Elevation data measurement method, device and equipment for large-fall region

Abstract

The invention provides an elevation data measurement method, device and equipment for a large-fall area, and relates to the technical field of engineering survey and geological survey. The method comprises the following steps: acquiring level gauge height difference data and measurement gravity data of each measurement point in a large-fall measurement area; carrying out regional division on the large-fall measurement region to obtain measurement sub-regions, and determining a gravity interference coefficient corresponding to a measurement point according to measurement gravity data and regional environment data of the measurement sub-regions; determining corrected gravity data based on the gravity interference coefficient and the measured gravity data, and determining an elevation correction item and measured elevation data; and correcting the measured elevation data according to the elevation correction item to obtain corrected elevation data corresponding to the large-fall measurement area. According to the technical scheme, the reliability and precision of elevation data measured by the level gauge in a large-fall area can be effectively improved.

Description

Background Art

[0002] In the process of measuring elevation data in areas with large elevation differences (such as mountain valleys 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 fluctuations, changes in gravity acceleration will have a direct impact on the elevation difference measurement of the level, resulting in the accumulation of systematic errors. Existing technologies usually use leveling instruments for measurement, but do not fully consider the impact of changes in the gravity field 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 into related technologies. For example, in the national first- and second-class leveling specifications, it is required to correct gravity anomalies for leveling measurements under certain conditions. However, in practical applications, due to the limitations of measurement costs and construction period pressure, 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 changes in the gravity field. It is easy to cause local error accumulation in the correction results, which is difficult to meet the high-precision requirements of engineering measurements.

[0004] At present, in the level gravity anomaly correction technology, either the impact of terrain undulation and gravity field changes on the level measurement accuracy is not fully considered, resulting in the difficulty in effectively correcting the elevation data errors obtained by the level measurement; or the gravity anomaly correction method is not accurate enough, especially in areas with complex terrain and drastic gravity changes, local errors accumulate seriously, the level measurement accuracy is low, and the reliability of the measurement results is low.

[0005] It should be noted that the information disclosed in the above background technology 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 ordinary technicians in the field. Summary of the invention

[0006] The purpose of the embodiments of the present disclosure is to provide a method for measuring elevation data in areas with large elevation differences, an apparatus for measuring elevation data in areas with large elevation differences, and electronic equipment, thereby effectively improving the reliability and accuracy of elevation data measured by a level in areas with large elevation differences.

[0007] According to a first aspect of an embodiment of the present disclosure, there is provided a method for measuring elevation data in a large drop area, comprising: 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, and determine the gravity interference coefficient corresponding to the measurement point according to the measurement gravity data and regional environmental data corresponding to each measurement sub-area; determining corrected gravity data based on the gravity disturbance coefficient and the measured 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.

[0008] According to a second aspect of an embodiment of the present disclosure, there is provided an elevation data measuring device for use in a large elevation difference area, comprising: 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, and determining the gravity interference coefficient corresponding to the measurement point according to the measurement gravity data and regional environmental data corresponding to each measurement sub-area; A gravity data correction module, configured to determine corrected gravity data based on the gravity interference coefficient and the measured 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.

[0009] According to a third aspect of an embodiment of the present disclosure, there is provided an electronic device, comprising: a processor; and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the elevation data measurement method for large drop areas in the first aspect is implemented.

[0010] According to a fourth aspect of an embodiment of the present disclosure, there is provided a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method for measuring elevation data for areas with large elevation differences in the first aspect is implemented.

[0011] The technical solution provided by the embodiments of the present disclosure may have the following beneficial effects: 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.

[0012] 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

[0013] 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.

[0014] 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.

[0015] Figure 2 The flowchart of dividing and obtaining measurement sub-areas according to some embodiments of the present disclosure is schematically shown.

[0016] Figure 3 A schematic diagram of a process for determining a gravity disturbance coefficient according to some embodiments of the present disclosure is schematically shown.

[0017] Figure 4A flowchart of constructing static interference terms according to some embodiments of the present disclosure is schematically shown.

[0018] Figure 5 A flowchart of constructing dynamic interference terms according to some embodiments of the present disclosure is schematically shown.

[0019] Figure 6 A flowchart of determining corrected gravity data according to some embodiments of the present disclosure is schematically shown.

[0020] Figure 7 A flowchart of real-time gravity field correction according to some embodiments of the present disclosure is schematically shown.

[0021] Figure 8 A schematic diagram of an elevation data measuring device for an area with a large elevation difference according to some embodiments of the present disclosure is schematically shown.

[0022] Fig. 9 A schematic diagram of the structure of a computer system of an electronic device according to some embodiments of the present disclosure is schematically shown.

[0023] Fig.10 A schematic diagram of a computer-readable storage medium according to some embodiments of the present disclosure is schematically shown.

[0024] In the drawings, the same or corresponding reference numerals represent the same or corresponding parts. DETAILED DESCRIPTION

[0025] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying 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 implementations described in the following exemplary embodiments do not represent all implementations consistent with this specification. Instead, they are merely examples of devices and methods consistent with some aspects of this specification as detailed in the appended claims.

[0026] Furthermore, the drawings are only schematic illustrations 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 separate entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0027] In this example embodiment, a method for measuring elevation data in areas with large elevation differences is first provided. The method for measuring elevation data in areas with large elevation differences can be applied to terminal devices or to servers. This example embodiment does not make any special limitation on this, and the following description will take the server executing the method as an example. 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. Figure 1 As shown, the elevation data measurement method for a large drop area may include the following steps: Step S110, obtaining the level 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; 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 regional environment data corresponding to each measurement sub-area; Step S130, determining corrected gravity data based on the gravity interference coefficient and the measured gravity data; Step S140, determining the elevation correction item of each of the measurement sub-areas by using the corrected gravity data, and determining the measurement elevation data of each of the measurement sub-areas by using the level height difference data; Step S150, correcting the measured elevation data according to the elevation correction item to obtain corrected elevation data corresponding to the large drop measurement area.

[0028] 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.

[0029] Next, the elevation data measurement method for areas with large elevation differences in this exemplary embodiment will be further described.

[0030] 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.

[0031] 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.

[0032] Measuring points refer to sampling points preset according to terrain features and measurement requirements in a large drop measurement area. The layout of measuring points can be reasonably planned according to the degree of terrain undulation, the trend of gravity field changes and engineering requirements. For example, the layout method can be regular layout (such as equidistant distribution) or irregular layout (such as layout along fault lines, landslide body boundaries and other key areas). This embodiment does not make special restrictions on this. In order to ensure the comprehensiveness and accuracy of the data read by the level instrument, the spacing between measuring points should be appropriately reduced in areas with complex terrain, and can be appropriately increased in areas with flat terrain; of course, the measuring points can also be automatically laid out by drones or all-terrain robots to improve the efficiency and safety of the layout.

[0033] The elevation difference data of the level refers to the data obtained by accurately measuring the elevation difference between adjacent measuring points through the leveling equipment arranged in the measuring area. The elevation data of each measuring point or measuring line can be obtained based on the accurate elevation data of the starting point. For example, the level can be a digital level with a high-precision reading function, or a laser level using laser ranging technology, or an automatic leveling level, but this example embodiment is not limited to this. The automatic leveling level can achieve automatic leveling through a built-in compensator to reduce human errors; the digital level can automatically read the scale data using photoelectric encoding technology to improve measurement accuracy and efficiency; the laser level can measure distance through a laser beam, which is suitable for long-distance measurement. During measurement, the level can obtain the elevation difference data between the two measuring points through the forward and rearward readings, and further accumulate and calculate to obtain the elevation data of each measuring point in the measuring area. In an optional embodiment, the level can be equipped with automatic data recording and wireless transmission functions to achieve real-time data upload and remote monitoring.

[0034] Measured gravity data refers to data used to describe the strength of the gravity field at each measuring point. Measured gravity data can be obtained in real time through a portable gravimeter. For example, the gravimeter can be a spring-type gravimeter, a superconducting gravimeter, or a micro gravity sensor based on a micro-electro-mechanical system (MEMS). The spring-type gravimeter is highly portable and easy to operate, 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 deployment. 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.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] In step S130, corrected gravity data is determined based on the gravity disturbance coefficient and the measured gravity data.

[0040] In an exemplary embodiment of the present disclosure, the corrected gravity data refers to accurate gravity data obtained by performing error correction on original gravity data, and the corrected gravity data may be determined based on the gravity interference coefficient and the measured gravity data. For example, in some optional embodiments, when the gravity interference coefficient is less than a preset threshold interval, a real-time gravity field correction method can be used. The real-time gravity field correction can include calculating free air correction and Bouguer correction. The free air correction is used to compensate for the gravity change caused by the height difference, and the Bouguer correction is used to compensate for the influence of terrain density on gravity. When the gravity interference coefficient is within the preset threshold interval, a method combining real-time gravity field correction with multimodal data fusion gravity estimation can be used. Multimodal data fusion refers to a processing method of inferring more accurate corrected gravity data by inputting multi-source data such as spatial position, terrain undulation, geological information, environmental parameters and historical gravity data into a multimodal fusion model. If the gravity interference coefficient exceeds the preset interval, a multimodal data fusion gravity estimation method can be used, using deep learning algorithms such as convolutional neural networks (CNN) or graph neural networks (GNN) to integrate multi-source information for gravity data estimation, so as to effectively deal with gravity data interference in complex environments.

[0041] In step S140, the elevation correction item of each of the measurement sub-areas is determined by using the corrected gravity data, and the measurement elevation data of each of the measurement sub-areas is determined by using the level height difference data.

[0042] 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 measuring 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 measuring point obtained by accumulating the level height difference data between adjacent measuring points. In order to improve the accuracy of the data, the measured elevation data can be smoothed by segmentation analysis and trend analysis methods to eliminate measurement errors.

[0043] In step S150, the measured elevation data is corrected according to the elevation correction item to obtain corrected elevation data corresponding to the large drop measurement area.

[0044] 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.

[0045] 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.

[0046] Next, the contents of step S110 to step S150 are described in detail.

[0047] 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: 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; Step S220, determining terrain complexity data of the large drop measurement area according to the terrain undulation characteristics; Step S230, determining geological complexity data of the large drop measurement area through geological exploration data corresponding to the measurement area; Step S240, weighted fusion is performed on the terrain complexity data and the geological complexity data 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.

[0048] Among them, terrain point cloud data refers to high-resolution three-dimensional terrain data obtained by using sensors carried by drones to conduct aerial inspections of large drop measurement areas. The drone can be a multi-rotor drone or a fixed-wing drone, and this embodiment is not limited to this. The sensor carried by the drone can be a laser radar, which is used to collect high-density terrain point cloud data. The laser radar can obtain surface reflection information and reconstruct the terrain three-dimensional model by transmitting 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 be equipped with an inertial navigation system (INS) and a global positioning system (GPS) to achieve high-precision positioning and attitude control to ensure accurate registration of terrain point cloud data. This embodiment does not specifically limit the way in which drones collect terrain point cloud data.

[0049] Terrain undulation refers to characteristic data extracted from terrain point cloud data that characterizes the changes in terrain undulation. For example, terrain undulation can be calculated from terrain point cloud data to obtain parameters such as terrain slope, slope aspect, curvature, surface roughness, and height difference change rate. These parameters can be used to quantitatively describe the changing trend and degree of undulation of the terrain. Specifically, slope can be used to reflect the degree of surface inclination, curvature can be used to indicate the rate of terrain change, and surface roughness can be used to characterize the complexity of the surface morphology.

[0050] Terrain complexity data refers to a data set used to characterize the degree of terrain undulation and spatial variation characteristics within the survey area. It can comprehensively reflect the complexity of the surface morphology in the survey area by conducting 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. Slope is used to describe the degree of inclination of the surface, aspect indicates the directional distribution of the slope, curvature is used to reflect the bending changes of the terrain surface, surface roughness is used to quantify the complexity of the surface morphology, and elevation gradient indicates the rate of elevation change. Terrain complexity data can be obtained by analyzing terrain point cloud data obtained by drones equipped with laser radar or high-resolution optical cameras using digital elevation models or digital surface models.

[0051] Geological exploration data refers to a data set used to characterize the underground geological conditions, lithology distribution, fault structure and geological unit characteristics of the survey area. Geological exploration data can be obtained through geological surveys, drilling, geological radar detection, geophysical exploration, etc. For example, geological exploration data may include information such as fault distribution, stratum dip angle, lithology type, and geological structure type. Fault distribution can reflect the direction and distribution of underground fault zones, stratum dip angle can be used to describe the degree of inclination of rock strata, lithology type can indicate the composition and properties of underground rocks, and geological structure type can describe the geological units and structural characteristics of the survey area.

[0052] Geological complexity data refers to a data set that quantifies the complexity of geological structures in a measurement area by analyzing geological exploration data. This data comprehensively reflects the geological structural characteristics of the measurement area and the potential impact of geological conditions on the measurement data. Geological complexity data can include indicators such as fault density, stratum dip angle, lithology diversity, and geological activity. Fault density can be used to describe the number and distribution of faults per unit area, stratum dip angle can reflect the stability and deformation trend of rock formations, lithology diversity can be used to indicate the degree of diversity of rock formation components, and geological activity can be used to measure the activity of fault zones and fold structures. Geological complexity data can be spatially superimposed on geological exploration data through a geographic information system, and combined with geophysical data such as gravity anomaly and seismic velocity for comprehensive analysis and verification.

[0053] Regional complexity refers to an indicator used to quantify the complexity of regional terrain obtained by comprehensive analysis and weighted fusion of terrain complexity data and geological complexity data in the measurement area; regional complexity is a comprehensive evaluation result of terrain undulation characteristics and geological structure characteristics, which can not only be used to guide the scientific division of the measurement area and the layout of measurement points, but also can be used to separately evaluate gravity anomalies in different regions under complex terrain, further improve the accuracy of gravity anomaly correction, and reduce gravity anomaly correction errors. The calculation of regional complexity can adopt the weighted overlay method to standardize and normalize the terrain complexity data and geological complexity data, and assign different weights according to the degree of influence of the two on the measurement accuracy to obtain a unified complexity score; of course, the terrain complexity data and geological complexity data can also be fused by a threshold-based method or a spatial clustering algorithm, and this embodiment is not limited to this. Areas with high regional complexity usually have dramatic terrain undulations and complex geological structures, so more dense measurement points should be deployed and a more stringent gravity correction strategy should be adopted. Areas with low regional complexity have flat terrain and stable geological conditions, so the density of measurement points can be appropriately reduced and the gravity data correction method can be simplified.

[0054] The terrain point cloud data obtained by drones equipped with laser radar or high-precision optical cameras can comprehensively and meticulously reflect the three-dimensional terrain characteristics of the survey area and obtain terrain complexity data, so that the terrain undulation characteristics can be quantified and described more scientifically, thereby achieving a comprehensive assessment of the terrain complexity; combining geological exploration data to analyze the geological characteristics of the large-drop measurement area can provide a deeper understanding of the lithology distribution, fault structure and geological structure in the area, and then divide the large-drop measurement area into measurement sub-areas based on the regional complexity determined by the terrain complexity and geological complexity, which can effectively determine gravity anomalies for different terrain and geological conditions, improve the accuracy and reliability of gravity anomaly correction, and thus effectively ensure the accuracy and reliability of the final corrected elevation data.

[0055] In an exemplary embodiment of the present disclosure, Figure 3 The steps in step S210 are implemented to determine the gravity interference coefficient corresponding to the measurement point according to the measured gravity data corresponding to each measurement sub-area and the regional environmental data, referring to Figure 3 As shown, it may specifically include: Step S310, performing static interference analysis on the measurement sub-area according to the measured gravity data to obtain static interference terms; Step S320, performing dynamic interference analysis on the measurement sub-area according to the measured gravity data and the regional environment data to obtain dynamic interference items; Step S330: weightedly fuse the static interference term and the dynamic interference term based on a preset weight coefficient to obtain a gravity interference coefficient of each measurement point in the measurement sub-area.

[0056] Among them, static interference analysis refers to the identification and quantification of systematic interference to gravity measurement caused by long-term stable factors such as topography, geological structure, etc. by conducting long-term stability analysis on gravity data collected at each measuring point in the measurement sub-area during the elevation data measurement process. Static interference analysis mainly focuses on interference sources that do not change or change slowly over time, and are usually closely related to factors such as geological structure, rock density, and terrain undulation. Its principle is to identify the long-term influence of gravity anomalies 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 fully reveal the stability and spatial distribution of gravity data.

[0057] The static interference term is the result of static interference analysis. It is the quantitative result of the long-term stability interference factors in the survey area, reflecting the systematic influence of stable factors such as geological structure, terrain undulation and underground medium distribution on the gravity measurement results. For example, static interference terms can include parameters such as gravity anomaly fluctuation coefficient (used to describe the intensity of gravity data fluctuation), Moran index (used to reflect the spatial aggregation and discreteness of gravity data) and spectrum energy ratio (used to describe the influence of different frequency components in the data). The calculation results of static interference terms are used to measure the degree of abnormal fluctuation of the measurement points in spatial distribution, providing an important basis for the correction of gravity data. Static interference terms have long-term stability and are not easily affected by short-term environmental changes. They mainly reflect the influence of geological structure and terrain conditions on gravity measurement results.

[0058] Dynamic interference analysis refers to the analysis of short-term fluctuations and environmental change trends in the time dimension of measured gravity data, identifying and quantifying the instantaneous or periodic interference caused by external environmental changes (such as temperature, humidity, air pressure, wind speed, etc.) on gravity measurement results. Dynamic interference analysis focuses on short-term interference factors that fluctuate over time. Its principle is to capture dynamic fluctuations in gravity data caused by environmental changes or equipment status 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 non-stability factors in the measurement process, ensuring the reliability and accuracy of gravity data in a dynamic environment.

[0059] The dynamic interference term is the result of dynamic interference analysis, which is the quantitative result of 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 changes over time) and environmental gravity anomalies (used to quantify the impact of environmental changes on gravity measurements). The short-term time series fluctuation characteristics capture the local fluctuation trend of the data through sliding time window analysis, and the environmental gravity anomalies are analyzed by correlating environmental parameters (such as temperature, humidity, and air pressure) with gravity data through a multivariate linear regression model to identify abnormal fluctuations caused by environmental changes. The dynamic interference term is real-time and sensitive, and can reflect the immediate interference caused by environmental changes or equipment status fluctuations during the measurement process, providing a dynamic basis for real-time gravity data correction.

[0060] Optionally, you can Figure 4 The steps in step S310 implement the static interference analysis of the measurement sub-area according to the measured gravity data to obtain the static interference term, refer to Figure 4 As shown, it may specifically include: Step S410, performing statistical analysis on the measured gravity data of each measuring point in the measuring sub-area, determining the gravity statistical features corresponding to the measuring sub-area, and calculating the gravity anomaly fluctuation coefficient according to the gravity statistical features; Step S420, performing spatial autocorrelation analysis on the measured sub-region according to the measured gravity data to obtain a Moran's index corresponding to the measured sub-region, wherein the Moran's index is used to characterize the spatial aggregation and discreteness of the gravity data; Step S430, performing Fourier transform on the measured gravity data to analyze the spectrum distribution and determine the spectrum energy intensity ratio of the measurement sub-area; Step S440: taking the gravity anomaly fluctuation coefficient, the Moran index and the spectrum energy intensity ratio as the static interference term.

[0061] Among them, gravity statistical characteristics refer to a set of statistical parameters used to describe the overall distribution and fluctuation trend of measured gravity data. Gravity statistical characteristics may include but are not limited to mean, variance, skewness, and kurtosis. The mean can reflect the central tendency of 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. By analyzing the statistical characteristics of gravity data, the fluctuation trend and outliers in the data can be identified, thereby providing basic support for subsequent data correction.

[0062] 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.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] The static analysis results such as gravity anomaly fluctuation coefficient, Moran's index and spectrum energy intensity ratio can be combined to form static interference terms. Static interference terms can comprehensively reflect the influence of long-term stable factors such as geological structure and terrain conditions on the measured gravity data, and provide a basis for subsequent gravity data correction and elevation data correction. In an optional implementation, principal component analysis (PCA) or weighted superposition method can be used to comprehensively evaluate the gravity anomaly fluctuation coefficient, Moran's index and spectrum energy intensity ratio to enhance the stability and reliability of static interference terms.

[0067] Static interference analysis can be used to comprehensively evaluate long-term systematic interference in the measured gravity data through statistical feature analysis, spatial autocorrelation analysis, spectrum analysis and other methods. By statistically analyzing the mean, variance, skewness and kurtosis of the measured data, the overall fluctuation trend and abnormal characteristics of the data in the measured sub-area can be revealed, providing a basis for identifying the impact of geological conditions and terrain changes on the data. Furthermore, combined with spatial autocorrelation analysis (such as Moran index calculation), the spatial aggregation and discreteness of the measured data can be quantified, accurately reflecting the interference effect of geological structure and terrain undulation on data distribution. In addition, by analyzing the energy distribution of gravity data at different frequency components through spectrum analysis, low-frequency components (related to geological structure) and high-frequency components (related to local anomalies or noise) can be effectively distinguished, and static interference sources can be fully identified. The interaction of this series of static analysis methods makes the identification of long-term interference characteristics in the survey area more comprehensive and in-depth, and can effectively reduce the systematic errors caused by long-term geological structure and terrain characteristics to elevation data.

[0068] Optionally, you can Figure 5 The steps in step S320 implement the dynamic interference analysis of the measurement sub-area according to the regional environmental data to obtain the dynamic interference term, refer to Figure 5 As shown, it may specifically include: Step S510, 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; Step S520, obtaining the regional environmental data, the regional environmental data at least including the temperature, humidity and air pressure of each measuring point, and inputting the regional environmental data into a pre-built multiple linear regression model to obtain an environmental gravity anomaly value; Step S530: taking the short-term time series fluctuation characteristics and the abnormal value of environmental gravity as the dynamic interference items.

[0069] Among them, the sliding time window is a time series processing method commonly used in dynamic data analysis. By setting a fixed length of time interval, the window range is sequentially slid to perform segmented processing and real-time analysis on the measured gravity data. The sliding time window can capture the fluctuation trend and local changes of the measured gravity data in the short term, and is convenient for identifying 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 frequency of environmental changes, and the present embodiment is not limited thereto.

[0070] Short-term time series fluctuation analysis is a method of performing short-term trend analysis on measured gravity data based on a sliding time window. By monitoring the fluctuation trend of gravity data within a continuous time period, it is possible to promptly discover mutation points or short-term fluctuation characteristics in the data. Short-term time series fluctuation analysis helps to identify data anomalies caused by environmental changes or equipment status fluctuations.

[0071] Short-term time series fluctuation characteristics are characteristic information extracted from short-term time series fluctuation analysis that reflects the short-term fluctuation trend and change law of gravity data. For example, short-term time series fluctuation characteristics can include indicators such as trend change, fluctuation amplitude, and change rate of data, which are used to identify and quantify short-term anomalies in measurement data caused by environmental factors or equipment fluctuations.

[0072] The multiple linear regression model is a statistical model used to analyze the relationship between multiple independent variables and a dependent variable. For example, the dependent variable can be assumed to be a linear combination of the respective variables and the data can be 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 gravity measurement results. It has the advantages of simple calculation and strong interpretability, and is suitable for joint analysis of multi-factor influences.

[0073] Environmental gravity anomaly values ​​are parameters calculated based on the multivariate linear regression model to characterize the degree of influence of environmental factors on gravity data. Environmental gravity anomaly values ​​can reflect the degree of deviation of gravity measurement data caused by environmental changes (such as temperature, humidity, and air pressure). The larger the environmental gravity anomaly value, the more significant the influence of environmental changes on gravity measurement results; the smaller the environmental gravity anomaly value, the smaller the influence of environmental changes on measurement data. Environmental gravity anomaly values ​​are used in dynamic interference analysis to help identify measurement errors caused by environmental factors and provide a basis for data correction.

[0074] The short-term time series fluctuation characteristics can be integrated with the environmental gravity anomalies for analysis to form dynamic interference items. The dynamic interference items can fully reflect the extent to which the measured data is affected by environmental changes in a short period of time, and provide a basis for real-time data correction. The dynamic interference items are real-time and sensitive, and can effectively reflect the immediate impact of environmental changes and emergencies on the measured data. In order to further improve the analysis accuracy of dynamic interference items, the Autoregressive Moving Average Model (ARMA) or Long Short-Term Memory (LSTM) can be introduced to perform deep learning analysis on the time series corresponding to the measured gravity data to enhance the adaptability to complex environmental changes.

[0075] Through dynamic interference analysis, short-term time series fluctuation analysis of measured gravity data and environmental data modeling, real-time monitoring and precise quantification of the impact of short-term environmental changes on gravity data can be achieved. Through sliding time windows, short-term time series fluctuation analysis of gravity data can be performed to capture abnormal fluctuation trends and mutation points in the data in real time, ensuring that potential measurement errors are discovered in a dynamic environment in a timely manner. Combined with the multivariate linear regression model, real-time regional environmental data (including temperature, humidity, air pressure, etc.) and measured gravity data are correlated and analyzed to quantify the degree of impact of environmental changes on measured gravity data, further extract environmental gravity anomalies, and compensate for the interference of environmental changes on measured gravity data. The combination of dynamic interference analysis and static interference analysis can form a comprehensive interference identification and correction mechanism for measured data.

[0076] Through the synergistic effect of static interference analysis and dynamic interference analysis, a dynamic and adaptive gravity data interference identification and correction mechanism has been formed. It can not only accurately identify static interference caused by long-term geological conditions and terrain changes, but also respond and correct dynamic fluctuations caused by environmental changes in real time, significantly improving the accuracy and reliability of elevation data measurement in large drop areas. This multi-level and multi-dimensional interference analysis and correction method has broken through the limitations of traditional single gravity anomaly correction strategies and achieved more efficient and stable data correction effects.

[0077] In an exemplary embodiment of the present disclosure, by Figure 6 The steps in step S130 are used to determine the corrected gravity data based on the gravity interference coefficient and the measured gravity data, referring to Figure 6 As shown, it may specifically include: Step S610, if it is determined that the gravity interference coefficient is less than a preset interference coefficient range, then real-time gravity field correction is performed on the measured gravity data to determine the corrected gravity data; or Step S620, 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 the first gravity data and the second gravity data, and weighted fusion is performed on the first gravity data and the second gravity data to obtain the corrected gravity data; or Step S630: If it is determined that the gravity interference coefficient is greater than the interference coefficient range, multimodal data fusion gravity estimation is performed on the measured gravity data to determine the corrected gravity data.

[0078] Among them, the preset interference coefficient section refers to the threshold range for classifying the degree of gravity interference, which is set in advance according to the environmental complexity, geological structure characteristics and historical gravity data fluctuations of the measurement area. 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 an adaptive gravity data correction strategy. The preset interference coefficient section can be divided into three ranges: a low interference section, a medium interference section and a high interference section. The gravity interference coefficient corresponding to the low interference section is 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 more complex interference sources; the high interference section indicates that the measurement point is highly disturbed, usually located in a geological fault, a sudden change in lithology or a strong environmental change area. The setting of the preset interference coefficient section can be based on historical measurement data, geological exploration data and environmental monitoring data, and can also 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. This example embodiment does not specifically limit the setting of the preset interference coefficient section.

[0079] Real-time gravity field correction refers to the rapid calculation and application of correction items for the measured gravity data collected in real time at the measuring point, based on the standard geophysical model and the measured environmental conditions, to eliminate the systematic errors caused by terrain undulation, geological density changes and elevation differences. Real-time gravity field correction can include free-air correction, Bouguer correction and gradient correction, etc.; among which free-air correction can be used to eliminate the influence of elevation changes on gravity data, Bouguer correction can be used to compensate for the disturbance of terrain and rock 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. It can perform correction processing while collecting data, and is suitable for survey areas with weak interference or relatively stable environment. In some optional implementations, 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.

[0080] Multimodal data fusion gravity estimation refers to a technical means of comprehensively estimating and correcting the gravity data of the measurement point by integrating multi-source data and using deep learning models or statistical analysis methods. The multimodal data fusion gravity estimation method integrates spatial location information, terrain undulation characteristics, geological structure information, environmental monitoring data (such as temperature, humidity, air pressure) and historical gravity measurement data. By inputting multimodal data into a pre-trained deep learning model (such as a convolutional neural network, a long short-term memory network or an integrated learning model), the model automatically extracts the potential associations and interference patterns between different data and outputs the gravity correction data that best matches the actual environment. Multimodal 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. In order to further enhance the adaptability and accuracy of the model, the attention mechanism can be used to optimize the model's attention to key data features, or the adaptive weighting strategy can be used to dynamically balance the influence of each data source. Multimodal data fusion gravity estimation breaks through the limitations of traditional single correction strategies, and can comprehensively and dynamically correct gravity data in complex environments, thereby improving measurement accuracy and data stability.

[0081] When the gravity interference coefficient is less than the preset interference coefficient range, it means that the measurement point is less disturbed, and the fluctuation of the measured gravity data mainly comes from the standard measurement error or the slight fluctuation of the environment. At this time, the 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 means that the interference to 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 the error. At this time, a strategy combining real-time gravity field correction with multimodal data fusion gravity estimation can be adopted; when the gravity interference coefficient is greater than the interference coefficient range, it means that the measurement point is seriously disturbed, and the traditional real-time gravity field correction cannot effectively correct the data. It must rely entirely on multimodal data fusion gravity estimation for correction. The multimodal data fusion model can accurately estimate the trend of gravity data changes in complex environments through deep learning and adaptive analysis of multidimensional data.

[0082] By combining the preset interference coefficient segments with the hierarchical correction strategy, different gravity anomaly correction methods are switched and combined in real time to form an all-round, multi-level, intelligent gravity anomaly correction mechanism, which not only effectively guarantees the real-time correction of gravity anomaly data, but also achieves the optimal correction effect under different interference intensities. It has high flexibility and adaptability in complex environments, breaking through the limitation of the unstable correction accuracy of traditional gravity anomaly correction strategies in variable environments. Through the dynamic identification of gravity interference coefficients and the intelligent selection of correction strategies, in complex geological environments and variable measurement conditions, it can effectively reduce measurement errors at different levels and dimensions, and significantly improve the accuracy of gravity data correction and the reliability of elevation measurement.

[0083] Optionally, you can Figure 7 The steps in step S610 are used to implement the real-time gravity field correction method. Figure 7 As shown, it may specifically include: Step S710, determining reference gravity data according to the level height difference data and latitude coordinates of each measuring point; Step S720, 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; Step S730, 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; Step S740: determining the corrected gravity data by using the reference gravity data, the gravity anomaly data and the gradient correction item.

[0084] Among them, reference gravity data refers to the theoretical gravity value calculated based on the geographic coordinates (mainly latitude) of the measurement point and the standard earth gravity model. Reference gravity data is used to characterize the gravity distribution of the earth under ideal conditions, and is used to compare with the actual measured gravity data to identify and correct gravity anomalies caused by topography, geology or other factors. Reference gravity data is usually calculated based on the international standard gravity formula or the global gravity field model (such as Earth GravitationalModel 2008, EGM2008). Specifically, the calculation of reference gravity data takes into account the influence of the earth's rotation and the flattening of the crust, 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 by the following relationship: ; in, Can represent the reference gravity data of the measurement point, It can represent the latitude coordinates of the measurement point. It can show the height difference data of the level at the measuring point.

[0085] The free air correction is used to correct the effect of the height difference of the measuring point on the gravity measurement results. Since gravity decreases with increasing altitude, in order to eliminate the effect of the height difference between the measuring point and the reference surface (usually sea level) on the gravity value, the measurement data needs to be corrected for free air. The basic principle of free air correction is to use the standard gravity gradient to correct the elevation of the measuring point. The gradient value usually used is about 0.3086 mGal / m. For example, the free air correction can be calculated using the following relationship: ; in, can be used to express the free air correction term, It can show the height difference data of the level at the measuring point.

[0086] The Bouguer correction is a correction made to eliminate the influence of the density of the material below the surface of the measuring point on the gravity measurement results. Unlike the free air correction, the Bouguer correction not only considers the elevation change, but also the gravitational effect of the rock and terrain mass between the measuring point and the reference surface 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 uniform density below the measuring point, and the contribution of the rock layer to gravity is calculated using the rock density and gravity constant. For example, the Bouguer correction can be calculated using the following relationship: ; in, can be expressed as the Bouguer correction term, can be expressed as the gravitational constant, It can represent the density of crustal rocks at the measuring point. It can show the height difference data of the level at the measuring point.

[0087] Gravity anomaly data refers to the difference between the actual gravity data measured at the measuring point and the reference gravity data. Gravity anomaly data can characterize the gravity field anomaly caused by factors such as crustal material distribution, geological structure and terrain undulation. Gravity anomaly data is the residual data obtained by performing free air correction and Bouguer correction on the measured gravity data in sequence, and is used to identify changes in underground geological structures. For example, positive gravity anomalies are usually associated with denser rock masses (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 using the following relationship: ; in, It can represent the gravity anomaly data of the measuring point. The measured gravity data of the measuring point can be expressed, It can represent the free air correction term of the measurement point, It can represent the Bouguer correction term of the measurement point.

[0088] 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 changes in gravity gradients, 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 points in three directions (X, Y, and Z axes). The construction of the gradient correction term usually uses the finite difference method or multiscale gradient analysis to quantify the spatial differences in gravity data of adjacent measurement points. By introducing the gradient correction term, the local errors caused by the differences in spatial positions between measurement points can be effectively corrected, and the accuracy and spatial consistency of gravity data can be improved. This correction method is particularly critical in measurement areas with complex terrain and drastic gravity changes, and can significantly improve the reliability of gravity data.

[0089] ; in, can represent the gradient correction term of the measurement point, The measured gravity data of the measuring point can be expressed, , and The partial derivatives of the measured gravity data of the measuring point in the X direction, Y direction and Z direction can be expressed respectively. , and It can represent the spatial distance difference between measurement points.

[0090] After obtaining the reference gravity data, gravity anomaly data and gradient correction items, the corrected gravity data can be determined by the reference gravity data, gravity anomaly data and gradient correction items. For example, the corrected gravity data can be determined by the following relationship: ; in, It can represent the corrected gravity data of the measurement point, and They can represent the weight data corresponding to the gravity anomaly data and the gradient correction term respectively.

[0091] Determining the corrected gravity data by referring to gravity data, gravity anomaly data and gradient correction items means combining standard reference values, system correction results and local gradient correction results to make comprehensive corrections to the original measurement data in an all-round and multi-level manner, so as to obtain accurate and reliable corrected gravity data. For example, a dynamic weighted fusion strategy can be used to comprehensively consider the weight of the influence of each correction item on the data to ensure that the corrected data can truly reflect the gravity field characteristics of the measurement area. The weight distribution can be dynamically adjusted based on the interference degree of the measurement point and the correction accuracy requirements; for example, in areas with less interference, the weights of free air correction items and Bouguer correction items can be increased; in areas with more interference, the proportion of gradient correction items can be increased. The final corrected gravity data has high accuracy and high stability, which can effectively eliminate the influence of environmental interference and geological changes on the measurement data, and provide reliable data support for subsequent elevation data correction and geological analysis.

[0092] In an exemplary embodiment of the present disclosure, the following steps may be used to implement multi-modal data fusion gravity estimation on measured gravity data and determine the content of corrected gravity data, which may specifically include: Multimodal terrain data corresponding to each measuring point can be obtained, and the multimodal terrain data can include at least two of spatial position, terrain undulation characteristics, geological information, environmental information and historical gravity measurement data; the multimodal terrain data can be input into a pre-trained multimodal data fusion gravity estimation model to infer estimated corrected gravity data.

[0093] Among them, multimodal terrain data refers to a multi-source heterogeneous data set obtained through a variety of data collection methods, which is used to comprehensively describe the terrain, geology and environmental characteristics of the survey area. It not only contains traditional terrain data, but also integrates information from a variety of different forms and sources to reflect the real environment of the survey area in a multi-dimensional and multi-level manner. For example, multimodal terrain data may 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 the global navigation satellite system, and used to accurately determine the three-dimensional coordinates (latitude, longitude and elevation) of each measuring point; terrain undulation characteristics can be obtained through high-density three-dimensional terrain point cloud data collected by drones, which is used to characterize detailed features such as slope, curvature, surface roughness, etc. of the survey area; geological information may include key parameters such as underground lithology distribution, fault structure, geological fault zone and rock density, which are used to reveal the geological structure of the survey area; environmental information may include temperature, humidity, air pressure, wind speed, etc. collected in real time; historical gravity measurement data refers to gravity measurement data that has been obtained in the survey area in the past. These data are used to establish gravity change trends, identify historical interference patterns, and provide data support for gravity data estimation and correction.

[0094] The multimodal data fusion gravity estimation model refers to a multi-source heterogeneous data fusion analysis model built based on deep learning or statistical analysis methods. It aims to use multimodal terrain data to make high-precision estimates and corrections of gravity field changes. The model can extract and fuse multidimensional data features through deep learning technology to achieve correction of gravity measurement errors under complex geological conditions and environmental changes. For example, in some feasible implementations, the multimodal 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), and use a long short-term memory network or a gated recurrent unit (GRU) to extract environmental information and time series features of historical gravity measurement data. In order 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 of different features; then the feature information of different modalities 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.

[0095] Gravity estimation through multimodal data fusion can make real-time and accurate corrections to measured gravity data based on the effective fusion and intelligent processing of multi-source terrain data. It not only solves the shortcomings of traditional methods in processing gravity anomaly data in complex environments, but also provides a highly flexible and adaptable measurement data correction solution, 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.

[0096] For example, after obtaining the corrected gravity data, the elevation correction item of each measurement sub-area can be determined by the corrected gravity data. The elevation correction item can be expressed by the following relationship: ; in, It can represent the elevation correction item of the measurement point, It can represent the height difference data of the level instrument at the measuring point. Then, the measured elevation data can be corrected according to the elevation correction item to obtain the corrected elevation data corresponding to the large drop measurement area. For example, the calculation of the corrected elevation data can be realized through the following relationship: in, Can represent corrected elevation data, Can represent survey elevation data.

[0097] It should be noted that, although the steps of the method in the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps, etc.

[0098] In addition, in this exemplary embodiment, a device for measuring elevation data for areas with large elevation differences is also provided. Figure 8 As shown, the elevation data measurement device 800 for large elevation difference areas includes: a level measurement module 810, a gravity interference analysis module 820, a gravity data correction module 830, an elevation correction item determination module 840 and an elevation data correction module 850. Among them: The level measurement module 810 is used to obtain the level height difference data collected at each preset measurement point in the large drop measurement area, and the measurement gravity data corresponding to each of the measurement points; The gravity interference analysis module 820 is used 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 regional environmental data corresponding to each measurement sub-area; A gravity data correction module 830, configured to determine corrected gravity data based on the gravity interference coefficient and the measured gravity data; An elevation correction item determination module 840, configured to determine an elevation correction item of each of the measurement sub-areas using the corrected gravity data, and to determine measurement elevation data of each of the measurement sub-areas using the level height difference data; The elevation data correction module 850 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.

[0099] In an exemplary embodiment of the present disclosure, based on the above solution, the gravity interference analysis module 820 is configured as follows: 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.

[0100] In an exemplary embodiment of the present disclosure, based on the above solution, the gravity interference analysis module 820 further includes: A static interference analysis unit, configured to perform static interference analysis on the measurement sub-area according to the measured gravity data to obtain a static interference term; A dynamic interference analysis unit, configured to 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; The gravity interference coefficient determination unit is used to perform weighted fusion of 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-area.

[0101] In an exemplary embodiment of the present disclosure, based on the above solution, the static interference analysis unit is configured as follows: 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 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.

[0102] In an exemplary embodiment of the present disclosure, based on the above solution, the dynamic interference analysis unit is configured as follows: 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.

[0103] In an exemplary embodiment of the present disclosure, based on the above solution, the gravity data correction module 830 is configured as follows: 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; or 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; or If it is determined that the gravity interference coefficient is greater than the interference coefficient range, multimodal data fusion gravity estimation is performed on the measured gravity data to determine the corrected gravity data.

[0104] In an exemplary embodiment of the present disclosure, based on the above solution, the gravity data correction module 830 is configured as follows: 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.

[0105] In an exemplary embodiment of the present disclosure, based on the above solution, the gravity data correction module 830 is configured as follows: 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.

[0106] The specific details of each module of the above-mentioned elevation data measurement device for large elevation difference areas have been described in detail in the corresponding elevation data measurement method for large elevation difference areas, so they will not be repeated here.

[0107] It should be noted that, although several modules or units of the elevation data measurement device for large drop 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 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 into multiple modules or units to be embodied.

[0108] In addition, in an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above-mentioned elevation data measurement method for areas with large elevation differences is also provided.

[0109] Those skilled in the art will appreciate that various aspects of the present disclosure may be implemented as systems, methods or program products. Therefore, various aspects of the present disclosure may be specifically implemented in the following forms, namely: complete hardware embodiments, complete software embodiments (including firmware, microcode, etc.), or embodiments combining hardware and software aspects, which may be collectively referred to herein as "circuits", "modules" or "systems".

[0110] Refer to the following Fig. 9 hereinafter describes an electronic device 900 according to such an embodiment of the present disclosure. Fig. 9 The electronic device 900 shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0111] like Fig. 9 As shown, the electronic device 900 is in the form of a general computing device. The components of the electronic device 900 may include but are not limited to: at least one processing unit 910, 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.

[0112] The storage unit stores a program code, which can be executed by the processing unit 910, so that the processing unit 910 performs 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 perform the following steps: Figure 1In step S110 shown in the figure, 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 are obtained; 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 measured gravity data corresponding to each of the measurement sub-areas and the regional environmental data; in step S130, the corrected gravity data is determined based on the gravity interference coefficient and the measured gravity data; in step S140, the elevation correction item of each of the measurement sub-areas is determined through the corrected gravity data, and the measured elevation data of each of the measurement sub-areas is determined through the level height difference data; in step S150, the measured elevation data is corrected according to the elevation correction item to obtain the corrected elevation data corresponding to the large drop measurement area.

[0113] The storage unit 920 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 921 and / or a cache memory unit 922 , and may further include a read-only memory unit (ROM) 923 .

[0114] The storage unit 920 may also include a program / utility 924 having a set (at least one) of program modules 925, such program modules 925 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0115] Bus 930 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0116] The electronic device 900 may also communicate with one or more external devices 970 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 900, and / or communicate with any device that enables the electronic device 900 to communicate with one or more other computing devices (e.g., routers, modems, etc.). This communication may be performed through an input / output (I / O) interface 950. In addition, the electronic device 900 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) through a network adapter 960. As shown, the network adapter 960 communicates with other modules of the electronic device 900 through a 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.

[0117] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the embodiment of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiment of the present disclosure.

[0118] In an exemplary embodiment of the present disclosure, a computer-readable storage medium is also provided, 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 a program code, and when the program product is run on a terminal device, the program code is used to enable the terminal device to perform the steps according to various exemplary embodiments of the present disclosure described in the above "Exemplary Method" section of the present specification.

[0119] refer to Fig.10 As shown, a program product 1000 for implementing the above-mentioned elevation data measurement method for a large drop area according to an embodiment of the present disclosure is described, which can adopt a portable compact disk read-only memory (CD-ROM) and include program code, and can be 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, a readable storage medium can be any tangible medium containing or storing a program, which can be used by or in combination with an instruction execution system, an apparatus or a device.

[0120] The program product may adopt any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0121] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0122] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0123] 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 conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a 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., using an Internet service provider to connect through the Internet).

[0124] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present disclosure, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0125] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the embodiment of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiment of the present disclosure.

[0126] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are to be considered exemplary only, and the true scope and spirit of the present disclosure are indicated by the claims.

[0127] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only 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, and determine the gravity interference coefficient corresponding to the measurement point according to the measurement gravity data and regional environmental data corresponding to each measurement sub-area; determining corrected gravity data based on the gravity disturbance coefficient and the measured 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 determining the gravity interference coefficient corresponding to the measuring point according to the measured gravity data corresponding to each of the measuring sub-areas and the regional environment data includes: Performing static interference analysis on the measurement sub-area according to the measured gravity data to obtain a static interference term; Performing 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; The static interference term and the dynamic interference term are weightedly fused based on a preset weight coefficient to obtain a gravity interference coefficient of each of the measurement points in the measurement sub-area.

4. The elevation data measurement method according to claim 3, 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 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.

5. The elevation data measurement method according to claim 3, 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.

6. The elevation data measurement method according to claim 1, characterized in that: The determining the corrected gravity data based on the gravity interference coefficient and the measured gravity data comprises: 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; or 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; or If it is determined that the gravity interference coefficient is greater than the interference coefficient range, multimodal data fusion gravity estimation is performed on the measured gravity data to determine the corrected gravity data.

7. The method for measuring elevation data in a large elevation difference area according to claim 6, 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.

8. The elevation data measurement method according to claim 6, 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.

9. An elevation data measuring device for areas with large elevation differences, 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, and determining the gravity interference coefficient corresponding to the measurement point according to the measurement gravity data and regional environmental data corresponding to each measurement sub-area; A gravity data correction module, configured to determine corrected gravity data based on the gravity interference coefficient and the measured 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.

10. 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 8.

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