Geological mineral exploration device and exploration method

Abstract

The invention discloses a geological mineral exploration device and an exploration method, and particularly relates to the technical field of geological exploration. Mining area data are collected in a plurality of preset time periods, and data analysis is performed in each period, so that whether an early sign of insufficient data quality exists or not is judged; if a quality problem is found, feature analysis is further carried out, and the precision level and the coverage degree of the data are evaluated; dividing a data acquisition period into a high-quality period and a low-quality period according to an evaluation result; for a low-quality acquisition period, resampling or supplementary acquisition is executed, so that the data precision and coverage rate are improved, and the integrity and accuracy of exploration data are ensured; according to the method, through multi-level evaluation of the precision index and the coverage rate index, the quality problem in data collection can be identified in time, multiple times of correction processing are carried out, the precision and the coverage rate of the collected data are remarkably improved, and it is ensured that the collected data can accurately reflect the real geological condition of a mining area.

Description

Technical Field

[0001] The present invention relates to the field of geological exploration technology, and more specifically, to a geological mineral exploration device and an exploration method. Background Art

[0002] Geological and mineral exploration refers to the process of identifying the existence and distribution of underground mineral resources through geological surveys, analysis, and research, so as to determine the scale, quality, grade, and economic value of mineral resources. The exploration process usually includes preliminary geological surveys, detailed surveys, prospecting, and other stages. Through sampling, analysis, and other technical means, scientific assessments are made on the types, distribution, and reserves of minerals in the mining area.

[0003] The first step in geological and mineral exploration is geological survey, which is to understand the surface and shallow geological conditions of a specific area to determine whether there are geological conditions for the formation of mineral resources. In this stage, potential mineral-rich areas are preliminarily screened out through observation, sampling and basic analysis, providing a basis for further exploration.

[0004] In geological and mineral exploration, the quality of the collected geological data directly affects the accuracy of the ore body model and the scientific nature of the mineral resource assessment. However, traditional geological exploration methods face many challenges in the data collection process. First, the accuracy and spatial coverage of the collected data are often difficult to guarantee, especially in environments with complex terrain or harsh mining conditions. The collected data may deviate from the actual situation, resulting in errors in resource reserve assessment. Secondly, traditional exploration usually relies on manual inspection of data quality, which makes it difficult to identify and correct errors in the collection in a timely manner. Problems such as abnormal fluctuations in data and insufficient coverage may be ignored or improperly handled, making the exploration results unreliable. Therefore, a geological and mineral exploration device and exploration method are proposed herein. Summary of the invention

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A geological mineral exploration method comprises the following steps:

[0007] Collect data from geological mining areas within multiple preset time periods;

[0008] Conduct data analysis on the collected data in each time period, and determine whether there are early signs of insufficient quality of geological and mineral exploration data based on the data analysis results;

[0009] Where there are early signs of insufficient quality of geological and mineral exploration data, the data for that time period shall be analysed for characteristics and the accuracy and coverage of the data collected during that time period shall be assessed respectively;

[0010] The data collection quality of the time period is determined according to the evaluation results, and the data collection period is divided into a high-quality collection period and a low-quality collection period;

[0011] For a low-quality acquisition period, resample or supplement the acquisition of data in the time period.

[0012] In a preferred embodiment, a correction process is performed after resampling or supplementary acquisition to reduce the deviation generated during the data acquisition process.

[0013] In a preferred embodiment, performing data analysis refers to:

[0014] In each time period, the data of all sampling points are statistically processed, including the mean And standard deviation σM, then perform abnormal data point detection:

[0015] Mi is the data value of the i-th sampling point, Di is the deviation value of the i-th sampling point, k is the preset threshold coefficient of anomaly detection, if Di>k*σM, the sampling point is marked as an anomaly point;

[0016] The data coverage rate of the actual data collected in the survey area during the calculation time period is obtained by dividing the number of data points actually collected by the preset theoretical number of data points;

[0017] Calculate the mean change rate of adjacent acquisition cycles to detect whether the data has abnormal fluctuations. The mean change rate calculation formula is as follows: t represents the index number of the current collection cycle, represents the mean value of the current collection period t, and Rt represents the rate of change of the mean value of the current collection period t.

[0018] In a preferred embodiment, judging whether there are early signs of insufficient quality of geological and mineral exploration data based on the data analysis results refers to:

[0019] Counting the number of abnormal points, and generating an abnormal signal if the number of abnormal points is greater than a preset first threshold;

[0020] If the data coverage actually collected in the survey area is greater than a preset second threshold, an abnormal signal is generated;

[0021] If the absolute value of the mean change rate of the current acquisition period t is greater than a preset third threshold, an abnormal signal is generated;

[0022] When abnormal signals are generated within the same acquisition cycle t, it indicates that there are early signs of insufficient quality of geological and mineral exploration data in the acquisition cycle t.

[0023] In a preferred embodiment, feature analysis refers to generating an accuracy index corresponding to the data acquisition accuracy based on the data acquisition accuracy information, and generating a coverage index corresponding to the data coverage based on the data coverage information.

[0024] In a preferred embodiment, the logic for obtaining the precision index is:

[0025] For each sampling point i within a time period, the acquisition error value is calculated using the following formula:

[0026] Ei=|Bi-Mi|; Mi is the data value of the i-th sampling point, Bi is the reference data value of the i-th sampling point, and Ei is the acquisition error value of the i-th sampling point;

[0027] Based on the error values ​​Ei of all sampling points, in order to quantify the acquisition accuracy, the mean square error MSE of the acquired data is calculated;

[0028] The precision index is calculated by inverse proportional normalization, and the formula is:

[0029] JDI is the precision index and α is the normalization coefficient.

[0030] In a preferred embodiment, the logic for obtaining the coverage index is:

[0031] The survey area is divided into multiple grid cells, and the actual number of acquisition points in each grid cell within a time period is obtained. nj is the actual number of acquisition points in grid cell j, Nj is the theoretical number of acquisition points in grid cell j, and the coverage ratio Cj of each grid cell is calculated:

[0032] In order to reflect the contribution of each grid cell to the overall coverage, the weighted average coverage is calculated

[0033] m is the total number of grid cells, wj is the proportional coefficient corresponding to grid cell j;

[0034] In order to evaluate the stability of the coverage, the standard deviation σC of the coverage ratio Cj of all grid cells is calculated to quantify the unevenness of the coverage;

[0035] The formula for calculating the coverage index is:

[0036] β is the preset adjustment coefficient, and ZFI is the coverage index.

[0037] In a preferred embodiment, dividing the data collection period into a high-quality collection period and a low-quality collection period means:

[0038] The coverage index and precision index within the same time period are input into the pre-trained machine learning model, and the output result is the period division value. If the period division value is greater than or equal to the preset division threshold, the time period is divided into a high-quality collection period. If the period division value is less than the preset division threshold, the time period is divided into a low-quality collection period.

[0039] In a preferred embodiment, the correction process refers to:

[0040] The acquisition error value Ei is compared with the preset error threshold. If the acquisition error value Ei is greater than the preset error threshold, it is marked as a correction data point, and the data of the adjacent high-quality acquisition points are used for linear interpolation correction. The average value of the high-quality acquisition point data before and after the correction data point is calculated to obtain the corrected data Mi′.

[0041] Then use sliding average for smoothing, set the sliding window W, and the calculation formula for the secondary corrected data Mi" within the sliding window is:

[0042]

[0043] Then the spatial deviation correction is performed, and the calculation formula of the data Mi"' after three corrections is:

[0044] Mi"'=Mi"*f(Δh, Δg); f(Δh, Δg) represents a correction factor based on the terrain height difference Δh and the geological feature difference Δg to correct the error caused by the terrain or geological characteristics;

[0045] After the calibration is completed, the three-calibrated data Mi"' are stored and updated to the database, and re-evaluated until the time period is divided into a high-quality acquisition period.

[0046] In a preferred embodiment, a geological and mineral exploration device comprises:

[0047] The data acquisition module collects data from the geological mining area within a preset multiple time periods and stores the collected data for subsequent analysis;

[0048] The data analysis module analyzes the collected data in each time period and determines whether there are early signs of insufficient quality of geological and mineral exploration data based on the analysis results. If there are early signs of insufficient data quality, the feature analysis module is triggered;

[0049] The feature analysis module further performs feature analysis on the data of the time period in which the data quality is insufficient, generates an accuracy index corresponding to the data collection accuracy, evaluates the accuracy level of the collected data, and generates a coverage index corresponding to the data coverage range, evaluates the coverage degree of the collected data;

[0050] The data quality assessment module determines the data collection quality of the time period according to the accuracy index and coverage index of the feature analysis module, and divides the data collection period into a high-quality collection period and a low-quality collection period. If the data quality meets the standard, it is marked as a high-quality collection period, otherwise it is marked as a low-quality collection period;

[0051] The data optimization processing module resamples or supplements the data of the time period marked as low quality, and executes the correction processing module after resampling or supplementing the data to reduce the deviation generated in the data collection process;

[0052] The correction processing module performs error correction on the re-collected data to reduce errors caused by collection time or spatial deviations, ensure that the accuracy and coverage of the collected data meet the preset standards, and optimize the overall data quality.

[0053] Technical effects and advantages of the present invention:

[0054] The present invention can timely identify quality problems in data collection through multi-level evaluation of precision index and coverage index, and perform multiple corrections according to the topographic and geological characteristics of the collection area, thereby significantly improving the precision and coverage of the collected data and ensuring that the collected data can accurately reflect the real geological conditions of the mining area.

[0055] The present invention uses a machine learning model to automatically judge the data quality within each time period and divide the acquisition period into high-quality and low-quality periods in real time. This automated quality detection method reduces the reliance on manual judgment and significantly improves the exploration efficiency and data processing accuracy. By performing targeted resampling or supplementary acquisition of data from low-quality acquisition periods, the present invention can effectively avoid unnecessary repeated acquisition, optimize the use of exploration resources, and reduce the manpower, equipment, and time costs during the exploration process.

[0056] Multi-level correction and processing steps, including abnormal data correction, smoothing and spatial deviation correction, ensure the continuity and stability of the collected data. This can reduce data fluctuations caused by environmental changes or equipment problems, make the data consistent in different time and space, and provide a reliable data basis for subsequent analysis.

[0057] The present invention takes into account the impact of terrain height differences and geological characteristics on the collected data, and can still provide an effective correction method under complex geological conditions, thereby improving the adaptability of the exploration method and enabling it to obtain high-quality data in various geological environments. By ensuring the high quality and consistency of data, the present invention provides more reliable data support for mineral resource assessment, ore body modeling and development decision-making, thereby improving the accuracy of resource assessment and reducing the risks of exploration and development. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0059] Figure 1 This is a schematic diagram of a geological and mineral exploration method in the present invention.

[0060] Figure 2 The schematic diagram of a geological and mineral exploration device in the present invention. DETAILED DESCRIPTION

[0061] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0062] Reference Figure 1 - Figure 2 The following embodiments are obtained:

[0063] Example 1: In the process of geological and mineral exploration, incomplete data collection or insufficient accuracy can lead to the following problems:

[0064] Inaccurate resource reserve estimation: Incomplete or low-precision data collection will lead to deviations in the size, distribution and reserve estimation of the ore body. Insufficient data collection may cause exploration personnel to underestimate or overestimate mineral resource reserves, which in turn affects the mining value and economic benefits of the mineral, resulting in unforeseen economic risks in the resource development process.

[0065] Incomplete information on ore body morphology and grade: Insufficient exploration data will affect the accurate grasp of the 3D structure of the ore body and the ore grade. For example, for complex deposits, insufficiently accurate data collection may make it difficult to reveal the true morphology and internal grade distribution of the ore body. This will affect the scientific nature of the mining plan, leading to insufficient mining of the ore body or waste of resources, and even affecting safe mining.

[0066] Inaccurate mining design and cost estimates: Due to inaccurate ore deposit models caused by insufficient data, mining design and equipment deployment may also be problematic. For example, ambiguous information about the ore body's orientation, thickness, and geological structure may lead to unreasonable mining equipment arrangements, resulting in geological difficulties or unnecessary operating costs during mining. Such deviations not only increase exploration and mining costs, but may also extend construction time.

[0067] Therefore, comprehensive and high-precision data collection is crucial for geological and mineral exploration, which helps to more scientifically evaluate mineral resources, design reasonable mining plans, and reduce mining risks. The present invention proposes a geological and mineral exploration method, comprising the following steps:

[0068] Collect data from geological mining areas within multiple preset time periods; by setting multiple time periods, regularly collect data from mining areas to ensure that the data has time continuity and coverage integrity. This operation provides basic data and lays a reliable foundation for subsequent quality analysis, feature analysis and data evaluation.

[0069] Perform data analysis on the collected data in each time period, and determine whether there are early signs of insufficient quality in geological and mineral exploration data based on the data analysis results; analyzing the data in each time period can promptly identify potential deficiencies in the quality of the collected data. This operation helps to discover data quality problems in advance and reduce misjudgments or analysis biases caused by insufficient data quality, thereby ensuring the accuracy and effectiveness of subsequent analysis.

[0070] In the case of early signs of insufficient quality of geological and mineral exploration data, feature analysis of the data of that time period is performed, and the accuracy level and coverage of the data collected during that time period are evaluated separately; feature analysis of the time period with insufficient data quality can deeply evaluate the accuracy and coverage of the collected data. Through separate evaluations of accuracy and coverage, the specific reasons for insufficient data (for example, low accuracy or insufficient coverage) can be clearly identified, providing a clear direction for subsequent data supplementation or re-collection.

[0071] The data collection quality of the time period is judged based on the evaluation results, and the data collection period is divided into high-quality collection period and low-quality collection period; based on the results of feature analysis, the data collection period is divided into high-quality and low-quality periods to ensure the reasonable hierarchical management of data. High-quality period data directly enters subsequent analysis or processing, while low-quality period data requires further supplementary collection or correction. This division helps to improve the overall reliability and accuracy of the entire data set.

[0072] For low-quality collection periods, resample or supplement the data of that time period. Resampling or supplementing the data of low-quality collection periods can help make up for the deficiencies in data quality and ensure that data coverage and collection accuracy meet standard requirements. This operation can reduce analysis errors caused by incomplete or inaccurate data and provide higher quality data support for subsequent processing and interpretation.

[0073] Correction processing is performed after resampling or supplementary collection to reduce the deviation caused by the data collection process. After resampling or supplementary collection is completed, correction processing is used to reduce the deviation caused by time or space differences in the collection process. Correction processing can effectively improve data consistency and accuracy, so that the supplementary collected data can be seamlessly integrated into the original data set, improve the integrity and accuracy of the overall data, and ensure the reliability of subsequent analysis and application.

[0074] The purpose of resampling is to obtain more reliable data in the existing collection area to solve the data quality problems caused by insufficient accuracy. The purpose of supplementary collection is to add new data collection points in the sub-areas with insufficient density in the original collection area to solve the data quality problems caused by insufficient coverage or insufficient sampling density. In actual use, the user can decide which sampling method to use.

[0075] Performing data analysis means:

[0076] In each time period, the data of all sampling points are statistically processed, including the mean And standard deviation σM, help to evaluate the overall trend and distribution of data. These statistics are an important basis for judging data quality, helping to identify possible abnormal data points, and then perform abnormal data point detection: Mi is the data value of the i-th sampling point, Di is the deviation value of the i-th sampling point, k is the preset threshold coefficient of anomaly detection, if Di>k*σM, then the sampling point is marked as an abnormal point; calculate the data coverage rate actually collected in the survey area within the time period, the data coverage rate is obtained by dividing the number of data points actually collected by the preset theoretical number of collection points; calculate the mean change rate of adjacent collection periods to detect whether the data has abnormal fluctuations, the mean change rate calculation formula is as follows: t represents the index number of the current collection cycle, represents the mean of the current acquisition cycle t, and Rt represents the mean change rate of the current acquisition cycle t. The mean change rate of adjacent acquisition cycles is calculated to detect whether there is abnormal fluctuation in the data. The mean change rate can quantify the consistency of the data over time. If the absolute value of the change rate is too large, there may be inconsistent acquisition or data quality problems, so as to issue an early warning in time to avoid further geological and mineral exploration and analysis of unstable data.

[0077] Judging from the data analysis results, early signs of insufficient quality of geological and mineral exploration data refer to:

[0078] The number of abnormal points is counted. If the number of abnormal points is greater than the preset first threshold, an abnormal signal is generated; if the data coverage actually collected in the survey area is greater than the preset second threshold, an abnormal signal is generated; if the absolute value of the mean change rate of the current acquisition cycle t is greater than the preset third threshold, an abnormal signal is generated; when an abnormal signal is generated in the same acquisition cycle t, it indicates that there are early signs of insufficient quality of geological and mineral exploration data in the acquisition cycle t. The number of abnormal points in the sampled data is counted and compared with the preset threshold (first threshold). When the number of abnormal points exceeds the first threshold, an abnormal signal is generated, indicating that the data may be abnormal. This indicator is used to evaluate data accuracy and ensure that the collected data is not interfered by noise or errors. Calculate the coverage of the data actually collected in the survey area and compare it with the preset coverage threshold (second threshold). If the coverage is insufficient, an abnormal signal is generated, indicating that the data may have uncovered areas in space. This indicator is used to evaluate the spatial integrity of the data and ensure that the collected data can fully cover the target area. Calculate the absolute value of the mean change rate of the current acquisition cycle and compare it with the preset change rate threshold (third threshold). When the absolute value of the mean change rate exceeds the third threshold, an abnormal signal is generated, indicating that the data fluctuates too much over time and may be unstable or inconsistent. This indicator is used to judge the temporal stability of the data and ensure that the data changes smoothly over consecutive periods. If any of the above indicators generates an abnormal signal within the same acquisition cycle, it is determined that there are early signs of insufficient quality of geological and mineral exploration data in that acquisition cycle. Through the comprehensive judgment of multiple indicators, data quality problems can be identified more comprehensively, ensuring that the data meets the exploration requirements and reducing the deviation of subsequent analysis. These judgment criteria help to detect data quality problems in a timely manner, ensure the accuracy, coverage and stability of geological and mineral exploration data, and provide a reliable data basis for subsequent analysis.

[0079] Feature analysis refers to generating an accuracy index corresponding to the data collection accuracy based on the data collection accuracy information, and generating a coverage index corresponding to the data coverage based on the data coverage information. The logic for obtaining the accuracy index is:

[0080] For each sampling point i within a time period, the acquisition error value is calculated using the following formula:

[0081] Ei=|Bi-Mi|; Mi is the data value of the i-th sampling point, Bi is the reference data value of the i-th sampling point (such as the mean or standard value of the adjacent high-quality acquisition points), and Ei is the acquisition error value of the i-th sampling point, which reflects the accuracy deviation of each sampling point and quantifies the difference between the acquired data and the standard data. The larger the error value, the lower the data accuracy of the sampling point.

[0082] Based on the error values ​​Ei of all sampling points, in order to quantify the acquisition accuracy, the mean square error MSE of the acquired data is calculated to quantify the data acquisition accuracy within the time period. The smaller the MSE value, the higher the overall accuracy of the acquired data, because the error value of each sampling point is lower.

[0083] The precision index is calculated by inverse proportional normalization, and the formula is:

[0084] JDI is the precision index, and α is the normalization coefficient, which adjusts the magnitude of the index. The precision index JDI ranges from 0 to 1. The larger the value, the higher the precision of the collected data. When JDI is close to 1, it means that the error of the collected data is very small and the precision is high; when the JDI value is low, it means that the error of the collected data is large and the precision is insufficient. The precision index JDI is an important indicator to measure the precision of collected data, and is used to determine whether the collected data meets the quality requirements.

[0085] The logic for obtaining the coverage index is:

[0086] The survey area is divided into multiple grid cells, and the actual number of acquisition points in each grid cell within a time period is obtained. nj is the actual number of acquisition points in grid cell j, Nj is the theoretical number of acquisition points in grid cell j, and the coverage ratio Cj of each grid cell is calculated: Assess coverage within each grid cell to ensure data collection achieves the expected coverage spatially.

[0087] In order to reflect the contribution of each grid cell to the overall coverage, the weighted average coverage is calculated

[0088] m is the total number of grid cells. The weighted average coverage can reflect the impact of different grid cells on the overall coverage. wj is the proportional coefficient corresponding to grid cell j, which can be defined as the ratio of the grid area or the ratio of the theoretical number of collection points of the grid cell to the theoretical number of collection points in the survey area.

[0089] In order to evaluate the stability of coverage, the standard deviation σC of the coverage ratio Cj of all grid cells is calculated to quantify the unevenness of the coverage and evaluate the stability of the coverage. The smaller the standard deviation, the more uniform the coverage is among the grid cells.

[0090] The formula for calculating the coverage index is:

[0091] β is a preset adjustment coefficient used to adjust the sensitivity to inhomogeneity (standard deviation). ZFI is the coverage index. The larger the coverage index ZFI is, the higher the coverage and the more uniform the distribution is. When the ZFI value is high, it means that the collected data in the survey area has achieved a high spatial coverage rate and the coverage distribution between different grid cells is also relatively uniform. A low ZFI value indicates insufficient coverage or uneven coverage distribution, which may require further supplementary collection to improve the spatial integrity of the data.

[0092] Dividing the data collection cycle into high-quality collection cycle and low-quality collection cycle means:

[0093] The coverage index and precision index within the same time period are input into the pre-trained machine learning model. The machine learning model is a polynomial regression model, which will not be described in detail here. The output result is the period division value. If the period division value is greater than or equal to the preset division threshold, the time period is divided into a high-quality collection period. If the period division value is less than the preset division threshold, the time period is divided into a low-quality collection period.

[0094] The coverage index and precision index are input into the pre-trained machine learning model, and the quality level of the collected data is automatically judged by the output period division value. This method reduces the subjectivity of manual judgment, improves the efficiency and consistency of evaluation, and ensures that the classification process of the collection period is objective and fast. The purpose of period division is to identify whether the data quality meets the exploration requirements. A high-quality collection period means that the data of this time period has sufficient coverage and collection accuracy and can be used for subsequent analysis. A low-quality collection period indicates that the data is insufficient or biased and needs further supplementation or resampling. This division can ensure that the data used for subsequent analysis has high accuracy and completeness. Through period division, time and resources can be concentrated on data processing of low-quality collection periods, such as recollection or supplementary collection, without additional processing of data of high-quality collection periods. This priority division improves the utilization efficiency of exploration resources, avoids unnecessary repeated collection, and reduces exploration costs. In the subsequent geological analysis, model construction or prediction process, the data of high-quality collection periods can provide reliable basic support, while low-quality data may bring deviations or instability. Through period division, data that meets quality requirements can be effectively screened out, providing more stable and reliable data support for subsequent analysis.

[0095] Correction processing refers to:

[0096] Compare the acquisition error value Ei with the preset error threshold. If the acquisition error value Ei is greater than the preset error threshold, it will be marked as a correction data point, and the data of the adjacent high-quality acquisition points will be used for linear interpolation correction. The average value of the high-quality acquisition point data before and after the correction data point is calculated to obtain the corrected data Mi′. This process is mainly to eliminate the abnormal data of individual sampling points and make them consistent with the surrounding high-quality data. A correction ensures that each sampling point will not deviate too far from the adjacent data, thereby initially improving the consistency and accuracy of the data as a whole.

[0097] Then use sliding average for smoothing, set the sliding window W, and the calculation formula for the secondary corrected data Mi" within the sliding window is:

[0098] This process can eliminate short-term fluctuations, smooth out slight differences between sampling points, and reduce accidental sampling errors. The secondary correction improves the continuity and stability of the data, making the data smoother in space and suitable for further analysis.

[0099] Then the spatial deviation correction is performed, and the calculation formula of the data Mi"' after three corrections is:

[0100] Mi”′=Mi”*f(Δh, Δg); f(Δh, Δg) represents the correction factor based on the terrain height difference Δh and the geological characteristic difference Δg, so as to correct the error caused by the terrain or geological characteristics; the tertiary correction is a spatial correction for the influence of geological characteristics and terrain characteristics, which further improves the accuracy of the data in geologically complex areas and makes the data more in line with the actual geological conditions.

[0101] After the calibration is completed, the three-calibrated data Mi"' are stored and updated to the database, and re-evaluated until the time period is divided into a high-quality acquisition period.

[0102] The first correction removes outliers first, and removes obvious anomalies in the data through linear interpolation with high-quality data to ensure the quality of preliminary data. The second correction smoothes the data, smoothes the data after the first correction, reduces fluctuations, makes the data more continuous and stable, and lays the foundation for spatial correction. The third correction considers the influence of geology and topography. On the basis of the smooth and stable data, further corrects the deviation caused by topography or geological characteristics to improve the spatial consistency and authenticity of the data. The data quality is gradually improved, from simple outlier processing to complex geological correction, ensuring the accuracy and stability of the data at each step.

[0103] The collected data at different locations and times may be affected by environmental, equipment or human factors, resulting in errors and deviations. Correction can eliminate these errors and improve the accuracy of the data. The first, second and third corrections successively eliminate outliers, smooth fluctuations and geological influences, making the data more consistent and continuous, and facilitating subsequent analysis and interpretation. The third correction specifically considers the topographic and geological characteristics, so that the data more realistically reflects the geological conditions of the exploration area, which is helpful for subsequent geological modeling and prediction. The data correction step is a necessary process to ensure the quality and accuracy of geological and mineral exploration data.

[0104] Embodiment 2: A geological and mineral exploration device, comprising:

[0105] The data acquisition module collects data from the geological mining area within a preset multiple time periods and stores the collected data for subsequent analysis;

[0106] The data analysis module analyzes the collected data in each time period and determines whether there are early signs of insufficient quality of geological and mineral exploration data based on the analysis results. If there are early signs of insufficient data quality, the feature analysis module is triggered;

[0107] The feature analysis module further performs feature analysis on the data of the time period in which the data quality is insufficient, generates an accuracy index corresponding to the data collection accuracy, evaluates the accuracy level of the collected data, and generates a coverage index corresponding to the data coverage range, evaluates the coverage degree of the collected data;

[0108] The data quality assessment module determines the data collection quality of the time period according to the accuracy index and coverage index of the feature analysis module, and divides the data collection period into a high-quality collection period and a low-quality collection period. If the data quality meets the standard, it is marked as a high-quality collection period, otherwise it is marked as a low-quality collection period;

[0109] The data optimization processing module resamples or supplements the data of the time period marked as low quality, and executes the correction processing module after resampling or supplementing the data to reduce the deviation generated in the data collection process;

[0110] The correction processing module performs error correction on the re-collected data to reduce errors caused by collection time or spatial deviations, ensure that the accuracy and coverage of the collected data meet the preset standards, and optimize the overall data quality.

[0111] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0112] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0113] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0114] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0115] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A geological and mineral exploration method, characterized in that: The following steps are involved: Collect data from geological mining areas within multiple preset time periods; Conduct data analysis on the collected data in each time period, and determine whether there are early signs of insufficient quality of geological and mineral exploration data based on the data analysis results; Where there are early signs of insufficient quality of geological and mineral exploration data, the data for that time period shall be analysed for characteristics and the accuracy and coverage of the data collected during that time period shall be assessed respectively; The data collection quality of the time period is determined according to the evaluation results, and the data collection period is divided into a high-quality collection period and a low-quality collection period; For a low-quality acquisition period, resample or supplement the acquisition of data in the time period.

2. A geological and mineral exploration method according to claim 1, characterized in that: Correction processing is performed after resampling or supplementary acquisition to reduce the bias introduced during data acquisition.

3. A geological and mineral exploration method according to claim 2, characterized in that: Performing data analysis means: In each time period, the data of all sampling points are statistically processed, including the mean And standard deviation σM, then perform abnormal data point detection: Mi is the data value of the i-th sampling point, Di is the deviation value of the i-th sampling point, k is the preset threshold coefficient of anomaly detection, if Di>k*σM, the sampling point is marked as an anomaly point; The data coverage rate of the actual data collected in the survey area during the calculation time period is obtained by dividing the number of data points actually collected by the preset theoretical number of data points; Calculate the mean change rate of adjacent acquisition cycles to detect whether the data has abnormal fluctuations. The mean change rate calculation formula is as follows: t represents the index number of the current collection cycle, represents the mean of the current collection period t, and Rt represents the rate of change of the mean of the current collection period t.

4. A geological and mineral exploration method according to claim 3, characterized in that: Judging from the data analysis results, early signs of insufficient quality of geological and mineral exploration data refer to: Counting the number of abnormal points, and generating an abnormal signal if the number of abnormal points is greater than a preset first threshold; If the data coverage actually collected in the survey area is greater than a preset second threshold, an abnormal signal is generated; If the absolute value of the mean change rate of the current acquisition period t is greater than a preset third threshold, an abnormal signal is generated; When abnormal signals are generated within the same acquisition cycle t, it indicates that there are early signs of insufficient quality of geological and mineral exploration data in the acquisition cycle t.

5. A geological and mineral exploration method according to claim 4, characterized in that: Feature analysis refers to generating an accuracy index corresponding to the data collection accuracy based on the data collection accuracy information, and generating a coverage index corresponding to the data coverage based on the data coverage information.

6. A geological and mineral exploration method according to claim 5, characterized in that: The logic for obtaining the precision index is: For each sampling point i within a time period, the acquisition error value is calculated using the following formula: Ei=|Bi-Mi|; Mi is the data value of the i-th sampling point, Bi is the reference data value of the i-th sampling point, and Ei is the acquisition error value of the i-th sampling point; Based on the error values ​​Ei of all sampling points, in order to quantify the acquisition accuracy, the mean square error MSE of the acquired data is calculated; The precision index is calculated by inverse proportional normalization, and the formula is: JDI is the precision index and α is the normalization coefficient.

7. A geological and mineral exploration method according to claim 6, characterized in that: The logic for obtaining the coverage index is: The survey area is divided into multiple grid cells, and the actual number of acquisition points in each grid cell within a time period is obtained. nj is the actual number of acquisition points in grid cell j, Nj is the theoretical number of acquisition points in grid cell j, and the coverage ratio Cj of each grid cell is calculated: In order to reflect the contribution of each grid cell to the overall coverage, the weighted average coverage is calculated m is the total number of grid cells, wj is the proportional coefficient corresponding to grid cell j; In order to evaluate the stability of the coverage, the standard deviation σC of the coverage ratio Cj of all grid cells is calculated to quantify the unevenness of the coverage; The formula for calculating the coverage index is: β is the preset adjustment coefficient, and ZFI is the coverage index.

8. A geological and mineral exploration method according to claim 7, characterized in that: Dividing the data collection cycle into high-quality collection cycle and low-quality collection cycle means: The coverage index and precision index within the same time period are input into the pre-trained machine learning model, and the output result is the period division value. If the period division value is greater than or equal to the preset division threshold, the time period is divided into a high-quality collection period. If the period division value is less than the preset division threshold, the time period is divided into a low-quality collection period.

9. A geological and mineral exploration method according to claim 8, characterized in that: Correction processing refers to: The acquisition error value Ei is compared with the preset error threshold. If the acquisition error value Ei is greater than the preset error threshold, it is marked as a correction data point, and the data of the adjacent high-quality acquisition points are used for linear interpolation correction. The average value of the high-quality acquisition point data before and after the correction data point is calculated to obtain the corrected data Mi′. Then use sliding average for smoothing, set the sliding window W, and the calculation formula for the secondary corrected data Mi" within the sliding window is: Then the spatial deviation correction is performed, and the calculation formula of the data Mi"' after three corrections is: Mi''=Mi''*f(Δh, Δg); f(Δh, Δg) represents a correction factor based on the terrain height difference Δh and the geological feature difference Δg to correct the error caused by the terrain or geological characteristics; After the calibration is completed, the three-calibrated data Mi"' are stored and updated to the database, and re-evaluated until the time period is divided into a high-quality acquisition period.

10. A geological and mineral exploration device, implemented based on a geological and mineral exploration method according to any one of claims 1 to 9, characterized in that: include: The data acquisition module collects data from the geological mining area within a preset multiple time periods and stores the collected data for subsequent analysis; The data analysis module analyzes the collected data in each time period and determines whether there are early signs of insufficient quality of geological and mineral exploration data based on the analysis results. If there are early signs of insufficient data quality, the feature analysis module is triggered; The feature analysis module further performs feature analysis on the data of the time period in which the data quality is insufficient, generates an accuracy index corresponding to the data collection accuracy, evaluates the accuracy level of the collected data, and generates a coverage index corresponding to the data coverage range, evaluates the coverage degree of the collected data; The data quality assessment module determines the data collection quality of the time period according to the accuracy index and coverage index of the feature analysis module, and divides the data collection period into a high-quality collection period and a low-quality collection period. If the data quality meets the standard, it is marked as a high-quality collection period, otherwise it is marked as a low-quality collection period; The data optimization processing module resamples or supplements the data of the time period marked as low quality, and executes the correction processing module after resampling or supplementing the data to reduce the deviation generated in the data collection process; The correction processing module performs error correction on the re-collected data to reduce errors caused by collection time or spatial deviations, ensure that the accuracy and coverage of the collected data meet the preset standards, and optimize the overall data quality.

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