Optimized prospecting engineering method based on data analysis and related equipment

Abstract

The invention discloses a data analysis-based prospecting engineering optimization method and related equipment. According to the method, firstly, resource quantity estimation is conducted on an ore body through original drill holes and design drill holes, the original estimated resource quantity and the design estimated resource quantity are obtained, and the design drill holes comprise encrypted upgrading design drill holes and peripheral newly-added design drill holes; verifying correlation characteristics of the original estimated resource quantity and the designed estimated resource quantity by using data analysis; and finally, if the verification is not passed, optimizing and adjusting the designed drill hole. In the process, a design drilling hole and a variance analysis method are set, the accuracy of resource quantity estimation is improved, the effectiveness of encryption upgrading or peripheral newly-added design is verified through a statistical method, and therefore more reliable data support is provided for subsequent development plans.

Description

Technical Field

[0001] The present application relates to the technical field of prospecting engineering, and in particular to a method for optimizing prospecting engineering based on data analysis and related equipment. Background Art

[0002] In prospecting projects, in order to accurately estimate the amount of underground mineral resources, it is usually necessary to obtain sample data through drilling and estimate the grade and reserves of the minerals based on this data.

[0003] Existing prospecting engineering designs mainly rely on drilling holes at fixed intervals to obtain sample data, and then use the block method to estimate resource volume. However, this method often results in large deviations in resource volume estimation when encountering areas with large changes in ore body grade and thickness, resulting in inaccurate resource volume estimation, which in turn affects the accuracy of decision-making. Summary of the invention

[0004] The present application provides an optimized prospecting engineering method and related equipment based on data analysis, which uses data analysis methods to analyze and judge the estimated resource volume, optimizes the drilling design, and improves the accuracy of resource volume estimation.

[0005] In a first aspect, the present application provides a method for optimizing prospecting engineering based on data analysis, the method comprising:

[0006] The resource volume of the ore body is estimated by using the original drill holes and the designed drill holes respectively to obtain the original estimated resource volume and the designed estimated resource volume, wherein the designed drill holes include the infilled upgraded designed drill holes and the peripheral newly added designed drill holes;

[0007] Use data analysis to verify the correlation characteristics between the original estimated resource volume and the designed estimated resource volume;

[0008] If the verification fails, optimize and adjust the drilling design.

[0009] Optionally, data analysis is used to verify the correlation characteristics between the original resource estimate and the design resource estimate, including:

[0010] The null hypothesis is that there is no correlation between the original estimated resource volume and the designed estimated resource volume;

[0011] Calculate the mean square of each group of segment block data in the original estimated resource volume and the designed estimated resource volume;

[0012] Calculate the mean square error between the original estimated resource and the designed estimated resource;

[0013] Using the mean square and error mean square of each group of segment data, the variance estimate is obtained;

[0014] Convert the variance estimator into a probability value, and determine whether the original hypothesis is valid based on the size of the probability value.

[0015] Optionally, judging whether the original hypothesis is valid based on the probability value includes:

[0016] If the probability value is greater than the preset value, the null hypothesis is established to determine that the verification fails;

[0017] If the probability value is less than the preset value, the original hypothesis is not established, so that the verification is confirmed to be successful.

[0018] Optionally, calculating the mean square of each group of segment block data in the original estimated resource quantity and the designed estimated resource quantity includes:

[0019] Calculate the sum of squares of each group of segment block data in the original estimated resource volume and the designed estimated resource volume;

[0020] The mean square of each group of segment block data is calculated using the sum of the squares of each group of segment block data.

[0021] Optionally, calculating the mean square error between the original estimated resource quantity and the designed estimated resource quantity includes:

[0022] The sum of square errors of each group of segment data is calculated using the mean square of each group of segment data;

[0023] The mean square error is calculated using the sum of squared errors of each group of segment data.

[0024] Optionally, if the verification fails, the drilling design is optimized and adjusted, including:

[0025] Readjust the grade thickness values ​​between the designed drill holes until they are verified.

[0026] Optionally, the method of estimating the resource volume of the ore body using the original drill hole and the designed drill hole respectively is the block method.

[0027] In a second aspect, the present application provides an optimized prospecting engineering device based on data analysis, the device comprising:

[0028] An acquisition unit is used to estimate the resource volume of the ore body by using the original drill hole and the designed drill hole respectively, and obtain the original estimated resource volume and the designed estimated resource volume, wherein the designed drill hole includes the infill upgraded designed drill hole and the peripheral newly added designed drill hole;

[0029] A verification unit, used to verify the correlation characteristics between the original estimated resource quantity and the designed estimated resource quantity by using data analysis;

[0030] The adjustment unit is used to optimize and adjust the designed drilling if the verification fails.

[0031] Optionally, the verification unit includes:

[0032] Establishing subunits for establishing the null hypothesis that there is no correlation between the original estimated resource volume and the designed estimated resource volume;

[0033] A calculation subunit, used for calculating the mean square of each group of segment block data in the original estimated resource quantity and the designed estimated resource quantity;

[0034] A calculation subunit, used for calculating the mean square error between the original estimated resource quantity and the designed estimated resource quantity;

[0035] A processing subunit is used for obtaining a variance estimate by using the mean square and the error mean square of each group of segment block data;

[0036] The judgment subunit is used to convert the variance estimator into a probability value and judge whether the original hypothesis is valid according to the size of the probability value.

[0037] Optionally, the judging subunit is specifically used for:

[0038] If the probability value is greater than the preset value, the null hypothesis is established to determine that the verification fails;

[0039] If the probability value is less than the preset value, the original hypothesis is not true, so the verification is confirmed to be successful.

[0040] Optionally, when the calculation subunit calculates the mean square of each group of segment block data in the original estimated resource quantity and the designed estimated resource quantity, it is specifically used to:

[0041] Calculate the sum of squares of each group of segment block data in the original estimated resource volume and the designed estimated resource volume;

[0042] The mean square of each group of segment block data is calculated using the sum of the squares of each group of segment block data.

[0043] Optionally, when the calculation subunit calculates the mean square error between the original estimated resource quantity and the designed estimated resource quantity, it is specifically used to:

[0044] The sum of square errors of each group of segment data is calculated using the mean square of each group of segment data;

[0045] The mean square error is calculated using the sum of squared errors of each group of segment data.

[0046] Optionally, the adjustment unit is specifically used for:

[0047] Readjust the grade thickness values ​​between the designed drill holes until they are verified.

[0048] Optionally, the method of estimating the resource volume of the ore body using the original drill hole and the designed drill hole respectively is the block method.

[0049] In a third aspect, the present application provides an electronic device, the electronic device comprising a memory and a processor:

[0050] The memory is used to store computer programs;

[0051] The processor is used to execute the method provided by the first aspect according to the computer program.

[0052] In a fourth aspect, the present application further provides a computer-readable storage medium, which is used to store a computer program, and the computer program is used to execute the method provided in the first aspect above.

[0053] It can be seen that this application has the following beneficial effects:

[0054] The present application provides an optimized prospecting engineering method based on data analysis. First, the original borehole and the designed borehole are used to estimate the resource volume of the ore body, and the original estimated resource volume and the designed estimated resource volume are obtained. The designed borehole includes the encrypted upgraded designed borehole and the peripheral newly added designed borehole; then the data analysis is used to verify the correlation characteristics of the original estimated resource volume and the designed estimated resource volume; finally, if the verification fails, the designed borehole is optimized and adjusted. In this process, the designed borehole is set and the variance analysis method is used, which not only improves the accuracy of the resource volume estimation, but also verifies the effectiveness of the encrypted upgraded or peripheral newly added design through statistical methods, thereby providing more reliable data support for subsequent development plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present application. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0056] Figure 1 A schematic diagram of a flow chart of an embodiment of a method for optimizing prospecting engineering based on data analysis in an embodiment of the present application;

[0057] Figure 2 A schematic flow chart of another embodiment of a method for optimizing prospecting engineering based on data analysis in the embodiments of the present application;

[0058] Figure 3 This is a schematic diagram of resource estimation at different spacings in an embodiment of the present application;

[0059] Figure 4 This is a schematic diagram of the variance analysis results of average grade and projected thickness in the examples of this application;

[0060] Figure 5 This is a schematic diagram of the first linear fitting and the second linear fitting before and after the drilling of the encryption upgrade design in the embodiment of the present application;

[0061] Figure 6 This is a schematic diagram of the structure of an optimized prospecting engineering device based on data analysis in an embodiment of the present application;

[0062] Figure 7 This is a schematic diagram of the structure of an electronic device in an embodiment of the present application. DETAILED DESCRIPTION

[0063] 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.

[0064] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions.

[0065] The following is a detailed description of the technical terms mentioned in the embodiments of the present application:

[0066] (1) Sum of Squares (SS): It plays an important role in statistics. It is mainly used to quantify the degree of difference between each observation in a data set and the mean of the set. By calculating the sum of squares, researchers can understand the dispersion of the data, that is, how tightly the data points are distributed relative to the mean. This is crucial for assessing the internal consistency of the data and conducting further statistical tests (such as analysis of variance) because it provides a basis for measuring differences between different groups and helps determine whether the observed differences are statistically significant. In short, the sum of squares is a basic and key concept in statistical analysis that helps reveal data patterns and support scientific decision-making.

[0067] (2) Mean Square (MS) is used in statistical analysis to measure the variability of data in a standardized way. It adjusts for differences in variability due to different sample sizes by dividing the sum of squares by the degrees of freedom, thus providing a fair basis for comparison. The calculation of the mean square allows researchers to more accurately assess the degree of dispersion of the data and helps distinguish whether differences between different groups or treatment conditions are statistically significant in situations such as analysis of variance (ANOVA). Therefore, the mean square is a key metric for measuring and comparing variability within and between different data sets.

[0068] (3) Error Sum of Squares (SSE) is used in statistical analysis to measure the internal variability of the data, that is, the degree of fluctuation of the observed value around its expected value (usually the mean value within the group) after excluding the influence of known factors. By calculating SSE, researchers can evaluate the part of the variation that cannot be explained by the model or treatment measures, so as to understand the extent of the influence of random noise or unidentified variables in the data that are not captured by the model. This means that the smaller the SSE, the stronger the ability of the model or experimental design to explain the variability of the data. Conversely, it indicates that the model may be insufficient or there are other factors in the data that have not been considered. Therefore, SSE is a key indicator for evaluating the goodness of fit of the model and the effectiveness of the experimental design.

[0069] (4) Error Mean Square (MSE) is a method in statistical analysis that is used to measure the internal variation of data in a standardized manner. The significance of the error mean square is that it provides a measure of the average variation of each group of data. Since MSE is obtained by standardizing the sum of squares (dividing by the degrees of freedom), it eliminates the effect of sample size on the variation measure, allowing the variation between samples or groups of different sizes to be directly compared. In addition, in analysis of variance (ANOVA), the error mean square is also used to compare with the variation mean square generated by the treatment effect (such as the between-group mean square) to evaluate the significance of the treatment effect. If the MSE is small, it means that the variation within the group is small, that is, the data points are more concentrated, otherwise it means that the data points are more widely distributed. In this way, the error mean square becomes an important tool for evaluating the internal consistency of the data and the quality of model fitting.

[0070] (5) Variance estimators: The process of comparing variance estimators often occurs in Analysis of Variance (ANOVA), especially when we want to assess whether the differences between different groups are significant. In this process, the so-called F-statistic, also known as the F value, is calculated by comparing the between-group mean square (MSB) with the within-group mean square (MSE). The significance of the F value is that it provides the ratio of the between-group variation to the within-group variation. If the F value is large, it means that the between-group variation is large relative to the within-group variation, which may indicate that there are substantial differences between different groups. On the contrary, if the F value is close to 1, it means that the between-group variation is comparable to the within-group variation, suggesting that there may be no significant differences between the groups. In statistics, we will compare the calculated F value with the critical value in the theoretical F distribution table, or directly calculate the P value corresponding to the F value to decide whether to reject the null hypothesis (that the means of each group are the same). If the P value is less than the pre-set significance level (such as 0.05), the difference between the groups is considered significant and the null hypothesis is rejected; otherwise, the null hypothesis is accepted and the difference between the groups is considered insignificant.

[0071] At present, the existing prospecting engineering design mainly relies on fixed-spacing boreholes to obtain sample data, and then uses the block method to estimate the resource volume. However, this method often has large deviations in resource estimation when encountering areas where the grade and thickness of the ore body vary greatly. Therefore, in order to improve the accuracy of resource estimation, this application proposes an optimized prospecting engineering method based on data analysis, which can analyze and judge the resource estimation to optimize the drilling design and improve the accuracy of resource estimation. In specific implementation, the method may include, for example: first, respectively estimate the resource volume of the ore body using the original borehole and the designed borehole to obtain the original estimated resource volume and the designed estimated resource volume, and the designed borehole includes encrypted upgraded designed boreholes and peripheral newly added designed boreholes; then use data analysis to verify the correlation characteristics of the original estimated resource volume and the designed estimated resource volume; finally, if the verification fails, optimize and adjust the designed borehole.

[0072] It can be seen that the method provided by the implementation of this application, which sets up designed drilling and uses variance analysis, not only improves the accuracy of resource estimation, but also verifies the effectiveness of encrypted upgrades or peripheral new designs through statistical methods, thereby providing more reliable data support for subsequent development plans.

[0073] To facilitate understanding of the specific implementation of an optimized prospecting engineering method based on data analysis provided in an embodiment of the present application, it will be described below with reference to the accompanying drawings.

[0074] It should be noted that the subject implementing the method for optimizing prospecting engineering based on data analysis may be an optimized prospecting engineering device based on data analysis provided in the embodiment of the present application, and the optimized prospecting engineering device based on data analysis may be carried in an electronic device or a functional module of an electronic device. The electronic device in the embodiment of the present application may be any device capable of implementing the method for optimizing prospecting engineering based on data analysis in the embodiment of the present application, such as an Internet of Things (IoT) device.

[0075] Figure 1 The present invention provides a flow chart of a method for optimizing prospecting engineering based on data analysis. The method can be applied to an optimized prospecting engineering device based on data analysis, which can be, for example, Figure 6 The optimized prospecting engineering device 600 based on data analysis is shown, or the optimized prospecting engineering device based on data analysis can also be integrated in Figure 7 Functional modules in the electronic device 700 are shown.

[0076] like Figure 1 As shown, the method includes the following S101 to S104:

[0077] S101: Estimate the resource volume of the ore body using the original drill holes and the designed drill holes respectively to obtain the original estimated resource volume and the designed estimated resource volume. The designed drill holes include encrypted upgraded designed drill holes and peripheral newly added designed drill holes.

[0078] In order to optimize the prospecting project and thus improve the accuracy of resource estimation, the present application first estimates the resource volume of the ore body using the original borehole and the designed borehole respectively, obtains the original estimated resource volume and the designed estimated resource volume, and then uses data analysis to verify the correlation characteristics of the original estimated resource volume and the designed estimated resource volume; finally, if the verification fails, the designed borehole is optimized and adjusted to improve the accuracy of resource estimation. In this regard, the present application embodiment obtains the original estimated resource volume and the designed estimated resource volume through S101, making preliminary preparations for the subsequent correlation feature verification analysis.

[0079] In the embodiment of the present application, the block method is used to estimate the resource quantity of the ore body. First, the original drill hole is selected to estimate the resource quantity of a certain area of ​​the ore body using the block method, and the original estimated resource quantity of the control level of 160*160m can be obtained. Subsequently, the original engineering spacing can be upgraded from the control level of 160*160m to the proven level of 80*80m by encrypting and upgrading the design drill holes. The block method is also used to estimate the resource quantity to obtain the designed estimated resource quantity.

[0080] In the embodiment of the present application, the resource quantity can also be estimated by newly designed drilling holes in the periphery to obtain the designed estimated resource quantity. The specific implementation method will not be described in detail here.

[0081] In the embodiment of the present application, due to the use of encrypted upgraded design drilling, the engineering spacing can be upgraded from the control level of 160*160m to the proven level of 80*80m, which makes the resource volume change significantly. This also shows that the grade and thickness changes in this area have a greater impact on the resource volume. In order to improve the reliability of resource estimation, the embodiment of the present application needs to use data analysis to verify the correlation characteristics of the original estimated resource volume and the designed estimated resource volume, so as to evaluate the reliability of the resource volume.

[0082] S102: Use data analysis to verify the correlation characteristics between the original estimated resource quantities and the designed estimated resource quantities.

[0083] As an example, S102 may include: S1021, establishing the null hypothesis that there is no correlation between the original estimated resource quantities and the designed estimated resource quantities; S1022, calculating the mean square of each group of segment block data in the original estimated resource quantities and the designed estimated resource quantities; S1023, calculating the mean square of the error between the original estimated resource quantities and the designed estimated resource quantities; S1024, using the mean square of each group of segment block data and the mean square of the error to obtain a variance estimator; S1025, converting the variance estimator into a probability value, and judging whether the null hypothesis is valid based on the size of the probability value.

[0084] Among them, the above-mentioned data utilization method is to use the variance method to analyze and test the original estimated resource volume and the designed estimated resource volume, specifically to analyze and test the average grade of each block segment. For this, the original hypothesis is first established that there is no correlation between the original estimated resource volume and the designed estimated resource volume. The "no correlation" here actually means that from a statistical point of view, the use of designed drilling has not led to a significant change in the resource volume. Subsequently, it is necessary to calculate the probability value to infer whether the designed drilling has led to a significant change in the resource volume, so as to judge the accuracy of the above-mentioned resource estimation using the block segment method.

[0085] As an example, the above S1022 may include: calculating the sum of squares of each group of segment block data in the original estimated resource amount and the designed estimated resource amount; and calculating the mean square of each group of segment block data using the sum of squares of each group of segment block data.

[0086] The process of obtaining the sum of squares of each group of segment data may include: calculating the square of the difference between each segment data and the mean for each group of segment data, and adding the square values ​​to obtain the sum of squares of each group of segment data. In more complex statistical analysis, such as variance analysis, the calculation of the within-group sum of squares and the between-group sum of squares is involved to facilitate the evaluation of the impact of different factors on the overall variation. The formula for the sum of squares is as follows:

[0087]

[0088] Among them, SSTR represents the sum of squares, n j represents the number of segment block data of the jth group, x j represents the segment block data of the jth group, represents the overall average of all segment data, and k represents the number of groups.

[0089] The process of obtaining the mean square of each group of segment data may include: dividing the sum of squares of each group of data by the degree of freedom of the group of data (i.e., the number of data minus 1) to obtain the mean square. The degree of freedom here is used to reflect the uncertainty or variability of the data. The calculation of the mean square helps to eliminate the influence of the sample size on the variability measurement, thereby making the comparison between samples of different sizes more fair. The formula of the mean square is as follows:

[0090]

[0091] Among them, SSTR represents the sum of squares, k represents the number of segment block data in the group, and k-1 represents the degree of freedom.

[0092] As an example, the above S1023 may include: using the mean square of each group of segment block data to calculate the sum of square errors of each group of segment block data; and using the sum of square errors of each group of segment block data to calculate the mean square error.

[0093] The process of obtaining the sum of squared errors for each group of segment data may include: adding the mean squares of all group segment data to obtain the sum of squared errors. This value reflects the degree of variation between groups of data, that is, the degree of difference between each group of data and the overall mean. Calculating the sum of squared errors is actually a process for measuring the internal variability of the data. The larger the value of the sum of squared errors, the greater the variability of the data within the group even if the differences between groups are excluded; conversely, if the value of the sum of squared errors is small, it means that most of the variation can be explained by the differences between groups, and the data within the group is relatively consistent. The sum of squared errors is an important component used to evaluate the goodness of fit of the model in analysis of variance. The formula for the sum of squared errors is as follows:

[0094]

[0095] Among them, SSE represents the sum of squared errors, n j represents the number of segment block data of the jth group, x j represents the segment block data of the jth group, x ij It represents the value of the i-th segment block data in the j-th group, and k represents the number of groups.

[0096] The process of obtaining the above-mentioned mean square error may include: dividing the sum of squared errors by the total degrees of freedom (i.e., the number of all data minus 1) to obtain the mean square error. This value represents the average degree of variation of each group of data. Calculating the mean square error is a method in statistical analysis that is used to measure the internal variation of data in a standardized manner. The mean square error is calculated by dividing the sum of squared errors by the degrees of freedom of the errors. The degrees of freedom here usually refer to the total number of all data points minus the number of estimated parameters, usually minus 1 (because the mean value of each group has been estimated when calculating the sum of squared errors). The formula for the mean square error is as follows:

[0097]

[0098] Among them, MSE represents the mean square error, SSE represents the sum of square errors, n T Indicates the total number of segment block data, k indicates the number of segment block data in the group, n T -k represents the degrees of freedom.

[0099] As an example, S1024 may include: using the mean square of each group of segment data divided by the error mean square to obtain a variance estimator F. The significance of the F value is that it provides the ratio of inter-group variation to intra-group variation. If the F value is large, it means that the inter-group variation is large, which may indicate that there are substantial differences between different groups relative to the intra-group variation. On the contrary, if the F value is close to 1, it means that the inter-group variation is equivalent to the intra-group variation, suggesting that there may be no significant differences between the groups. Therefore, this application will evaluate whether the differences between different groups are significant by comparing variance estimators. In this way, the F statistic becomes a powerful tool for determining whether there are significant differences in data under different treatments or classifications, thereby helping us make data-based decisions. The specific formula for the variance estimator is as follows:

[0100]

[0101] Among them, F represents the variance estimator, MSTR represents the mean square, and MSE represents the mean square error.

[0102] As an example, S1025 may include: obtaining a corresponding probability P value by looking up the variance estimate F obtained above or converting it by software. If the probability value is greater than a preset value, the hypothesis is established to determine that the verification fails; if the probability value is less than the preset value, the null hypothesis is not established to determine that the verification passes. For example, if the value of P is less than the significance level of 0.05, the null hypothesis can be rejected (i.e., it is believed that there is a correlation between the original estimated resource quantity and the designed estimated resource quantity), otherwise the null hypothesis is not rejected.

[0103] In the embodiment of the present application, the P value verification shows that the original estimated resource and the designed estimated resource are reasonable. If the verification result shows that the P value is low, it means that the original estimated resource and the designed estimated resource are highly correlated, so there is no need to adjust the grade and thickness of the designed borehole. On the contrary, if the verification result shows that the P value is high, it means that the original estimated resource and the designed estimated resource are not highly correlated, indicating that the grade and thickness data in the designed borehole may be unreasonable.

[0104] S103: If the verification fails, the drilling design is optimized and adjusted.

[0105] As an example, S103 may include: if the probability value is greater than a preset value, the assumption is established to determine that the verification fails, indicating that the correlation between the original estimated resource volume and the designed estimated resource volume is not large, indicating that the grade and thickness data in the designed boreholes may be unreasonable, and therefore it is necessary to readjust the grade and thickness values ​​between the designed boreholes until the verification passes. That is, it is necessary to check the correlation between the boreholes and use the weighted average of the grade and thickness between different boreholes to re-estimate the grade and thickness of the designed boreholes until the P value verification passes.

[0106] It can be seen that the embodiment of the present application not only improves the accuracy of resource estimation by setting up designed drilling holes and utilizing variance analysis, but also verifies the effectiveness of encrypted upgrades or peripheral new designs through statistical methods, thereby providing more reliable data support for subsequent development plans.

[0107] In order to make the method provided in the embodiment of the present application clearer and easier to understand, Figure 2 A specific example of this method is described.

[0108] like Figure 2 As shown, the implementation may include:

[0109] S201: Estimate the resource volume of the ore body using the original drill holes and the designed drill holes respectively to obtain the original estimated resource volume and the designed estimated resource volume. The designed drill holes include encrypted upgraded designed drill holes and peripheral newly added designed drill holes.

[0110] In the embodiment of the present application, the block method is used to estimate the resource volume, which may specifically include: according to the core part of the x area, from the north to the south between the EW008, EW000 and EW007 lines, the (original drill hole) originally constructed drill hole is selected, and the block method is used to estimate the resource volume of the ore-bearing part of a certain ore body, and the ore volume of the controlled level of 160*160m is 7.6077 million tons, the lead grade is 6.65%, the lead metal content is 506,000 tons, the zinc grade is 24.93%, and the zinc metal content is 1.8964 million tons.

[0111] Similarly, the design drilling holes were selected to upgrade the original project spacing from the control level of 160*160m to the proven level of 80*80m. The block method was also used to estimate the resource volume, and the 80*80m control level ore volume was 6.8444 million tons, with a lead grade of 7.27%, a lead metal content of 497,300 tons, a zinc grade of 21.35%, and a zinc metal content of 1.4611 million tons. The specific data can be shown in Table 1 below.

[0112] Table 1 Statistics of resources at different spacings

[0113]

[0114] Schematic diagram of resource estimation at 160m (left) and 80m (right) intervals in this application example Figure 3 As shown, the specific resource quantities at different spacings are shown in Table 2 below.

[0115] Table 2 Specific statistics of resources at different spacings

[0116]

[0117] In this process, by comparing the resource volume data of the two estimates, it can be concluded that the resource volume has changed significantly. This shows that the change in grade thickness in this area has a greater impact on the resource volume, and it is necessary to design more exploration lines and drill holes to improve the resource level and the reliability of resource volume estimation. However, in order to determine whether this resource volume estimation is accurate, the variance method is used in the embodiment of this application to test the correlation between the original estimated resource volume and the designed estimated resource volume.

[0118] S202: Establish the null hypothesis that there is no correlation between the original estimated resource quantity and the designed estimated resource quantity.

[0119] The variance method is used in the embodiment of this application, so a hypothesis needs to be set first. The null hypothesis set in this application is that there is no correlation between the original estimated resource volume and the designed estimated resource volume, that is, the change of the designed drilling hole does not lead to a significant change in the resource volume. Therefore, the following steps need to calculate the probability value to determine whether to reject the null hypothesis.

[0120] S203: Calculate the square sum of each group of segment block data in the original estimated resource quantity and the designed estimated resource quantity.

[0121] First, it is necessary to calculate the sum of squares of each group of segment block data in the original estimated resource quantity and the designed estimated resource quantity. The specific calculation formula is shown in the above embodiment and will not be repeated here.

[0122] S204: Calculate the mean square of each group of segment block data using the sum of the squares of each group of segment block data.

[0123] The mean square needs to be processed subsequently, that is, the mean square of each group of segment block data is calculated using the sum of squares of each group of segment block data and the mean square formula. The specific calculation formula is shown in the above embodiment and will not be repeated here.

[0124] S205: Calculate the sum of square errors of each group of segment block data using the mean square of each group of segment block data.

[0125] After obtaining the mean square of each group of segment block data, it is necessary to calculate the sum of square errors of each group of segment block data. The sum of square errors of each group of segment block data can be calculated using the mean square and sum of square errors formula for each group of segment block data. The specific calculation formula is shown in the above embodiment and will not be repeated here.

[0126] S206: Calculate the mean square error using the sum of square errors of each group of segment block data.

[0127] After obtaining the error square sum of each group of segment block data, the error mean square can be calculated according to the error mean square formula. The specific calculation formula is shown in the above embodiment and will not be described in detail here.

[0128] S207: Obtain a variance estimate using the mean square and error mean square of each group of segment block data.

[0129] According to the mean square, error mean square and variance estimation formula of each group of segment block data, the variance estimation can be calculated. The specific calculation formula is shown in the above embodiment and will not be described here.

[0130] S208: Convert the variance estimator into a probability value.

[0131] Finally, in order to determine whether the original hypothesis is true, it is necessary to convert the variance estimator into a probability value. In the embodiment of the present application, the variance estimator can be converted into a probability value by looking up a table and using software. The variance analysis results obtained in the embodiment of the present application are as follows: Figure 4 As shown, it lists the sum of squares from regression and the sum of squares from error under SS; the mean square from regression and the mean square from error under MS; F represents the variance estimator, and P represents the probability value. Among them, Figure 4 The figure on the left is a schematic diagram of the variance analysis results of the average grade. Figure 4 The figure on the right in the middle is a schematic diagram of the variance analysis results of projection thickness.

[0132] In the present application, Figure 4 The variance analysis results of the average grade in the figure show that the P value is 0.004, and the variance analysis results of the projection thickness show that the P value is 0.000. The specific fitting diagram is shown in Figure 5 As shown, Figure 5 The left side shows a linear fit before and after the designed drilling, and Figure 5 The right side shows the quadratic linear fit before and after the designed drilling.

[0133] S209: Determine whether the probability value is greater than a preset value.

[0134] In the embodiment of the present application, the preset value is set to 0.05, that is, if the probability value P is greater than 0.05, the null hypothesis is considered to be true, that is, the original estimated resource quantity and the designed estimated resource quantity have no correlation, and S310 is executed; if the probability value P is less than 0.05, the null hypothesis is considered to be false, that is, the original estimated resource quantity and the designed estimated resource quantity have a correlation, and S311 is executed.

[0135] S210: If the probability value is greater than the preset value, the original hypothesis is established to determine that the verification fails, and the grade thickness value between the designed drill holes is readjusted until the verification passes.

[0136] If the probability value is greater than the preset value, the original hypothesis is established to determine that the verification fails. Failure of the verification indicates that the designed drilling design is unreasonable and needs to be readjusted, that is, the grade thickness value between the designed drilling holes needs to be adjusted until the verification passes.

[0137] S211: If the probability value is less than the preset value, the original hypothesis is not established, so as to determine whether the verification is passed.

[0138] The variance analysis results of the average grade in the examples of this application show that the P value is 0.004, and the variance analysis results of the projected thickness show that the P value is 0.000, both of which are less than 0.05, indicating that the original estimated resource volume and the designed estimated resource volume have a strong correlation. This proves that the designed values ​​of the average grade and projected thickness of the designed borehole are reasonable, further verifying the reliability of the resource volume estimation in the examples of this application.

[0139] This embodiment provides a method for optimizing prospecting engineering based on data analysis. By designing boreholes, for example, the control level of the original 160*160m can be upgraded to the proven level of 80*80m by encrypting and upgrading the design boreholes. This means that after the encrypted upgrade design, the distance between the boreholes is closer, and the changes inside the ore body can be captured more finely, thereby improving the accuracy of resource estimation. The designed boreholes can more accurately reflect the changes in resource estimation, thereby reducing the estimation error caused by the large borehole spacing. In addition, the present invention also verifies the correlation of resource data before and after the designed boreholes by variance analysis. The results show that the resource data before and after the designed boreholes have a strong correlation (the P value of the average grade is 0.004, and the P value of the projected thickness is 0.000), which is far below the significance level of 0.05, which further proves the reliability of resource estimation after the designed boreholes. The resource estimation results after the designed boreholes are closer to the actual situation, providing a more reliable decision-making basis for prospecting projects, and helping to plan subsequent mining activities more reasonably. In summary, the embodiments of the present application effectively improve the accuracy of resource estimation and provide important technical support for prospecting projects.

[0140] See also Figure 6 The embodiment of the present application provides an optimized prospecting engineering device 600 based on data analysis, and the device includes:

[0141] An obtaining unit 601 is used to estimate the resource volume of the ore body by using the original drill hole and the designed drill hole respectively, and obtain the original estimated resource volume and the designed estimated resource volume, wherein the designed drill hole includes the infilled upgraded designed drill hole and the peripheral newly added designed drill hole;

[0142] A verification unit 602, used to verify the correlation characteristics between the original estimated resource quantity and the designed estimated resource quantity by using data analysis;

[0143] The adjustment unit 603 is used to optimize and adjust the drilling design if the verification fails.

[0144] Optionally, the verification unit 602 includes:

[0145] Establishing subunits for establishing the null hypothesis that there is no correlation between the original estimated resource volume and the designed estimated resource volume;

[0146] A calculation subunit, used for calculating the mean square of each group of segment block data in the original estimated resource quantity and the designed estimated resource quantity;

[0147] A calculation subunit, used for calculating the mean square error between the original estimated resource quantity and the designed estimated resource quantity;

[0148] A processing subunit is used for obtaining a variance estimate by using the mean square and the error mean square of each group of segment block data;

[0149] The judgment subunit is used to convert the variance estimator into a probability value and judge whether the original hypothesis is valid according to the size of the probability value.

[0150] Optionally, the judging subunit is specifically used for:

[0151] If the probability value is greater than the preset value, the null hypothesis is established to determine that the verification fails;

[0152] If the probability value is less than the preset value, the original hypothesis is not true, so the verification is confirmed to be successful.

[0153] Optionally, when the calculation subunit calculates the mean square of each group of segment block data in the original estimated resource quantity and the designed estimated resource quantity, it is specifically used to:

[0154] Calculate the sum of squares of each group of segment block data in the original estimated resource volume and the designed estimated resource volume;

[0155] The mean square of each group of segment block data is calculated using the sum of the squares of each group of segment block data.

[0156] Optionally, when the calculation subunit calculates the mean square error between the original estimated resource quantity and the designed estimated resource quantity, it is specifically used to:

[0157] The sum of square errors of each group of segment data is calculated using the mean square of each group of segment data;

[0158] The mean square error is calculated using the sum of squared errors of each group of segment data.

[0159] Optionally, the adjusting unit 603 is specifically configured to:

[0160] Readjust the grade thickness values ​​between the designed drill holes until they are verified.

[0161] Optionally, the method of estimating the resource volume of the ore body using the original drill hole and the designed drill hole respectively is the block method.

[0162] It should be noted that the specific implementation method and effect of the optimized prospecting engineering device 600 based on data analysis can be found in the above Figure 1 or Figure 2 The relevant descriptions in the provided methods will not be repeated here.

[0163] The present application embodiment also provides an electronic device 700, such as Figure 7 As shown, the device 700 includes a memory 701 and a processor 702:

[0164] The memory 701 is used to store computer programs;

[0165] The processor 702 is used to execute the above Figure 1 or Figure 2The method provided.

[0166] In addition, the present application also provides a computer-readable storage medium, the computer-readable storage medium is used to store a computer program, and the computer program is used to execute Figure 1 or Figure 2 The method provided.

[0167] Through the description of the above implementation methods, it can be known that those skilled in the art can clearly understand that all or part of the steps in the above-mentioned embodiment method can be implemented by means of software plus a general hardware platform. Based on such an understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a storage medium, such as a read-only memory (ROM) / RAM, a magnetic disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network communication device such as a router) to execute the methods described in each embodiment of the present application or some parts of the embodiments.

[0168] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The device embodiment described above is only schematic, in which the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the goals of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative work.

[0169] The above description is merely an exemplary embodiment of the present application and is not intended to limit the protection scope of the present application.

Claims

1. An optimized prospecting engineering method based on data analysis, characterized in that: The method comprises: The resource volume of the ore body is estimated by using the original drill holes and the designed drill holes respectively to obtain the original estimated resource volume and the designed estimated resource volume, wherein the designed drill holes include the infilled upgraded designed drill holes and the peripheral newly added designed drill holes; Verifying the correlation characteristics between the original estimated resource volume and the designed estimated resource volume by using data analysis; If the verification fails, the designed drilling is optimized and adjusted.

2. The method according to claim 1, characterized in that The method of verifying the correlation characteristics between the original estimated resource quantity and the designed estimated resource quantity by using data analysis includes: Establishing the null hypothesis that there is no correlation between the original estimated resource volume and the designed estimated resource volume; Calculating the mean square of each group of segment block data in the original estimated resource quantity and the designed estimated resource quantity; Calculate the mean square error between the original estimated resource quantity and the designed estimated resource quantity; Obtain a variance estimate using the mean square of each group of segment block data and the mean square of the error; The variance estimate is converted into a probability value, and whether the original hypothesis is established is determined based on the size of the probability value.

3. The method according to claim 2, characterized in that The determining whether the original hypothesis is established according to the size of the probability value includes: If the probability value is greater than a preset value, the null hypothesis is established, so as to determine that the verification fails; If the probability value is less than the preset value, the original hypothesis is not established, so as to determine that the verification is passed.

4. The method according to claim 1, characterized in that: The calculating the mean square of each group of segment block data in the original estimated resource quantity and the designed estimated resource quantity comprises: Calculating the sum of squares of each group of segment block data in the original estimated resource quantity and the designed estimated resource quantity; The mean square of each group of segment block data is calculated using the sum of squares of each group of segment block data.

5. The method according to claim 4, characterized in that The calculating the mean square error between the original estimated resource quantity and the designed estimated resource quantity comprises: Calculating the sum of square errors of each group of segment block data using the mean square of each group of segment block data; The error mean square is calculated using the error square sum of each group of segment block data.

6. The method according to claim 1, characterized in that If the verification fails, optimizing and adjusting the designed drilling includes: The grade thickness values ​​between the designed drill holes are readjusted until the verification is passed.

7. The method according to claim 1, characterized in that The method of estimating the resource volume of the ore body using the original drill hole and the designed drill hole respectively is the block method.

8. An optimized prospecting engineering method based on data analysis, characterized in that: The method comprises: An acquisition unit is used to estimate the resource volume of the ore body by using the original drill hole and the designed drill hole respectively, and obtain the original estimated resource volume and the designed estimated resource volume, wherein the designed drill hole includes the infill upgraded designed drill hole and the peripheral newly added designed drill hole; A verification unit, used for verifying the correlation characteristics between the original estimated resource quantity and the designed estimated resource quantity by using data analysis; An adjustment unit is used to optimize and adjust the designed drilling if the verification fails.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, wherein the processor is used to execute a program stored in the memory to run the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store a computer program, and the computer program is used to execute the method according to any one of claims 1 to 7.

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