog digital elevation model representing phase t. This represents the variance of the layer.
[0022] S3: Based on a multi-dimensional rule set, the elevation change raster is filtered to generate candidate change areas; the multi-dimensional rule set includes: vegetation index filtering rules, slope filtering rules, uncertainty filtering rules, and continuity filtering rules.
[0023] To ensure that volume estimation is confined to stable surface regions, this application constructs a multi-dimensional rule set: Vegetation index selection rules: Calculate vegetation indices based on corresponding remote sensing images. ,in, The vegetation index, For green band reflectivity, For red band reflectivity, For blue band reflectivity, The empirical weighting coefficient is a≈0.667; pixels with vegetation indices higher than the vegetation index threshold are removed using the OTSU adaptive thresholding method.
[0024] Slope selection rules: Calculate cell slope based on the first derivative of the simulated digital elevation model. ,in, For pixel slope, This is the elevation value. Let x be the rate of change of elevation in the x-direction. The elevation change rate in the y-direction; only pixels whose slope falls within a preset slope range are retained. This application retains only... Pixels.
[0025] Uncertainty filtering rule: Pixels with pixel-level variance greater than a variance threshold are removed. This application only retains pixels with pixel-level variance. Cells ≤ 5.0 (logarithmic field) are used to ensure the robustness of the input data.
[0026] Temporal continuity filtering rules: Only images that meet the vegetation index filtering rules, slope filtering rules, and uncertainty filtering rules in the same spatial location for three consecutive years or more are retained.
[0027] "Candidate" means potential interannual topographic changes, and topographic changes are more likely to be related to industrial forces associated with open-pit mining.
[0028] S4: For each candidate patch within the candidate change region, extract a multi-temporal elevation change sequence; and perform linear fitting on the multi-temporal elevation change sequence to obtain the filtered candidate patches based on the goodness of fit.
[0029] For each candidate patch, its spatial location is fixed, and at that fixed location, the ΔE value of the corresponding pixel is extracted periodically from multiple elevation change raster cells. These values are then arranged in chronological order to form a multi-temporal elevation change sequence for the candidate patch.
[0030] Linear fitting of multi-temporal elevation change sequences: In the formula, Let t be the elevation change over time phase t, k be the linear fitting slope, and b be the intercept constant.
[0031] When the goodness of fit R 2 When the value is ≥0.5, the change at that point is considered to have stabilized, and the corresponding candidate patch is retained as a selected candidate patch.
[0032] When 0.15≤R 2 When the value is less than 0.5, the corresponding candidate patch is retained as the filtered candidate patch; When R 2 When the value is less than 0.15, the corresponding candidate patches are removed.
[0033] Under this mechanism, three types of earthwork fluctuation trends can be automatically distinguished: continuous decline (excavation), phased filling (filling), and oscillating changes (disturbance). This allows for further optimization or attribute classification of candidate land parcels.
[0034] Of the three formulas below, the first formula ensures the initial location of plots exhibiting a continuous downward trend (active mining areas) or an upward trend (tailings accumulation near mining areas, i.e., x+m and y+n), where (x,y) represents spatial coordinates and t represents any year (t'). The second formula explains how to eliminate invalid candidate plots using variance: First, excessively large variance values will not be included in the formula calculation; second, a negative Z-value represents a downward trend in elevation, and when Z is less than -1.00, it indicates a significant change; third, the first formula is essentially a two-sample Z-test in statistics, or Kalman filtering in signal processing; fourth, to avoid interference from unpredictable events such as mine closures and underground mining activities, this application only considers topographic changes over the past four years, i.e., from t' to t'+3. The third formula is used to construct non-overlapping confidence intervals. Mathematically, if subsequent values are statistically significantly lower than previous values, the difference cannot be explained by random noise. This application sets k=k'=0.2 to improve the fault tolerance of the discrimination process. These mathematical rules ensure the feasibility, credibility, and interpretability of the S4 steps.
[0035] or ; In this embodiment, t = t', t'+1, t'+2, t'+3. This represents the elevation of point (x, y). The standard deviation of the model's predictions (not a single elevation estimate, but rather a set of estimates for each point, with the standard deviation denoted as...) ), This represents the mean. The control coefficient is usually taken as an empirical value such as 0.5, 1.0, or 1.5.
[0036] S5: Cluster the selected candidate patches into mining area units based on spatial proximity, accumulate the volume changes of all pixels within each mining area unit, calculate the annual volume change of earthwork in open-pit mines, and realize the monitoring of annual earthwork changes in open-pit mines.
[0037] Candidate patches with similar distances after screening can be clustered into mining area units, and the formula for calculating volume change can be defined as: Annual earthwork volume change: Annual metal production estimates: in, This refers to the annual earthwork volume of an open-pit mine. , These are the elevation values of the simulated digital elevation model for adjacent time phases; Let T be the pixel area and T be the time span. ΔM represents the annual volume change of earth and rock in the open-pit mine, ΔM represents the annual metal production, c represents the ore-rock ratio coefficient, ρ represents the ore density, and C represents the average grade.
[0038] like With variance The variance of annual metal production is then assessed as follows: The confidence limits for annual metal production can be obtained by converting them using the same multiples as volume: In the formula, Upper represents the upper limit and Lower represents the lower limit.
[0039] This application uses a set of empirical values. To estimate the annual volume change of earthwork and rock in open-pit mines and the annual metal production. It is a set used to provide statistical descriptions of volume changes, such as "upper bound, lower bound, and mode," where i is the open-pit mine number. In the formula, This represents the mean volume change of candidate patch i; This represents the variance of the volume change of candidate patch i; This indicates the corresponding mode.
[0040] Based on this set, three sets of inputs can be generated to calculate the corresponding values. (Lower bound, upper bound, and recommended value), where the generated mode is considered the recommended value, and the upper and lower bounds are considered its fluctuation range. Users must collect real, high-precision DEM data from the demonstration area to understand the error or the pattern of error changes before extending it to unknown areas. Finally, these statistical results can be labeled on the DEM base map patch by patch or compiled into a table.
[0041] Based on the same inventive concept, this application also provides a system for implementing the above-mentioned method for monitoring interannual earthwork changes in open-pit mines based on an analog digital elevation model. The solution provided by this system is similar to the implementation described in the above method. Therefore, the specific limitations of one or more embodiments of the system for monitoring interannual earthwork changes in open-pit mines based on an analog digital elevation model provided below can be found in the limitations of the method for monitoring interannual earthwork changes in open-pit mines based on an analog digital elevation model described above, and will not be repeated here.
[0042] In one exemplary embodiment, a monitoring system for interannual earthwork variation in open-pit mines based on a simulated digital elevation model is provided. This system is used to execute the aforementioned method for monitoring interannual earthwork variation in open-pit mines based on a simulated digital elevation model. The system includes the following modules.
[0043] The simulated digital elevation model generation module is used to acquire remote sensing images of the same open-pit mine at different time phases and generate corresponding simulated digital elevation models through artificial intelligence neural networks or stereo image pairs in 3D inversion; the simulated digital elevation model sequence contains pixel-level elevation values and pixel-level variance.
[0044] The differential operation module is used to perform differential operations on the analog digital elevation model of adjacent time phases to obtain an elevation change raster.
[0045] The candidate change region generation module is used to filter the elevation change raster based on a multi-dimensional rule set to generate candidate change regions; the multi-dimensional rule set includes: vegetation index filtering rules, slope filtering rules, uncertainty filtering rules, and continuity filtering rules.
[0046] The candidate patch determination module after screening is used to extract a multi-temporal elevation change sequence for each candidate patch within the candidate change region; and to perform linear fitting on the multi-temporal elevation change sequence to obtain the screened candidate patches based on the goodness of fit.
[0047] The volume change calculation module is used to cluster the selected candidate patches into mining area units based on spatial proximity, accumulate the volume changes of all pixels in each mining area unit, calculate the annual volume change of earthwork in open-pit mines, and realize the monitoring of annual earthwork changes in open-pit mines.
[0048] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments. The computer device may be a server or a terminal. The computer device includes a processor, a memory, an input / output interface (I / O), and a communication interface. The processor, memory, and I / O are connected via a system bus, and the communication interface is connected to the system bus via the I / O interface. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device stores data to be processed. The I / O interface of the computer device is used for exchanging information between the processor and external devices. The communication interface of the computer device is used for communicating with an external terminal via a network connection. When the computer program is executed by the processor, it implements the steps in the above-described method embodiments.
[0049] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.
[0050] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.
[0051] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).
[0052] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.
[0053] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0054] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.
Claims
1. A method for monitoring interannual earthwork and rockfall variations in open-pit mines based on a simulated digital elevation model, characterized in that, include: Remote sensing images of the same open-pit mine at different times are acquired, and corresponding simulated digital elevation models are generated through three-dimensional inversion using artificial intelligence neural networks or stereo image pairs. Differential calculations are performed on the simulated digital elevation models of adjacent time phases to obtain an elevation change raster. The elevation change grid is filtered based on a multi-dimensional rule set to generate candidate change areas; The multi-dimensional rule set includes: vegetation index screening rules, slope screening rules, uncertainty screening rules, and continuity screening rules; For each candidate patch within the candidate change region, a multi-temporal elevation change sequence is extracted; and the multi-temporal elevation change sequence is linearly fitted, and the filtered candidate patches are obtained based on the goodness of fit. The selected candidate patches are clustered into mining area units based on spatial proximity. The volume changes of all pixels within each mining area unit are accumulated to calculate the annual volume change of earthwork in open-pit mines, thereby realizing the monitoring of annual earthwork changes in open-pit mines.
2. The method for monitoring interannual earthwork variation in open-pit mines based on a simulated digital elevation model according to claim 1, characterized in that, The vegetation index screening rule is as follows: calculate the vegetation index based on the corresponding remote sensing image, and remove pixels with vegetation indices higher than the vegetation index threshold using the OTSU adaptive threshold method.
3. The method for monitoring interannual earthwork variation in open-pit mines based on a simulated digital elevation model according to claim 1, characterized in that, The slope selection rule is as follows: the pixel slope is calculated based on the first derivative of the simulated digital elevation model, and only pixels whose pixel slope is within the preset pixel slope range are retained.
4. The method for monitoring interannual earthwork variation in open-pit mines based on a simulated digital elevation model according to claim 1, characterized in that, The uncertainty screening rule is as follows: remove pixels whose pixel-level variance is greater than the variance threshold.
5. The method for monitoring interannual earthwork variation in open-pit mines based on a simulated digital elevation model according to claim 1, characterized in that, The temporal continuity screening rule is as follows: only images that meet the vegetation index screening rule, slope screening rule, and uncertainty screening rule in the same spatial location for three consecutive years or more are retained.
6. The method for monitoring interannual earthwork variation in open-pit mines based on a simulated digital elevation model according to claim 1, characterized in that, The candidate patches are obtained after screening based on the goodness of fit, specifically including: When R 2 When R is ≥0.5, the corresponding candidate polygons are retained as the filtered candidate polygons; where R 2 For goodness of fit When 0.15≤R 2 When the value is less than 0.5, the corresponding candidate patch is retained as the filtered candidate patch; When R 2 When the value is less than 0.15, the corresponding candidate patches are removed.
7. The method for monitoring interannual earthwork variation in open-pit mines based on a simulated digital elevation model according to claim 1, characterized in that, The calculation of the interannual volume change of earthwork in open-pit mines also includes: The annual metal production is calculated using the formula ΔM=(ΔV×c)×ρ×C; where ΔM is the annual metal production, c is the ore-rock ratio coefficient, ρ is the ore density, C is the average grade, and ΔV is the annual earthwork volume of the open-pit mine.
8. A monitoring system for interannual earthwork variation in open-pit mines based on a simulated digital elevation model, characterized in that, The system is used to execute the open-pit mine interannual earthwork variation monitoring method based on a simulated digital elevation model as described in any one of claims 1-7, the system comprising: The simulated digital elevation model generation module is used to acquire remote sensing images of the same open-pit mine at different time phases, and generate corresponding simulated digital elevation models through artificial intelligence neural networks or stereo image pairs in 3D inversion; the simulated digital elevation model sequence contains pixel-level elevation values and pixel-level variance. The differential operation module is used to perform differential operations on the analog digital elevation model of adjacent time phases to obtain the elevation change raster. The candidate change region generation module is used to filter the elevation change raster based on a multi-dimensional rule set to generate candidate change regions; the multi-dimensional rule set includes: vegetation index filtering rules, slope filtering rules, uncertainty filtering rules, and continuity filtering rules; The candidate patch determination module after screening is used to extract a multi-temporal elevation change sequence for each candidate patch within the candidate change region; and to perform linear fitting on the multi-temporal elevation change sequence to obtain the screened candidate patches based on the goodness of fit. The volume change calculation module is used to cluster the selected candidate patches into mining area units based on spatial proximity, accumulate the volume changes of all pixels in each mining area unit, calculate the annual volume change of earthwork in open-pit mines, and realize the monitoring of annual earthwork changes in open-pit mines.
9. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the method for monitoring interannual earthwork changes in open-pit mines based on an analog digital elevation model, as described in any one of claims 1-7.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the method for monitoring interannual earthwork changes in open-pit mines based on an analog digital elevation model, as described in any one of claims 1-7.