Geochemical abnormal data management system and method for gold mine exploration

Abstract

The invention discloses a geomaterialization abnormal data management system and method for gold mine exploration, and relates to the technical field of data analysis. The system comprises a historical data management module, a feature extraction and analysis module, a validity evaluation model construction module and a real-time data acquisition and validity evaluation module. The historical data management module divides historical geomorphic abnormal data into historical valid data and historical invalid data; the feature extraction and analysis module analyzes deviation between historical valid data and historical invalid data, and extracts and marks valid key features; the effectiveness evaluation model construction module constructs an effectiveness comprehensive evaluation model based on the effectiveness key features, and calculates an effectiveness comprehensive evaluation index threshold; the real-time data acquisition and validity evaluation module acquires real-time geochemical abnormal data, extracts validity key features, calculates a real-time validity comprehensive evaluation index and identifies real-time valid data.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and in particular to a geophysical and chemical anomaly data management system and method for gold mine exploration. Background Art

[0002] Gold exploration plays a vital role in mineral resource exploration, especially in the research of gold mineralization and prospecting models, where significant progress has been made. With the continuous increase in exploration depth, the detection depth of traditional surface geophysical and geochemical exploration methods has been gradually limited, gradually exposing its limitations in the exploration of deep ore bodies or concealed ore bodies. The effectiveness of surface abnormal signals in deep ore body detection has significantly decreased, and traditional geophysical and geochemical exploration methods are difficult to provide sufficient information to support this process. The detection and exploration of deep ore bodies and concealed ore bodies usually require more refined and in-depth geological, geophysical and geochemical information. In this regard, deep drilling provides more direct and effective prospecting information for gold exploration, especially when detecting deep ore bodies, the measuring points in the well are closer to the target body, which can obtain stronger response signals and have strong anti-interference ability. Therefore, the application of deep drilling geophysical and geochemical exploration technology has become a research hotspot in the field of gold exploration, especially in deep detection, the advantages of well geophysical and geochemical exploration technology are becoming more and more prominent.

[0003] However, the geophysical and chemical anomaly data obtained through deep drilling geophysical and chemical exploration technology is affected by many factors, which requires exploration personnel to rely on a lot of experience and repeated verification to accurately define the abnormal data area. At present, most gold mine exploration systems still rely on traditional data analysis methods, which often lack intelligent and automated analysis tools. Therefore, for the analysis of the effectiveness of geophysical and chemical anomaly data, it is urgent to introduce advanced intelligent analysis methods to improve analysis efficiency and accuracy. Summary of the invention

[0004] The object of the present invention is to provide a geophysical and chemical anomaly data management system and method for gold mine exploration to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: A method for managing geophysical and chemical anomaly data for gold mine exploration comprises the following steps: Step S100. Obtain historical geophysical anomaly data and historical event records in the area where the gold exploration task has been completed from the database, and divide the historical geophysical anomaly data into historical valid data and historical invalid data according to the historical event records; Step S200. Analyze the deviation relationship between historical valid data and historical invalid data, thereby identifying the distinguishing features between the two, and mark the distinguishing features between the historical valid data and the historical invalid data as key features of effectiveness; Step S300. Based on the key features of effectiveness, combined with historical effective data and historical invalid data, a comprehensive effectiveness evaluation model of geophysical and chemical anomaly data is constructed to obtain a comprehensive effectiveness evaluation index; based on the historical effective data and historical invalid data, the corresponding comprehensive effectiveness evaluation index is calculated to obtain a comprehensive effectiveness evaluation threshold; Step S400. Collect real-time geophysical and chemical anomaly data of the gold mining exploration area, and extract key features of effectiveness from the real-time geophysical and chemical anomaly data; calculate the real-time effectiveness comprehensive evaluation index based on the key features of effectiveness and in combination with the effectiveness comprehensive evaluation model; compare the real-time effectiveness comprehensive evaluation index with the effectiveness comprehensive evaluation threshold, and extract real-time effective data from the real-time geophysical and chemical anomaly data based on the comparison result.

[0006] Furthermore, step S100 includes: The historical event record refers to the historical geophysical anomaly data in the area where the gold mine exploration task has been completed, and the record information is used to judge whether the historical geophysical anomaly data is valid by comparing the actual usage of the historical geophysical anomaly data; according to the historical event record, the historical geophysical anomaly data is divided into historical valid data and historical invalid data, and a historical valid data set A and a historical invalid data set B are constructed, wherein the historical valid data set A and the historical invalid data set B are respectively expressed as: A={a1,a2,...,an}, B={b1,b2,...,bm}, wherein A represents the historical valid data set, a1 represents the first element in the historical valid data set A, a2 represents the second element in the historical valid data set A, and so on, an represents the nth element in the historical valid data set A; B represents the historical invalid data set, b1 represents the first element in the historical invalid data set B, b2 represents the second element in the historical invalid data set B, and so on, bm represents the mth element in the historical invalid data set B.

[0007] Among them, historical geophysical and chemical anomaly data refers to data detected in the past geophysical, geochemical and geological exploration processes that are significantly different from the normal background values ​​at that time, and the geophysical and chemical anomaly data specifically include geological exploration anomaly data, geophysical anomaly data and geochemical anomaly data; the geological exploration anomaly data refers to data discovered in the geological exploration process that are significantly different from the surrounding geological environment or expected geological characteristics, which may include sudden changes in rock types, abnormal formation structures, sudden changes in geological structures, etc. These data often reveal the complexity of underground geological structures or the existence of special geological events; the geophysical anomaly data refers to data detected in geophysical exploration by measuring geophysical fields (such as gravity fields, magnetic fields, electric fields) , seismic wave field, etc.) that are significantly different from the normal background value. These data may manifest as gravity anomalies, magnetic anomalies, electrical anomalies, or abnormal changes in parameters such as seismic wave velocity and attenuation. They are usually related to the physical properties of underground geological bodies (such as density, magnetism, conductivity, etc.), and provide important information for inferring underground geological structures; the geochemical anomaly data refers to data that are significantly different from the normal background value found by analyzing the content of elements or compounds in surface or underground samples in geochemical exploration. These data may indicate the enrichment or depletion of certain elements or compounds, and are usually related to the existence of underground ore bodies, the activity of geological structures, or the migration of geochemical elements, and are important clues in mineral exploration and geological research.

[0008] Further, step S200 includes: S201. Based on the historical valid data set A and the historical invalid data set B, feature extraction is performed on each element in the historical valid data set A and the historical invalid data set B, and normalization is performed, so that each element in the historical valid data set A and the historical invalid data set B is represented as a feature vector, and is represented as: V_ai=[vai_1,vai_2,...,vai_p],V_bi=[vbi_1,vbi_2,...,vbi_p]; Among them, ai represents the i-th element in the historical valid data set A, and i ranges from 1 to n; V_ai represents the eigenvector corresponding to the i-th element in the historical valid data set A, vai_1 represents the eigenvalue of the first dimension of the eigenvector V_ai, vai_2 represents the eigenvalue of the second dimension of the eigenvector V_ai, and so on, vai_p represents the eigenvalue of the p-th dimension of the eigenvector V_ai, and p is the characteristic dimension, covering multiple indicators such as geology, geophysics, and geochemistry; similarly, V_bi represents the eigenvector corresponding to the i-th element in the historical invalid data set B, vbi_1 represents the eigenvalue of the first dimension of the eigenvector V_bi, vbi_2 represents the eigenvalue of the second dimension of the eigenvector V_bi, and so on, vbi_p represents the eigenvalue of the p-th dimension of the eigenvector V_ai; Due to the significant characteristics of geophysical anomaly data such as high dimensionality and spatial distribution, commonly used algorithms include PCA, ICA, wavelet transform, autoencoder, SVM, K-means clustering and neural network. The specific algorithm to be chosen depends on the characteristics of the data (such as noise, dimension, spatial distribution, nonlinearity, etc.) and the requirements of the specific task. For example, if the data contains a lot of noise and requires dimensionality reduction, PCA or ICA may be more appropriate; if it is necessary to capture multi-scale anomalies, wavelet transform may be more effective; for complex nonlinear relationships, neural networks and autoencoders may be better choices; the specific algorithm should be selected by the relevant personnel.

[0009] S202. For each element in the historical valid data set A, the eigenvalues ​​of each dimension of the eigenvectors V_ax and V_ay corresponding to two different elements ax and ay are calculated in turn, so as to obtain the minimum and maximum absolute values ​​of the difference of the eigenvalues ​​of each dimension of the eigenvectors in the historical valid data set A, which are respectively expressed as: minΔvai_t and maxΔvai_t, where i ranges from 1 to n, and t ranges from 1 to p; the eigenvalues ​​of each dimension of the eigenvector V_bi corresponding to each element in the historical invalid data set B are calculated in turn with the eigenvalues ​​of the corresponding dimension of V_ai corresponding to each element in the historical valid data set A, so as to obtain the absolute value of the difference Δvi_t; the absolute value of the difference Δvi_t of each dimension of the eigenvector V_bi corresponding to each element in the historical invalid data set B is summarized, so as to form the difference Set Ct, and Ct={Δv1_t,Δv2_t,...,Δvn_t}, where Δv1_t represents the absolute value of the difference between the eigenvalue of the tth dimension in the eigenvector V_bi of the corresponding element in the historical invalid data set B and the eigenvalue of the tth dimension in the eigenvector V_a1 corresponding to the first element in the historical valid data set A, Δv2_t represents the absolute value of the difference between the eigenvalue of the tth dimension in the eigenvector V_bi of the corresponding element in the historical invalid data set B and the eigenvalue of the tth dimension in the eigenvector V_a2 corresponding to the second element in the historical valid data set A, and so on, Δvn_t represents the absolute value of the difference between the eigenvalue of the tth dimension in the eigenvector V_bi of the corresponding element in the historical invalid data set B and the eigenvalue of the tth dimension in the eigenvector V_an corresponding to the nth element in the historical valid data set A; S203. For each element in the historical invalid data set B corresponding to the difference set Ct, compare the minimum minΔvai_t and the maximum maxΔvai_t of the absolute value of the difference between each element in the difference set Ct and the eigenvalue of the corresponding dimension of the eigenvector in the historical valid data set A, and count the number N of elements in the difference set Ct that do not belong to the interval [minΔvai_t, maxΔvai_t]. If N / n≥d, the features corresponding to the dimensions of the difference set Ct are used as the distinguishing features between the historical valid data and the historical invalid data, where d represents the ratio threshold; for each element in the historical invalid data set B corresponding to the difference set Ct, the corresponding distinguishing features are summarized to form a distinguishing feature set Qt; perform intersection calculation on all distinguishing feature sets Qt, and mark the distinguishing features corresponding to the intersection calculation results as validity key features.

[0010] Furthermore, step S300 includes: S301. For the key features of effectiveness, search in the feature vectors corresponding to the historical valid data and the historical invalid data, find the eigenvalues ​​of the key features in the eigenvectors corresponding to the historical valid data and the historical invalid data, and calculate the average absolute value of the difference of the eigenvalues ​​of the dimensions corresponding to the key features of effectiveness, which are expressed as: sk and mk, respectively, where sk represents the average absolute value of the difference of the eigenvalues ​​of the dimensions corresponding to the key features of effectiveness in the historical valid data set A, and mk represents the average absolute value of the difference of the eigenvalues ​​of the dimensions corresponding to the key features of effectiveness in the historical invalid data set B; calculate the weight coefficient wk of the key features of effectiveness, and the specific calculation formula is: wk=|sk-mk| / σk, where σk represents the standard deviation of the eigenvalues ​​corresponding to the key features of effectiveness k; traverse all key features of effectiveness to obtain the weight coefficients corresponding to all key features of effectiveness; S302. According to the weight coefficients corresponding to each key feature of effectiveness, combined with the eigenvalues ​​corresponding to the key features of effectiveness, a comprehensive evaluation model for the effectiveness of geophysical anomaly data is constructed, and the corresponding calculation formula is: E=∑ R k=1 (wk×vk); wherein E represents the effectiveness comprehensive evaluation index, R represents the number of effectiveness key features, vk represents the eigenvalue corresponding to the kth effectiveness key feature, and the eigenvalue corresponding to the effectiveness key feature is found from the historical effective data set A or the historical invalid data set B; according to the effectiveness comprehensive evaluation model, the effectiveness comprehensive evaluation index E corresponding to each element in the historical effective data set A and the historical invalid data set B is calculated, and according to the division of the historical effective data set A and the historical invalid data set B, the effectiveness comprehensive evaluation index set EA of the historical effective data set A and the effectiveness comprehensive evaluation index set EB of the historical invalid data set B are obtained respectively, and the effectiveness comprehensive evaluation index threshold E0 is obtained according to the effectiveness comprehensive evaluation index set EA and the effectiveness comprehensive evaluation index set EB; according to the effectiveness comprehensive evaluation index threshold E0, it is judged whether the effectiveness comprehensive evaluation model needs to be adjusted, and corresponding adjustments are made according to the judgment result.

[0011] Furthermore, the effectiveness comprehensive evaluation index threshold E0 is obtained according to the effectiveness comprehensive evaluation index set EA and the effectiveness comprehensive evaluation index set EB. The specific analysis content is as follows: The values ​​corresponding to the effectiveness comprehensive evaluation index set EA and the effectiveness comprehensive evaluation index set EB are represented on the number axis, and the intersection of the effectiveness comprehensive evaluation index set EA and the effectiveness comprehensive evaluation index set EB is calculated. If the intersection calculation result is an empty set, the minimum value EA_min and the maximum value EA_max in the effectiveness comprehensive evaluation index set EA, as well as the minimum value EB_min and the maximum value EB_max in the effectiveness comprehensive evaluation index set EB are extracted; if EA_min>EB_max, EA_min is used as the effectiveness comprehensive evaluation index threshold value E0; if EA_max<EB_min, EA_max is used as the effectiveness comprehensive evaluation index threshold value E0; If the intersection calculation result is not an empty set, compare the maximum value EA_max in the effectiveness comprehensive evaluation index set EA with the maximum value EB_max in the effectiveness comprehensive evaluation index set EB. If EA_max≤EB_max, then the elements in the intersection area are taken as the pending effectiveness comprehensive evaluation index threshold E1. Under the pending effectiveness comprehensive evaluation index threshold E1, the corresponding values ​​of true positive TP, false positive FP, true negative TN and false negative FN are obtained in turn, where true positive TP represents the number of samples successfully and correctly classified as effectiveness comprehensive evaluation index in the intersection area; false positive FP represents the number of samples misclassified as effectiveness comprehensive evaluation index in the intersection area; true negative TN represents the number of samples successfully and correctly classified as invalid comprehensive evaluation index outside the intersection area. ; False negative FN represents the number of samples that are misjudged as invalid comprehensive evaluation index outside the intersection area; Calculate the true rate TPR and false positive rate FPR, where the specific calculation formula is: TPR=TP / (TP+FN), FPR=FP / (FP+TN); Summarize the true rate TPR and false positive rate FPR under each pending effectiveness comprehensive evaluation index threshold E1, draw the ROC curve, and the abscissa of the ROC curve is FPR, and the ordinate is TPR; Select the pending effectiveness comprehensive evaluation index threshold E1 corresponding to the point farthest from the (0,1) point in the ROC curve as the effectiveness comprehensive evaluation index threshold E0; Similarly, if EB_max≤EA_max, analyze according to the analysis process under the case of EA_max≤EB_max, so as to obtain the effectiveness comprehensive evaluation index threshold E0; According to the effectiveness comprehensive evaluation index threshold E0, it is judged whether the effectiveness comprehensive evaluation model needs to be adjusted, and corresponding adjustments are made according to the judgment results. The specific contents are as follows: When the intersection result of the effectiveness comprehensive evaluation index set EA and the effectiveness comprehensive evaluation index set EB is an empty set, the current effectiveness comprehensive evaluation model does not need to be adjusted; when the intersection result of the effectiveness comprehensive evaluation index set EA and the effectiveness comprehensive evaluation index set EB is not an empty set, and the intersection calculation results are both true subsets of the effectiveness comprehensive evaluation index set EA and the effectiveness comprehensive evaluation index set EB, the current effectiveness comprehensive evaluation model needs to be adjusted, and the specific adjustment process is as follows: The adaptive adjustment mechanism is used to dynamically adjust the weight coefficient of the key features of effectiveness. The corresponding specific calculation formula is: w'k=αk×wk+βk, where αk and βk represent the adjustment factor and bias term corresponding to the key feature k of effectiveness respectively; the weight coefficients of all key features of effectiveness are traversed to obtain several adjusted weight coefficients of key features of effectiveness, and normalized to ensure that the sum of the weight coefficients of all adjusted key features of effectiveness is equal to 1; and the expression of the adjusted comprehensive effectiveness evaluation model is: E_end=∑ R k=1 (w'k×vk); According to the adjusted effectiveness comprehensive evaluation model, the effectiveness comprehensive evaluation index E_end corresponding to each element in the historical valid data set A and the historical invalid data set B is calculated, so as to obtain the corresponding effectiveness comprehensive evaluation index set EA_end and the effectiveness comprehensive evaluation index set EB_end, and the intervals represented by the adjusted effectiveness comprehensive evaluation index set EA_end and the effectiveness comprehensive evaluation index set EB_end on the number axis do not have an intersection, so as to obtain the corresponding effectiveness comprehensive evaluation index threshold E0.

[0012] Furthermore, the specific analysis process of comparing the real-time effectiveness comprehensive evaluation index with the effectiveness comprehensive evaluation threshold in step S400 and extracting real-time effective data from the real-time geophysical abnormal data according to the comparison result is as follows: When the intersection of the effectiveness comprehensive evaluation index set EA and the effectiveness comprehensive evaluation index set EB is an empty set, and EA_min>EB_max, EA_min is used as the effectiveness comprehensive evaluation index threshold E0; the real-time effectiveness comprehensive evaluation index E' is compared with the effectiveness comprehensive evaluation index threshold E0, and when E'≥E0, the real-time geomaterialized abnormal data is real-time valid data; when the intersection of the effectiveness comprehensive evaluation index set EA and the effectiveness comprehensive evaluation index set EB is an empty set, and EA_max<EB_min, EA_max is used as the effectiveness comprehensive evaluation index threshold E0; the real-time effectiveness comprehensive evaluation index E' is compared with the effectiveness comprehensive evaluation index threshold E0, and when E'≤E0, the real-time geomaterialized abnormal data is real-time valid data; When the intersection of the effectiveness comprehensive evaluation index set EA and the effectiveness comprehensive evaluation index set EB is not an empty set, and EA_max≤EB_max; compare the real-time effectiveness comprehensive evaluation index E' with the effectiveness comprehensive evaluation index threshold E0, and when E'≤E0, the real-time geo-materialized abnormal data is real-time valid data; when the intersection of the effectiveness comprehensive evaluation index set EA and the effectiveness comprehensive evaluation index set EB is not an empty set, and EB_max≤EA_max, compare the real-time effectiveness comprehensive evaluation index E' with the effectiveness comprehensive evaluation index threshold E0, and when E'≥E0, the real-time geo-materialized abnormal data is real-time valid data.

[0013] A geophysical and chemical anomaly data management system for gold mine exploration, comprising: a historical data management module, a feature extraction and analysis module, a validity evaluation model construction module, and a real-time data acquisition and validity evaluation module; The historical data management module obtains historical geophysical and chemical anomaly data and historical event records in the completed gold mine exploration task area from the database, and divides the historical geophysical and chemical anomaly data into historical valid data and historical invalid data according to the historical event records; The feature extraction and analysis module analyzes the deviation relationship between historical valid data and historical invalid data, thereby identifying the distinguishing features between the two, and marking the distinguishing features between the historical valid data and the historical invalid data as key features of effectiveness; The effectiveness evaluation model building module builds a comprehensive effectiveness evaluation model for geophysical and chemical anomaly data based on the key effectiveness features and combines historical effective data with historical invalid data, thereby obtaining a comprehensive effectiveness evaluation index; based on historical effective data and historical invalid data, the corresponding comprehensive effectiveness evaluation index is calculated, thereby obtaining a comprehensive effectiveness evaluation threshold; The real-time data collection and effectiveness evaluation module collects real-time geophysical and chemical anomaly data in the gold mining exploration area, extracts key effectiveness features from the real-time geophysical and chemical anomaly data; calculates the real-time effectiveness comprehensive evaluation index based on the key effectiveness features and combined with the effectiveness comprehensive evaluation model; compares the real-time effectiveness comprehensive evaluation index with the effectiveness comprehensive evaluation threshold, and extracts real-time effective data from the real-time geophysical and chemical anomaly data based on the comparison results.

[0014] The historical data management module includes a historical data acquisition unit and a historical data division and classification unit; The historical data acquisition unit obtains the historical geophysical anomaly data and the corresponding historical event records in the area where the gold mine exploration task has been completed from the database; the historical data division and classification unit judges the validity of the geophysical anomaly data based on the historical event records, divides the historical geophysical anomaly data into valid data and invalid data, and constructs historical valid data sets and historical invalid data sets.

[0015] The feature extraction and analysis module includes a feature extraction and normalization unit and a feature difference analysis unit; The feature extraction and normalization unit extracts various features for each element in the historical valid data set and the historical invalid data set, and performs normalization processing to convert them into feature vectors; the feature difference analysis unit analyzes the feature differences between the historical valid data set and the invalid data set, thereby identifying the distinguishing features between the two and marking them as key features of validity.

[0016] The effectiveness evaluation model building module includes a feature weight calculation unit and a comprehensive evaluation model building unit; The feature weight calculation unit calculates the weight coefficient of each key feature of effectiveness based on the difference between the key features of effectiveness in historical valid data and invalid data; the comprehensive evaluation model construction unit constructs a comprehensive evaluation model of the effectiveness of geophysical and chemical anomaly data using the weight coefficients of each key feature of effectiveness; the comprehensive evaluation index of effectiveness corresponding to the historical geophysical and chemical anomaly data is calculated through the comprehensive evaluation model of effectiveness, and the comprehensive evaluation threshold of effectiveness is determined; The real-time data collection and effectiveness evaluation module includes a real-time data collection unit and a real-time effectiveness evaluation unit; The real-time data collection unit collects real-time geophysical and chemical anomaly data in the gold mine exploration area and extracts the key features of the effectiveness of the real-time geophysical and chemical anomaly data; the real-time effectiveness evaluation unit calculates the real-time effectiveness comprehensive evaluation index based on the key features of the effectiveness of the real-time data, and compares it with the effectiveness comprehensive evaluation threshold to identify real-time effective data.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: through the deviation analysis between historical valid data and historical invalid data, the present invention can accurately distinguish the key features between the two, and use the key features of effectiveness to build a comprehensive effectiveness evaluation model, avoiding the problem of relying on experience judgment and repeated verification in traditional methods, and improving the accuracy of data judgment. Through feature extraction and normalization processing, each abnormal data is represented as a feature vector, and difference calculation is performed, so as to identify the distinguishing features between abnormal data and normal data, and use these distinguishing features to build a comprehensive effectiveness evaluation model; this multi-dimensional feature analysis, combined with weighted effectiveness key features, can more accurately judge the effectiveness of abnormal data, unlike the traditional method that relies solely on experience judgment. By calculating and analyzing the intersection of the effectiveness comprehensive evaluation index set of historical data, and using the ROC curve to select the best comprehensive effectiveness evaluation index threshold, this method is more automated and intelligent than the traditional method, and can dynamically adjust the effectiveness judgment threshold according to real-time data, reducing manual intervention. For the geophysical and chemical abnormal data obtained in real time during gold mine exploration, the present invention can extract key features in real time, and compare the constructed comprehensive effectiveness evaluation model with the threshold, automatically identify and extract valid data, and provide real-time support for exploration decisions. The present invention can help surveyors to more scientifically judge the validity of geophysical and chemical anomaly data through intelligent analysis methods, thereby effectively avoiding misjudgment and missed judgment, improving the scientificity and accuracy of gold mine exploration decisions, and reducing prospecting costs and risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 The present invention is a module schematic diagram of a geophysical and chemical anomaly data management system for gold mine exploration. DETAILED DESCRIPTION

[0019] 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.

[0020] See also Figure 1 , the present invention provides a technical solution: A geophysical and chemical anomaly data management system for gold mine exploration, comprising: a historical data management module, a feature extraction and analysis module, a validity evaluation model construction module, and a real-time data acquisition and validity evaluation module; The historical data management module obtains historical geophysical and chemical anomaly data and historical event records in the completed gold mine exploration task area from the database, and divides the historical geophysical and chemical anomaly data into historical valid data and historical invalid data according to the historical event records; The feature extraction and analysis module analyzes the deviation relationship between historical valid data and historical invalid data, thereby identifying the distinguishing features between the two, and marking the distinguishing features between the historical valid data and the historical invalid data as key features of effectiveness; The effectiveness evaluation model building module builds a comprehensive effectiveness evaluation model for geophysical and chemical anomaly data based on the key effectiveness features and combines historical effective data with historical invalid data, thereby obtaining a comprehensive effectiveness evaluation index; based on historical effective data and historical invalid data, the corresponding comprehensive effectiveness evaluation index is calculated, thereby obtaining a comprehensive effectiveness evaluation threshold; The real-time data collection and effectiveness evaluation module collects real-time geophysical and chemical anomaly data in the gold mining exploration area, extracts key effectiveness features from the real-time geophysical and chemical anomaly data; calculates the real-time effectiveness comprehensive evaluation index based on the key effectiveness features and combined with the effectiveness comprehensive evaluation model; compares the real-time effectiveness comprehensive evaluation index with the effectiveness comprehensive evaluation threshold, and extracts real-time effective data from the real-time geophysical and chemical anomaly data based on the comparison results.

[0021] The historical data management module includes a historical data acquisition unit and a historical data division and classification unit; The historical data acquisition unit obtains the historical geophysical anomaly data and the corresponding historical event records in the area where the gold mine exploration task has been completed from the database; the historical data division and classification unit judges the validity of the geophysical anomaly data based on the historical event records, divides the historical geophysical anomaly data into valid data and invalid data, and constructs historical valid data sets and historical invalid data sets.

[0022] The feature extraction and analysis module includes a feature extraction and normalization unit and a feature difference analysis unit; The feature extraction and normalization unit extracts various features for each element in the historical valid data set and the historical invalid data set, and performs normalization processing to convert them into feature vectors; the feature difference analysis unit analyzes the feature differences between the historical valid data set and the invalid data set, thereby identifying the distinguishing features between the two and marking them as key features of validity.

[0023] The effectiveness evaluation model building module includes a feature weight calculation unit and a comprehensive evaluation model building unit; The feature weight calculation unit calculates the weight coefficient of each key feature of effectiveness based on the difference between the key features of effectiveness in historical valid data and invalid data; the comprehensive evaluation model construction unit constructs a comprehensive evaluation model of the effectiveness of geophysical and chemical anomaly data using the weight coefficients of each key feature of effectiveness; the comprehensive evaluation index of effectiveness corresponding to the historical geophysical and chemical anomaly data is calculated through the comprehensive evaluation model of effectiveness, and the comprehensive evaluation threshold of effectiveness is determined; The real-time data collection and effectiveness evaluation module includes a real-time data collection unit and a real-time effectiveness evaluation unit; The real-time data collection unit collects real-time geophysical and chemical anomaly data in the gold mine exploration area and extracts the key features of the effectiveness of the real-time geophysical and chemical anomaly data; the real-time effectiveness evaluation unit calculates the real-time effectiveness comprehensive evaluation index based on the key features of the effectiveness of the real-time data, and compares it with the effectiveness comprehensive evaluation threshold to identify real-time effective data.

[0024] A method for managing geophysical and chemical anomaly data for gold mine exploration comprises the following steps: Step S100. Obtain historical geophysical anomaly data and historical event records in the area where the gold exploration task has been completed from the database, and divide the historical geophysical anomaly data into historical valid data and historical invalid data according to the historical event records; Step S200. Analyze the deviation relationship between historical valid data and historical invalid data, thereby identifying the distinguishing features between the two, and mark the distinguishing features between the historical valid data and the historical invalid data as key features of effectiveness; Step S300. Based on the key features of effectiveness, combined with historical effective data and historical invalid data, a comprehensive effectiveness evaluation model of geophysical and chemical anomaly data is constructed to obtain a comprehensive effectiveness evaluation index; based on the historical effective data and historical invalid data, the corresponding comprehensive effectiveness evaluation index is calculated to obtain a comprehensive effectiveness evaluation threshold; Step S400. Collect real-time geophysical and chemical anomaly data of the gold mining exploration area, and extract key features of effectiveness from the real-time geophysical and chemical anomaly data; calculate the real-time effectiveness comprehensive evaluation index based on the key features of effectiveness and in combination with the effectiveness comprehensive evaluation model; compare the real-time effectiveness comprehensive evaluation index with the effectiveness comprehensive evaluation threshold, and extract real-time effective data from the real-time geophysical and chemical anomaly data based on the comparison result.

[0025] Step S100 includes: The historical event record refers to the historical geophysical anomaly data in the area where the gold mine exploration task has been completed, and the record information is used to judge whether the historical geophysical anomaly data is valid by comparing the actual usage of the historical geophysical anomaly data; according to the historical event record, the historical geophysical anomaly data is divided into historical valid data and historical invalid data, and a historical valid data set A and a historical invalid data set B are constructed, wherein the historical valid data set A and the historical invalid data set B are respectively expressed as: A={a1,a2,...,an}, B={b1,b2,...,bm}, wherein A represents the historical valid data set, a1 represents the first element in the historical valid data set A, a2 represents the second element in the historical valid data set A, and so on, an represents the nth element in the historical valid data set A; B represents the historical invalid data set, b1 represents the first element in the historical invalid data set B, b2 represents the second element in the historical invalid data set B, and so on, bm represents the mth element in the historical invalid data set B.

[0026] Among them, historical geophysical and chemical anomaly data refers to data detected in the past geophysical, geochemical and geological exploration processes that are significantly different from the normal background values ​​at that time, and the geophysical and chemical anomaly data specifically include geological exploration anomaly data, geophysical anomaly data and geochemical anomaly data; the geological exploration anomaly data refers to data discovered in the geological exploration process that are significantly different from the surrounding geological environment or expected geological characteristics, which may include sudden changes in rock types, abnormal formation structures, sudden changes in geological structures, etc. These data often reveal the complexity of underground geological structures or the existence of special geological events; the geophysical anomaly data refers to data detected in geophysical exploration by measuring geophysical fields (such as gravity fields, magnetic fields, electric fields) , seismic wave field, etc.) that are significantly different from the normal background value. These data may manifest as gravity anomalies, magnetic anomalies, electrical anomalies, or abnormal changes in parameters such as seismic wave velocity and attenuation. They are usually related to the physical properties of underground geological bodies (such as density, magnetism, conductivity, etc.), and provide important information for inferring underground geological structures; the geochemical anomaly data refers to data that are significantly different from the normal background value found by analyzing the content of elements or compounds in surface or underground samples in geochemical exploration. These data may indicate the enrichment or depletion of certain elements or compounds, and are usually related to the existence of underground ore bodies, the activity of geological structures, or the migration of geochemical elements, and are important clues in mineral exploration and geological research.

[0027] In this embodiment, the specific numerical values ​​of geological exploration abnormal data, geophysical abnormal data and geochemical abnormal data are as follows: 1. Geological exploration abnormal data Sudden changes in rock types: For example, during geological exploration, a large amount of light-colored igneous rocks suddenly appear in an area that was originally a dark metamorphic rock system. This type of anomaly can be discovered through core sampling or surface outcrop surveys. Numerical manifestations may include: Changes in rock properties: For example, the sudden appearance of granite or dikes in metamorphic rocks often means changes in geological structure or fault activity.

[0028] Abnormal stratigraphic structure: If abnormal folds or faults are found in a certain layer, it may appear as irregularities in the stratigraphic position and thickness.

[0029] 2. Geophysical anomaly data Gravity anomaly: By measuring the gravity values ​​at different levels underground, abnormal changes in the gravity field can be discovered. For example: In a given area, gravity values ​​may vary from the normal 9.81 m / s² (the Earth's normal gravitational acceleration) to 9.90 m / s² or 9.75 m / s². These deviations may indicate unusual distribution of material underground, such as high-density ore bodies or low-density rock bodies.

[0030] Magnetic anomalies: When measuring the underground magnetic field using a magnetometer, some areas may show abnormal magnetic field strength. For example: The magnetic field strength of underground rock formations may deviate from the conventional background value, such as the normal value of 50 nT (nanotesla), and abnormal values ​​may appear as 25 nT or less, indicating the presence of weakly magnetic minerals or demagnetized fault zones in the area.

[0031] Electrical anomaly: Abnormal changes in underground resistivity detected through electrical exploration. For example: The general background resistivity is 20Ω·m. If the resistivity in a certain area suddenly changes to 5Ω·m or 100Ω·m, this may indicate different conductivity characteristics of mineralization or water sources.

[0032] Seismic wave anomaly: In seismic exploration, the velocity of reflected waves from underground geological layers changes, such as: The sudden drop in P-wave velocity from the normal 6.5 km / s to 4.2 km / s may indicate the presence of softer sediments or fluid-filled pore structures in the subsurface.

[0033] 3. Geochemical anomaly data Element enrichment anomaly: Through geochemical exploration and analysis of element concentrations in underground samples, abnormal data that is significantly different from the background value is found. For example: The normal background gold content is 20 ppb, but the gold concentration in a certain area may reach 1000 ppb or more, which is usually a sign of enriched ore body.

[0034] The background value of copper is 10 ppm, but in one mining area, copper content as high as 500 ppm may be found.

[0035] Chemical Anomalies: Unusual changes in certain chemical compositions in soil or rock samples may indicate mineral deposits or geological activity. For example: The sulfide concentration is about 0.1% in the background area, but it can reach 5% or higher in the mineralized zone or structural zone, indicating that there may be relatively rich sulfide ores in the area.

[0036] Step S200 includes: S201. Based on the historical valid data set A and the historical invalid data set B, feature extraction is performed on each element in the historical valid data set A and the historical invalid data set B, and normalization is performed, so that each element in the historical valid data set A and the historical invalid data set B is represented as a feature vector, and is represented as: V_ai=[vai_1,vai_2,...,vai_p],V_bi=[vbi_1,vbi_2,...,vbi_p]; Among them, ai represents the i-th element in the historical valid data set A, and i ranges from 1 to n; V_ai represents the eigenvector corresponding to the i-th element in the historical valid data set A, vai_1 represents the eigenvalue of the first dimension of the eigenvector V_ai, vai_2 represents the eigenvalue of the second dimension of the eigenvector V_ai, and so on, vai_p represents the eigenvalue of the p-th dimension of the eigenvector V_ai, and p is the characteristic dimension, covering multiple indicators such as geology, geophysics, and geochemistry; similarly, V_bi represents the eigenvector corresponding to the i-th element in the historical invalid data set B, vbi_1 represents the eigenvalue of the first dimension of the eigenvector V_bi, vbi_2 represents the eigenvalue of the second dimension of the eigenvector V_bi, and so on, vbi_p represents the eigenvalue of the p-th dimension of the eigenvector V_ai; Due to the significant characteristics of geophysical anomaly data such as high dimensionality and spatial distribution, commonly used algorithms include PCA, ICA, wavelet transform, autoencoder, SVM, K-means clustering and neural network. The specific algorithm to be chosen depends on the characteristics of the data (such as noise, dimension, spatial distribution, nonlinearity, etc.) and the requirements of the specific task. For example, if the data contains a lot of noise and requires dimensionality reduction, PCA or ICA may be more appropriate; if it is necessary to capture multi-scale anomalies, wavelet transform may be more effective; for complex nonlinear relationships, neural networks and autoencoders may be better choices; the specific algorithm should be selected by the relevant personnel.

[0037] S202. For each element in the historical valid data set A, the eigenvalues ​​of each dimension of the eigenvectors V_ax and V_ay corresponding to two different elements ax and ay are calculated in turn, so as to obtain the minimum and maximum absolute values ​​of the difference of the eigenvalues ​​of each dimension of the eigenvectors in the historical valid data set A, which are respectively expressed as: minΔvai_t and maxΔvai_t, where i ranges from 1 to n, and t ranges from 1 to p; the eigenvalues ​​of each dimension of the eigenvector V_bi corresponding to each element in the historical invalid data set B are calculated in turn with the eigenvalues ​​of the corresponding dimension of V_ai corresponding to each element in the historical valid data set A, so as to obtain the absolute value of the difference Δvi_t; the absolute value of the difference Δvi_t of each dimension of the eigenvector V_bi corresponding to each element in the historical invalid data set B is summarized, so as to form the difference Set Ct, and Ct={Δv1_t,Δv2_t,...,Δvn_t}, where Δv1_t represents the absolute value of the difference between the eigenvalue of the tth dimension in the eigenvector V_bi of the corresponding element in the historical invalid data set B and the eigenvalue of the tth dimension in the eigenvector V_a1 corresponding to the first element in the historical valid data set A, Δv2_t represents the absolute value of the difference between the eigenvalue of the tth dimension in the eigenvector V_bi of the corresponding element in the historical invalid data set B and the eigenvalue of the tth dimension in the eigenvector V_a2 corresponding to the second element in the historical valid data set A, and so on, Δvn_t represents the absolute value of the difference between the eigenvalue of the tth dimension in the eigenvector V_bi of the corresponding element in the historical invalid data set B and the eigenvalue of the tth dimension in the eigenvector V_an corresponding to the nth element in the historical valid data set A; S203. For each element in the historical invalid data set B corresponding to the difference set Ct, compare the minimum minΔvai_t and the maximum maxΔvai_t of the absolute value of the difference between each element in the difference set Ct and the eigenvalue of the corresponding dimension of the eigenvector in the historical valid data set A, and count the number N of elements in the difference set Ct that do not belong to the interval [minΔvai_t, maxΔvai_t]. If N / n≥d, the features corresponding to the dimensions of the difference set Ct are used as the distinguishing features between the historical valid data and the historical invalid data, where d represents the ratio threshold; for each element in the historical invalid data set B corresponding to the difference set Ct, the corresponding distinguishing features are summarized to form a distinguishing feature set Qt; perform intersection calculation on all distinguishing feature sets Qt, and mark the distinguishing features corresponding to the intersection calculation results as validity key features.

[0038] In this embodiment, the feature vectors corresponding to the elements in the historical valid data set A and the historical invalid data set B are respectively expressed as: V_ai=[vai_1,vai_2,...,vai_p], V_bi=[vbi_1,vbi_2,...,vbi_p]; assuming that the dimension of the feature vector p=4, and the number of elements in the historical valid data set A and the historical invalid data set B are 4 and 3 respectively, then the corresponding relationship between the elements in the historical valid data set A and the feature vector V_ai is: a1→V_a1=[va1_1,va1_2,va1_3,va1_4]; a2→V_a2=[va2_1,va2_2,va2_3,va2_4]; a3→V_a3=[va3_1,va3_2,va3_3,va3_4]; a4→V_a4=[va4_1,va4_2,va4_3,va4_4]; Similarly, the corresponding relationship between the elements in the historical invalid data set B and the feature vector V_bi is: b1→V_b1=[vb1_1,vb1_2,vb1_3,vb1_4]; b2→V_b2=[vb2_1,vb2_2,vb2_3,vb2_4]; b3→V_b3=[vb3_1,vb3_2,vb3_3,vb3_4]; For each element in the historical valid data set A, the eigenvalues ​​of each dimension of the eigenvectors V_ax and V_ay corresponding to two different elements ax and ay are calculated in turn, so as to obtain the minimum and maximum absolute values ​​of the difference of the eigenvalues ​​of each dimension of the eigenvectors in the historical valid data set A. For example, the calculation process of minΔva1_1 is: min{|va1_1-va2_1|,|va1_1-va3_1|,|va1_1-va4_1|,|va2_1-va3_1|,|va2_1-va4_1|,|va3_1-va4_1|}=minΔva1_1; Similarly, the calculation process of maxΔva1_1 is: max{|va1_1-va2_1|,|va1_1-va3_1|,|va1_1-va4_1|,|va2_1-va3_1|,|va2_1-va4_1|, |va3_1-va4_1|}=maxΔva1_1; Taking the feature vector V_b1 as an example, the feature value of each dimension of the feature vector V_b1 is calculated in turn with the feature value of the corresponding dimension of the feature vector corresponding to each element in the historical valid data set A, so as to obtain the absolute value of the difference, and the absolute value of the difference is summarized to form a difference set Ct; when t=1, the corresponding element of the difference set C1 is: |vb1_1-va1_1|,|vb1_1-va2_1|,|vb1_1-va3_1|,|vb1_1-va4_1|; Compare each element in the difference set C1 with the interval [minΔva1_1,maxΔva1_1] consisting of the minimum minΔva1_1 and maximum maxΔva1_1 of the absolute value of the difference of the eigenvalue of the first dimension of the eigenvector in the historical valid data set A. Assuming that |vb1_1-va1_1|, |vb1_1-va2_1|, |vb1_1-va3_1| do not belong to the interval [minΔva1_1,maxΔva1_1], then the number of elements in the difference set Ct that do not belong to the interval [minΔvai_t,maxΔvai_t] is N=3 , and N / n=3 / 4=0.75, assuming that the ratio threshold d=0.5, since N / n=3 / 4=0.75>d=0.5, then the feature corresponding to the feature value of the first dimension is used as the distinguishing feature between the historical valid data and the historical invalid data; traverse the feature values ​​of all dimensions of the feature vector V_b1, assuming that the distinguishing features corresponding to the feature vector V_b1 are: the features corresponding to the feature value of the first dimension, the features corresponding to the feature value of the third dimension, and the features corresponding to the feature value of the fourth dimension, therefore, the distinguishing feature set Q1 corresponding to the feature vector V_b1={features of the first dimension, features of the third dimension, features of the fourth dimension}; Similarly, the feature vector V_b2 and the feature vector V_b3 are analyzed in the same way as the feature vector V_b1 to obtain the corresponding distinguishing feature set Q2 and the distinguishing feature set Q3. Assuming that the distinguishing feature set Q1 ∩ the distinguishing feature set Q2 ∩ the distinguishing feature set Q3 = {first dimension features, third dimension features}, then the first dimension features and the third dimension features are used as key features for effectiveness.

[0039] Step S300 includes: S301. For the key features of effectiveness, search in the feature vectors corresponding to the historical valid data and the historical invalid data, find the eigenvalues ​​of the key features in the eigenvectors corresponding to the historical valid data and the historical invalid data, and calculate the average absolute value of the difference of the eigenvalues ​​of the dimensions corresponding to the key features of effectiveness, which are expressed as: sk and mk, respectively, where sk represents the average absolute value of the difference of the eigenvalues ​​of the dimensions corresponding to the key features of effectiveness in the historical valid data set A, and mk represents the average absolute value of the difference of the eigenvalues ​​of the dimensions corresponding to the key features of effectiveness in the historical invalid data set B; calculate the weight coefficient wk of the key features of effectiveness, and the specific calculation formula is: wk=|sk-mk| / σk, where σk represents the standard deviation of the eigenvalues ​​corresponding to the key features of effectiveness k; traverse all key features of effectiveness to obtain the weight coefficients corresponding to all key features of effectiveness; S302. According to the weight coefficients corresponding to each key feature of effectiveness, combined with the eigenvalues ​​corresponding to the key features of effectiveness, a comprehensive evaluation model for the effectiveness of geophysical anomaly data is constructed, and the corresponding calculation formula is: E=∑ R k=1 (wk×vk); wherein E represents the effectiveness comprehensive evaluation index, R represents the number of effectiveness key features, vk represents the eigenvalue corresponding to the kth effectiveness key feature, and the eigenvalue corresponding to the effectiveness key feature is found from the historical effective data set A or the historical invalid data set B; according to the effectiveness comprehensive evaluation model, the effectiveness comprehensive evaluation index E corresponding to each element in the historical effective data set A and the historical invalid data set B is calculated, and according to the division of the historical effective data set A and the historical invalid data set B, the effectiveness comprehensive evaluation index set EA of the historical effective data set A and the effectiveness comprehensive evaluation index set EB of the historical invalid data set B are obtained respectively, and the effectiveness comprehensive evaluation index threshold E0 is obtained according to the effectiveness comprehensive evaluation index set EA and the effectiveness comprehensive evaluation index set EB; according to the effectiveness comprehensive evaluation index threshold E0, it is judged whether the effectiveness comprehensive evaluation model needs to be adjusted, and corresponding adjustments are made according to the judgment result.

[0040] In this embodiment, the feature vectors corresponding to the elements in the known historical valid data set A and the historical invalid data set B are respectively expressed as: V_ai=[vai_1,vai_2,...,vai_p], V_bi=[vbi_1,vbi_2,...,vbi_p], assuming that the dimension of the feature vector is p=4; and after analysis, the key features of validity are obtained as the first dimension features and the third dimension features, then the key feature values ​​of validity for the historical valid data set A are: vai_1 and vai_3, and the key feature values ​​of validity for the historical valid data set B are: vbi_1 and vbi_3, therefore, the feature values ​​corresponding to the key features of validity are uniformly expressed as: v1 and v3; Taking the eigenvalue v1 corresponding to the key feature of effectiveness as an example, calculate the average value of the eigenvalue vai_1 of the eigenvalue v1 in the historical effective data set A, expressed as s1; calculate the average value of the eigenvalue vbi_1 of the eigenvalue v1 in the historical effective data set B, expressed as m1; summarize the corresponding eigenvalues ​​vai_1 and eigenvalues ​​vbi_1 in the historical effective data set A and the historical effective data set B, calculate the corresponding standard deviation, and thus obtain σ1, calculate the weight coefficient w1 of the key feature 1 of effectiveness, and w1=|s1-m1| / σ1, assuming w1=0.6; perform the same analysis on the eigenvalue v3 corresponding to the key feature of effectiveness, assuming w3=0.2; then according to the formula: E=∑ R k=1 (wk×vk), calculate the effectiveness comprehensive evaluation index corresponding to each element in the historical valid data set A and the historical invalid data set B respectively, and the effectiveness comprehensive evaluation index corresponding to the elements in the historical valid data set A is E=0.6×vai_1+0.4×vai_3, and the effectiveness comprehensive evaluation index corresponding to the elements in the historical valid data set B is E=0.6×vbi_1+0.4×vbi_3, thus obtaining the effectiveness comprehensive evaluation index set EA and the effectiveness comprehensive evaluation index set EB.

[0041] The values ​​corresponding to the effectiveness comprehensive evaluation index set EA and the effectiveness comprehensive evaluation index set EB are represented on the number axis, and the effectiveness comprehensive evaluation index threshold E0 is obtained according to the effectiveness comprehensive evaluation index set EA and the effectiveness comprehensive evaluation index set EB. The specific analysis content is as follows: Calculate the intersection of the effectiveness comprehensive evaluation index set EA and the effectiveness comprehensive evaluation index set EB. If the intersection calculation result is an empty set, extract the minimum value EA_min and the maximum value EA_max in the effectiveness comprehensive evaluation index set EA, and the minimum value EB_min and the maximum value EB_max in the effectiveness comprehensive evaluation index set EB; if EA_min>EB_max, use EA_min as the effectiveness comprehensive evaluation index threshold E0; if EA_max<EB_min, use EA_max as the effectiveness comprehensive evaluation index threshold E0; If the intersection calculation result is not an empty set, compare the maximum value EA_max in the effectiveness comprehensive evaluation index set EA with the maximum value EB_max in the effectiveness comprehensive evaluation index set EB. If EA_max≤EB_max, then the elements in the intersection area are taken as the pending effectiveness comprehensive evaluation index threshold E1. Under the pending effectiveness comprehensive evaluation index threshold E1, the corresponding values ​​of true positive TP, false positive FP, true negative TN and false negative FN are obtained in turn, where true positive TP represents the number of samples successfully and correctly classified as effectiveness comprehensive evaluation index in the intersection area; false positive FP represents the number of samples misclassified as effectiveness comprehensive evaluation index in the intersection area; true negative TN represents the number of samples successfully and correctly classified as invalid comprehensive evaluation index outside the intersection area. ; False negative FN represents the number of samples that are misjudged as invalid comprehensive evaluation index outside the intersection area; Calculate the true rate TPR and false positive rate FPR, where the specific calculation formula is: TPR=TP / (TP+FN), FPR=FP / (FP+TN); Summarize the true rate TPR and false positive rate FPR under each pending effectiveness comprehensive evaluation index threshold E1, draw the ROC curve, and the abscissa of the ROC curve is FPR, and the ordinate is TPR; Select the pending effectiveness comprehensive evaluation index threshold E1 corresponding to the point farthest from the (0,1) point in the ROC curve as the effectiveness comprehensive evaluation index threshold E0; Similarly, if EB_max≤EA_max, analyze according to the analysis process under the case of EA_max≤EB_max, so as to obtain the effectiveness comprehensive evaluation index threshold E0; According to the effectiveness comprehensive evaluation index threshold E0, it is judged whether the effectiveness comprehensive evaluation model needs to be adjusted, and corresponding adjustments are made according to the judgment results. The specific contents are as follows: When the intersection result of the effectiveness comprehensive evaluation index set EA and the effectiveness comprehensive evaluation index set EB is an empty set, the current effectiveness comprehensive evaluation model does not need to be adjusted; when the intersection result of the effectiveness comprehensive evaluation index set EA and the effectiveness comprehensive evaluation index set EB is not an empty set, and the intersection calculation results are both true subsets of the effectiveness comprehensive evaluation index set EA and the effectiveness comprehensive evaluation index set EB, the current effectiveness comprehensive evaluation model needs to be adjusted, and the specific adjustment process is as follows: The adaptive adjustment mechanism is used to dynamically adjust the weight coefficient of the key features of effectiveness. The corresponding specific calculation formula is: w'k=αk×wk+βk, where αk and βk represent the adjustment factor and bias term corresponding to the key feature k of effectiveness respectively; the weight coefficients of all key features of effectiveness are traversed to obtain several adjusted weight coefficients of key features of effectiveness, and normalized to ensure that the sum of the weight coefficients of all adjusted key features of effectiveness is equal to 1; and the expression of the adjusted comprehensive effectiveness evaluation model is: E_end=∑ Rk=1 (w'k×vk); According to the adjusted effectiveness comprehensive evaluation model, the effectiveness comprehensive evaluation index E_end corresponding to each element in the historical valid data set A and the historical invalid data set B is calculated, so as to obtain the corresponding effectiveness comprehensive evaluation index set EA_end and the effectiveness comprehensive evaluation index set EB_end, and the intervals represented by the adjusted effectiveness comprehensive evaluation index set EA_end and the effectiveness comprehensive evaluation index set EB_end on the number axis do not have an intersection, so as to obtain the corresponding effectiveness comprehensive evaluation index threshold E0.

[0042] In this embodiment, the values ​​corresponding to the effectiveness comprehensive evaluation index set EA and the effectiveness comprehensive evaluation index set EB are represented on the number axis, and the intersection of the effectiveness comprehensive evaluation index set EA and the effectiveness comprehensive evaluation index set EB is calculated. Assuming that the interval corresponding to the effectiveness comprehensive evaluation index set EA represented on the number axis is [1,5], and the interval corresponding to the effectiveness comprehensive evaluation index set EB is [4,10], so the intersection interval is [4,5]. Then, the elements in the intersection area [4,5] are taken as the pending effectiveness comprehensive evaluation index threshold E1; in the pending effectiveness comprehensive evaluation index Under the condition of the threshold E1, the values ​​corresponding to the true positive TP, false positive FP, true negative TN and false negative FN are obtained in turn, and the true positive rate TPR and the false positive rate FPR are calculated; the true positive rate TPR and the false positive rate FPR under each undetermined comprehensive effectiveness evaluation index threshold E1 are summarized, and the ROC curve is drawn, and the abscissa of the ROC curve is FPR and the ordinate is TPR; the undetermined comprehensive effectiveness evaluation index threshold E1 corresponding to the point farthest from the point (0,1) in the ROC curve is selected as the comprehensive effectiveness evaluation index threshold E0, assuming that the comprehensive effectiveness evaluation index threshold E0=5.

[0043] When the effectiveness comprehensive evaluation index threshold E0=5, the elements in the historical invalid data set will be misjudged as historical valid data. Therefore, the current effectiveness comprehensive evaluation model needs to adjust parameters. The adaptive adjustment mechanism is used to dynamically adjust the weight coefficient of the effectiveness key feature. The corresponding specific calculation formula is: w'k=αk×wk+βk. Assuming that the weight coefficients corresponding to the adjusted effectiveness key features are 0.3 and 0.7 respectively, the expression of the adjusted effectiveness comprehensive evaluation model is: E_end=0.3×vbi_1+0.7×vbi_3. According to the calculation method that the intersection of the effectiveness comprehensive evaluation index set EA and the effectiveness comprehensive evaluation index set EB is an empty set, the corresponding effectiveness comprehensive evaluation index threshold E0 is obtained. Assuming E0=4.5, and according to the adjusted effectiveness comprehensive evaluation model, the interval corresponding to the effectiveness comprehensive evaluation index set EA is [0,4.5], and the interval corresponding to the effectiveness comprehensive evaluation index set EB is (4.5,7].

[0044] The specific analysis process of comparing the real-time effectiveness comprehensive evaluation index with the effectiveness comprehensive evaluation threshold in step S400 and extracting real-time effective data from the real-time geophysical abnormal data according to the comparison result is as follows: When the intersection of the effectiveness comprehensive evaluation index set EA and the effectiveness comprehensive evaluation index set EB is an empty set, and EA_min>EB_max, EA_min is used as the effectiveness comprehensive evaluation index threshold E0; the real-time effectiveness comprehensive evaluation index E' is compared with the effectiveness comprehensive evaluation index threshold E0, and when E'≥E0, the real-time geomaterialized abnormal data is real-time valid data; when the intersection of the effectiveness comprehensive evaluation index set EA and the effectiveness comprehensive evaluation index set EB is an empty set, and EA_max<EB_min, EA_max is used as the effectiveness comprehensive evaluation index threshold E0; the real-time effectiveness comprehensive evaluation index E' is compared with the effectiveness comprehensive evaluation index threshold E0, and when E'≤E0, the real-time geomaterialized abnormal data is real-time valid data; When the intersection of the effectiveness comprehensive evaluation index set EA and the effectiveness comprehensive evaluation index set EB is not an empty set, and EA_max≤EB_max; compare the real-time effectiveness comprehensive evaluation index E' with the effectiveness comprehensive evaluation index threshold E0, and when E'≤E0, the real-time geo-materialized abnormal data is real-time valid data; when the intersection of the effectiveness comprehensive evaluation index set EA and the effectiveness comprehensive evaluation index set EB is not an empty set, and EB_max≤EA_max, compare the real-time effectiveness comprehensive evaluation index E' with the effectiveness comprehensive evaluation index threshold E0, and when E'≥E0, the real-time geo-materialized abnormal data is real-time valid data.

[0045] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0046] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A geophysical and chemical anomaly data management system for gold mine exploration, characterized by: The system includes: a historical data management module, a feature extraction and analysis module, an effectiveness evaluation model construction module, and a real-time data collection and effectiveness evaluation module; The historical data management module obtains historical geophysical and chemical anomaly data and historical event records in the completed gold mine exploration task area from the database, and divides the historical geophysical and chemical anomaly data into historical valid data and historical invalid data according to the historical event records; The feature extraction and analysis module analyzes the deviation relationship between the historical valid data and the historical invalid data, thereby identifying the distinguishing features between the two, and marking the distinguishing features between the historical valid data and the historical invalid data as key features of effectiveness; The effectiveness evaluation model construction module constructs a comprehensive effectiveness evaluation model for geophysical and chemical anomaly data based on effectiveness key features and combines historical effective data with historical invalid data, thereby obtaining a comprehensive effectiveness evaluation index; calculates the corresponding comprehensive effectiveness evaluation index based on historical effective data and historical invalid data, thereby obtaining a comprehensive effectiveness evaluation threshold; The real-time data collection and effectiveness evaluation module collects real-time geophysical and chemical anomaly data in the gold mine exploration area, extracts effectiveness key features from the real-time geophysical and chemical anomaly data; calculates a real-time effectiveness comprehensive evaluation index based on the effectiveness key features and in combination with an effectiveness comprehensive evaluation model; compares the real-time effectiveness comprehensive evaluation index with an effectiveness comprehensive evaluation threshold, and extracts real-time effective data from the real-time geophysical and chemical anomaly data based on the comparison result.

2. A geophysical and chemical anomaly data management system for gold mine exploration according to claim 1, characterized in that: The historical data management module includes a historical data acquisition unit and a historical data division and classification unit; The historical data acquisition unit acquires historical geophysical anomaly data and corresponding historical event records in the area where the gold mine exploration task has been completed from the database; the historical data division and classification unit judges the validity of the geophysical anomaly data based on the historical event records, divides the historical geophysical anomaly data into valid data and invalid data, and constructs a historical valid data set and a historical invalid data set.

3. The geophysical and chemical anomaly data management system for gold mine exploration according to claim 1, characterized in that: The feature extraction and analysis module includes a feature extraction and normalization unit and a feature difference analysis unit; The feature extraction and normalization unit extracts various features for each element in the historical valid data set and the historical invalid data set, and performs normalization processing to convert them into feature vectors; The feature difference analysis unit analyzes the feature differences between the historical valid data set and the invalid data set, thereby identifying the distinguishing features between the two and marking them as validity key features.

4. The geophysical and chemical anomaly data management system for gold mine exploration according to claim 1, characterized in that: The effectiveness evaluation model construction module includes a feature weight calculation unit and a comprehensive evaluation model construction unit; The feature weight calculation unit calculates the weight coefficient of each key feature of effectiveness based on the difference between the key features of effectiveness in historical valid data and invalid data; the comprehensive evaluation model construction unit constructs a comprehensive evaluation model of the effectiveness of geophysical anomaly data using the weight coefficients of each key feature of effectiveness; The effectiveness comprehensive evaluation index corresponding to the historical geophysical anomaly data is calculated through the effectiveness comprehensive evaluation model, and the effectiveness comprehensive evaluation threshold is determined; The real-time data acquisition and effectiveness evaluation module includes a real-time data acquisition unit and a real-time effectiveness evaluation unit; The real-time data acquisition unit collects real-time geophysical and chemical anomaly data in the gold mine exploration area and extracts the key features of the effectiveness of the real-time geophysical and chemical anomaly data; the real-time effectiveness evaluation unit calculates the real-time effectiveness comprehensive evaluation index based on the key features of the effectiveness of the real-time data, and compares it with the effectiveness comprehensive evaluation threshold, thereby identifying real-time effective data.

5. A geophysical anomaly data management method for gold mine exploration, applied to a geophysical anomaly data management system for gold mine exploration as claimed in any one of claims 1 to 4, characterized in that: The method comprises the following steps: Step S100. Obtain historical geophysical anomaly data and historical event records in the area where the gold exploration task has been completed from the database, and divide the historical geophysical anomaly data into historical valid data and historical invalid data according to the historical event records; Step S200. Analyze the deviation relationship between historical valid data and historical invalid data, thereby identifying the distinguishing features between the two, and mark the distinguishing features between the historical valid data and the historical invalid data as key features of effectiveness; Step S300. Based on the key features of effectiveness, combined with historical effective data and historical invalid data, a comprehensive effectiveness evaluation model of geophysical and chemical anomaly data is constructed to obtain a comprehensive effectiveness evaluation index; based on the historical effective data and historical invalid data, the corresponding comprehensive effectiveness evaluation index is calculated to obtain a comprehensive effectiveness evaluation threshold; Step S400. Collect real-time geophysical and chemical anomaly data of the gold mining exploration area, and extract key features of effectiveness from the real-time geophysical and chemical anomaly data; calculate the real-time effectiveness comprehensive evaluation index based on the key features of effectiveness and in combination with the effectiveness comprehensive evaluation model; compare the real-time effectiveness comprehensive evaluation index with the effectiveness comprehensive evaluation threshold, and extract real-time effective data from the real-time geophysical and chemical anomaly data based on the comparison result.

6. A method for managing geophysical and chemical anomaly data for gold mine exploration according to claim 5, characterized in that: The step S100 includes: The historical event record refers to the historical geophysical anomaly data in the area where the gold mine exploration task has been completed, and the record information is used to judge whether the historical geophysical anomaly data is valid by comparing the actual usage of the historical geophysical anomaly data; according to the historical event record, the historical geophysical anomaly data is divided into historical valid data and historical invalid data, and a historical valid data set A and a historical invalid data set B are constructed, wherein the historical valid data set A and the historical invalid data set B are respectively expressed as: A={a1,a2,...,an}, B={b1,b2,...,bm}, wherein A represents the historical valid data set, a1 represents the first element in the historical valid data set A, a2 represents the second element in the historical valid data set A, and so on, an represents the nth element in the historical valid data set A; B represents the historical invalid data set, b1 represents the first element in the historical invalid data set B, b2 represents the second element in the historical invalid data set B, and so on, bm represents the mth element in the historical invalid data set B.

7. A method for managing geophysical and chemical anomaly data for gold mine exploration according to claim 6, characterized in that: The step S200 includes: S201. Based on the historical valid data set A and the historical invalid data set B, feature extraction is performed on each element in the historical valid data set A and the historical invalid data set B, and normalization is performed, so that each element in the historical valid data set A and the historical invalid data set B is represented as a feature vector, and is represented as: V_ai=[vai_1,vai_2,...,vai_p],V_bi=[vbi_1,vbi_2,...,vbi_p]; Wherein, ai represents the i-th element in the historical valid data set A, and i ranges from 1 to n; V_ai represents the eigenvector corresponding to the i-th element in the historical valid data set A, vai_1 represents the eigenvalue of the first dimension of the eigenvector V_ai, vai_2 represents the eigenvalue of the second dimension of the eigenvector V_ai, and so on, vai_p represents the eigenvalue of the p-th dimension of the eigenvector V_ai, and p is the feature dimension; similarly, V_bi represents the eigenvector corresponding to the i-th element in the historical invalid data set B, vbi_1 represents the eigenvalue of the first dimension of the eigenvector V_bi, vbi_2 represents the eigenvalue of the second dimension of the eigenvector V_bi, and so on, vbi_p represents the eigenvalue of the p-th dimension of the eigenvector V_ai; S202. For each element in the historical valid data set A, the eigenvalues ​​of each dimension of the eigenvectors V_ax and V_ay corresponding to two different elements ax and ay are calculated in turn, so as to obtain the minimum and maximum absolute values ​​of the difference of the eigenvalues ​​of each dimension of the eigenvectors in the historical valid data set A, which are respectively expressed as: minΔvai_t and maxΔvai_t, where i ranges from 1 to n, and t ranges from 1 to p; the eigenvalues ​​of each dimension of the eigenvector V_bi corresponding to each element in the historical invalid data set B are calculated in turn with the eigenvalues ​​of the corresponding dimension of V_ai corresponding to each element in the historical valid data set A, so as to obtain the absolute value of the difference Δvi_t; the absolute value of the difference Δvi_t of each dimension of the eigenvector V_bi corresponding to each element in the historical invalid data set B is summarized, so as to form the difference Set Ct, and Ct={Δv1_t,Δv2_t,...,Δvn_t}, where Δv1_t represents the absolute value of the difference between the eigenvalue of the tth dimension in the eigenvector V_bi of the corresponding element in the historical invalid data set B and the eigenvalue of the tth dimension in the eigenvector V_a1 corresponding to the first element in the historical valid data set A, Δv2_t represents the absolute value of the difference between the eigenvalue of the tth dimension in the eigenvector V_bi of the corresponding element in the historical invalid data set B and the eigenvalue of the tth dimension in the eigenvector V_a2 corresponding to the second element in the historical valid data set A, and so on, Δvn_t represents the absolute value of the difference between the eigenvalue of the tth dimension in the eigenvector V_bi of the corresponding element in the historical invalid data set B and the eigenvalue of the tth dimension in the eigenvector V_an corresponding to the nth element in the historical valid data set A; S203. For each element in the historical invalid data set B corresponding to the difference set Ct, compare the minimum minΔvai_t and the maximum maxΔvai_t of the absolute value of the difference between each element in the difference set Ct and the eigenvalue of the corresponding dimension of the eigenvector in the historical valid data set A, and count the number N of elements in the difference set Ct that do not belong to the interval [minΔvai_t, maxΔvai_t]. If N / n≥d, the features corresponding to the dimensions of the difference set Ct are used as the distinguishing features between the historical valid data and the historical invalid data, where d represents the ratio threshold; for each element in the historical invalid data set B corresponding to the difference set Ct, the corresponding distinguishing features are summarized to form a distinguishing feature set Qt; perform intersection calculation on all distinguishing feature sets Qt, and mark the distinguishing features corresponding to the intersection calculation results as validity key features.

8. A method for managing geophysical and chemical anomaly data for gold mine exploration according to claim 7, characterized in that: The step S300 includes: S301. For the key features of effectiveness, search in the feature vectors corresponding to the historical valid data and the historical invalid data, find the eigenvalues ​​of the key features in the eigenvectors corresponding to the historical valid data and the historical invalid data, and calculate the average absolute value of the difference of the eigenvalues ​​of the dimensions corresponding to the key features of effectiveness, which are expressed as: sk and mk, respectively, where sk represents the average absolute value of the difference of the eigenvalues ​​of the dimensions corresponding to the key features of effectiveness in the historical valid data set A, and mk represents the average absolute value of the difference of the eigenvalues ​​of the dimensions corresponding to the key features of effectiveness in the historical invalid data set B; calculate the weight coefficient wk of the key features of effectiveness, and the specific calculation formula is: wk=|sk-mk| / σk, where σk represents the standard deviation of the eigenvalues ​​corresponding to the key features of effectiveness k; traverse all key features of effectiveness to obtain the weight coefficients corresponding to all key features of effectiveness; S302. According to the weight coefficients corresponding to each key feature of effectiveness, combined with the eigenvalues ​​corresponding to the key features of effectiveness, a comprehensive evaluation model for the effectiveness of geophysical anomaly data is constructed, and the corresponding calculation formula is: E=∑ R k=1 (wk×vk); where E represents the effectiveness comprehensive evaluation index, R represents the number of effectiveness key features, vk represents the eigenvalue corresponding to the kth effectiveness key feature, and the eigenvalue corresponding to the effectiveness key feature is found from the historical effective data set A or the historical invalid data set B; according to the effectiveness comprehensive evaluation model, the effectiveness comprehensive evaluation index E corresponding to each element in the historical effective data set A and the historical invalid data set B is calculated, and according to the division of the historical effective data set A and the historical invalid data set B, the effectiveness comprehensive evaluation index set EA of the historical effective data set A and the effectiveness comprehensive evaluation index set EB of the historical invalid data set B are obtained respectively, and the effectiveness comprehensive evaluation index threshold E0 is obtained according to the effectiveness comprehensive evaluation index set EA and the effectiveness comprehensive evaluation index set EB; according to the effectiveness comprehensive evaluation index threshold E0, it is judged whether the effectiveness comprehensive evaluation model needs to be adjusted, and corresponding adjustments are made according to the judgment result. .

9. A method for managing geophysical and chemical anomaly data for gold mine exploration according to claim 8, characterized in that: The effectiveness comprehensive evaluation index threshold E0 is obtained according to the effectiveness comprehensive evaluation index set EA and the effectiveness comprehensive evaluation index set EB. The specific analysis content is as follows: The values ​​corresponding to the effectiveness comprehensive evaluation index set EA and the effectiveness comprehensive evaluation index set EB are represented on the number axis, and the intersection of the effectiveness comprehensive evaluation index set EA and the effectiveness comprehensive evaluation index set EB is calculated. If the intersection calculation result is an empty set, the minimum value EA_min and the maximum value EA_max in the effectiveness comprehensive evaluation index set EA, as well as the minimum value EB_min and the maximum value EB_max in the effectiveness comprehensive evaluation index set EB are extracted; if EA_min>EB_max, EA_min is used as the effectiveness comprehensive evaluation index threshold value E0; if EA_max<EB_min, EA_max is used as the effectiveness comprehensive evaluation index threshold value E0; If the intersection calculation result is not an empty set, and the intersection calculation results are both true subsets of the effectiveness comprehensive evaluation index set EA and the effectiveness comprehensive evaluation index set EB, compare the maximum value EA_max in the effectiveness comprehensive evaluation index set EA with the maximum value EB_max in the effectiveness comprehensive evaluation index set EB, if EA_max≤EB_max; then the elements in the intersection area are taken as the pending effectiveness comprehensive evaluation index threshold E1, and when the pending effectiveness comprehensive evaluation index threshold E1 is determined, the corresponding values ​​of true positive TP, false positive FP, true negative TN and false negative FN are obtained in turn, where true positive TP represents the number of samples successfully classified as effectiveness comprehensive evaluation index in the intersection area; false positive FP represents the number of samples misclassified as effectiveness comprehensive evaluation index in the intersection area; true negative TN represents the number of samples misclassified as effectiveness comprehensive evaluation index outside the intersection area. The false negative FN represents the number of samples that are correctly classified as invalid comprehensive evaluation index; the false negative FN represents the number of samples that are misclassified as invalid comprehensive evaluation index outside the intersection area; the true positive rate TPR and the false positive rate FPR are calculated, and the specific calculation formula is: TPR=TP / (TP+FN), FPR=FP / (FP+TN); the true positive rate TPR and the false positive rate FPR under each pending effectiveness comprehensive evaluation index threshold E1 are summarized, and the ROC curve is drawn, and the abscissa of the ROC curve is FPR and the ordinate is TPR; the pending effectiveness comprehensive evaluation index threshold E1 corresponding to the point farthest from the (0,1) point in the ROC curve is selected as the effectiveness comprehensive evaluation index threshold E0; similarly, if EB_max≤EA_max, the analysis process under the case of EA_max≤EB_max is performed to obtain the effectiveness comprehensive evaluation index threshold E0; The effectiveness comprehensive evaluation index threshold E0 is used to determine whether the effectiveness comprehensive evaluation model needs to be adjusted, and corresponding adjustments are made according to the judgment result. The specific contents are as follows: When the intersection result of the effectiveness comprehensive evaluation index set EA and the effectiveness comprehensive evaluation index set EB is an empty set, the current effectiveness comprehensive evaluation model does not need to be adjusted; When the intersection result of the effectiveness comprehensive evaluation index set EA and the effectiveness comprehensive evaluation index set EB is not an empty set, and the intersection calculation results are all true subsets of the effectiveness comprehensive evaluation index set EA and the effectiveness comprehensive evaluation index set EB, the current effectiveness comprehensive evaluation model needs to be adjusted. The specific adjustment process is as follows: The adaptive adjustment mechanism is used to dynamically adjust the weight coefficient of the key features of effectiveness. The corresponding specific calculation formula is: w'k=αk×wk+βk, where αk and βk represent the adjustment factor and bias term corresponding to the key feature k of effectiveness respectively; the weight coefficients of all key features of effectiveness are traversed to obtain several adjusted weight coefficients of key features of effectiveness, and normalized to ensure that the sum of the weight coefficients of all adjusted key features of effectiveness is equal to 1; and the expression of the adjusted comprehensive effectiveness evaluation model is: E_end=∑ R k=1 (w'k×vk); According to the adjusted effectiveness comprehensive evaluation model, the effectiveness comprehensive evaluation index E_end corresponding to each element in the historical valid data set A and the historical invalid data set B is calculated, so as to obtain the corresponding effectiveness comprehensive evaluation index set EA_end and the effectiveness comprehensive evaluation index set EB_end, and the intervals represented by the adjusted effectiveness comprehensive evaluation index set EA_end and the effectiveness comprehensive evaluation index set EB_end on the number axis do not have an intersection, so as to obtain the corresponding effectiveness comprehensive evaluation index threshold E0.

10. A method for managing geophysical and chemical anomaly data for gold mine exploration according to claim 9, characterized in that: The specific analysis process of comparing the real-time effectiveness comprehensive evaluation index with the effectiveness comprehensive evaluation threshold in step S400 and extracting real-time effective data from the real-time geomaterialized abnormal data according to the comparison result is as follows: When the intersection of the effectiveness comprehensive evaluation index set EA and the effectiveness comprehensive evaluation index set EB is an empty set, and EA_min>EB_max, EA_min is used as the effectiveness comprehensive evaluation index threshold E0; the real-time effectiveness comprehensive evaluation index E' is compared with the effectiveness comprehensive evaluation index threshold E0, and when E'≥E0, the real-time geomaterialized abnormal data is real-time valid data; when the intersection of the effectiveness comprehensive evaluation index set EA and the effectiveness comprehensive evaluation index set EB is an empty set, and EA_max<EB_min, EA_max is used as the effectiveness comprehensive evaluation index threshold E0; the real-time effectiveness comprehensive evaluation index E' is compared with the effectiveness comprehensive evaluation index threshold E0, and when E'≤E0, the real-time geomaterialized abnormal data is real-time valid data; When the intersection of the effectiveness comprehensive evaluation index set EA and the effectiveness comprehensive evaluation index set EB is not an empty set, and EA_max≤EB_max; compare the real-time effectiveness comprehensive evaluation index E' with the effectiveness comprehensive evaluation index threshold E0, and when E'≤E0, the real-time geo-materialized abnormal data is real-time valid data; when the intersection of the effectiveness comprehensive evaluation index set EA and the effectiveness comprehensive evaluation index set EB is not an empty set, and EB_max≤EA_max, compare the real-time effectiveness comprehensive evaluation index E' with the effectiveness comprehensive evaluation index threshold E0, and when E'≥E0, the real-time geo-materialized abnormal data is real-time valid data.

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