Geological exploration data analysis system and method based on multi-source data fusion

Abstract

This invention discloses a geological exploration data analysis system and method based on multi-source data fusion, belonging to the field of data analysis technology. The invention utilizes multi-source sensors to collect general characteristic data in geology, calculates real-time feature values ​​to construct an environmental feature matrix; calculates the similarity between the real-time environmental feature matrix and each type of geological data in a knowledge base, and selects and judges the real-time geological exploration type; calculates the data fusion weight of the real-time geological exploration type using quality scores and relevance; uses the calculated data fusion weights to perform weighted fusion of the real-time data stream during geological exploration; calculates and standardizes the anomaly thresholds for different geological types based on historical exploration data of different types, generating an anomaly threshold database; extracts the anomaly thresholds for the real-time geological exploration type from the anomaly threshold database, and uses the anomaly thresholds to judge the fused data, determining whether there are anomalies in the real-time geological exploration and issuing an early warning.

Description

Technical Field

[0001] This invention relates to the field of data analysis technology, specifically to a geological exploration data analysis system and method based on multi-source data fusion. Background Technology

[0002] Geological exploration aims to comprehensively understand the geological structure and mineral resource distribution of the Earth's surface and interior. However, single data sources, such as geological mapping, geophysical exploration, geochemical exploration, and remote sensing, provide limited information, making it difficult to fully depict complex geological phenomena and accurately predict mineral resource locations. Multi-source data fusion technology can integrate data from different sources, leveraging their respective advantages to improve exploration efficiency and accuracy. With the development of computer technology, computers have begun to be applied in the geological field, mainly for processing geophysical and geochemical data, such as simple data filtering and statistical analysis. Initially, attempts were made to comprehensively analyze different types of geophysical data (such as gravity and magnetic data) to infer subsurface geological structures, but the fusion methods were relatively simple, mostly based on intuitive comparisons based on experience and manually drawn composite maps. GIS technology has gradually matured and is widely used in geological exploration, providing a unified spatial analysis platform for multi-source data fusion. With the continuous advancement of information technology, various specialized geological exploration data processing and analysis systems have emerged and are gradually developing towards integration.

[0003] However, the geological environment varies greatly on Earth, with some special geological environments existing. Current geological survey data analysis systems typically use common analysis methods and judgment standards suitable for most common geological conditions. However, for the analysis of special geological conditions, general analysis systems cannot provide accurate analysis and judgment results. Therefore, it is crucial to know how to change the analysis and judgment standards according to different geological types and reduce judgment errors. Summary of the Invention

[0004] The purpose of this invention is to provide a geological exploration data analysis system and method based on multi-source data fusion to solve the problems raised in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A geological exploration data analysis method based on multi-source data fusion, the method comprising the following steps:

[0007] S100. Screen environmental data from all types of geology, extract all existing environmental data and obtain common features through professional knowledge review, collect common feature data from geology using multi-source sensors, calculate real-time feature values ​​and construct an environmental feature matrix.

[0008] Furthermore, the specific steps for calculating real-time feature values ​​and constructing the environmental feature matrix are as follows:

[0009] S101. Collect environmental data for all types of geology, identify the types of environmental data that exist in all types of geology, and use expert knowledge to review and screen the types of environmental data that exist in all types of geology to obtain the common characteristics of all types of geology.

[0010] S102. During real-time geological surveys, sensors are used to collect general characteristic data of the real-time geological surveys. Characteristic values ​​are obtained by calculating and analyzing the general characteristic data using the following formula:

[0011] ;

[0012] In the formula, e i f represents the calculated i-th eigenvalue. i Let represent the i-th feature extraction function, which includes, but is not limited to, the mean slope and water depth; Sen represents the general feature data; for each type of general feature data, feature values ​​are calculated and standardized;

[0013] S103. Construct an environmental feature matrix E = [e1, e2, e3, ..., e] using all calculated eigenvalues. n ] T e1, e2, e3, ..., e n This represents the 1st, 2nd, 3rd, ..., nth eigenvalues ​​calculated, where n is a positive integer.

[0014] By filtering common environmental data across all geological types (such as soil moisture, rock stress, and geomagnetic intensity), data fragmentation caused by differences in geological types is avoided, laying a unified foundation for subsequent cross-type analysis. An environmental feature matrix is ​​constructed to facilitate rapid computer processing of massive amounts of real-time data, providing structured data support for subsequent real-time analysis.

[0015] S200: Generate a knowledge base by predefining the common feature range of each type of geology based on professional knowledge; calculate the similarity between the real-time environmental feature matrix and the geological data of each type in the knowledge base, and select and judge to obtain the real-time geological survey type;

[0016] Furthermore, the specific steps for selecting and determining the real-time geological type are as follows:

[0017] S201. Based on professional knowledge, predefine the common feature range for each type of geology, construct a geological database containing all types, and generate a type label g for each type of geology in the geological database. Within the geological database, calculate the mean and standard deviation of different common data for each type of geology, and calculate the similarity between the real-time environmental feature matrix and each type of geology in the geological database. The formula is:

[0018] ;

[0019] In the formula, Sim k E represents the similarity between the real-time environmental feature matrix and the k-th type of geology in the geological database. i μ represents the i-th eigenvalue in the real-time environment feature matrix. ki sd represents the average value of the i-th general characteristic in the k-th geological type. ki This represents the standard deviation of the i-th general characteristic in the k-th geological type;

[0020] S202. Construct a similarity filtering mechanism, the formula of which is:

[0021] ;

[0022] In the formula, g t This represents the type label for real-time geological surveys. The formula means selecting from all geological types k that Sim makes available. k The type label corresponding to the maximum value k is used as the real-time geological type label g. t ;

[0023] A similarity filtering mechanism is used to judge and filter the similarity between the real-time environmental feature matrix and all types of geology in the geological database, so as to obtain the type label of the real-time surveyed geology.

[0024] By matching real-time data with a knowledge base through similarity calculations, complex geological types can be automatically identified, avoiding subjective errors from manual interpretation. Pre-determining geological types provides "type labels" for subsequent data fusion and anomaly detection, overcoming the shortcomings of general-purpose systems' "one-size-fits-all" analysis.

[0025] S300. Calculate the quality score and relevance of real-time general feature data, and use the calculated quality score and relevance to calculate the data fusion weight of real-time geological survey types respectively;

[0026] Furthermore, the specific steps for calculating the data fusion weights for real-time geological types using the calculated quality scores and relevance are as follows:

[0027] S301. For real-time geological type labels, expert knowledge is used to score each common feature in the real-time geological type, and the historical accuracy of each common feature is calculated using the following formula: In the formula, Lz represents the historical accuracy of the general feature, Cr represents the number of correct detections of the general feature in history, and Cz represents the total number of times the general feature has been used in history; the average of the expert score and historical accuracy of each general feature is calculated as the relevance of the general feature;

[0028] Collect the signal-to-noise ratio, theoretical maximum signal-to-noise ratio, and data coverage of each general feature from historical data sources, and calculate the quality score for each general feature using the following formula:

[0029] ;

[0030] In the formula, q represents the quality score of the general feature, and SNR represents the signal-to-noise ratio of the general feature in the historical data source. max This represents the theoretical maximum signal-to-noise ratio in historical data sources, and Cover represents the coverage of general features;

[0031] S302. Calculate the fusion weight using the relevance and quality score of each general feature, using the following formula:

[0032] ;

[0033] In the formula, w j Represents the fusion weight of the j-th general feature, Rel j q represents the relevance of the j-th general feature. j represents the quality score of the j-th general feature; m represents the total number of general features. The fusion weights of all general features are calculated by repeating the calculation.

[0034] By combining quality scores and relevance, noisy data (such as outliers caused by sensor malfunctions) is eliminated, improving data reliability. Dynamic weight calculations automatically increase the weight of anti-interference sensors (such as fiber optic strain sensors) for special geological environments (such as areas with high magnetic interference), reducing the impact of interference-prone data (such as traditional electromagnetic sensors) and enhancing system adaptability.

[0035] S400. During real-time geological surveys, environmental data is collected using sensors to generate a real-time data stream. The calculated data fusion weights are then used to perform weighted fusion of the real-time data stream during geological surveys.

[0036] Furthermore, the specific steps for weighted fusion of real-time data streams during geological surveys using calculated data fusion weights are as follows:

[0037] S401. During real-time geological surveys, environmental data is collected using sensors to generate a real-time data stream. Real-time common feature data values ​​are extracted from the real-time data stream, and weighted fusion is performed using fusion weights. The formula is as follows:

[0038] ;

[0039] In the formula, Rt represents the weighted fused data, and Sd j This represents the real-time data value of the j-th general feature.

[0040] Weighted fusion of real-time data streams can mitigate sudden interference and ensure data continuity. Integrating multi-dimensional data such as geological, hydrological, and meteorological data creates a "three-dimensional geological profile," addressing the limitations of single-dimensional data analysis. Through weight adjustments, while ensuring real-time data updates (e.g., second-level updates), it avoids the accumulation of errors caused by rapid data acquisition.

[0041] S500: Based on different types of geological historical survey data, calculate and standardize the anomaly thresholds for different types of geology to generate an anomaly threshold database.

[0042] Furthermore, the specific steps for generating the anomaly threshold database are as follows:

[0043] S501. For each geological type, extract and standardize the eigenvalues ​​from the historical environmental feature matrix, calculate the mean and standard deviation of the historical eigenvalues, and use the eigenvalues ​​to calculate the environmental variation coefficient for each geological type. The formula is as follows:

[0044] ;

[0045] In the formula, EnvVar represents the environmental variability coefficient for each type of geology, e i μ represents the i-th eigenvalue in the real-time environment feature matrix. i sd represents the average value of the i-th historical feature. i This represents the standard deviation of the i-th historical eigenvalue;

[0046] S502. Extract the average and standard deviation of the common data for each type of geological formation from the historical data, fuse them, and standardize them. Calculate the anomaly threshold for each type of geological formation using the following formula:

[0047] ;

[0048] In the formula, τ k Rμ represents the anomaly threshold for the k-th type of geology. k Rsd represents the average historical fusion data of the k-th geological type. k denoted by , where represents the standard deviation of the historical fusion data for the k-th geological type, and α represents the sensitivity coefficient;

[0049] An anomaly threshold database is constructed using anomaly thresholds for all types of geology.

[0050] Based on historical survey data of different geological types, establish anomaly standards that are more in line with actual scenarios to avoid misjudgments caused by general thresholds.

[0051] S600. Extract the anomaly threshold of the real-time geological survey type from the anomaly threshold database, and use the anomaly threshold to judge the fused data to determine whether there are anomalies in the real-time geological survey and issue an early warning.

[0052] Furthermore, the specific steps for obtaining real-time geological anomalies and issuing early warnings are as follows:

[0053] S601. Extract the anomaly threshold τ for real-time geological survey types from the anomaly threshold database. gt The anomaly threshold of real-time geological survey types is used to judge the fused data of real-time geological surveys. When Rt>τ gt When an anomaly risk is detected in the real-time geological survey, an early warning is issued; when Rt≤τ gt At that time, it was determined that there were no abnormal risks in the real-time geological survey.

[0054] By extracting corresponding thresholds based on real-time geological types (e.g., the threshold for cave development in karst landforms differs from the threshold for landslides in the Loess Plateau), the underreporting caused by the "uniform standard" in general systems can be reduced. Combining fused data with thresholds enables multi-level early warning (e.g., yellow alert → red alert), providing a time window for emergency decision-making.

[0055] A geological exploration data analysis system based on multi-source data fusion includes a data acquisition module, a feature analysis module, a survey type judgment module, a fusion module, an anomaly threshold module, and an anomaly judgment module.

[0056] The data acquisition module is used to collect environmental data of all types of geology and extract feature values ​​from the historical environmental feature matrix.

[0057] The feature analysis module is used to extract common features of all types of geology and to calculate and analyze the common feature data to obtain feature values.

[0058] The survey type determination module is used to calculate the similarity between real-time feature values ​​and each type of geology in the geological database, and filter the maximum value to obtain the real-time survey geological type.

[0059] The fusion module is used to calculate the fusion weight of each general feature, and to perform weighted fusion of the general features in the real-time data stream using the fusion weight;

[0060] The anomaly threshold module is used to calculate the anomaly threshold using the average value, standard deviation and environmental coefficient of variation of historical fused data;

[0061] The anomaly detection module is used to judge the fused data of real-time geological surveys using anomaly thresholds, to determine whether there are anomalies in the real-time geological surveys and to issue an early warning.

[0062] The feature analysis module includes general feature units and eigenvalue units;

[0063] The general feature unit is used to review and filter the types of environmental data that exist in all types of geology using expert knowledge, so as to obtain the general features of all types of geology.

[0064] The feature value unit is used to collect general feature data of real-time geological surveys using sensors, and to calculate and analyze the general feature data to obtain feature values.

[0065] The fusion module includes a fusion weight unit and a data fusion unit;

[0066] The fusion weight unit is used to calculate the relevance and quality score of each general feature, and to calculate the fusion weight of each general feature;

[0067] The data fusion unit is used to generate a real-time data stream by collecting environmental data from sensors, extract real-time general feature data values ​​from the real-time data stream, and perform weighted fusion using fusion weights.

[0068] Compared with the prior art, the beneficial effects of the present invention are:

[0069] 1. This invention achieves "one policy for one location" analysis by judging the geological type in real time and calculating the anomaly threshold separately; different judgment standards are adopted for different geological types, making geological judgment more accurate and avoiding misjudgment.

[0070] 2. By fusing general features, this invention can reduce the interference of a single sensor in special scenarios such as high magnetic fields and high seismic activity areas (e.g., volcanic activity zones) through weighted fusion of multiple source sensors. Attached Figure Description

[0071] Figure 1 This is a module distribution diagram of the geological exploration data analysis system based on multi-source data fusion of the present invention;

[0072] Figure 2 This is a schematic diagram illustrating the steps of the geological exploration data analysis method based on multi-source data fusion according to the present invention. Detailed Implementation

[0073] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0074] Example: Figures 1-2 As shown, the present invention provides a technical solution.

[0075] A geological exploration data analysis method based on multi-source data fusion, the method comprising the following steps:

[0076] S100. Screen environmental data from all types of geology, extract all existing environmental data and obtain common features through professional knowledge review, collect common feature data from geology using multi-source sensors, calculate real-time feature values ​​and construct an environmental feature matrix.

[0077] The specific steps for calculating real-time feature values ​​and constructing the environmental feature matrix are as follows:

[0078] S101. Collect environmental data for all types of geology, identify the types of environmental data that exist in all types of geology, and use expert knowledge to review and screen the types of environmental data that exist in all types of geology to obtain the common characteristics of all types of geology.

[0079] S102. During real-time geological surveys, sensors are used to collect general characteristic data of the real-time geological surveys. Characteristic values ​​are obtained by calculating and analyzing the general characteristic data using the following formula:

[0080] ;

[0081] In the formula, e i f represents the calculated i-th eigenvalue. i Let represent the i-th feature extraction function, which includes, but is not limited to, the mean slope and water depth; Sen represents the general feature data; for each type of general feature data, feature values ​​are calculated and standardized;

[0082] S103. Construct an environmental feature matrix E = [e1, e2, e3, ..., e] using all calculated eigenvalues. n ] T e1, e2, e3, ..., e n This represents the 1st, 2nd, 3rd, ..., nth eigenvalues ​​calculated, where n is a positive integer.

[0083] By filtering common environmental data across all geological types (such as soil moisture, rock stress, and geomagnetic intensity), data fragmentation caused by differences in geological types is avoided, laying a unified foundation for subsequent cross-type analysis. An environmental feature matrix is ​​constructed to facilitate rapid computer processing of massive amounts of real-time data, providing structured data support for subsequent real-time analysis.

[0084] S200: Generate a knowledge base by predefining the common feature range of each type of geology based on professional knowledge; calculate the similarity between the real-time environmental feature matrix and the geological data of each type in the knowledge base, and select and judge to obtain the real-time geological survey type;

[0085] The specific steps for selecting and determining the real-time geological type are as follows:

[0086] S201. Based on professional knowledge, predefine the common feature range for each type of geology, construct a geological database containing all types, and generate a type label g for each type of geology in the geological database. Within the geological database, calculate the mean and standard deviation of different common data for each type of geology, and calculate the similarity between the real-time environmental feature matrix and each type of geology in the geological database. The formula is:

[0087] ;

[0088] In the formula, Sim k E represents the similarity between the real-time environmental feature matrix and the k-th type of geology in the geological database. i μ represents the i-th eigenvalue in the real-time environment feature matrix. ki sd represents the average value of the i-th general characteristic in the k-th geological type. ki This represents the standard deviation of the i-th general characteristic in the k-th geological type;

[0089] S202. Construct a similarity filtering mechanism, the formula of which is:

[0090] ;

[0091] In the formula, g t This represents the type label for real-time geological surveys. The formula means selecting from all geological types k that Sim makes available. k The type label corresponding to the maximum value k is used as the real-time geological type label g. t ;

[0092] A similarity filtering mechanism is used to judge and filter the similarity between the real-time environmental feature matrix and all types of geology in the geological database, so as to obtain the type label of the real-time surveyed geology.

[0093] By matching real-time data with a knowledge base through similarity calculations, complex geological types can be automatically identified, avoiding subjective errors from manual interpretation. Pre-determining geological types provides "type labels" for subsequent data fusion and anomaly detection, overcoming the shortcomings of general-purpose systems' "one-size-fits-all" analysis.

[0094] S300. Calculate the quality score and relevance of real-time general feature data, and use the calculated quality score and relevance to calculate the data fusion weight of real-time geological survey types respectively;

[0095] The specific steps for calculating the data fusion weights for real-time geological survey types using the calculated quality scores and relevance are as follows:

[0096] S301. For real-time geological type labels, expert knowledge is used to score each common feature in the real-time geological type, and the historical accuracy of each common feature is calculated using the following formula: In the formula, Lz represents the historical accuracy of the general feature, Cr represents the number of correct detections of the general feature in history, and Cz represents the total number of times the general feature has been used in history; the average of the expert score and historical accuracy of each general feature is calculated as the relevance of the general feature;

[0097] Collect the signal-to-noise ratio, theoretical maximum signal-to-noise ratio, and data coverage of each general feature from historical data sources, and calculate the quality score for each general feature using the following formula:

[0098] ;

[0099] In the formula, q represents the quality score of the general feature, and SNR represents the signal-to-noise ratio of the general feature in the historical data source. max This represents the theoretical maximum signal-to-noise ratio in historical data sources, and Cover represents the coverage of general features;

[0100] S302. Calculate the fusion weight using the relevance and quality score of each general feature, using the following formula:

[0101] ;

[0102] In the formula, w j Represents the fusion weight of the j-th general feature, Rel j q represents the relevance of the j-th general feature. j represents the quality score of the j-th general feature; m represents the total number of general features. The fusion weights of all general features are calculated by repeating the calculation.

[0103] By combining quality scores and relevance, noisy data (such as outliers caused by sensor malfunctions) is eliminated, improving data reliability. Dynamic weight calculations automatically increase the weight of anti-interference sensors (such as fiber optic strain sensors) for special geological environments (such as areas with high magnetic interference), reducing the impact of interference-prone data (such as traditional electromagnetic sensors) and enhancing system adaptability.

[0104] S400. During real-time geological surveys, environmental data is collected using sensors to generate a real-time data stream. The calculated data fusion weights are then used to perform weighted fusion of the real-time data stream during geological surveys.

[0105] The specific steps for weighted fusion of real-time data streams during geological surveys using calculated data fusion weights are as follows:

[0106] S401. During real-time geological surveys, environmental data is collected using sensors to generate a real-time data stream. Real-time common feature data values ​​are extracted from the real-time data stream, and weighted fusion is performed using fusion weights. The formula is as follows:

[0107] ;

[0108] In the formula, Rt represents the weighted fused data, and Sd j This represents the real-time data value of the j-th general feature.

[0109] Weighted fusion of real-time data streams can mitigate sudden interference and ensure data continuity. Integrating multi-dimensional data such as geological, hydrological, and meteorological data creates a "three-dimensional geological profile," addressing the limitations of single-dimensional data analysis. Through weight adjustments, while ensuring real-time data updates (e.g., second-level updates), it avoids the accumulation of errors caused by rapid data acquisition.

[0110] S500: Based on different types of geological historical survey data, calculate and standardize the anomaly thresholds for different types of geology to generate an anomaly threshold database.

[0111] The specific steps for generating the anomaly threshold database are as follows:

[0112] S501. For each geological type, extract and standardize the eigenvalues ​​from the historical environmental feature matrix, calculate the mean and standard deviation of the historical eigenvalues, and use the eigenvalues ​​to calculate the environmental variation coefficient for each geological type. The formula is as follows:

[0113] ;

[0114] In the formula, EnvVar represents the environmental variability coefficient for each type of geology, e i μ represents the i-th eigenvalue in the real-time environment feature matrix. i sd represents the average value of the i-th historical feature. i This represents the standard deviation of the i-th historical eigenvalue;

[0115] S502. Extract the average and standard deviation of the common data for each type of geological formation from the historical data, fuse them, and standardize them. Calculate the anomaly threshold for each type of geological formation using the following formula:

[0116] ;

[0117] In the formula, τ k Rμ represents the anomaly threshold for the k-th type of geology. k Rsd represents the average historical fusion data of the k-th geological type. k denoted by , where represents the standard deviation of the historical fusion data for the k-th geological type, and α represents the sensitivity coefficient;

[0118] An anomaly threshold database is constructed using anomaly thresholds for all types of geology.

[0119] Based on historical survey data of different geological types, establish anomaly standards that are more in line with actual scenarios to avoid misjudgments caused by general thresholds.

[0120] S600. Extract the anomaly threshold of the real-time geological survey type from the anomaly threshold database, and use the anomaly threshold to judge the fused data to determine whether there are anomalies in the real-time geological survey and issue an early warning.

[0121] The specific steps for obtaining real-time geological anomalies and issuing early warnings are as follows:

[0122] S601. Extract the anomaly threshold τ for real-time geological survey types from the anomaly threshold database. gt The anomaly threshold of real-time geological survey types is used to judge the fused data of real-time geological surveys. When Rt>τ gt When an anomaly risk is detected in the real-time geological survey, an early warning is issued; when Rt≤τ gt At that time, it was determined that there were no abnormal risks in the real-time geological survey.

[0123] By extracting corresponding thresholds based on real-time geological types (e.g., the threshold for cave development in karst landforms differs from the threshold for landslides in the Loess Plateau), the underreporting caused by the "uniform standard" in general systems can be reduced. Combining fused data with thresholds enables multi-level early warning (e.g., yellow alert → red alert), providing a time window for emergency decision-making.

[0124] A geological exploration data analysis system based on multi-source data fusion includes a data acquisition module, a feature analysis module, a survey type judgment module, a fusion module, an anomaly threshold module, and an anomaly judgment module.

[0125] The data acquisition module is used to collect environmental data of all types of geology and extract feature values ​​from the historical environmental feature matrix.

[0126] The feature analysis module is used to extract common features of all types of geology and to calculate and analyze the common feature data to obtain feature values.

[0127] The survey type determination module is used to calculate the similarity between real-time feature values ​​and each type of geology in the geological database, and filter the maximum value to obtain the real-time survey geological type.

[0128] The fusion module is used to calculate the fusion weight of each general feature, and to perform weighted fusion of the general features in the real-time data stream using the fusion weight;

[0129] The anomaly threshold module is used to calculate the anomaly threshold using the average value, standard deviation and environmental coefficient of variation of historical fused data;

[0130] The anomaly detection module is used to judge the fused data of real-time geological surveys using anomaly thresholds, to determine whether there are anomalies in the real-time geological surveys and to issue an early warning.

[0131] The feature analysis module includes general feature units and eigenvalue units;

[0132] The general feature unit is used to review and filter the types of environmental data that exist in all types of geology using expert knowledge, so as to obtain the general features of all types of geology.

[0133] The feature value unit is used to collect general feature data of real-time geological surveys using sensors, and to calculate and analyze the general feature data to obtain feature values.

[0134] The fusion module includes a fusion weight unit and a data fusion unit;

[0135] The fusion weight unit is used to calculate the relevance and quality score of each general feature, and to calculate the fusion weight of each general feature;

[0136] The data fusion unit is used to generate a real-time data stream by collecting environmental data from sensors, extract real-time general feature data values ​​from the real-time data stream, and perform weighted fusion using fusion weights.

[0137] Example 1: A knowledge base derived from expert knowledge. Table 1 is an example:

[0138]

[0139] Table 1

[0140] Example 2: Assuming the common features are magnetic field and slope; the correlation of each common data for mining geology is calculated to be 0.9 and 0.2; the quality scores of the data sources for the common features of magnetic field and slope are calculated to be 0.782 and 0.80, respectively.

[0141] The fusion weights for calculating the general characteristics of the magnetic field are 0.81 and 0.19.

[0142] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A geological exploration data analysis method based on multi-source data fusion, characterized in that: The method includes the following steps: S100. Screen environmental data from all types of geology, extract all existing environmental data and obtain common features through professional knowledge review, collect common feature data from geology using multi-source sensors, calculate real-time feature values ​​and construct an environmental feature matrix. S200: Generate a knowledge base by predefining the common feature range of each type of geology based on professional knowledge; calculate the similarity between the real-time environmental feature matrix and the geological data of each type in the knowledge base, and select and judge to obtain the real-time geological survey type; S300. Calculate the quality score and relevance of real-time general feature data, and use the calculated quality score and relevance to calculate the data fusion weight of real-time geological survey types respectively; The specific steps for calculating the data fusion weights for real-time geological survey types using the calculated quality scores and relevance are as follows: S301. For real-time geological type labels, expert knowledge is used to score each common feature in the real-time geological type, and the historical accuracy of each common feature is calculated using the following formula: In the formula, Lz represents the historical accuracy of the general feature, Cr represents the number of correct detections of the general feature in history, and Cz represents the total number of times the general feature has been used in history; the average of the expert score and historical accuracy of each general feature is calculated as the relevance of the general feature; Collect the signal-to-noise ratio, theoretical maximum signal-to-noise ratio, and data coverage of each general feature from historical data sources, and calculate the quality score for each general feature using the following formula: ； In the formula, q represents the quality score of the general feature, and SNR represents the signal-to-noise ratio of the general feature in the historical data source. max This represents the theoretical maximum signal-to-noise ratio in historical data sources, and Cover represents the coverage of general features; S302. Calculate the fusion weight using the relevance and quality score of each general feature, using the following formula: ； In the formula, w j Represents the fusion weight of the j-th general feature, Rel j q represents the relevance of the j-th general feature. j represents the quality score of the j-th general feature; m represents the total number of general features, and the fusion weights of all general features are calculated repeatedly. S400. During real-time geological surveys, environmental data is collected using sensors to generate a real-time data stream. The calculated data fusion weights are then used to perform weighted fusion of the real-time data stream during geological surveys. S500: Based on different types of geological historical survey data, calculate and standardize the anomaly thresholds for different types of geology to generate an anomaly threshold database. S600. Extract the anomaly threshold of the real-time geological survey type from the anomaly threshold database, and use the anomaly threshold to judge the fused data to determine whether there are anomalies in the real-time geological survey and issue an early warning.

2. The geological exploration data analysis method based on multi-source data fusion according to claim 1, characterized in that: The specific steps for calculating real-time feature values ​​and constructing the environmental feature matrix in S100 are as follows: S101. Collect environmental data for all types of geology, identify the types of environmental data that exist in all types of geology, and use expert knowledge to review and screen the types of environmental data that exist in all types of geology to obtain the common characteristics of all types of geology. S102. During real-time geological surveys, sensors are used to collect general characteristic data of the real-time geological surveys. Characteristic values ​​are obtained by calculating and analyzing the general characteristic data using the following formula: ； In the formula, e i f represents the calculated i-th eigenvalue. i Let represent the i-th feature extraction function, which includes, but is not limited to, the mean slope and water depth; Sen represents the general feature data; for each type of general feature data, feature values ​​are calculated and standardized; S103. Construct an environmental feature matrix E=[e1, e2, e3, ..., e] using all calculated eigenvalues. n ] T e1, e2, e3, ..., e n This represents the 1st, 2nd, 3rd, ..., nth eigenvalues ​​calculated, where n is a positive integer.

3. The geological exploration data analysis method based on multi-source data fusion according to claim 2, characterized in that: The specific steps for determining the real-time geological type in S200 are as follows: S201. Based on professional knowledge, predefine the common feature range for each type of geology, construct a geological database containing all types, and generate a type label g for each type of geology in the geological database. Within the geological database, calculate the mean and standard deviation of different common data for each type of geology, and calculate the similarity between the real-time environmental feature matrix and each type of geology in the geological database. The formula is: ； In the formula, Sim k E represents the similarity between the real-time environmental feature matrix and the k-th type of geology in the geological database. i μ represents the i-th eigenvalue in the real-time environment feature matrix. ki sd represents the average value of the i-th general characteristic in the k-th geological type. ki This represents the standard deviation of the i-th general characteristic in the k-th type of geology; S202. Construct a similarity filtering mechanism, the formula of which is: ； In the formula, g t This represents the type label for real-time geological surveys. The formula indicates that Sim selects the type of geological data from all geological types k. k The type label corresponding to the maximum value k is used as the real-time geological type label g. t ; A similarity filtering mechanism is used to judge and filter the similarity between the real-time environmental feature matrix and all types of geology in the geological database, so as to obtain the type label of the real-time surveyed geology.

4. The geological exploration data analysis method based on multi-source data fusion according to claim 3, characterized in that: The specific steps in S400 for weighted fusion of real-time data streams during geological surveys using calculated data fusion weights are as follows: S401. During real-time geological surveys, environmental data is collected using sensors to generate a real-time data stream. Real-time common feature data values ​​are extracted from the real-time data stream, and weighted fusion is performed using fusion weights. The formula is as follows: ； In the formula, Rt represents the weighted fused data, and Sd j This represents the real-time data value of the j-th general feature.

5. The geological exploration data analysis method based on multi-source data fusion according to claim 4, characterized in that: The specific steps for generating the anomaly threshold database in S500 are as follows: S501. For each geological type, extract and standardize the eigenvalues ​​from the historical environmental feature matrix, calculate the mean and standard deviation of the historical eigenvalues, and use the eigenvalues ​​to calculate the environmental variation coefficient for each geological type. The formula is as follows: ； In the formula, EnvVar represents the environmental variability coefficient for each type of geology, e i μ represents the i-th eigenvalue in the real-time environment feature matrix. i sd represents the average value of the i-th historical feature. i This represents the standard deviation of the i-th historical feature value; S502. Extract the average and standard deviation of the common data for each type of geological formation from the historical data, fuse them, and standardize them. Calculate the anomaly threshold for each type of geological formation using the following formula: ； In the formula, τ k Rμ represents the anomaly threshold for the k-th type of geology. k Rsd represents the average historical fusion data of the k-th geological type. k α represents the standard deviation of the historical fusion data for the k-th type of geology, and α represents the sensitivity coefficient. An anomaly threshold database is constructed using anomaly thresholds for all types of geology.

6. The geological exploration data analysis method based on multi-source data fusion according to claim 5, characterized in that: The specific steps in S600 for obtaining real-time geological anomalies and issuing early warnings are as follows: S601. Extract the anomaly threshold τ for real-time geological survey types from the anomaly threshold database. gt The anomaly threshold of real-time geological survey types is used to judge the fused data of real-time geological surveys. When Rt>τ gt When an anomaly risk is detected in the real-time geological survey, an early warning is issued; when Rt≤τ gt At that time, it was determined that there were no abnormal risks in the real-time geological survey.

7. A geological exploration data analysis system based on multi-source data fusion, characterized in that: The geological exploration data analysis system includes a data acquisition module, a feature analysis module, an exploration type determination module, a fusion module, an anomaly threshold module, and an anomaly judgment module. The data acquisition module is used to collect environmental data of all types of geology and extract feature values ​​from the historical environmental feature matrix. The feature analysis module is used to extract common features of all types of geology and to calculate and analyze the common feature data to obtain feature values. The survey type determination module is used to calculate the similarity between real-time feature values ​​and each type of geology in the geological database, and filter the maximum value to obtain the real-time survey geological type. The fusion module is used to calculate the fusion weight of each general feature, and to perform weighted fusion of the general features in the real-time data stream using the fusion weight; The anomaly threshold module is used to calculate the anomaly threshold using the average value, standard deviation and environmental coefficient of variation of historical fused data; The anomaly detection module is used to judge the fused data of real-time geological surveys using anomaly thresholds, to determine whether there are anomalies in the real-time geological surveys and to issue an early warning.

8. The geological exploration data analysis system based on multi-source data fusion according to claim 7, characterized in that: The feature analysis module includes general feature units and feature value units; The general feature unit is used to review and filter the types of environmental data that exist in all types of geology using expert knowledge, so as to obtain the general features of all types of geology. The feature value unit is used to collect general feature data of real-time geological surveys using sensors, and to calculate and analyze the general feature data to obtain feature values.

9. The geological exploration data analysis system based on multi-source data fusion according to claim 7, characterized in that: The fusion module includes a fusion weighting unit and a data fusion unit; The fusion weight unit is used to calculate the relevance and quality score of each general feature, and to calculate the fusion weight of each general feature; The data fusion unit is used to generate a real-time data stream by collecting environmental data from sensors, extract real-time general feature data values ​​from the real-time data stream, and perform weighted fusion using fusion weights.

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