A method and device and equipment for measuring elevation data in areas with large elevation differences

Abstract

The present disclosure provides a method, device, and equipment for measuring elevation data in areas with large elevation differences, relating to the technical fields of engineering surveying and geological exploration. The method includes: obtaining the level difference data and measured gravity data of each measurement point in the large elevation difference measurement area; dividing the large elevation difference measurement area into measurement sub-areas, and determining the gravity interference coefficient corresponding to the measurement point according to its measured gravity data and regional environmental data; determining the corrected gravity data based on the gravity interference coefficient and the measured gravity data, and determining the elevation correction term and the measured elevation data; and then obtaining the corrected elevation data corresponding to the large elevation difference measurement area by correcting the measured elevation data according to the elevation correction term. This technical solution can effectively improve the reliability and accuracy of the elevation data measured by the level in areas with large elevation differences.

Description

Background Art

[0002] During the elevation data measurement in large elevation difference areas (such as alpine canyons and hydropower station construction areas), traditional leveling methods face significant challenges. Due to the complexity of the spatial distribution of the earth's gravity field, especially in areas with drastic terrain undulations, the change in gravitational acceleration will directly affect the height difference measurement of the level, resulting in the accumulation of systematic errors. Existing technologies usually use levels for measurement, but do not fully consider the influence of gravity field changes on elevation data, making it difficult to meet the high requirements for measurement accuracy in complex terrain environments.

[0003] In order to reduce measurement errors, gravity anomaly correction methods have been gradually introduced in related technologies. For example, in the national first- and second-class leveling specifications, it has been required to perform gravity anomaly correction on leveling under certain conditions. However, in practical applications, limited by measurement costs and construction period pressures, the Bouguer anomaly interpolation method is usually used to correct gravity anomalies. However, this method has low accuracy, especially in areas with complex terrain and drastic gravity field changes, which easily leads to the accumulation of local errors in the correction results and is difficult to meet the high-precision requirements of engineering surveys.

[0004] Currently, in the gravity anomaly correction technology of levels, either the influence of terrain undulations and gravity field changes on the leveling accuracy is not fully considered, resulting in the difficulty of effectively correcting the elevation data errors obtained by level measurements; or the accuracy of the gravity anomaly correction method is insufficient, especially in areas with complex terrain and drastic gravity changes, where local errors accumulate seriously, the leveling accuracy is low, and the reliability of the measurement results is relatively low.

[0005] It should be noted that the information disclosed in the above background art section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0006] The purpose of the embodiments of the present disclosure is to provide a method for measuring elevation data in large elevation difference areas, a device for measuring elevation data in large elevation difference areas, and an electronic device, so as to effectively improve the reliability and accuracy of the elevation data measured by the level in large elevation difference areas.

[0007] According to the first aspect of the embodiments of the present disclosure, a method for measuring elevation data in large elevation difference areas is provided, including:

[0008] Obtain the height difference data of the level collected at each preset measurement point in the large elevation difference measurement area, and the measured gravity data corresponding to each of the measurement points;

[0009] Divide the large elevation difference measurement area into measurement sub-areas, and determine the gravity interference coefficient corresponding to the measurement point according to the measurement gravity data and regional environment data corresponding to each measurement sub-area;

[0010] Determine the corrected gravity data based on the gravity interference coefficient and the measurement gravity data;

[0011] Determine the elevation correction term for each measurement sub-area through the corrected gravity data, and determine the measured elevation data for each measurement sub-area through the level height difference data;

[0012] Correct the measured elevation data according to the elevation correction term to obtain the corrected elevation data corresponding to the large elevation difference measurement area.

[0013] According to the second aspect of the embodiments of the present disclosure, there is provided an elevation data measurement device for large elevation difference areas, including:

[0014] A level measurement module for obtaining the level height difference data collected at each preset measurement point in the large elevation difference measurement area, and the measurement gravity data corresponding to each measurement point;

[0015] A gravity interference analysis module for dividing the large elevation difference measurement area into measurement sub-areas, and determining the gravity interference coefficient corresponding to the measurement point according to the measurement gravity data and regional environment data corresponding to each measurement sub-area;

[0016] A gravity data correction module for determining the corrected gravity data based on the gravity interference coefficient and the measurement gravity data;

[0017] An elevation correction term determination module for determining the elevation correction term for each measurement sub-area through the corrected gravity data, and determining the measured elevation data for each measurement sub-area through the level height difference data;

[0018] An elevation data correction module for correcting the measured elevation data according to the elevation correction term to obtain the corrected elevation data corresponding to the large elevation difference measurement area.

[0019] According to the third aspect of the embodiments of the present disclosure, there is provided an electronic device, including: a processor; and a memory, on which computer-readable instructions are stored, and when the computer-readable instructions are executed by the processor, the elevation data measurement method for large elevation difference areas in the first aspect is implemented.

[0020] According to the fourth aspect of the embodiments of the present disclosure, there is provided a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the elevation data measurement method for large elevation difference areas in the first aspect is implemented.

[0021] The technical solution provided by the embodiments of the present disclosure may have the following beneficial effects:

[0022] The elevation data measurement method for areas with large elevation differences in the example embodiments of the present disclosure divides the measurement area into a plurality of measurement sub-areas, and determines the gravity interference coefficient corresponding to the measurement point according to the measured gravity data of each measurement sub-area and the regional environmental data, so as to realize the refined management and classification processing of the measurement area environment, realize the differentiated analysis and processing of different sub-areas, so that the gravity interference situation of each area can be quantified, which is helpful to select the appropriate correction strategy in a targeted manner, effectively reduce the influence of the local change of the gravity field on the elevation measurement result, and improve the accuracy and reliability of the elevation measurement result; the correction gravity data is further determined based on the gravity interference coefficient and the measured gravity data, which is different from the traditional method of using Bouguer anisotropy. The correction method using the constant interpolation method can more accurately reflect the changes in the gravity field in the survey area. This correction strategy based on multiple factors can dynamically respond to the actual environmental changes in the survey area, making the correction results more in line with the actual situation, avoiding the problem of local error accumulation, and improving the accuracy and reliability of the output elevation data. The elevation correction items of each survey sub-area are determined by correcting the gravity data, and the measured elevation data determined by the level height difference data are combined to perform multi-level corrections on the survey data to further improve the accuracy of the corrected elevation data. In the process of data correction, the complementary advantages of the corrected gravity data and the level height difference data are fully utilized to effectively eliminate the measurement errors caused by gravity field changes and terrain undulations.

[0023] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification are used to explain the principles of the present disclosure. Obviously, the accompanying drawings described below are only some embodiments of the present disclosure, and for ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0025] Figure 1 The flowchart of the elevation data measurement method for a large elevation difference area according to some embodiments of the present disclosure is schematically shown.

[0026] Figure 2 The flowchart of dividing and obtaining measurement sub-areas according to some embodiments of the present disclosure is schematically shown.

[0027] Figure 3Schematically shows a flowchart of determining a gravity interference coefficient according to some embodiments of the present disclosure.

[0028] Figure 4 Schematically shows a flowchart of constructing a static interference term according to some embodiments of the present disclosure.

[0029] Figure 5 Schematically shows a flowchart of constructing a dynamic interference term according to some embodiments of the present disclosure.

[0030] Figure 6 Schematically shows a flowchart of determining corrected gravity data according to some embodiments of the present disclosure.

[0031] Figure 7 Schematically shows a flowchart of real-time gravity field correction according to some embodiments of the present disclosure.

[0032] Figure 8 Schematically shows a schematic diagram of an elevation data measurement device for large drop areas according to some embodiments of the present disclosure.

[0033] Figure 9 Schematically shows a schematic diagram of the computer system of an electronic device according to some embodiments of the present disclosure.

[0034] Figure 10 Schematically shows a schematic diagram of a computer-readable storage medium according to some embodiments of the present disclosure.

[0035] In the drawings, the same or corresponding reference numerals indicate the same or corresponding parts. Detailed Description of the Embodiments

[0036] Here, exemplary embodiments will be described in detail, and examples thereof are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this specification. On the contrary, they are merely examples of devices and methods consistent with some aspects of this specification as detailed in the appended claims.

[0037] In addition, the drawings are only schematic diagrams and are not necessarily drawn to scale. The block diagrams shown in the drawings are only functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices.

[0038] In the present exemplary embodiment, first, a method for measuring elevation data in a large drop area is provided. The method for measuring elevation data in a large drop area can be applied to a terminal device or a server. The present exemplary embodiment does not make any special limitation on this, and hereinafter, the case where the server executes this method will be taken as an example for illustration. Figure 1 Schematically shows a flowchart of a method for measuring elevation data in a large drop area according to some embodiments of the present disclosure. Refer to Figure 1 As shown, the method for measuring elevation data in a large drop area may include the following steps:

[0039] Step S110, obtaining the level difference data collected by a level at each preset measurement point in the large drop measurement area, and the measurement gravity data corresponding to each of the measurement points;

[0040] Step S120, dividing the large drop measurement area into measurement sub-areas, and determining the gravity interference coefficient corresponding to the measurement point according to the measurement gravity data and the regional environment data corresponding to each of the measurement sub-areas;

[0041] Step S130, determining the corrected gravity data based on the gravity interference coefficient and the measurement gravity data;

[0042] Step S140, determining the elevation correction term for each of the measurement sub-areas through the corrected gravity data, and determining the measured elevation data for each of the measurement sub-areas through the level difference data;

[0043] Step S150, correcting the measured elevation data according to the elevation correction term to obtain the corrected elevation data corresponding to the large drop measurement area.

[0044] According to the elevation data measurement method for areas with large elevation differences in this example embodiment, by dividing the measurement area into multiple measurement sub-areas, and determining the gravity interference coefficient corresponding to the measurement point according to the measured gravity data of each measurement sub-area and the regional environmental data, it is possible to achieve refined management and classification processing of the measurement area environment, and to achieve differentiated analysis and processing of different sub-areas, so that the gravity interference situation of each area can be quantified, which helps to select a suitable correction strategy in a targeted manner, effectively reduce the impact of local changes in the gravity field on the elevation measurement results, and improve the accuracy and reliability of the elevation measurement results; further determining the corrected gravity data based on the gravity interference coefficient and the measured gravity data is different from the traditional method of using Bouguer anomaly The correction method using the interpolation method can more accurately reflect the changes in the gravity field in the survey area. This correction strategy based on multiple factors can dynamically respond to the actual environmental changes in the survey area, making the correction results more in line with the actual situation, avoiding the problem of local error accumulation, and improving the accuracy and reliability of the output elevation data. The elevation correction items of each survey sub-area are determined by correcting the gravity data, and the measured elevation data determined by the level height difference data are combined to perform multi-level corrections on the survey data to further improve the accuracy of the corrected elevation data. In the process of data correction, the complementary advantages of the corrected gravity data and the level height difference data are fully utilized to effectively eliminate the measurement errors caused by gravity field changes and terrain undulations.

[0045] Next, the elevation data measurement method for areas with large elevation differences in this exemplary embodiment will be further described.

[0046] In step S110, the level instrument height difference data collected at each preset measuring point in the large drop measurement area and the measured gravity data corresponding to each of the measuring points are obtained.

[0047] In an example embodiment of the present disclosure, a large drop measurement area refers to an area to be measured with significant terrain undulations and large changes in surface elevation. For example, a large drop measurement area may be a mountainous area, a canyon, a mining area, or a large-scale water conservancy project construction area. These areas usually have complex terrain structures and variable geological conditions, which makes the measurement data susceptible to interference from changes in the gravity field and terrain effects. The large drop measurement area can be screened and determined by using a digital elevation model (DEM), a digital surface model (DSM), or a laser radar (Light Detection and Ranging, LiDAR) technology measured historically. In some optional implementations, satellite remote sensing images or historical topographic mapping data can also be used to preliminarily identify and divide the large drop measurement area. This embodiment does not specifically limit the method of screening or determining the large drop measurement area.

[0048] A measurement point refers to a sampling point preset according to topographic features and measurement requirements within a large drop measurement area. The layout of measurement points can be reasonably planned according to the degree of terrain undulation, the change trend of the gravity field, and engineering requirements. For example, the layout method can be regular layout (such as equidistant distribution), or irregular layout (such as layout along key areas such as fault lines and landslide boundaries), and this embodiment does not make special limitations in this regard. To ensure the comprehensiveness and accuracy of the data read by the level, the spacing of measurement points should be appropriately reduced in complex terrain areas, and can be appropriately increased in flat terrain areas; of course, measurement points can also be automatically laid out by drones or all-terrain robots to improve the layout efficiency and safety.

[0049] The level height difference data refers to the data obtained by precisely measuring the elevation difference between adjacent measurement points through the level measurement equipment deployed in the measurement area, and the elevation data of each measurement point or survey line can be obtained based on the precise elevation data of the starting point. For example, the level can be a digital level with high-precision reading function, a laser level using laser ranging technology, or an automatic level, and this exemplary embodiment is not limited thereto. The automatic level can achieve automatic leveling through a built-in compensator to reduce human error; the digital level can use optoelectronic coding technology to automatically read the scale data to improve measurement accuracy and efficiency; the laser level can measure distances through laser beams and is suitable for long-distance measurements. During measurement, the level can obtain the height difference data between two measurement points through foresight and backsight readings, and further accumulate and calculate to obtain the elevation data of each measurement point in the survey area. In an optional implementation manner, the level can be equipped with functions of automatic data recording and wireless transmission to achieve real-time data upload and remote monitoring.

[0050] The measured gravity data refers to the data used to describe the intensity of the gravity field at each measurement point. The measured gravity data can be obtained in real time through a portable gravimeter. For example, the gravimeter can be a spring gravimeter, a superconducting gravimeter, or a micro-gravity sensor based on Micro-Electro-Mechanical System (MEMS). The spring gravimeter has the characteristics of strong portability and simple operation and is suitable for field measurements; the superconducting gravimeter has high sensitivity and high stability and is suitable for high-precision measurements; the MEMS gravity sensor has the advantages of small size, low cost, and low power consumption and is suitable for large-scale layout. The measured gravity data can be uploaded to the data processing terminal in real time or stored in the device for subsequent analysis. Optionally, the measured gravity data can also be preliminarily corrected through a global gravity field model (such as the EGM96 model) to improve the consistency and accuracy of the data.

[0051] In step S120, the large drop measurement area is divided into measurement sub-areas, and the gravity interference coefficient corresponding to the measurement point is determined according to the measurement gravity data and regional environmental data corresponding to each measurement sub-area.

[0052] In an exemplary embodiment of the present disclosure, regional division refers to the process of subdividing a large drop measurement area into multiple measurement sub-areas according to terrain and geological characteristics. For example, the terrain point cloud data obtained by scanning with a laser radar (LiDAR) carried by an unmanned aerial vehicle can be used to analyze the surface undulations, and the geological structure can be judged in combination with geological exploration data, so as to scientifically divide the measurement sub-areas; of course, remote sensing images taken by satellites and orthophotos taken by aerial surveys can also be used to realize the division of large drop measurement areas, and multiple measurement sub-areas with different terrain characteristics can be obtained. Measuring gravity data is an important reference for dividing measurement sub-areas, and the division of measurement sub-areas can be further optimized by measuring the spatial distribution characteristics of gravity data (such as gravity gradient, gravity anomaly). For example, spatial autocorrelation analysis (such as Moran index) or gravity field gradient calculation can be used to identify areas with drastic changes in the gravity field and stable areas, so as to divide different measurement sub-areas; of course, machine learning models such as support vector machines (SVM) or random forests (RF) can also be used to intelligently divide large drop measurement areas.

[0053] Regional environmental data refers to environmental condition data related to the measurement sub-area. For example, regional environmental data may include, but are not limited to, temperature, humidity, air pressure, wind speed, and geological structure information. These data may be obtained in real time through environmental monitoring sensors or patrol drones deployed in the measurement area, or from meteorological monitoring stations or remote sensing data associated with the measurement sub-area. This embodiment is not limited thereto. The combination of measured gravity data and regional environmental data helps to identify gravity anomalies caused by environmental changes.

[0054] The gravity interference coefficient refers to a parameter used to characterize the degree of interference of the terrain features of the measurement sub-area with the gravity acceleration of the measurement point. For example, the gravity interference coefficient can be obtained by performing static interference analysis and dynamic interference analysis on the measurement point respectively; static interference analysis can adopt methods such as gravity anomaly analysis, Moran index calculation and spectrum analysis, and dynamic interference analysis can be obtained by correlating environmental data with gravity data through a multivariate linear regression model. In some optional implementations, a machine learning model can also be used to perform weighted fusion of static and dynamic interference data to more accurately estimate the gravity interference coefficient.

[0055] In step S130, corrected gravity data is determined based on the gravity disturbance coefficient and the measured gravity data.

[0056] In an exemplary embodiment of the present disclosure, the calibrated gravity data refers to the precise gravity data obtained by correcting the errors of the original gravity data, and the calibrated gravity data can be determined based on the gravity interference coefficient and the measured gravity data. For example, in some alternative embodiments, when the gravity interference coefficient is less than the preset threshold range, a real-time gravity field correction method can be adopted. The real-time gravity field correction may include calculating the free-air correction and the Bouguer correction. The free-air correction is used to compensate for the gravity change caused by the elevation difference, and the Bouguer correction is used to compensate for the influence of the terrain density on gravity. When the gravity interference coefficient is within the preset threshold range, a method combining real-time gravity field correction and multi-modal data fusion gravity estimation can be adopted. Multi-modal data fusion refers to a processing method in which multi-source data such as spatial position, terrain undulation, geological information, environmental parameters, and historical gravity data are input into a multi-modal fusion model to infer more precise calibrated gravity data. If the gravity interference coefficient exceeds the preset range, a multi-modal data fusion gravity estimation method can be adopted, and deep learning algorithms such as Convolutional Neural Networks (CNN) or Graph Neural Network (GNN) are used to comprehensively estimate gravity data from multi-source information to effectively cope with the gravity data interference in complex environments.

[0057] In step S140, the elevation correction term of each of the measured sub-regions is determined by the calibrated gravity data, and the measured elevation data of each of the measured sub-regions is determined by the level difference data.

[0058] In an exemplary embodiment of the present disclosure, the elevation correction term is calculated based on the gravity anomaly value and the standard gravity value of the measurement point, and is used to correct the elevation deviation caused by the gravity anomaly. The measured elevation data refers to the measured elevation data of each measurement point obtained by accumulating the level difference data between adjacent measurement points. To improve the accuracy of the data, the measured elevation data can be smoothed by methods such as segment analysis and trend analysis to eliminate measurement errors.

[0059] In step S150, the measured elevation data is corrected according to the elevation correction term to obtain the corrected elevation data corresponding to the large-drop measurement region.

[0060] In an example embodiment of the present disclosure, the correction process refers to the process of using the calculated elevation correction items to perform error compensation and data optimization on the preliminary elevation data. Corrected elevation data refers to the accurate elevation information obtained after multi-factor correction and optimization processing on the basis of the original leveling measurement data. Its essence is to perform multi-dimensional and multi-level corrections on the level measurement data through elevation correction items, fully eliminating the systematic errors and random errors in the measurement results caused by factors such as gravity anomalies, terrain undulations and environmental changes, and then reflecting the true terrain elevation characteristics of the measurement area; the corrected elevation data not only has higher accuracy and stability, but also has stronger environmental adaptability, and is particularly suitable for large drop areas with complex terrain and drastic changes in gravity fields.

[0061] In the implementation process, the elevation data measured by the level can be corrected by the calculated elevation correction item. The elevation correction item reflects the comprehensive influence of gravity anomaly and terrain effect on elevation measurement. Its calculation process fully considers local gravity anomaly, free air correction, Bouguer correction and terrain effect correction. Secondly, the measured elevation data and the elevation correction item can be linearly superimposed or dynamically weighted fused to effectively eliminate the systematic error in the height difference data. The dynamic weighted fusion method can adaptively adjust the weight distribution of measured data and corrected data according to the gravity interference coefficient of the measuring point, so that the correction result is more in line with the actual terrain conditions of the measuring area. Specifically, a dynamic weighted fusion strategy can be selected to assign different weights to the measured elevation data and the elevation correction item of the level according to the interference degree of the measuring point (such as the gravity interference coefficient). For example, when the gravity interference coefficient of the measuring point is small, more emphasis is placed on the measured data of the level; when the gravity interference coefficient is large, more emphasis is placed on the correction effect of the elevation correction item. Through this flexible fusion strategy, the optimal data correction effect can be achieved under different environmental conditions. In order to further improve the spatial continuity and overall consistency of the corrected elevation data, spatial interpolation algorithms (such as Kriging interpolation and inverse distance weighted interpolation) can be optionally used to supplement and optimize the elevation data between measuring points, which can effectively make up for the limitations of the measurement point layout and generate continuous and smooth elevation distribution data.

[0062] Next, the contents of step S110 to step S150 are described in detail.

[0063] In an exemplary embodiment of the present disclosure, Figure 2 The steps in step S210 are implemented to divide the large drop measurement area into regions to obtain the content of the measurement sub-regions, refer to Figure 2 As shown, it may specifically include:

[0064] Step S210, obtaining terrain point cloud data obtained by inspecting the large drop measurement area based on a drone, and determining terrain undulation characteristics according to the terrain point cloud data;

[0065] Step S220: Determine the terrain complexity data of the large drop measurement area according to the terrain undulation characteristics;

[0066] Step S230: Determine the geological complexity data of the large drop measurement area through the geological exploration data corresponding to the measurement area;

[0067] Step S240: Perform weighted fusion on the terrain complexity data and the geological complexity data to obtain the area complexity corresponding to the large drop measurement area, and perform area division on the measurement area based on the area complexity to obtain measurement sub-areas.

[0068] Among them, the terrain point cloud data refers to the high-resolution three-dimensional terrain data obtained by using a sensor carried by a drone to conduct aerial inspections on the large drop measurement area. The drone can be a multi-rotor drone or a fixed-wing drone. This embodiment is not limited thereto. The sensor carried by the drone can be a lidar for collecting high-density terrain point cloud data. The lidar can obtain surface reflection information and reconstruct a three-dimensional terrain model by emitting and receiving laser pulse signals. The sensor carried by the drone can also use a high-precision optical camera to extract terrain information by obtaining multi-view images and performing image matching and three-dimensional reconstruction. Of course, the drone can also carry an Inertial Navigation System (INS) and a Global Positioning System (GPS) to achieve high-precision positioning and attitude control, ensuring the accurate registration of the terrain point cloud data. This embodiment does not make special limitations on the method of the drone collecting terrain point cloud data.

[0069] The terrain undulation characteristics refer to the characteristic data extracted from the terrain point cloud data that characterize the terrain undulation changes. For example, the terrain undulation characteristics can be parameters such as terrain slope, aspect, curvature, surface roughness, and elevation difference change rate calculated from the terrain point cloud data. These parameters can be used to quantitatively describe the change trend and undulation degree of the terrain. Specifically, the slope can be used to reflect the inclination degree of the ground surface, the curvature can be used to represent the rate of terrain change, and the surface roughness can be used to depict the complexity of the ground surface form.

[0070] Terrain complexity data refers to a set of data used to characterize the undulation degree and spatial variation characteristics of the terrain within the measurement area. It can comprehensively reflect the complexity of the surface morphology in the surveyed area through multi-dimensional quantitative analysis of the geometric morphological characteristics of the terrain. For example, terrain complexity data can include indicators such as slope, aspect, curvature, surface roughness, and elevation gradient. The slope is used to describe the inclination degree of the surface, the aspect represents the direction distribution of the slope surface, the curvature is used to reflect the bending change of the terrain surface, the surface roughness is used to quantify the complexity of the surface morphology, and the elevation gradient represents the rate of elevation change. Terrain complexity data can be obtained from terrain point cloud data acquired by an unmanned aerial vehicle equipped with lidar or a high-resolution optical camera, and analyzed using a digital elevation model or a digital surface model.

[0071] Geological exploration data refers to a set of data used to characterize the underground geological conditions, lithology distribution, fault structures, and geological unit characteristics within the measurement area. Geological exploration data can be obtained through geological surveys, drilling, geological radar detection, geophysical exploration, etc. For example, geological exploration data can include information such as fault distribution, stratum dip angle, lithology type, and geological structure type. The fault distribution can reflect the strike and distribution of underground fault zones, the stratum dip angle can be used to describe the inclination degree of the strata, the lithology type can represent the composition and properties of underground rocks, and the geological structure type can describe the geological units and structural characteristics of the surveyed area.

[0072] Geological complexity data refers to a set of data obtained by analyzing geological exploration data to quantify the complexity of the geological structure within the measurement area. This data comprehensively reflects the geological structural characteristics of the surveyed area and the potential impact of geological conditions on measurement data. Geological complexity data can include indicators such as fault density, stratum dip angle, lithology diversity, and geological activity. The fault density can be used to describe the number and distribution of faults per unit area, the stratum dip angle can reflect the stability and deformation trend of the strata, the lithology diversity can be used to represent the degree of diversification of stratum compositions, and the geological activity can be used to measure the activity degree of fault zones and fold structures. Geological complexity data can be obtained through spatial overlay analysis of geological exploration data using a geographic information system, and comprehensively analyzed and verified in combination with geophysical data such as gravity anomaly and seismic velocity.

[0073] The regional complexity refers to an index used to quantify the complexity of the regional terrain obtained through comprehensive analysis and weighted fusion of the terrain complexity data and geological complexity data within the measurement area. The regional complexity is the comprehensive evaluation result of the terrain undulation characteristics and geological structure characteristics, which can not only be used to guide the scientific division of the survey area and the layout of measurement points, but also be used to separately evaluate the gravity anomalies in different regions under complex terrains, further improving the accuracy of gravity anomaly correction and reducing the gravity anomaly correction error. The calculation of the regional complexity can adopt the Weighted Overlay Method, standardize and normalize the terrain complexity data and geological complexity data, and assign different weights according to the influence degrees of the two on the measurement accuracy to obtain a unified complexity score. Of course, it is also possible to adopt a threshold-based method or a spatial clustering algorithm to fuse the terrain complexity data and geological complexity data, and this embodiment is not limited thereto. Regions with high regional complexity usually have drastic terrain undulations and complex geological structures, and denser measurement points should be arranged and stricter gravity correction strategies should be adopted; regions with low regional complexity have gentle terrains and stable geological conditions, and the density of measurement points can be appropriately reduced and the gravity data correction method can be simplified.

[0074] The terrain point cloud data obtained by drones carrying lidar or high-precision optical cameras can comprehensively and meticulously reflect the three-dimensional terrain characteristics of the measurement area, obtain the terrain complexity data, enabling the terrain undulation characteristics to be more scientifically quantified and described, and then realizing the comprehensive evaluation of the terrain complexity. Analyzing the geological characteristics of the large-drop measurement area in combination with geological exploration data can provide a deeper understanding of the lithology distribution, fault structure, and geological structure within the area. Then, based on the regional complexity determined by the terrain complexity and geological complexity, the large-drop measurement area can be divided into measurement sub-areas, which can effectively determine the gravity anomalies according to different terrain and geological conditions, improving the accuracy and reliability of gravity anomaly correction, and thus effectively ensuring the accuracy and reliability of the finally obtained corrected elevation data.

[0075] In an exemplary embodiment of the present disclosure, the steps in Figure 3 can be used to determine the gravity interference coefficient corresponding to the measurement point according to the measurement gravity data and regional environment data corresponding to each measurement sub-area in step S210. As shown in Figure 3 , it specifically may include:

[0076] Step S310: Perform static interference analysis on the measurement sub-area according to the measurement gravity data to obtain a static interference term;

[0077] Step S320: Perform dynamic interference analysis on the measurement sub-area according to the measurement gravity data and the regional environment data to obtain a dynamic interference term;

[0078] Step S330, based on the preset weight coefficients, weight and fuse the static interference term and the dynamic interference term to obtain the gravity interference coefficients of the measurement points in the measurement sub-region.

[0079] Among them, static interference analysis refers to, during the elevation data measurement process, through long-term stability analysis of the gravity data collected at each measurement point in the measurement sub-region, identifying and quantifying the systematic interference caused by long-term stable factors such as topography, geological structure, etc. to gravity measurement. Static interference analysis mainly focuses on interference sources that do not change with time or change slowly, and is usually closely related to factors such as geological structure, rock layer density, and terrain undulation. Its principle is to identify the long-existing gravity anomaly effects through statistical analysis, spatial distribution feature analysis, and spectral characteristic analysis of gravity data. Static interference analysis generally uses methods such as statistical characteristics of gravity data (such as mean, variance, skewness, kurtosis), spatial autocorrelation analysis (such as Moran's index), and spectral analysis to comprehensively reveal the stability and spatial distribution law of gravity data.

[0080] The static interference term is the result of static interference analysis, which is the quantification result of long-term stability interference factors in the measurement area, reflecting the systematic influence of stable factors such as geological structure, terrain undulation, and underground medium distribution on the gravity measurement result. For example, the static interference term can include parameters such as the gravity anomaly fluctuation coefficient (used to describe the fluctuation intensity of gravity data), Moran's index (used to reflect the spatial aggregation and discreteness of gravity data), and spectral energy ratio (used to describe the influence of different frequency components in the data). The calculation result of the static interference term is used to measure the abnormal fluctuation degree of the measurement points in spatial distribution, providing an important basis for the correction of gravity data. The static interference term has long-term stability and is not easily affected by short-term environmental changes, mainly reflecting the influence of geological structure and terrain conditions on the gravity measurement result.

[0081] Dynamic interference analysis refers to analyzing the short-term fluctuations and environmental change trends of the measured gravity data in the time dimension, identifying and quantifying the instantaneous or periodic interference caused by external environmental changes (such as temperature, humidity, air pressure, wind speed, etc.) to the gravity measurement result. Dynamic interference analysis mainly focuses on short-term interference factors that fluctuate with time. Its principle is to capture the dynamic fluctuations in gravity data caused by environmental changes or equipment state changes through methods such as short-term time series analysis, sliding time window analysis, and environmental factor regression analysis. Dynamic interference analysis can timely discover and quantify the non-stable factors in the measurement process, ensuring the reliability and accuracy of gravity data in a dynamic environment.

[0082] The Dynamic Interference Term is the result of dynamic interference analysis and is the quantification result of the interference caused by short-term environmental changes or random fluctuations during the measurement process. The dynamic interference term mainly includes short-term time-series fluctuation characteristics (used to describe the trend and fluctuation of data over time) and environmental gravity anomaly values (used to quantify the impact of environmental changes on gravity measurement). The short-term time-series fluctuation characteristics capture the local fluctuation trend of data through sliding time-window analysis, and the environmental gravity anomaly values correlate environmental parameters (such as temperature, humidity, and air pressure) with gravity data through a multiple linear regression model to identify abnormal fluctuations caused by environmental changes. The dynamic interference term is real-time and sensitive, capable of reflecting the immediate interference caused by environmental changes or equipment state fluctuations during the measurement process, providing a dynamic basis for real-time gravity data correction.

[0083] Optionally, step S310 of performing static interference analysis on the measurement sub-region based on the measured gravity data to obtain a static interference term can be implemented through the steps in Figure 4 As shown in Figure 4 , it can specifically include:

[0084] Step S410: Statistically analyze the measured gravity data of each measurement point in the measurement sub-region, determine the gravity statistical characteristics corresponding to the measurement sub-region, and calculate the gravity anomaly fluctuation coefficient through the gravity statistical characteristics;

[0085] Step S420: Perform spatial autocorrelation analysis on the measurement sub-region based on the measured gravity data to obtain the Moran's index corresponding to the measurement sub-region, where the Moran's index is used to characterize the clustering and dispersion of gravity data in space;

[0086] Step S430: Perform Fourier transform on the measured gravity data to analyze the frequency spectrum distribution and determine the spectral energy intensity ratio of the measurement sub-region;

[0087] Step S440: Use the gravity anomaly fluctuation coefficient, the Moran's index, and the spectral energy intensity ratio as the static interference term.

[0088] Among them, the gravity statistical characteristics refer to a set of statistical parameters used to describe the overall distribution and fluctuation trend of the measured gravity data. The gravity statistical characteristics can include, but are not limited to, mean, variance, skewness, and kurtosis. The mean can reflect the central tendency of the gravity data, the variance can describe the degree of dispersion of the data, the skewness can be used to measure the symmetry of the data distribution, and the kurtosis can be used to describe the sharpness of the data distribution. Through the analysis of the statistical characteristics of the gravity data, the fluctuation trend and outliers in the data can be identified, providing basic support for subsequent data correction.

[0089] The gravity anomaly fluctuation coefficient is a parameter used to quantify the intensity of fluctuations in the spatial and temporal dimensions of the measured gravity data. The gravity anomaly fluctuation coefficient can reflect the degree of fluctuation and discreteness of the gravity data in the measurement area. It is calculated by the statistical characteristics of the gravity data. The larger the gravity anomaly fluctuation coefficient, the more drastic the change in the gravity data, which may be affected by the local geological structure or terrain undulations; the smaller the gravity anomaly fluctuation coefficient, the relatively stable gravity data, which is less disturbed by the terrain. This parameter can be used to evaluate the stability and degree of interference of the gravity field in the measurement sub-area where the measurement point is located.

[0090] Spatial autocorrelation analysis is a method used to measure and analyze the clustering or discreteness of spatial data in geographic spatial distribution. This analysis mainly reveals the patterns and laws of data in spatial distribution by calculating the correlation of spatial data. Spatial autocorrelation analysis can help identify the changing trends of gravity data at different spatial scales, and then discover systematic errors that may be caused by changes in geological structure or terrain.

[0091] Moran's I is a commonly used metric in spatial autocorrelation analysis, which is used to characterize the clustering or discreteness of spatial data. The value range of Moran's I is usually between -1 and 1. When the value is close to 1, it means that the measured gravity data in the measurement sub-area are spatially clustered; when the value is close to -1, it means that the measured gravity data in the measurement sub-area are spatially discrete; when the value is close to 0, it means that the measured gravity data in the measurement sub-area are spatially randomly distributed. Moran's I can be used to determine whether the gravity data in the measurement area is affected by regional geological conditions or terrain changes.

[0092] The spectrum energy intensity ratio is a parameter used to analyze the energy distribution ratio of gravity data at different frequency components. By performing spectrum analysis (such as Fourier transform) on gravity data, the gravity data can be decomposed into different frequency components, and then the energy ratio of high-frequency and low-frequency components can be calculated. Low-frequency components are mainly related to large-scale geological structures and terrain undulations, while high-frequency components are usually related to local geological anomalies or measurement errors. The spectrum energy intensity ratio is used to evaluate the influence of interference sources of different scales in the data.

[0093] The static analysis results such as the gravity anomaly fluctuation coefficient, Moran index, and spectral energy intensity ratio can be combined to form a static interference term. The static interference term can comprehensively reflect the influence caused by long-term stable factors such as geological structures and terrain conditions in the measured gravity data, providing a basis for subsequent gravity data correction and elevation data modification. In an alternative implementation, principal component analysis (PCA) or weighted superposition method can be used to comprehensively evaluate the gravity anomaly fluctuation coefficient, Moran index, and spectral energy intensity ratio to enhance the stability and reliability of the static interference term.

[0094] Through static interference analysis, a comprehensive evaluation of long-term systematic interferences is achieved through various methods such as statistical feature analysis, spatial autocorrelation analysis, and spectral analysis of the measured gravity data. By statistically analyzing the mean, variance, skewness, and kurtosis of the measured data, the overall fluctuation trend and abnormal characteristics of the data within the measurement sub-region can be revealed, providing a basis for identifying the influence of geological conditions and terrain changes on the data. Further, combined with spatial autocorrelation analysis (such as Moran index calculation), the clustering and discreteness of the measured data in space can be quantified, accurately reflecting the interference effect of geological structures and terrain undulations on the data distribution. In addition, by analyzing the energy distribution of gravity data at different frequency components through spectral analysis, the low-frequency components (related to geological structures) and high-frequency components (related to local anomalies or noise) can be effectively distinguished, comprehensively identifying static interference sources. The interaction of this series of static analysis methods makes the identification of long-term interference characteristics in the measurement area more comprehensive and in-depth, effectively reducing the systematic error caused by long-term geological structures and terrain features on elevation data.

[0095] Optionally, it can be achieved through Figure 5 the steps in to perform dynamic interference analysis on the measurement sub-region according to the regional environmental data in step S320 to obtain a dynamic interference term. As shown in Figure 5 it specifically may include:

[0096] Step S510, performing short-term time series fluctuation analysis on the measured gravity data through a preset sliding time window to obtain the short-term time series fluctuation characteristics corresponding to the measured gravity data;

[0097] Step S520, obtaining the regional environmental data, where the regional environmental data at least includes the temperature, humidity, and air pressure of each measurement point, and inputting the regional environmental data into a pre-constructed multiple linear regression model to obtain an environmental gravity anomaly value;

[0098] Step S530, using the short-term time series fluctuation characteristics and the environmental gravity anomaly value as the dynamic interference term.

[0099] Among them, the sliding time window is a commonly used time series processing method in dynamic data analysis. By setting a time interval with a fixed length and sliding the window range sequentially, the measured gravity data can be segmented and analyzed in real time. The sliding time window can capture the short-term fluctuation trend and local changes in the measured gravity data, facilitating the identification of possible instantaneous anomalies in the measured gravity data. The setting of the window length can be flexibly adjusted according to the actual measurement requirements and the environmental change frequency, and this embodiment is not limited thereto.

[0100] The short-term time series fluctuation analysis is a method for analyzing the short-term trend of measured gravity data based on the sliding time window. By monitoring the fluctuation trend of gravity data within a continuous time period, the mutation points or short-term fluctuation characteristics in the data can be detected in a timely manner. The short-term time series fluctuation analysis helps to identify data anomalies caused by environmental changes or equipment state fluctuations.

[0101] The short-term time series fluctuation characteristics are the characteristic information reflecting the short-term fluctuation trend and change law of gravity data extracted from the short-term time series fluctuation analysis. For example, the short-term time series fluctuation characteristics can include indicators such as the trend change, fluctuation amplitude, and change rate of the data, which are used to identify and quantify the short-term anomalies caused by environmental factors or equipment fluctuations in the measured data.

[0102] The multiple linear regression model is a statistical model used to analyze the relationship between multiple independent variables and a dependent variable. For example, it can be assumed that the dependent variable is a linear combination of the independent variables, and the data is fitted by the least squares method. In the dynamic interference analysis of gravity data, the multiple linear regression model can be used to establish a relationship model between regional environmental data (such as temperature, humidity, air pressure, wind speed) and measured gravity data to quantify the impact of environmental changes on the gravity measurement results. It has the advantages of simple calculation and strong interpretability and is suitable for joint analysis of multi-factor influences.

[0103] The environmental gravity anomaly value is a parameter calculated based on the multiple linear regression model and used to characterize the degree of influence of environmental factors on gravity data. The environmental gravity anomaly value can reflect the deviation degree of environmental changes (such as temperature, humidity, air pressure) from the gravity measurement data. The larger the environmental gravity anomaly value, the more significant the impact of environmental changes on the gravity measurement results; the smaller the environmental gravity anomaly value, the smaller the impact of environmental changes on the measurement data. The environmental gravity anomaly value is used in dynamic interference analysis to help identify the measurement errors caused by environmental factors and provide a basis for data correction.

[0104] The short-term temporal fluctuation characteristics can be fused with the environmental gravity anomaly values to form a dynamic interference term. The dynamic interference term can comprehensively reflect the degree to which the measurement data is affected by environmental changes in a short period of time, providing a basis for real-time data correction. The dynamic interference term has real-time and sensitivity, and can effectively reflect the immediate impact of environmental changes and emergencies on the measurement data. To further improve the analysis accuracy of the dynamic interference term, an Autoregressive Moving Average Model (ARMA) or a Long Short-Term Memory (LSTM) network can be introduced to perform deep learning analysis on the time series corresponding to the measured gravity data, enhancing the adaptability to complex environmental changes.

[0105] Through the dynamic interference analysis of the short-term temporal fluctuation analysis of the measured gravity data and the environmental data modeling, the real-time monitoring and accurate quantification of the impact of short-term environmental changes on the gravity data are realized. The short-term temporal fluctuation analysis of the gravity data is carried out through a sliding time window to capture the abnormal fluctuation trend and mutation points in the data in real time, ensuring the timely discovery of potential measurement errors in a dynamic environment; combining the multiple linear regression model to perform correlation analysis on the real-time regional environmental data (including temperature, humidity, air pressure, etc.) and the measured gravity data can quantify the degree of influence of environmental changes on the measured gravity data, further extract the environmental gravity anomaly values, compensate for the interference of environmental changes on the measured gravity data. The combination of dynamic interference analysis and static interference analysis can form an all-round interference identification and correction mechanism for the measurement data.

[0106] Through the synergistic effect of static interference analysis and dynamic interference analysis, a dynamic and adaptive gravity data interference identification and correction mechanism is formed. It can not only accurately identify the static interference caused by long-term geological conditions and terrain changes, but also respond and correct the dynamic fluctuations caused by environmental changes in real time, significantly improving the accuracy and reliability of the elevation data measurement in large drop areas. This multi-level and multi-dimensional interference analysis and correction method breaks through the limitations of traditional single gravity anomaly correction strategies and achieves a more efficient and stable data correction effect.

[0107] In an exemplary embodiment of the present disclosure, the steps in Figure 6 are used to implement the determination of the corrected gravity data based on the gravity interference coefficient and the measured gravity data in step S130. As shown in Figure 6 , it may specifically include:

[0108] Step S610, if it is determined that the gravity interference coefficient is less than the preset interference coefficient section, perform real-time gravity field correction on the measured gravity data to determine the corrected gravity data; or

[0109] Step S620, if it is determined that the gravity interference coefficient belongs to the interference coefficient section, perform real-time gravity field correction and multi-modal data fusion gravity estimation on the measured gravity data respectively, determine the first gravity data and the second gravity data, and perform weighted fusion on the first gravity data and the second gravity data to obtain the corrected gravity data; or

[0110] Step S630, if it is determined that the gravity interference coefficient is greater than the interference coefficient section, perform multi-modal data fusion gravity estimation on the measured gravity data to determine the corrected gravity data.

[0111] Among them, the preset interference coefficient section refers to a threshold range preset according to the environmental complexity, geological structure characteristics, and historical gravity data fluctuation conditions of the measurement area, and is used to classify the gravity interference degree. The interference coefficient section can be used to divide the gravity interference coefficient of the measurement point into different levels to guide the selection of appropriate gravity data correction strategies. The preset interference coefficient section can be divided into three ranges: low interference section, medium interference section, and high interference section. The gravity interference coefficient corresponding to the low interference section is relatively small, indicating that the measurement point is less affected by the terrain and environment; the gravity interference coefficient corresponding to the medium interference section is in the critical range, and there are relatively complex interference sources; the high interference section indicates that the measurement point is highly interfered, usually located in areas such as geological faults, lithological mutations, or strong environmental change areas. The setting of the preset interference coefficient section can be based on historical measurement data, geological exploration data, and environmental monitoring data, or can be dynamically adjusted through statistical analysis or machine learning methods (such as clustering algorithms) to ensure that the correction strategy matches the actual situation of the measurement area. In this exemplary embodiment, the setting of the preset interference coefficient section is not specially limited.

[0112] Real-time gravity field correction refers to, for the measured gravity data collected in real time at the measurement point, based on the standard geophysical model and the measured environmental conditions, quickly calculating and applying correction terms to eliminate the systematic errors caused by terrain undulation, geological density change, and elevation difference effects. Real-time gravity field correction can include Free-Air Correction, Bouguer Correction, Gradient Correction, etc.; among them, Free-Air Correction can be used to eliminate the influence of elevation change on gravity data, Bouguer Correction can be used to compensate for the disturbance of terrain and rock layer density on gravity data, and Gradient Correction can adjust data deviation through gravity gradient information. Real-time gravity field correction has the characteristics of fast response speed and strong real-time performance, can perform correction processing while data is being collected, and is suitable for measurement areas with weak interference or relatively stable environments. In some alternative embodiments, a global gravity field model (such as EGM2008) or a regional gravity field model can be introduced as a reference benchmark to further improve the correction accuracy.

[0113] Multi-modal data fusion gravity estimation refers to a technical means of comprehensively estimating and correcting gravity data at measurement points by integrating multi-source data and using deep learning models or statistical analysis methods. The multi-modal data fusion gravity estimation method integrates spatial position information, terrain undulation characteristics, geological structure information, environmental monitoring data (such as temperature, humidity, air pressure), and historical gravity measurement data. By inputting multi-modal data into a pre-trained deep learning model (such as a convolutional neural network, long short-term memory network, or ensemble learning model), the model automatically extracts the potential correlations and interference patterns between different data and outputs gravity correction data that best matches the actual environment. Multi-modal data fusion gravity estimation can effectively identify data anomalies and trend changes under complex environmental conditions and is particularly suitable for measurement areas with complex geological conditions or strong environmental disturbances. To further enhance the adaptability and accuracy of the model, an attention mechanism can be used to optimize the model's attention to key data features, or an adaptive weighting strategy can be adopted to dynamically balance the influence of each data source. Multi-modal data fusion gravity estimation breaks through the limitations of traditional single correction strategies and can comprehensively and dynamically correct gravity data in complex environments, improving measurement accuracy and data stability.

[0114] When the gravity interference coefficient is less than the preset interference coefficient range, it indicates that the measurement point is less affected by interference, and the fluctuations in the measured gravity data mainly come from standard measurement errors or weak environmental fluctuations. At this time, a real-time gravity field correction method can be used to correct the measured gravity data; when the gravity interference coefficient belongs to the interference coefficient range, it indicates that the interference on the measurement point is at a medium level, and there are certain complex interference factors. A single real-time gravity field correction cannot completely eliminate errors. At this time, a strategy combining real-time gravity field correction and multi-modal data fusion gravity estimation can be adopted; when the gravity interference coefficient is greater than the interference coefficient range, it indicates that the measurement point is severely affected by interference, and traditional real-time gravity field correction cannot effectively correct the data. It is necessary to rely entirely on multi-modal data fusion gravity estimation for correction. The multi-modal data fusion model can accurately estimate the trend of gravity data changes in complex environments through deep learning and adaptive analysis of multi-dimensional data.

[0115] By presetting the interference coefficient section and combining the hierarchical correction strategy, different gravity anomaly correction methods are switched and combined in real time to form an all-round, multi-level, and intelligent gravity anomaly correction mechanism. This not only effectively ensures the real-time nature of gravity anomaly data correction but also achieves the optimal correction effect under different interference intensities, with high flexibility and self-adaptability in complex environments, breaking through the limitation of the traditional gravity anomaly correction strategy in terms of unstable correction accuracy in a changing environment. Through the dynamic discrimination of the gravity interference coefficient and the intelligent selection of the correction strategy, under complex geological environments and changing measurement conditions, measurement errors can be effectively reduced at different levels and in different dimensions, significantly improving the accuracy of gravity data correction and the reliability of elevation measurement.

[0116] Optionally, the real-time gravity field correction method in step S610 can be implemented through the steps in Figure 7 as shown in Figure 7 and specifically may include:

[0117] Step S710: Determine the reference gravity data based on the level difference data and latitude coordinates of each measurement point;

[0118] Step S720: Calculate the free-air correction term and Bouguer correction term based on the level difference data, and determine the gravity anomaly data through the free-air correction term, the Bouguer correction term, the measured gravity data, and the reference gravity data;

[0119] Step S730: Calculate the partial derivative of the measured gravity data in the spatial direction of the measurement point, and construct a gradient correction term based on the partial derivative;

[0120] Step S740: Determine the corrected gravity data through the reference gravity data, the gravity anomaly data, and the gradient correction term.

[0121] Among them, the reference gravity data refers to the theoretical gravity value calculated based on the geographical coordinates (mainly latitude) of the measurement point and the standard Earth gravity model. The reference gravity data is used to characterize the gravity distribution of the Earth under ideal conditions, used to compare with the actually measured gravity data to identify and correct gravity anomalies caused by terrain, geology, or other factors. The reference gravity data is usually calculated according to the international standard gravity formula or the global gravity field model (such as Earth Gravitational Model 2008, EGM2008). Specifically, the calculation of the reference gravity data takes into account the influence of the Earth's rotation and crustal oblateness, so that the obtained theoretical gravity value can more accurately match the actual geographical location of the measurement point. For example, the reference gravity data can be determined through the following relational expression:

[0122] ;

[0123] Among them, can represent the reference gravity data of the measurement point, can represent the latitude coordinate of the measurement point, can represent the level difference data of the measurement point.

[0124] The free-air correction term is a correction term used to correct the influence of the change in elevation difference at the measurement point on the gravity measurement result. Since gravity decreases with the increase in height, in order to eliminate the influence of the elevation difference between the measurement point and the reference plane (usually the sea level) on the gravity value, it is necessary to perform free-air correction on the measurement data. The basic principle of free-air correction is to correct the elevation of the measurement point using the standard gravity gradient, and the commonly used gradient value is about 0.3086 mGal / m. For example, the free-air correction term can be calculated through the following relational expression:

[0125] ;

[0126] Among them, can represent the free-air correction term, can represent the level difference data of the measurement point.

[0127] The Bouguer correction term is a correction made to eliminate the influence of the density of the material below the surface of the measurement point on the gravity measurement result. Different from the free-air correction, the Bouguer correction not only considers the elevation change, but also considers the gravitational effect of the rock and terrain mass between the measurement point and the reference plane on gravity. The calculation of the Bouguer correction can be based on the Bouguer plate model, that is, assuming that there is an infinite horizontal rock layer with a uniform density below the measurement point, and calculating the contribution of this rock layer to gravity using the rock density and the gravitational constant. For example, the Bouguer correction term can be calculated through the following relational expression:

[0128] ;

[0129] Among them, can represent the Bouguer correction term, can represent the universal gravitational constant, can represent the crustal rock density of the measurement point, can represent the level difference data of the measurement point.

[0130] Gravity anomaly data refers to the difference between the actually measured gravity data at a measurement point and the reference gravity data. Gravity anomaly data can characterize the gravity field anomalies caused by factors such as the distribution of crustal materials, geological structures, and terrain undulations. Gravity anomaly data is the residual data obtained by successively performing free-air correction and Bouguer correction on the measured gravity data, and is used to identify changes in underground geological structures. For example, positive gravity anomalies are usually associated with rock masses with higher densities (such as basalt and iron ore bodies), while negative gravity anomalies may correspond to crustal thinning, fault zones, or low-density rock layers. For example, gravity anomaly data can be calculated by the following relational expression:

[0131] ;

[0132] where, can represent the gravity anomaly data at the measurement point, can represent the measured gravity data at the measurement point, can represent the free-air correction term at the measurement point, can represent the Bouguer correction term at the measurement point.

[0133] The gradient correction term is a correction term for the gradient change in the spatial distribution of gravity data between measurement points. This correction term is used to compensate for data deviations caused by gravity gradient changes, especially in areas with large terrain undulations or complex geological structures. The gravity gradient represents the rate of change of the gravity field in space, and its calculation is based on the partial derivatives of the measurement point in three directions (X, Y, Z axes). The construction of the gradient correction term usually uses the Finite Difference Method or Multiscale Gradient Analysis to quantitatively analyze the spatial differences in gravity data of adjacent measurement points. By introducing the gradient correction term, local errors caused by spatial position differences between measurement points can be effectively corrected, improving the accuracy and spatial consistency of gravity data. This correction method is particularly crucial in survey areas with complex terrain and drastic gravity changes, and can significantly improve the reliability of gravity data.

[0134] ;

[0135] where, can represent the gradient correction term at the measurement point, can represent the measured gravity data at the measurement point, 、 and can respectively represent the partial derivatives of the measured gravity data at the measurement point in the X direction, Y direction, and Z direction, 、 and can represent the spatial distance differences between measurement points.

[0136] After obtaining the reference gravity data, gravity anomaly data, and gradient correction term, the corrected gravity data can be determined based on the reference gravity data, gravity anomaly data, and gradient correction term. For example, the corrected gravity data can be determined by the following relational expression:

[0137] ;

[0138] wherein, can represent the corrected gravity data of the measurement point, and can respectively represent the weight data corresponding to the gravity anomaly data and the gradient correction term.

[0139] Determining the corrected gravity data based on the reference gravity data, gravity anomaly data, and gradient correction term means combining the standard reference value, system correction result, and local gradient correction result to comprehensively correct the original measurement data in all aspects and at multiple levels to obtain accurate and reliable corrected gravity data. For example, through a dynamic weighted fusion strategy, the influence weights of each correction term on the data can be comprehensively considered to ensure that the corrected data can truly reflect the gravity field characteristics of the measurement area. The weight allocation can be dynamically adjusted based on the interference degree of the measurement point and the accuracy requirement of the correction. For example, in areas with less interference, the weights of the free-air correction term and Bouguer correction term can be increased; in areas with more interference, the proportion of the gradient correction term can be increased. The finally formed corrected gravity data has high precision and high stability, can effectively eliminate the influence of environmental interference and geological changes on the measurement data, and provides reliable data support for subsequent elevation data correction and geological analysis.

[0140] In an exemplary embodiment of the present disclosure, the multi-modal data fusion gravity estimation of the measured gravity data can be implemented through the following steps to determine the content of the corrected gravity data, which can specifically include:

[0141] The multi-modal terrain data corresponding to each measurement point can be obtained. The multi-modal terrain data can include at least two of spatial position, terrain undulation characteristics, geological information, environmental information, and historical gravity measurement data. The multi-modal terrain data can be input into a pre-trained multi-modal data fusion gravity estimation model to infer the estimated corrected gravity data.

[0142] Among them, multi-modal terrain data refers to a multi-source heterogeneous data set obtained through multiple data acquisition methods for comprehensively describing the terrain, geology, and environmental characteristics of the measurement area. It not only includes traditional terrain data but also integrates various forms and sources of information to reflect the real environment of the measurement area in multiple dimensions and at multiple levels. For example, multi-modal terrain data can include at least two of spatial position, terrain undulation characteristics, geological information, environmental information, and historical gravity measurement data; spatial position information can be collected in real time through the Global Positioning System or Global Navigation Satellite System for accurately determining the three-dimensional coordinates (latitude, longitude, and elevation) of each measurement point; terrain undulation characteristics can be obtained from the high-density three-dimensional terrain point cloud data collected by drones for depicting details such as slopes, curvatures, and surface roughness in the measurement area; geological information can include key parameters such as underground lithology distribution, fault structures, geological fracture zones, and rock densities for revealing the geological structure of the measurement area; environmental information can include temperature, humidity, air pressure, wind speed, etc. collected in real time; historical gravity measurement data refers to the gravity measurement data previously obtained in the measurement area, which is used to establish the gravity change trend, identify historical interference patterns, and provide data support for gravity data estimation and correction.

[0143] The multi-modal data fusion gravity estimation model refers to a multi-source heterogeneous data fusion analysis model constructed based on deep learning or statistical analysis methods, aiming to use multi-modal terrain data to perform high-precision estimation and correction of gravity field changes. This model can extract and fuse multi-dimensional data features through deep learning techniques to achieve the correction of gravity measurement errors under complex geological conditions and environmental changes. For example, in some feasible implementation manners, the multi-modal data fusion gravity estimation model can use a convolutional neural network to extract the spatial features of spatial data (such as terrain point clouds and remote sensing images), use long short-term memory networks or gated recurrent units (GRUs) to extract the time series features of environmental information and historical gravity measurement data. To enhance the model's ability to capture key features, a self-attention mechanism or a multi-head attention mechanism can also be introduced to dynamically adjust the attention weights for different features; then, different modal feature information can be integrated through a feature fusion layer, and the fusion method can be simple feature splicing, weighted superposition, or cross-modal alignment to achieve effective interaction and information complementarity between different data types.

[0144] Through multi-modal data fusion, gravity estimation can perform real-time and accurate correction of measured gravity data based on the effective fusion and intelligent processing of multi-source terrain data. It not only solves the deficiencies of traditional methods in processing gravity anomaly data in complex environments but also provides a highly flexible and adaptable measurement data correction scheme, which can improve the reliability of corrected gravity data under complex geological and environmental conditions, thereby ensuring the accuracy and effectiveness of the output corrected elevation data.

[0145] For example, after obtaining the corrected gravity data, the elevation correction term for each measurement sub-region can be determined from the corrected gravity data, and the elevation correction term can be expressed by the following relational expression:

[0146] ;

[0147] Wherein, can represent the elevation correction term of the measurement point, can represent the level difference data of the level instrument at the measurement point. Furthermore, the measured elevation data can be corrected according to the elevation correction term to obtain the corrected elevation data corresponding to the large-drop measurement region. For example, the calculation of the corrected elevation data can be achieved through the following relational expression:

[0148]

[0149] Wherein, can represent the corrected elevation data, can represent the measured elevation data.

[0150] It should be noted that although the steps of the method in the present disclosure are described in a specific order in the accompanying drawings, this does not require or imply that these steps must be executed in that specific order, or that all the shown steps must be executed to achieve the desired result. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step for execution, and / or one step may be decomposed into multiple steps for execution, etc.

[0151] In addition, in the present exemplary embodiment, an elevation data measurement device for large-drop regions is also provided. Referring to Figure 8 shown, the elevation data measurement device 800 for large-drop regions includes: a level measurement module 810, a gravity interference analysis module 820, a gravity data correction module 830, an elevation correction term determination module 840, and an elevation data correction module 850. Wherein:

[0152] The level measurement module 810 is configured to obtain the level difference data collected by the level instrument at each preset measurement point in the large-drop measurement region, as well as the measured gravity data corresponding to each of the measurement points;

[0153] The gravity interference analysis module 820 is configured to divide the large-drop measurement area into measurement sub-areas, and determine the gravity interference coefficient corresponding to the measurement point according to the measurement gravity data and the regional environmental data corresponding to each of the measurement sub-areas;

[0154] The gravity data correction module 830 is configured to determine the corrected gravity data based on the gravity interference coefficient and the measurement gravity data;

[0155] The elevation correction term determination module 840 is configured to determine the elevation correction term of each of the measurement sub-areas through the corrected gravity data, and determine the measured elevation data of each of the measurement sub-areas through the level difference data;

[0156] The elevation data correction module 850 is configured to correct the measured elevation data according to the elevation correction term to obtain the corrected elevation data corresponding to the large-drop measurement area.

[0157] In an exemplary embodiment of the present disclosure, based on the foregoing solution, the gravity interference analysis module 820 is configured to:

[0158] Obtain the terrain point cloud data obtained by inspecting the large-drop measurement area based on the unmanned aerial vehicle, and determine the terrain undulation characteristics according to the terrain point cloud data;

[0159] Determine the terrain complexity data of the large-drop measurement area according to the terrain undulation characteristics;

[0160] Determine the geological complexity data of the large-drop measurement area through the geological exploration data corresponding to the measurement area;

[0161] Perform weighted fusion on the terrain complexity data and the geological complexity data to obtain the regional complexity corresponding to the large-drop measurement area, and divide the measurement area into measurement sub-areas based on the regional complexity.

[0162] In an exemplary embodiment of the present disclosure, based on the foregoing solution, the gravity interference analysis module 820 further includes:

[0163] The static interference analysis unit is configured to perform static interference analysis on the measurement sub-area according to the measurement gravity data to obtain a static interference term;

[0164] The dynamic interference analysis unit is configured to perform dynamic interference analysis on the measurement sub-area according to the measurement gravity data and the regional environmental data to obtain a dynamic interference term;

[0165] A gravity interference coefficient determination unit, configured to perform weighted fusion on the static interference term and the dynamic interference term based on a preset weight coefficient to obtain the gravity interference coefficient of each measurement point in the measurement sub-region.

[0166] In an exemplary embodiment of the present disclosure, based on the foregoing solution, the static interference analysis unit is configured to:

[0167] Perform statistical analysis on the measured gravity data of each measurement point in the measurement sub-region, determine the gravity statistical characteristics corresponding to the measurement sub-region, and calculate the gravity anomaly fluctuation coefficient through the gravity statistical characteristics;

[0168] Perform spatial autocorrelation analysis on the measurement sub-region according to the measured gravity data to obtain the Moran index corresponding to the measurement sub-region, and the Moran index is used to characterize the aggregation and discreteness of the gravity data in space;

[0169] Perform Fourier transform on the measured gravity data to analyze the frequency spectrum distribution and determine the spectral energy intensity ratio of the measurement sub-region;

[0170] Take the gravity anomaly fluctuation coefficient, the Moran index, and the spectral energy intensity ratio as the static interference term.

[0171] In an exemplary embodiment of the present disclosure, based on the foregoing solution, the dynamic interference analysis unit is configured to:

[0172] Perform short-term time series fluctuation analysis on the measured gravity data through a preset sliding time window to obtain the short-term time series fluctuation characteristics corresponding to the measured gravity data;

[0173] Obtain the regional environmental data, where the regional environmental data at least includes the temperature, humidity, and air pressure of each measurement point, and input the regional environmental data into a pre-constructed multiple linear regression model to obtain the environmental gravity anomaly value;

[0174] Take the short-term time series fluctuation characteristics and the environmental gravity anomaly value as the dynamic interference term.

[0175] In an exemplary embodiment of the present disclosure, based on the foregoing solution, the gravity data correction module 830 is configured to:

[0176] If it is determined that the gravity interference coefficient is less than the preset interference coefficient section, perform real-time gravity field correction on the measured gravity data to determine the corrected gravity data; or

[0177] If it is determined that the gravity interference coefficient belongs to the interference coefficient section, perform real-time gravity field correction and multi-modal data fusion gravity estimation on the measured gravity data respectively, determine the first gravity data and the second gravity data, and perform weighted fusion on the first gravity data and the second gravity data to obtain the corrected gravity data; or

[0178] If it is determined that the gravity interference coefficient is greater than the interference coefficient section, perform multi-modal data fusion gravity estimation on the measured gravity data to determine the corrected gravity data.

[0179] In an exemplary embodiment of the present disclosure, based on the foregoing solution, the gravity data correction module 830 is configured to:

[0180] Determine the reference gravity data according to the level difference data and the latitude coordinates of each measurement point;

[0181] Calculate the free-air correction term and the Bouguer correction term based on the level difference data, and determine the gravity anomaly data through the free-air correction term, the Bouguer correction term, the measured gravity data and the reference gravity data;

[0182] Calculate the partial derivative of the measured gravity data in the spatial direction of the measurement point, and construct a gradient correction term based on the partial derivative;

[0183] Determine the corrected gravity data through the reference gravity data, the gravity anomaly data and the gradient correction term.

[0184] In an exemplary embodiment of the present disclosure, based on the foregoing solution, the gravity data correction module 830 is configured to:

[0185] Obtain the multi-modal terrain data corresponding to each measurement point, and the multi-modal terrain data includes at least two of spatial position, terrain undulation characteristics, geological information, environmental information and historical gravity measurement data;

[0186] Input the multi-modal terrain data into a pre-trained multi-modal data fusion gravity estimation model, and infer the estimated corrected gravity data.

[0187] The specific details of each module of the above elevation data measurement device for large drop areas have been described in detail in the corresponding elevation data measurement method for large drop areas, so they will not be repeated here.

[0188] It should be noted that although several modules or units of the elevation data measurement device for large elevation difference areas are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of the two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0189] In addition, in an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above elevation data measurement method for large elevation difference areas is also provided.

[0190] Those skilled in the art can understand that various aspects of the present disclosure can be implemented as a system, a method, or a program product. Therefore, various aspects of the present disclosure can be specifically implemented in the following forms, namely: a complete hardware embodiment, a complete software embodiment (including firmware, microcode, etc.), or an embodiment combining hardware and software aspects, which can be collectively referred to as "circuit", "module", or "system" here.

[0191] The following refers to Figure 9 to describe the electronic device 900 according to this embodiment of the present disclosure. Figure 9 The electronic device 900 shown is only an example and should not impose any limitations on the functions and usage scope of the embodiments of the present disclosure.

[0192] As Figure 9 shown, the electronic device 900 is presented in the form of a general-purpose computing device. The components of the electronic device 900 may include, but are not limited to: the above at least one processing unit 910, the above at least one storage unit 920, a bus 930 connecting different system components (including the storage unit 920 and the processing unit 910), and a display unit 940.

[0193] Among them, the storage unit stores program codes, and the program codes can be executed by the processing unit 910, so that the processing unit 910 executes the steps according to various exemplary embodiments of the present disclosure described in the above "Exemplary Method" section of this specification. For example, the processing unit 910 can execute as Figure 1In step S110 shown in the figure, obtain the level difference data of the level instrument collected at each preset measurement point in the large drop measurement area, and the measurement gravity data corresponding to each of the measurement points; in step S120, divide the large drop measurement area into measurement sub-areas, and determine the gravity interference coefficient corresponding to the measurement point according to the measurement gravity data and the regional environment data corresponding to each of the measurement sub-areas; in step S130, determine the corrected gravity data based on the gravity interference coefficient and the measurement gravity data; in step S140, determine the elevation correction term of each of the measurement sub-areas through the corrected gravity data, and determine the measured elevation data of each of the measurement sub-areas through the level difference data of the level instrument; in step S150, correct the measured elevation data according to the elevation correction term to obtain the corrected elevation data corresponding to the large drop measurement area.

[0194] The storage unit 920 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 921 and / or a cache storage unit 922, and may further include a read-only storage unit (ROM) 923.

[0195] The storage unit 920 may further include a program / utilities 924 having a set (at least one) of program modules 925. Such program modules 925 include, but are not limited to: an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment.

[0196] The bus 930 may represent one or more of several types of bus architectures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0197] The electronic device 900 may also communicate with one or more external devices 970 (such as a keyboard, a pointing device, a Bluetooth device, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 900, and / or may communicate with any device (such as a router, a modem, etc.) that enables the electronic device 900 to communicate with one or more other computing devices. Such communication may be carried out through the input / output (I / O) interface 950. Moreover, the electronic device 900 may also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through the network adapter 960. As shown in the figure, the network adapter 960 communicates with other modules of the electronic device 900 through the bus 930. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 900, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0198] Through the description of the above embodiments, those skilled in the art can easily understand that the exemplary embodiments described herein can be implemented by software or by a combination of software and necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure can be embodied in the form of a software product, and the software product can be stored in a non-volatile storage medium (which may be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

[0199] In an exemplary embodiment of the present disclosure, there is also provided a computer-readable storage medium, on which a program product capable of implementing the above method of the present specification is stored. In some possible embodiments, various aspects of the present disclosure may also be implemented in the form of a program product, which includes program code. When the program product runs on a terminal device, the program code is used to cause the terminal device to execute the steps according to various exemplary embodiments of the present disclosure described in the above "Exemplary Method" section of the present specification.

[0200] Referring Figure 10 As shown, a program product 1000 for implementing the above method for measuring elevation data in a large drop area according to an embodiment of the present disclosure is described. It may adopt a portable compact disc read-only memory (CD-ROM) and include program code, and may run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto. In this document, the readable storage medium may be any tangible medium that contains or stores a program, and the program may be used by or in combination with an instruction execution system, apparatus, or device.

[0201] The program product may employ any combination of one or more readable media. The readable media may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0202] A computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which the readable program code is carried. Such a propagated data signal may take many forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the foregoing. The readable signal medium may also be any readable medium other than the readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device.

[0203] The program code contained on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0204] The program code for performing the operations of the present disclosure may be written in any combination of one or more programming languages, including object-oriented programming languages such as Java, C++, etc., and also including conventional procedural programming languages such as the "C" language or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, executed as a stand-alone software package, partially on the user computing device and partially on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0205] In addition, the above-mentioned drawings are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present disclosure, rather than for limiting purposes. It is easy to understand that the processes shown in the above-mentioned drawings do not indicate or limit the time sequence of these processes. Additionally, it is also easy to understand that these processes may be executed, for example, synchronously or asynchronously in multiple modules.

[0206] From the description of the above embodiments, those skilled in the art can easily understand that the exemplary embodiments described herein can be implemented by software or by a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (such as a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

[0207] After considering the specification and practicing the invention disclosed herein, those skilled in the art will readily conceive of other embodiments of the present disclosure. This application is intended to cover any variations, uses, or adaptations of the present disclosure, which follow the general principles of the present disclosure and include known common knowledge or conventional technical means in the technical field not disclosed by the present disclosure. The specification and embodiments are only regarded as exemplary, and the true scope and spirit of the present disclosure are pointed out by the claims.

[0208] It should be understood that the present disclosure is not limited to the exact structures already described and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present disclosure is only limited by the appended claims.

Claims

1. A method for measuring elevation data in areas with large elevation differences, characterized in that: include: Acquire the level height difference data collected from each preset measuring point in the large drop measurement area, and the measured gravity data corresponding to each of the measuring points; Divide the large drop measurement area into measurement sub-areas, perform static interference analysis on the measurement sub-areas according to the measurement gravity data to obtain static interference terms, perform dynamic interference analysis on the measurement sub-areas according to the measurement gravity data and the regional environment data to obtain dynamic interference terms, and perform weighted fusion of the static interference terms and the dynamic interference terms based on a preset weight coefficient to obtain a gravity interference coefficient of each measurement point in the measurement sub-area; If it is determined that the gravity interference coefficient is less than a preset interference coefficient range, performing real-time gravity field correction on the measured gravity data to determine the corrected gravity data; If it is determined that the gravity interference coefficient belongs to the interference coefficient section, real-time gravity field correction and multimodal data fusion gravity estimation are respectively performed on the measured gravity data to determine first gravity data and second gravity data, and weighted fusion is performed on the first gravity data and the second gravity data to obtain corrected gravity data; If it is determined that the gravity interference coefficient is greater than the interference coefficient range, performing multimodal data fusion gravity estimation on the measured gravity data to determine the corrected gravity data; Determining the elevation correction item of each of the measurement sub-areas through the corrected gravity data, and determining the measurement elevation data of each of the measurement sub-areas through the level height difference data; The measured elevation data is corrected according to the elevation correction item to obtain corrected elevation data corresponding to the large drop measurement area.

2. The elevation data measurement method according to claim 1, characterized in that: The step of dividing the large drop measurement area into measurement sub-areas includes: Acquire terrain point cloud data obtained by inspecting the large drop measurement area based on a drone, and determine terrain undulation characteristics based on the terrain point cloud data; Determining terrain complexity data of the large drop measurement area according to the terrain undulation characteristics; Determine the geological complexity data of the large drop measurement area through the geological exploration data corresponding to the measurement area; The terrain complexity data and the geological complexity data are weightedly fused to obtain the regional complexity corresponding to the large drop measurement area, and the measurement area is divided into measurement sub-areas based on the regional complexity.

3. The elevation data measurement method according to claim 1, characterized in that: The step of performing static interference analysis on the measurement sub-area according to the measured gravity data to obtain static interference terms includes: Performing statistical analysis on the measured gravity data of each measuring point in the measuring sub-area, determining the gravity statistical characteristics corresponding to the measuring sub-area, and calculating the gravity anomaly fluctuation coefficient through the gravity statistical characteristics; Performing spatial autocorrelation analysis on the measurement sub-area according to the measured gravity data to obtain a Moran's index corresponding to the measurement sub-area, wherein the Moran's index is used to characterize the spatial aggregation and discreteness of the gravity data; Performing Fourier transformation on the measured gravity data to analyze the spectrum distribution and determine the spectrum energy intensity ratio of the measured sub-area; The gravity anomaly fluctuation coefficient, the Moran index and the spectrum energy intensity ratio are used as the static interference terms.

4. The elevation data measurement method according to claim 1, characterized in that: The performing dynamic interference analysis on the measurement sub-area according to the regional environment data to obtain dynamic interference items includes: Performing short-term time series fluctuation analysis on the measured gravity data through a preset sliding time window to obtain short-term time series fluctuation characteristics corresponding to the measured gravity data; Acquire the regional environmental data, the regional environmental data at least including the temperature, humidity and air pressure of each measuring point, and input the regional environmental data into a pre-built multiple linear regression model to obtain an environmental gravity anomaly value; The short-term time series fluctuation characteristics and the abnormal value of environmental gravity are used as the dynamic interference items.

5. The method for measuring elevation data in a large elevation difference area according to claim 1, characterized in that: Performing real-time gravity field correction on the measured gravity data to determine the corrected gravity data includes: Determine reference gravity data based on the level height difference data and latitude coordinates of each measuring point; Calculating a free air correction term and a Bouguer correction term based on the level height difference data, and determining gravity anomaly data through the free air correction term, the Bouguer correction term, the measured gravity data and the reference gravity data; Calculating the partial derivative of the measured gravity data in the spatial direction of the measurement point, and constructing a gradient correction term based on the partial derivative; The corrected gravity data is determined by the reference gravity data, the gravity anomaly data and the gradient correction term.

6. The elevation data measurement method according to claim 1, characterized in that: Performing multimodal data fusion gravity estimation on the measured gravity data to determine the corrected gravity data includes: Acquire multimodal terrain data corresponding to each measuring point, wherein the multimodal terrain data includes at least two of spatial position, terrain relief characteristics, geological information, environmental information and historical gravity measurement data; The multimodal terrain data is input into a pre-trained multimodal data fusion gravity estimation model, and estimated corrected gravity data is obtained by inference.

7. An elevation data measurement device for use in areas with large elevation differences, used to perform the elevation data measurement method according to any one of claims 1 to 6, characterized in that: include: A level measurement module, used to obtain level height difference data collected at each preset measurement point in a large drop measurement area, and measurement gravity data corresponding to each of the measurement points; A gravity interference analysis module, used for dividing the large drop measurement area into measurement sub-areas, performing static interference analysis on the measurement sub-areas according to the measurement gravity data to obtain static interference terms, performing dynamic interference analysis on the measurement sub-areas according to the measurement gravity data and the regional environment data to obtain dynamic interference terms, and weightedly fusing the static interference terms and the dynamic interference terms based on a preset weight coefficient to obtain a gravity interference coefficient of each measurement point in the measurement sub-area; A gravity data correction module, configured to perform real-time gravity field correction on the measured gravity data to determine the corrected gravity data if it is determined that the gravity interference coefficient is less than a preset interference coefficient range; If it is determined that the gravity interference coefficient belongs to the interference coefficient section, real-time gravity field correction and multimodal data fusion gravity estimation are respectively performed on the measured gravity data to determine first gravity data and second gravity data, and weighted fusion is performed on the first gravity data and the second gravity data to obtain the corrected gravity data; If it is determined that the gravity interference coefficient is greater than the interference coefficient range, performing multimodal data fusion gravity estimation on the measured gravity data to determine the corrected gravity data; An elevation correction item determination module, used to determine the elevation correction item of each of the measurement sub-areas through the corrected gravity data, and to determine the measurement elevation data of each of the measurement sub-areas through the level height difference data; The elevation data correction module is used to correct the measured elevation data according to the elevation correction item to obtain the corrected elevation data corresponding to the large drop measurement area.

8. An electronic device, characterized in that: include: processor; as well as A memory having computer-readable instructions stored thereon, wherein the computer-readable instructions, when executed by the processor, implement the elevation data measurement method for a large elevation difference area as described in any one of claims 1 to 6.

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