Exploration data extraction and deletion method and system

Abstract

The invention provides an exploration data extraction and deletion method. The method comprises the following steps: acquiring elevation data of a target area; performing data interpolation on the elevation data, and replacing the data of the measuring point position; defining an extraction area according to measured data, and extracting or deleting data in the area; and carrying out gridding processing on the extracted or deleted data. The data processing precision is improved by adopting the interpolation algorithm, the accuracy and efficiency of extracting or deleting the specific data from the mass data are improved by defining the extraction area with the self-defined size, the data processing is more flexible by dynamically adjusting the grid size, automatic data extraction and deletion are achieved, and the data processing efficiency is improved. The data processing speed and processing precision in geological exploration and topographic survey are improved, and the method is more in line with application scenarios where massive data need to be extracted and deleted.

Description

Technical Field

[0001] The present invention belongs to the fields of geological exploration and topographic surveying, and particularly relates to a method and system for extracting and deleting exploration data. Background Art

[0002] In the fields of geological exploration and topographic surveying, with the development of measurement technologies and the enhancement of data acquisition capabilities, a large amount of high-density and high-precision elevation data has been generated; these data are of great value for aspects such as geological structure analysis, resource exploration, environmental monitoring, and topographic modeling; however, processing and analyzing massive data has become a challenge, requiring efficient and accurate technical means; currently, the conventional techniques in the industry mainly rely on basic data processing methods, including standard data interpolation techniques and manually defined data processing procedures; these methods often struggle when dealing with massive data and are unable to efficiently process and analyze large-scale data sets; especially when it is necessary to extract specific elevation data or delete unnecessary data from massive data, the existing technologies often lack flexibility and precision.

[0003] There are multiple drawbacks in the existing technologies when dealing with massive elevation data. First, traditional data interpolation methods often cannot guarantee the accuracy of data in the face of complex terrains; second, the method of manually defining data extraction regions is not only time-consuming but also inefficient, especially when it is necessary to extract key information from a large amount of data.

[0004] In view of the above problems, the present invention provides an efficient processing method for massive data; by adopting advanced interpolation algorithms, such as bilinear interpolation or cubic interpolation, it can provide higher-precision data processing under complex terrain conditions; at the same time, by automatically defining the extraction region based on measured data, the present invention significantly improves the efficiency and accuracy of extracting or deleting specific data from massive data; in addition, the method of dynamically adjusting the grid size makes data processing more flexible and can adapt to different data distributions and geological characteristics; the main advantages of the present invention lie in its high efficiency and accuracy in dealing with massive data, and the advanced interpolation method and automated data extraction process significantly improve the ability to process large-scale data sets; through dynamic grid processing, the present invention can better adapt to the characteristics of different data and provide more accurate data analysis results; these characteristics make the present invention have significant advantages over traditional methods in dealing with massive data, especially in application scenarios where precise extraction or deletion of specific data is required. Summary of the Invention

[0005] The purpose of the present application is to provide a method and system for extracting and deleting exploration data to solve the above problems.

[0006] The purpose of the present application is achieved by adopting the following technical solutions:

[0007] First aspect, this application provides a method for extracting and deleting exploration data, including the following steps: collecting elevation data of a target area; performing data interpolation on the elevation data and replacing the data at the measuring point positions; defining an extraction area according to measured data, and extracting or deleting data within the area; performing grid processing on the extracted or deleted data.

[0008] The beneficial effects of this technical solution are as follows: By using the method of data interpolation, the elevation data can be processed more accurately, especially in areas with complex terrain, which helps to better understand the geological structure and improve the reliability of terrain data; By automatically defining the extraction area according to the measured data, the time and labor for manually defining the area are saved, and at the same time, the accuracy and efficiency of extracting or deleting specific data from a large amount of data are improved; Due to the aforementioned method, this technical solution reduces manual intervention through automated and intelligent processing, improves the processing speed, and at the same time ensures the consistency and accuracy of data processing; At the same time, this technical solution can better adapt to data with different geological structures and terrain characteristics through the strategy of dynamically adjusting the grid size, giving this technical solution a greater advantage over traditional technical solutions when dealing with unevenly distributed data; Finally, this technical solution effectively improves the overall quality and usability of the data set by deleting unnecessary or incorrect data and optimizing the retained data, providing more accurate data support for subsequent geological analysis and decision-making.

[0009] Further, the data interpolation of the elevation data uses a bilinear interpolation or cubic interpolation algorithm.

[0010] The beneficial effects of this technical solution are as follows: The bilinear interpolation or cubic interpolation algorithm can more finely process the spatial relationship between elevation data points. Compared with simple linear interpolation, the above interpolation methods can provide higher spatial resolution, which helps to distinguish more complex terrains; The above interpolation methods can better adapt to the natural changes of the terrain and reduce the errors in the interpolation process, which is very important for fine geological analysis; Cubic interpolation provides a smoother and more continuous approximation of terrain features, especially suitable for areas with large terrain undulations. Compared with traditional interpolation methods, cubic interpolation can generate a smoother surface when dealing with areas with large height changes. At the same time, the data processed using these interpolation algorithms can present a smoother and more natural terrain contour during terrain visualization, which helps to more accurately interpret and display geological data; By providing more accurate and continuous interpolation results, this technical solution provides higher-quality basic data for subsequent data analysis (such as geological modeling, resource assessment, etc.).

[0011] Further, the extraction area is the largest rectangular area defined based on the measured data, and an area expanded by a preset ratio is added in four directions.

[0012] The beneficial effects of this technical solution are as follows: By expanding the maximum rectangular area of the measured data, it can ensure that key data is not omitted. Especially in the edge area of the data, this technical solution provides a systematic way to determine the scope of data processing, ensuring the integrity and coherence of the data set; By adjusting the preset expansion ratio, this technical solution can flexibly adapt to different exploration requirements and geological conditions, enabling this method to work effectively in diverse application scenarios; At the same time, the automated definition of the extraction area reduces manual intervention, thereby reducing errors and inconsistencies caused by subjective judgment and improving the objectivity and repeatability of the data extraction process; Moreover, by precisely defining the extraction area, computing resources can be more effectively allocated, avoiding processing unnecessary data areas, thereby improving the overall data processing efficiency and speed.

[0013] Further, the grid processing includes dividing the data according to a fixed grid size;

[0014] The beneficial effects of this technical solution are as follows: The fixed grid size provides a consistent standard for data processing, ensuring the uniformity and comparability of data division; This technical solution simplifies the complexity of data processing through grid processing with a fixed size, making the data easier to manage and analyze; At the same time, data with a fixed grid size is easy to integrate and share with other data, allowing data from different sources to be uniformly processed and compared, thereby enhancing data interoperability; In terms of the convenience of interacting with other systems, in geographic information systems (GIS) and other visualization tools, the fixed grid size helps generate consistent and standardized visual outputs, making data interpretation more intuitive and consistent; For subsequent processing, the fixed grid size provides a uniform basis for subsequent data analysis and geological modeling.

[0015] Further, the determination of data extraction or deletion is based on the distance from the measured point. If the distance is less than the preset threshold, the data is retained; if the distance is greater than the preset threshold, the data is deleted.

[0016] The beneficial effects of this technical solution are as follows: Through the distance-based determination mechanism, it can ensure that the retained data has a high correlation with the measured points; deleting the data far from the measured points helps to remove irrelevant or redundant data, thereby reducing the burden of data processing and storage. When the data volume is small, there is no significant burden on data storage and processing. However, when faced with a large sample, that is, a large amount of data, deleting redundant data becomes very important. In the current conventional technical solutions, most of the data processed is of a relatively small volume, while this technical solution focuses on processing massive data; this technical solution can also improve the overall quality of the data set because it ensures the accuracy and usability of the data through the determination mechanism, while excluding the data far from the measurement points that may be misleading; at the same time, the automated distance determination method simplifies the data processing process, avoids cumbersome manual data screening, and improves the efficiency and speed of data processing. Compared with manual screening, manual screening is possible when the data volume is small, but when the data volume is extremely large, the error rate of manual screening is extremely high and the processing efficiency is extremely low; for subsequent steps, this technical solution helps to ensure that each data point in the data set is closely related to the measured points, which is conducive to improving the accuracy of subsequent data analysis and interpretation.

[0017] Further, the data extraction or deletion is further subjected to time series analysis, and the time series analysis is used to identify the change trend of the data over time.

[0018] The beneficial effects of this technical solution are as follows: Time series analysis can reveal the change trend of geological data over time. By analyzing these trends, it is possible to better predict future geological changes; at the same time, this technical solution is not limited to the data analysis at a single time point, but considers the evolution of the data over time, thereby providing a deeper insight and increasing the dimension and depth of data analysis; time series analysis helps to identify specific geological events and patterns, such as periodic changes or abnormal activities of geological structures, which provides a large amount of valuable comparison data for geological exploration and environmental monitoring; at the same time, for geological exploration projects that require long-term monitoring, time series analysis provides an effective tool to evaluate and predict the long-term changes in terrain and geological conditions. For the transformation of geological exploration information, by providing insights into the changes in geological data over time, time series analysis helps to guide the formulation of geological exploration and development plans, as well as the adjustment of environmental management and protection measures.

[0019] Further, while the fixed grid size is dynamically adjusted based on the coefficient of variation of the extracted or deleted data.

[0020] The beneficial effects of this technical solution are as follows: By considering the coefficient of variation of the data to dynamically adjust the grid size, it can better adapt to the heterogeneity of the data in different regions; using a smaller grid size in areas with large data variability can improve the accuracy of data processing in these regions, thus ensuring that key features and subtle geological changes are not overlooked; dynamically adjusting the grid size helps to allocate computing resources more efficiently. Larger grids can be used in regions with lower variability to reduce the computational burden, while smaller grids are used in key regions to ensure the accuracy of the analysis; the application of different grid sizes can provide a more detailed view during data visualization, especially in areas with complex terrain and geological structures, which helps to better interpret and understand the data; by adjusting the grid size based on the variability of the data, it can ensure that the basic data used in subsequent analyses (such as geological modeling, resource assessment, etc.) has higher quality and applicability.

[0021] In a second aspect, the present invention also provides an exploration data extraction and deletion system, including a data collection unit for collecting elevation data of a target area; a data processing unit for performing data interpolation and replacement on the collected elevation data; a data extraction and deletion unit for extracting or deleting the elevation data within a specified area; and a grid processing unit for performing grid processing on the processed elevation data.

[0022] The beneficial effects of this technical solution are as follows: The data processing unit performs advanced interpolation and replacement on the collected data, enhancing the accuracy and practicality of the data.

[0023] Furthermore, the first client also records the device information of the active key that unlocks or locks the passive lock.

[0024] The beneficial effects of this technical solution are as follows: Recording the device information of each active key enables tracking of which specific device performed a particular operation, increasing the transparency and traceability of the system; being able to identify and record which specific active key was used for a particular unlocking or locking operation helps to quickly locate and take measures in case of security issues; this record ensures that responsibility can be determined when needed, helping to address any potential security vulnerabilities or improper use issues.

[0025] Furthermore, the data collection unit includes a GPS device and an elevation measurement instrument.

[0026] Furthermore, the data processing unit includes at least one microprocessor and a storage device.

[0027] Furthermore, the data extraction and deletion unit uses an algorithm to perform data extraction or deletion based on measured data and preset rules.

[0028] The beneficial effects of this technical solution are as follows: By using an algorithm to automatically perform data extraction or deletion tasks, manual intervention is reduced, the risk of human error is lowered, and the consistency and accuracy of the processing process are ensured; The algorithm can intelligently select the data to be retained or deleted according to complex preset rules, and this intelligent selection is more efficient and accurate than simple manual screening, and this advantage is particularly prominent in processing massive data; Through algorithm processing based on measured data and preset rules, the high relevance and quality of the final data set are ensured, making subsequent analysis more accurate and reliable.

[0029] The preset rules at least include geographical location screening, elevation threshold, range from the measured point, data density requirement, and time series change.

[0030] For example: Geographical location screening: If the geographical location of a data point is within a specific geological area of interest (e.g., a known mineral-rich area or a geologically complex area), then retain this data point; otherwise, delete this data point. Elevation threshold: Set an elevation threshold (e.g., 200 meters above sea level). For data points above or below this threshold, decide whether to retain or delete them according to the exploration purpose. For example, when looking for mineral resources in high areas, it may be chosen to retain all data points above 200 meters. Range from the measured point: Define a distance range (such as 50 meters), and only retain data points that are no more than this range from any measured point. This helps to focus on the most relevant area around the measured point. Data density requirement: If the data point density in a specific area is lower than the preset standard (such as less than 10 data points per square kilometer), then extract additional data from adjacent areas to ensure sufficient data density. Time series change: For continuously monitored areas, if data points show abnormal changes (such as rapid elevation changes) within a specific time window (e.g., in the past year), then mark and retain these data points for further analysis. Of course, the specific data and included preset rules in the examples can be changed according to the actual situation in time operations.

[0031] Furthermore, the data processing unit further includes a data compression module, and the data compression module is used to reduce the storage space requirement of the processed data.

[0032] The beneficial effects of this technical solution are as follows: By effectively compressing data, the demand for storage space is reduced, thereby lowering the cost of data storage; Data compression reduces the file size, making the transmission and loading of data more efficient. This is particularly beneficial for exploration projects that require remote access or frequent data transmission; The compressed data occupies less physical space, making data management and backup easier and more efficient, which helps improve the manageability and security of data; For data that needs to be shared or accessed remotely via a network, compression can significantly increase the transmission speed, reduce waiting time, and improve work efficiency; Since the data occupies less space, it can be used and analyzed more effectively in resource-constrained environments such as mobile devices or remote exploration tools.

[0033] Further, the grid processing unit supports dynamic adjustment of the grid size.

[0034] The beneficial effects of this technical solution are as follows: Dynamically adjusting the grid size can better adapt to the data characteristics of different regions, such as terrain complexity or data point density, which makes the grid processing more accurate and targeted, especially in complex or changeable geological environments; Setting grids with different densities for data of different importance levels helps improve the flexibility of data processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The accompanying drawings herein are incorporated into the specification and constitute a part of this specification, showing embodiments consistent with the present disclosure and, together with the specification, are used to explain the principles of the present disclosure.

[0036] To more clearly illustrate the technical solutions in the embodiments of the present disclosure or in the related art, the following will briefly introduce the accompanying drawings required for use in the description of the embodiments or the related art. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0037] Figure 1 Schematically shows a flowchart of a method according to an embodiment of the present disclosure;

[0038] Figure 2 Schematically shows a schematic diagram of a device according to an embodiment of the present disclosure;

[0039] Figure 3 Schematically shows a schematic diagram of the abnormal state of elevation data according to an embodiment of the present disclosure;

[0040] Figure 4 Schematically shows a schematic diagram of data points according to an embodiment of the present disclosure;

[0041] Figure 5Schematically shown is a schematic diagram of the point distribution map after data extraction according to an embodiment of the present disclosure. Detailed implementation manners

[0042] To make the objectives, technical solutions, and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure. Apparently, the described embodiments are some but not all of the embodiments of the present disclosure. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present disclosure without creative efforts shall fall within the scope of protection of the present disclosure.

[0043] It should be noted that in this document, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise", or any other variant thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device including a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article, or device. Without further limitation, an element defined by the phrase "including a..." does not exclude the existence of additional identical elements in the process, method, article, or device including the element.

[0044] First, the technical field involved in this embodiment will be explained.

[0045] Bilinear interpolation algorithm. The bilinear interpolation algorithm is a method for interpolation in a two-dimensional space. It performs linear interpolation in two directions and is commonly used in fields such as image processing and digital map making. In bilinear interpolation, given an interpolation point to be processed, the algorithm first finds two nearest neighbor points in one direction (e.g., the horizontal direction) for linear interpolation, and then performs linear interpolation in the other direction (e.g., the vertical direction) as well. The final interpolation result is obtained based on four surrounding data points. This algorithm is suitable for processing grid data, especially when the data is unevenly distributed in space. Compared with simple linear interpolation, bilinear interpolation provides a smoother interpolation result.

[0046] In this embodiment, bilinear interpolation estimates the value of the interpolation point by considering the surrounding four data points, providing a more accurate result than simple linear interpolation because they often need to accurately reflect the subtle changes in the terrain; in the processing of elevation data of complex terrain, bilinear interpolation can generate a more realistic and smooth terrain surface because it can better adapt to local terrain changes; the smooth gradient effect produced by bilinear interpolation is very beneficial for the visualization processing of elevation data, making topographic maps and other related views clearer and more realistic; in applications such as geological modeling and resource assessment, the elevation data obtained based on bilinear interpolation can provide a more reliable analysis basis due to its higher accuracy.

[0047] The cubic interpolation algorithm is an interpolation method commonly used in mathematics and computer graphics. It estimates the value of the intermediate point by using the adjacent four points (in the one-dimensional case) or sixteen points (in the two-dimensional case). Compared with linear interpolation or bilinear interpolation, cubic interpolation provides a smoother curve and higher accuracy; in cubic interpolation, the interpolation function is a cubic polynomial, which enables it to more accurately simulate the changes between data points. Cubic interpolation is particularly suitable for scenarios that require high precision and smooth curves, such as image processing, audio signal processing, and geographic information systems (GIS).

[0048] In this embodiment, cubic interpolation can provide a more accurate result than bilinear interpolation because it uses a higher-order polynomial; the result produced by cubic interpolation is smoother than that of bilinear interpolation, which is very beneficial for generating a continuous terrain surface without obvious broken lines, especially in areas with complex terrain undulations; cubic interpolation can better capture the subtle changes and details of the terrain, making the representation of the terrain more real and delicate.

[0049] As Figure 1 shown, this embodiment provides a method for extracting and deleting exploration data, including the following steps: collecting elevation data of the target area; performing data interpolation on the elevation data and replacing the data at the measurement point positions; defining the extraction area according to the measured data and extracting or deleting data within the area; performing grid processing on the extracted or deleted data.

[0050] Among them, collecting elevation data of the target area also includes downloading the elevation data of this block;

[0051] Performing data interpolation on the elevation data and replacing the data at the measurement point positions also includes collecting geophysical exploration data of the target area, determining coordinates, and spreading them in a three-dimensional map as needed;

[0052] Define the extraction area according to the measured data, and extract or delete data within the area. Specifically, construct an extraction radius of R, search the elevation data volume, and extract or delete the data within the corresponding range. First, obtain the largest rectangle A on the plane occupied by the measured data, expand it outward in four directions by about a preset ratio to form a new rectangle B, and determine whether the data points to be extracted are within the range of rectangle B. Perform integer grid division on rectangle B according to a fixed area to form grid C, mark each division grid line, and assign each measured point P1 to the grid. Then, mark each point P of the data within the range of rectangle B to the point P2 in the grid in grid C in turn, and obtain all the measured data points P1 within the grid of a preset size around the grid point P2. Calculate the distance between each point P1 and point P. If the distance is less than the preset threshold, the requirement is met and the data is retained; otherwise, the data is excluded.

[0053] After the grid processing of the extracted or deleted data, it also includes backfilling the data and generating grid maps as needed to improve the accuracy of the original data; or inputting the data into the next operation.

[0054] In a specific embodiment, take an application example in a certain area in the western part of China;

[0055] A method for extracting and deleting exploration data includes:

[0056] S1. Collect the elevation data of the target area, including downloading the elevation data of the block (as Figure 3 shown);

[0057] S2. Perform data interpolation on the elevation data, replace the data at the measuring point positions, and also collect the geophysical exploration data of the target area, determine the coordinates, and spread them in a 3D map as needed (as Figure 4 shown);

[0058] S3. Define the extraction area according to the measured data, and extract or delete data within the area. Specifically, construct an extraction radius of, search the elevation data volume, and extract or delete the data within the corresponding range. The specific steps are as follows: First, obtain the largest rectangle A on the plane occupied by the measured data, expand it outward in four directions by about 20m * 1.1 times to form a new rectangle B, and determine whether the massive data points to be extracted are within the range of rectangle B. Second, perform integer grid division on rectangle B with a grid size of 20m to form grid C, mark each division grid line, and assign each measured point P1 to the grid. Third, mark each point P of the massive data within the range of rectangle B to the point P2 in the grid in grid C in turn, and obtain all the measured data points P1 within the 3 * 3 grid around the grid point P2. Calculate the distance between each point P1 and point P. If the distance is less than 20m, the requirement is met and the data is retained; otherwise, the data is excluded.

[0059] S4. Grid the extracted or deleted data. After extracting the data, data backfilling and grid mapping can be performed as needed to improve the accuracy of the original data; alternatively, the data can be subjected to further calculations (as shown in Figure 5 below).

[0060] Among them, the data replacement at the measurement point location refers to comparing and replacing the original measurement point data with the interpolated data to improve the accuracy and consistency of the data. For example, if the interpolated data provides a more accurate estimate at a specific measurement point, the original measurement point data can be replaced with the interpolated data;

[0061] The extraction radius is determined based on the degree of attention to a specific geological target;

[0062] The gridded data can also be used for further calculations and analyses, such as geological trend analysis, resource estimation, or environmental impact assessment.

[0063] As shown in Figure 2 the following, this embodiment provides a system for extracting and deleting exploration data, including a data collection unit for collecting elevation data of a target area; a data processing unit for performing data interpolation and replacement on the collected elevation data; a data extraction and deletion unit for extracting or deleting the elevation data within a specified area; and a grid processing unit for gridding the processed elevation data.

[0064] Among them, the data collection unit includes a GPS device and an elevation measurement instrument; the data processing unit includes at least one microprocessor and a storage device; the data extraction and deletion unit executes data extraction or deletion based on measured data and preset rules using an algorithm; the data processing unit further includes a data compression module for reducing the storage space requirement of the processed data; the grid processing unit supports dynamic adjustment of the grid size.

[0065] In this embodiment, the data collection unit uses a CPS, an elevation measurement instrument, or calls a database to obtain the elevation data of the acquisition target area, and sends the obtained elevation data to the data processing unit. The data processing unit has a storage function and processes the received data after receiving the data from the data collection unit, including interpolating and replacing the data to improve the data accuracy. After the processing is completed, the data processing unit outputs the processed data to the data extraction and deletion unit. The data extraction and deletion unit extracts or deletes the elevation data within the specified area in a preset manner (parameters). After the data extraction and deletion unit finishes processing, it outputs the processed data to the grid processing unit, and the grid processing unit grids the received data.

[0066] Among them, the grid processing is to make the data present a unified format in space, which helps to standardize the data representation, and the grid-processed data is more suitable for visualization processing. The grid-formatted data can be directly used to generate various types of visual representations such as topographic maps and heat maps.

[0067] As mentioned above, only the preferred specific embodiments of the present invention are described, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and should be covered within the protection scope of the present invention.

Claims

1. A method for extracting and deleting exploration data, characterized in that, It includes the following steps: Collect elevation data of the target area; Perform data interpolation on the elevation data and replace the data at the measuring point positions; Define an extraction area according to the measured data and extract or delete data within the area; Perform grid processing on the extracted or deleted data.

2. The exploration data extraction and deletion method according to claim 1, characterized in that, The data interpolation of the elevation data adopts bilinear interpolation or cubic interpolation algorithm.

3. A method for extracting and deleting exploration data according to claim 1, characterized in that The extraction area is the largest rectangular area defined based on the measured data and expands the area by a preset ratio in four directions.

4. A method for extracting and deleting exploration data according to claim 1, characterized in that, The grid processing includes dividing the data according to a fixed grid size.

5. A method for extracting and deleting exploration data according to claim 1, characterized in that The determination of the extracted or deleted data is based on the distance from the measured points. If the distance is less than the preset threshold, the data is retained; if the distance is greater than the preset threshold, the data is deleted.

6. A method for extracting and deleting exploration data according to claim 1, characterized in that The extracted or deleted data is further subjected to time series analysis, and the time series analysis is used to identify the change trend of the data over time.

7. A method for extracting and deleting exploration data according to claim 4, characterized in that The fixed grid size is dynamically adjusted based on the coefficient of variation of the extracted or deleted data.

8. An exploration data extraction and deletion system, characterized in that, It includes A data collection unit for collecting elevation data of the target area; A data processing unit for performing data interpolation and replacement on the collected elevation data; A data extraction and deletion unit for extracting or deleting the elevation data within a specified area; A grid processing unit for performing grid processing on the processed elevation data.

9. The exploration data extraction and deletion system according to claim 8, characterized in that, The data collection unit includes a GPS device and an elevation measuring instrument.

10. The exploration data extraction and deletion system according to claim 8, characterized in that, The data processing unit includes at least one microprocessor and a storage device.

11. A system for extracting and deleting exploration data according to claim 8, characterized in that The data extraction and deletion unit executes data extraction or deletion based on an algorithm based on the measured data and preset rules.

12. A system for extracting and deleting exploration data according to claim 8, characterized in that The data processing unit further includes a data compression module, and the data compression module is used to reduce the storage space requirement of the processed data.

13. A prospecting data extraction and deletion system according to claim 8, characterized in that, The grid processing unit supports dynamic adjustment of the grid size.

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