Geological surveying and mapping information extraction method and system based on multi-source data fusion

Abstract

The invention provides a geological surveying and mapping information extraction method and system based on multi-source data fusion, and relates to the technical field of data processing, and the method comprises the steps: collecting multi-source geological data, including satellite remote sensing data, geological exploration data, geophysical data, geochemical data and ground actual measurement data; preprocessing the multi-source geological data to obtain preprocessed multi-source geological data; based on the amount of information provided by each data source, the frequency of data updating and the accuracy of historical data, calculating a dynamic adjustment factor of each data source; fusing the preprocessed multi-source geological data by using a dynamic adjustment factor to obtain fused geological data; based on the fused geological data, extracting target geological surveying and mapping information by using a geological information extraction algorithm; and performing information screening on the extracted target geological surveying and mapping information to form a final geological surveying and mapping information extraction result. According to the invention, the precision and efficiency of data fusion can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and system for extracting geological surveying and mapping information based on multi-source data fusion. Background Art

[0002] In the prior art, there are some defects in the processing and analysis of multi-source geological data.

[0003] For example, some traditional geological information extraction methods simply overlay or average multi-source data, failing to fully consider the differences and complementarities between different data sources. This processing method may not only lead to the loss or distortion of key information, but also introduce unnecessary noise and errors, thus affecting the accuracy and reliability of the final geological mapping information. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a geological surveying and mapping information extraction method and system based on multi-source data fusion, so that the data fusion process is more flexible and intelligent, and can be dynamically adjusted according to the characteristics and real-time status of different data sources, thereby improving the accuracy and efficiency of data fusion.

[0005] In order to solve the above technical problems, the technical solution of the present invention is as follows: In a first aspect, a method for extracting geological surveying and mapping information based on multi-source data fusion is provided, the method comprising: Step 1: Collect multi-source geological data, including satellite remote sensing data, geological exploration data, geophysical data, geochemical data and ground measured data; Step 2, preprocessing the multi-source geological data to obtain preprocessed multi-source geological data; Step 3, based on the amount of information provided by each data source, the frequency of data updates, and the accuracy of historical data, calculate the dynamic adjustment factor of each data source; Step 4, using the dynamic adjustment factor, fusing the preprocessed multi-source geological data to obtain fused geological data; Step 5, based on the fused geological data, using a geological information extraction algorithm to extract target geological mapping information; Step 6: Screen the extracted target geological surveying and mapping information to form the final geological surveying and mapping information extraction result.

[0006] Furthermore, multi-source geological data are collected, including: Determine the data sources and types to form the space for data collection; randomly generate a set of combinations of data sources as the initial population, each combination represents a solution, and a solution represents a set of selected data sources; Construct a fitness function to evaluate the quality of each data source combination; based on the fitness function, select the corresponding individuals from the current population as the parent generation to generate the next generation; Combine the selected parent individuals to generate new offspring individuals; perform random mutation on the newly generated offspring individuals, replace part of the old population with the newly generated offspring individuals to form a new generation population, and repeat the process of selection, crossover, mutation and generation of new population until the preset number of iterations is reached to obtain the optimized data source combination; According to the optimized data source combination, the corresponding multi-source geological data are collected.

[0007] Furthermore, the fitness function The calculation formula is: ; in, Indicates The number of valid, non-missing data entries in each data source; Indicates The total number of data entries in the data source; , and is the weight parameter; Indicates the total number of data sources; and It is the index of the data source, which is used to traverse and identify different data sources; represents the complementarity score; Representative data sources and Redundancy scores between data sources; It is The correlation score between each data source and the target geological mapping information; ; in, Indicates Data source The value of a data item; Indicates The average value of all data in a data source; Indicates the target geological mapping information The value of a data item; It represents the average value of all data in the target geological mapping information; Indicates the total number of data entries; It is the index of the data entry, which is used to traverse the specific data points in the data source.

[0008] Further, data sources and Redundancy score between data sources The calculation formula is: ; in, Indicates Data source The value of the data item; complementarity score The calculation formula is: ; in, Indicates data sources and The amount of information contained in the data sources; Indicates data sources and The total amount of information after merging the data sources.

[0009] Furthermore, the dynamic adjustment factor The calculation formula is: ; in, , and represents the weight parameter; Indicates The amount of information in each data source; Indicates The frequency of data updates for each data source; Indicates Data source Historical data points; Indicates True value; Represents the total number of data points, that is, when calculating the The number of data points to consider for historical data accuracy of a data source; represents the total number of data sources, i.e., the number of different data sources considered during the dynamic adjustment process; It is used to traverse all data sources to calculate the total amount; Represents a method used to iterate over all data points of a particular data source to calculate accuracy.

[0010] Furthermore, the pre-processed multi-source geological data are fused using dynamic adjustment factors to obtain fused geological data, including: Multiply the data in each data source by the corresponding dynamic adjustment factor to obtain weighted data source data; Determine the unit for average calculation, and for each unit for average calculation, traverse the data source and find the data point matching the current unit, so as to extract the corresponding data value from all weighted data source data; For each unit, all extracted weighted data values ​​were averaged; By extracting the corresponding data values ​​and averaging, a comprehensive geological data value integrating information from multiple data sources is calculated for each unit. The comprehensive geological data value is the integrated geological data.

[0011] Furthermore, based on the fused geological data, the target geological mapping information is extracted using a geological information extraction algorithm, including: Conduct statistical analysis on the fused geological data to determine the numerical range and distribution characteristics of the data; A threshold is set based on the specific representation of the target geological feature in the data; Traverse the fused geological data and judge each data point to see whether it exceeds the set threshold. If the value of the data point exceeds the set threshold, the data point is marked as a target geological feature point; otherwise, it is marked as a non-target point; Create a new data table to store the extracted target geological mapping information. For each data point marked as a target geological feature, add its location information and attribute value to the data table to obtain the target geological mapping information.

[0012] In the second aspect, a geological surveying and mapping information extraction system based on multi-source data fusion comprises: The collection module is used to collect multi-source geological data, including satellite remote sensing data, geological exploration data, geophysical data, geochemical data and ground measured data; A preprocessing module, used for preprocessing multi-source geological data to obtain preprocessed multi-source geological data; A calculation module, used to calculate the dynamic adjustment factor of each data source based on the amount of information provided by each data source, the frequency of data update and the accuracy of historical data; A fusion module is used to fuse the pre-processed multi-source geological data using a dynamic adjustment factor to obtain fused geological data; An extraction module is used to extract target geological surveying and mapping information based on the fused geological data using a geological information extraction algorithm; The screening module is used to screen the extracted target geological surveying and mapping information to form the final geological surveying and mapping information extraction result.

[0013] According to a third aspect, a computing device includes: one or more processors; The storage device is used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method described.

[0014] In a fourth aspect, a computer-readable storage medium stores a program, and when the program is executed by a processor, the method described is implemented.

[0015] The above scheme of the present invention includes at least the following beneficial effects.

[0016] The present invention realizes comprehensive and multi-angle capture of geological information by collecting and integrating multi-source geological data such as satellite remote sensing data, geological exploration data, geophysical data, geochemical data and ground measured data, thereby improving the integrity and accuracy of geological surveying and mapping information.

[0017] The present invention innovatively introduces a dynamic adjustment factor, which can dynamically adjust the weight of data fusion according to the amount of information provided by each data source, the frequency of data updates and the accuracy of historical data. This not only makes the data fusion process more flexible, but also can reflect the reliability and timeliness of different data sources in real time, further improving the accuracy of the fused geological data.

[0018] By preprocessing multi-source geological data, the present invention effectively eliminates the problems of format differences and inconsistent coordinate systems among data, simplifies the data processing flow, and improves the efficiency of geological information extraction.

[0019] After extracting the target geological surveying and mapping information, the present invention also performs an information screening step, which helps to remove redundant and erroneous information and ensure that the final geological surveying and mapping information extraction result is more accurate and reliable. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a flow chart of a geological surveying and mapping information extraction method based on multi-source data fusion provided by an embodiment of the present invention.

[0021] Figure 2 It is a schematic diagram of a geological surveying and mapping information extraction system based on multi-source data fusion provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0023] like Figure 1 As shown, an embodiment of the present invention proposes a geological surveying and mapping information extraction method based on multi-source data fusion, and the method comprises the following steps: Step 1: Collect multi-source geological data, including satellite remote sensing data, geological exploration data, geophysical data, geochemical data and ground measured data; Step 2, preprocessing the multi-source geological data to obtain preprocessed multi-source geological data; Step 3, based on the amount of information provided by each data source, the frequency of data updates, and the accuracy of historical data, calculate the dynamic adjustment factor of each data source; Step 4, using the dynamic adjustment factor, fusing the preprocessed multi-source geological data to obtain fused geological data; Step 5, based on the fused geological data, using a geological information extraction algorithm to extract target geological mapping information; Step 6: Screen the extracted target geological surveying and mapping information to form the final geological surveying and mapping information extraction result.

[0024] In an embodiment of the present invention, by collecting multiple data sources including satellite remote sensing data, geological exploration data, geophysical data, geochemical data and ground measured data, the geological situation can be reflected more comprehensively and from multiple angles. The preprocessing step can remove noise, correct errors, unify data formats and coordinate systems, and ensure the quality and consistency of the data. By considering the amount of information, update frequency and historical accuracy of each data source, a dynamic adjustment factor is assigned to each data source, so that the contribution of different data sources can be weighed more accurately during data fusion, and the accuracy of the fused data can be improved. Multi-source data fusion using dynamic adjustment factors can effectively integrate information from different data sources. Through the geological information extraction algorithm, the target geological surveying and mapping information can be quickly and accurately extracted from the fused geological data, improving the efficiency and accuracy of information acquisition. By screening the extracted geological surveying and mapping information, redundant and erroneous data can be removed to ensure that the final extracted geological surveying and mapping information is more accurate and reliable.

[0025] In a preferred embodiment of the present invention, the above step 1, collecting multi-source geological data, may include: Step 11, determine the data sources and types to form a space for data collection; randomly generate a set of combinations of data sources as an initial population, each combination represents a solution, and a solution represents a set of selected data sources; Step 12, construct a fitness function for evaluating the quality of each data source combination; according to the fitness function, select the corresponding individual from the current population as the parent generation to generate the next generation; Step 13, combining the selected parent individuals to generate new offspring individuals; performing random mutation on the newly generated offspring individuals, replacing part of the old population with the newly generated offspring individuals to form a new generation population, and repeating the process of selection, crossover, mutation and generation of new population until the preset number of iterations is reached to obtain an optimized data source combination; Step 14: Collect corresponding multi-source geological data based on the optimized data source combination.

[0026] In the embodiment of the present invention, step 11, by clarifying the data source and type, can ensure the wide coverage and diversity of data, and provide a comprehensive perspective for subsequent analysis. Randomly generating the initial population increases the diversity of data source combinations, which helps to explore more possible solutions in the subsequent optimization process. Step 12, constructing a fitness function provides a quantitative standard for evaluating the pros and cons of data source combinations. Selecting the parent generation through the fitness function can ensure that the high-quality data source combination characteristics are inherited to the next generation, thereby improving the quality of the overall data source combination. Step 13, generating new offspring individuals through crossover and mutation operations, can explore more potential data source combinations. This evolutionary mechanism helps data source combinations with higher fitness gradually stand out during the iteration process, so as to find data source combinations that are more suitable for actual needs. Step 14, after the previous optimization process, the obtained data source combination is more in line with actual needs. Collecting data according to this optimized combination can improve the efficiency and accuracy of data collection, and ensure that the collected data has higher value for subsequent geological surveying and mapping information extraction. At the same time, this also reduces unnecessary data collection work and saves resources and time costs.

[0027] When applied specifically, the following steps are specifically included: Step 11, list the data sources: Satellite remote sensing data: provides information on the earth’s surface topography, landforms, vegetation coverage, etc.

[0028] Geological exploration data: including rock, soil and mineral data obtained through field exploration such as drilling and pit exploration.

[0029] Geophysical exploration data: data obtained by detecting underground structures through physical methods such as gravity, magnetism, and electricity.

[0030] Geochemical exploration data: Analyze the distribution of chemical elements in soil and rocks for mineral resource assessment, etc.

[0031] Ground measured data: including GPS measurement, leveling measurement and other direct ground observation data.

[0032] Determine the data type: For satellite remote sensing data, the data type can be multispectral images, hyperspectral images, radar images, etc. Geological exploration data can include core sample analysis, soil analysis reports, etc. Geophysical data can be gravity anomaly maps, magnetic anomaly maps, etc. Geochemical data involves element distribution maps, abnormal area identification, etc. Ground measurement data provides accurate geographic location and terrain elevation information.

[0033] Construct a data source combination space: Create a list or matrix that represents all possible combinations of data sources. For example, you can use binary encoding, where "1" indicates that the data source is selected and "0" indicates that it is not selected. For each data source, consider the case where it is selected or not selected, thus generating all possible combinations.

[0034] Randomly generate the initial population: Set the population size, that is, the number of random data source combinations to be generated. Use a random number generator to randomly select several combinations in the data source combination space constructed above. Ensure that each combination (individual) is unique, or accept a certain degree of repetition, depending on the design requirements of the algorithm. Each randomly generated data source combination represents an initial solution, that is, a possible data collection plan.

[0035] Step 12, according to the needs of geological mapping, design a fitness function to quantitatively evaluate the advantages and disadvantages of each data source combination; apply the fitness function to evaluate each individual in the initial population to obtain the fitness value of each individual, and select individuals with excellent performance as parents based on the fitness value. This is usually achieved through selection mechanisms in genetic algorithms such as roulette selection and tournament selection.

[0036] Step 13, pair the selected parent individuals, and exchange some of their genes, that is, some elements in the data source combination, through crossover operations (such as single-point crossover, multi-point crossover, etc.), to generate new offspring individuals. Perform random mutations on the newly generated offspring individuals, such as randomly changing the selection state of a data source, to increase the diversity of the population. Replace some individuals in the old population with the newly generated offspring individuals to form a new generation of population. This process usually follows a certain replacement strategy, such as retaining the best individual, random replacement, etc. Repeat the above selection, crossover, mutation and generation of a new population until the preset number of iterations is reached or other stopping conditions are met.

[0037] Step 14, after the iterative optimization is completed, the individual with the highest fitness is selected from the final population as the optimal data source combination; based on this optimal data source combination, the actual collection of multi-source geological data is carried out, which involves coordination with different data providers, conversion and unification of data formats, and preliminary verification and cleaning of data.

[0038] In a preferred embodiment of the present invention, the fitness function The calculation formula is: ; in, Indicates The number of valid, non-missing data entries in each data source; Indicates The total number of data entries in the data source; , and is the weight parameter; Indicates the total number of data sources; and It is the index of the data source, which is used to traverse and identify different data sources; represents the complementarity score; Representative data sources and Redundancy scores between data sources; It is The correlation score between each data source and the target geological mapping information; ; in, Indicates Data source The value of a data item; Indicates The average value of all data in a data source; Indicates the target geological mapping information The value of a data item; It represents the average value of all data in the target geological mapping information; Indicates the total number of data entries; It is the index of the data entry, which is used to traverse the specific data points in the data source.

[0039] In the embodiment of the present invention, by considering the ratio of the number of valid, non-missing data entries in each data source to the total number of data entries , the function can give priority to data sources with higher data quality and fewer missing values, thereby improving the quality of the overall data set. By calculating the complementarity score between data sources , the function can promote the selection of data sources that complement each other in content, so as to more comprehensively cover geological surveying and mapping information and reduce information blind spots. , the function can avoid selecting highly similar or repeated data sources, thereby reducing data redundancy and improving the efficiency of data processing and analysis. By calculating the relevance score of each data source with the target geological mapping information , the function can ensure that the selected data source is closely related to the target task, thereby improving the accuracy and reliability of geological mapping.

[0040] In a preferred embodiment of the present invention, data sources and Redundancy score between data sources The calculation formula is: ; in, Indicates Data source The value of the data item; complementarity score The calculation formula is: ; in, Indicates data sources and The amount of information contained in the data sources; Indicates data sources and The total amount of information after merging the data sources.

[0041] In this embodiment of the present invention, the redundancy score By comparing data sources and The value of the same data item in the two data sources is used to accurately quantify the degree of data redundancy between the two using the ratio of the minimum and maximum values. This helps to identify and avoid highly redundant data source combinations during the data source selection process, thereby improving the efficiency of data integration and processing. It combines the redundancy score with the information overlap between data sources. It not only considers the complementarity of data sources at the numerical level (reflected by the redundancy score), but also considers the complementarity of data sources in terms of information content, that is, the ratio of the amount of information contained together to the total amount of information after merging. This helps to select data source combinations that complement each other in content and can provide more comprehensive information coverage. By comprehensively considering the redundancy score and the complementarity score, a more optimized data source combination strategy can be formulated. While maintaining the necessary information overlap between data sources, the diversity and comprehensiveness of information content are maximized, thereby improving the accuracy and completeness of geological surveying and mapping information. Accurately identifying and removing redundant data sources can reduce unnecessary data processing and analysis work, save computing resources, and improve the efficiency of overall data processing. At the same time, the optimized data source combination also helps to simplify the process of data cleaning and integration.

[0042] Step 2: Preprocessing of multi-source geological data is implemented as follows: Collect geological data from various available data sources, which may include geological exploration reports, remote sensing images, map data, geological sample analysis results, etc. Categorize the collected data according to data type and source to ensure that the data is clearly organized and easy to process.

[0043] Check and remove duplicate data entries to avoid data redundancy. Identify and handle missing values, which can be done by interpolation, deletion, or statistical filling strategies. Correct or delete outliers, which may be caused by measurement errors or data entry errors. Convert data from different data sources into a unified format, such as CSV, GeoJSON, or Shapefile, to facilitate subsequent data integration and analysis. Ensure that all data uses the same coordinate reference system (CRS) for accurate alignment in spatial analysis. Standardize the data, such as scaling and centering, to eliminate the effects of different dimensions or units between different data sources. For categorical data, encoding (such as one-hot encoding) can be used to convert it into a format suitable for numerical analysis. Extract relevant features from the raw data, such as rock type, layer thickness, geological structure, etc., according to the needs of the geological mapping task. Use feature selection techniques (such as correlation analysis, principal component analysis, etc.) to reduce the number of features while retaining the most useful information for the task.

[0044] In a preferred embodiment of the present invention, the above step 3 dynamically adjusts the factor The calculation formula is: ; in, , and represents the weight parameter; Indicates The amount of information in each data source; Indicates The frequency of data updates for each data source; Indicates Data source Historical data points; Indicates True value; Represents the total number of data points, that is, when calculating the The number of data points to consider for historical data accuracy of a data source; represents the total number of data sources, i.e., the number of different data sources considered during the dynamic adjustment process; It is used to traverse all data sources to calculate the total amount; Represents a method used to iterate over all data points of a particular data source to calculate accuracy.

[0045] In an embodiment of the present invention: This formula takes into account the amount of information in the data source. , Data update frequency and the accuracy of historical data (by comparing and ), so that the quality of the data source can be comprehensively evaluated. , and , the importance of each evaluation dimension can be flexibly adjusted according to actual needs. The dynamic calculation of enables the system to adjust the weight of the data source in real time according to its performance, thereby selecting the better data source among multiple data sources. This dynamic optimization helps to improve the efficiency and accuracy of data processing. As an adjustment factor, the contribution of each data source can be dynamically adjusted according to its quality, thereby improving the overall quality of the fused data. By considering the accuracy of historical data, this formula can identify and reduce the impact of unstable or erroneous data sources to a certain extent, thereby enhancing the robustness of the system. The design of this formula takes into account multiple data sources (represented by q) and multiple data points (represented by p), so it has good scalability and can easily cope with the increase in the number of data sources and data volume.

[0046] In a preferred embodiment of the present invention, the above step 4, using a dynamic adjustment factor to fuse the pre-processed multi-source geological data to obtain fused geological data, includes: Step 41, multiplying the data in each data source by the corresponding dynamic adjustment factor to obtain weighted data source data; Step 42, determining the unit for average calculation, for each unit for average calculation, traversing the data source and searching for data points matching the current unit, so as to extract the corresponding data value from all weighted data source data; Step 43, for each unit, average all the extracted weighted data values; Step 44, by extracting corresponding data values ​​and averaging, a comprehensive geological data value integrating information from multiple data sources is calculated for each unit, and the comprehensive geological data value is the integrated geological data.

[0047] In an embodiment of the present invention, by weighting the data in each data source through a dynamic adjustment factor, the quality and reliability of different data sources can be fully considered, so that a higher-quality data source is given a greater weight in the fusion process. This helps to improve the accuracy and credibility of the fused geological data. The use of dynamic adjustment factors allows the weight of the data source to be dynamically adjusted according to its actual performance, which means that during the data fusion process, the system can optimize the weight distribution of the data source in real time. This flexibility helps to better cope with the situation of changes in the quality of the data source and ensure the stability and reliability of the fusion results. By averaging the data of multiple weighted data sources, the information provided by different data sources can be comprehensively considered to obtain a more comprehensive and representative comprehensive geological data value. This helps to improve the comprehensive analysis ability of geological data and provide more powerful data support for subsequent work such as geological exploration and resource assessment. In the process of data fusion, the data deviation and uncertainty that may be caused by a single data source can be reduced by averaging. The weighted fusion of multiple data sources helps to smooth data fluctuations and improve the stability and consistency of data. The fused geological data provides decision makers with a more accurate and comprehensive information basis, which helps to formulate more scientific and reasonable decision-making plans. In the fields of geological engineering, mineral resource development, etc., accurate fusion data is the key to formulating effective strategies. This method promotes the collaborative application of geological data from different sources and gives full play to the complementary advantages of multi-source data. Through fusion processing, information from different data sources can be effectively integrated, improving the overall utilization efficiency and value of data.

[0048] When applied specifically, the following steps are included: Step 41, obtain all pre-processed geological data sources and determine the dynamic adjustment factor corresponding to each data source. For each data point in each data source, multiply its value by the corresponding dynamic adjustment factor. This process is performed at the data source level, which means that all data points of the entire data source will be multiplied by the same factor. After completing the weighted processing, the weighted data source data is stored for use in subsequent steps.

[0049] Step 42, according to the fusion requirements, determine the unit of average calculation, which can be a geographic grid, a time interval, a specific geological feature, etc. For each determined calculation unit, traverse all weighted data sources. In each data source, find the data point that matches the current calculation unit. Once a matching data point is found, extract its weighted data value. These extracted data values ​​will be used for subsequent average calculations.

[0050] Step 43: For each calculation unit, all data values ​​extracted from the weighted data source are aggregated. These aggregated data values ​​are averaged to obtain the average weighted data value of the calculation unit. This value represents a comprehensive indicator that integrates information from multiple data sources.

[0051] Step 44, repeating steps 42 and 43 until all calculation units are processed. Eventually, each calculation unit will obtain a comprehensive geological data value. These values ​​constitute the fused geological data, which can be used for further geological analysis or decision support.

[0052] For example, suppose there are three geological data sources (A, B, C), each of which contains metal content data at different locations in a region. It is desired to fuse these data sources to obtain a more accurate estimate of the metal content at each location. Step 41, in this step, the three geological data sources (A, B, C) are first weighted. The metal content data contained in each data source will be multiplied by a dynamic adjustment factor based on its accuracy and resolution evaluation results. Specifically: Data source A, due to its higher accuracy and resolution, is given a larger weight, and its dynamic adjustment factor is 0.6. This means that each metal content data in data source A will be multiplied by 0.6.

[0053] Data source B, which has slightly lower accuracy and resolution than data source A, has a dynamic adjustment factor of 0.3. Each metal content data from data source B will be multiplied by this factor.

[0054] Data source C, which has lower accuracy and resolution than A and B, is given a smaller weight with a dynamic adjustment factor of 0.1. Similarly, all data in data source C will be multiplied by 0.1.

[0055] Through this weighted processing, the data can be adjusted accordingly according to the quality of the data source, laying the foundation for subsequent data fusion.

[0056] Step 42, in this step, the calculation unit is determined as a grid of geographical locations. This means that the entire region is divided into a number of grids, each grid representing a specific geographical location.

[0057] For each grid location, it is necessary to find the metal content data corresponding to the grid in the weighted data sources A, B, and C. Each grid has a center point or representative point whose geographic coordinates (longitude and latitude) are known. Next, parse the geographic coordinate information in each data source; read the longitude and latitude information in the data source, which can be stored in different formats, such as decimal degrees, degrees, minutes, and seconds, etc., and need to be converted into the same format for comparison. Then, use a coordinate matching algorithm to determine which grid each data point in the data source belongs to, which can be done by calculating the distance between the coordinates of the data point and the center point or boundary of each grid. If the coordinates of a data point fall within the boundary of a grid or are closest to the center point of a grid, the data point is considered to belong to the grid. If the grid to which the data point belongs is determined, the corresponding data (such as metal content) can be extracted from the data source and associated with the grid coordinates. This usually involves creating a data structure (such as a database table or array) to store the data values ​​of each grid.

[0058] Step 43, after extracting the weighted metal content data from data sources A, B, and C at each grid location, an average calculation is performed. The purpose of this step is to fuse information from different data sources to obtain a more accurate and comprehensive metal content estimate.

[0059] Taking a certain grid location as an example, suppose the weighted data extracted from data source A is 10, from data source B is 15, and from data source C is 20. In order to obtain the comprehensive metal content data value of this grid location, these three values ​​are added (10+15+20=45), and then divided by the number of data sources (i.e. 3) to obtain the average value of 15.

[0060] This average value represents the metal content estimate for that grid location after integrating the information from the three data sources. In this way, the advantages of multiple data sources can be utilized to improve the accuracy and reliability of metal content estimates.

[0061] Step 44, after completing the average calculation of all grid positions, a comprehensive metal content data value is finally obtained for each grid position. These data values ​​are based on the weighted and averaged results of multiple data sources, and are therefore more accurate and comprehensive than the information provided by a single data source. These comprehensive metal content data values ​​constitute the fused geological data, and by fusing the information of multiple data sources, geological resources can be better understood and utilized.

[0062] In a preferred embodiment of the present invention, the above step 5, based on the fused geological data, uses a geological information extraction algorithm to extract target geological mapping information, including: Step 51, performing statistical analysis on the fused geological data to determine the numerical range and distribution characteristics of the data, specifically including: performing statistical analysis on the fused geological data, which includes calculating the maximum value, minimum value, average value, standard deviation and other statistical quantities of the data to understand the overall numerical range of the data.

[0063] Step 52, according to the specific performance of the target geological features in the data, a threshold is set, specifically including: determining that the target is to identify areas with high metal content; consulting relevant geological research literature, reports or databases to understand the historical data and research results of metal content in the study area, and paying special attention to the metal content values ​​of the high metal content areas identified in previous studies. According to the statistical analysis results of the obtained fused geological data, the numerical range and distribution of the metal content are observed, and the central trend, degree of dispersion and possible outliers of the data are noted. Based on the values ​​of the high metal content areas identified in previous studies and combined with the distribution of the current data, a threshold of the metal content is preliminarily set, which can be a specific value or a range of values. Using the preliminarily set threshold, the data is screened to find data points that exceed the threshold. The screened high metal content areas are compared with the high metal content areas in the previous study to check the consistency. At the same time, the knowledge and experience of geological experts can also be used to verify the screening results. If it is found that the preliminarily set threshold is too high or too low, resulting in a large deviation between the screening results and the actual situation, it needs to be adjusted according to the verification results. The adjustment can be to increase or decrease the threshold until the screening results are consistent with the actual geological situation. The threshold after verification and adjustment will be determined as the final threshold.

[0064] Step 53, traverse the fused geological data, judge each data point to see whether it exceeds the set threshold, if the value of the data point exceeds the set threshold, mark the data point as a target geological feature point; otherwise, mark it as a non-target point; create a new data table for storing the extracted target geological mapping information, for each data point marked as a target geological feature, add its location information and attribute value to the data table to obtain the target geological mapping information, specifically including: after setting the threshold, start traversing each data point in the fused geological data set. For each data point, judge whether its attribute value (such as metal content, rock type, etc.) exceeds the set threshold. If the attribute value of a data point exceeds the threshold, then the data point is marked as a target geological feature point, indicating that it may belong to the geological feature area of ​​interest. On the contrary, if the attribute value of the data point does not exceed the threshold, it is marked as a non-target point.

[0065] Finally, create a new data table to store the extracted target geological mapping information. This new data table will contain all the relevant information marked as target geological feature points, such as their location coordinates (latitude and longitude), attribute values ​​(such as metal content, etc.). In this way, the extracted target geological feature information can be easily queried, analyzed and displayed.

[0066] In an embodiment of the present invention, by performing statistical analysis on the fused geological data, the numerical range and distribution characteristics of the data can be understood more accurately, which helps to identify and correct outliers or errors in the data, thereby improving the overall data quality. Setting specific thresholds to identify target geological features can achieve accurate capture of specific geological phenomena. This method can quickly filter out data points that meet specific conditions from a large amount of data, providing strong support for geological research and resource exploration. The process of automating traversal and judging whether data points exceed the set threshold can greatly reduce the time of manual analysis and processing, and improve the efficiency of geological mapping work. By creating a new data table to store the extracted target geological mapping information, systematic management and efficient query of this information can be achieved. This helps with subsequent data analysis and decision support, making geological information easier to access and use.

[0067] The above step 6, screening the extracted target geological mapping information to form the final geological mapping information extraction result, may include: Step 61, data quality assessment is performed on the extracted target geological mapping information. This can be done by checking for null values, outliers, or duplicate values ​​in the data. For example, if the attribute value of a data point is significantly deviated from other data points or is not within the expected value range, it may be considered an outlier.

[0068] Step 62, use the statistics (such as mean value, standard deviation, etc.) calculated in step 51 to set the screening conditions. For example, data points whose attribute values ​​are within the standard deviation range of the mean value plus or minus a certain multiple can be screened out, so that extreme outliers can be excluded. Traverse the extracted target geological mapping information. For each data point, filter it according to whether its attribute value meets the screening conditions. Data points that meet the conditions are retained, and data points that do not meet the conditions are eliminated.

[0069] Step 63, organizes the data points retained after screening into the final geological mapping information extraction result. This result can be a streamlined data set, which only contains high-quality target geological feature points that meet the screening conditions. In this way, step 6 can automatically screen out high-quality geological mapping information, avoid the subjectivity and tediousness of manual screening, and improve the efficiency and accuracy of screening.

[0070] like Figure 2As shown, an embodiment of the present invention further provides a geological surveying and mapping information extraction system based on multi-source data fusion, comprising: The collection module is used to collect multi-source geological data, including satellite remote sensing data, geological exploration data, geophysical data, geochemical data and ground measured data; A preprocessing module, used for preprocessing multi-source geological data to obtain preprocessed multi-source geological data; A calculation module, used to calculate the dynamic adjustment factor of each data source based on the amount of information provided by each data source, the frequency of data update and the accuracy of historical data; A fusion module is used to fuse the pre-processed multi-source geological data using a dynamic adjustment factor to obtain fused geological data; An extraction module is used to extract target geological surveying and mapping information based on the fused geological data using a geological information extraction algorithm; The screening module is used to screen the extracted target geological surveying and mapping information to form the final geological surveying and mapping information extraction result.

[0071] It should be noted that the system is a system corresponding to the above method, and all implementation methods in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0072] The embodiment of the present invention further provides a computing device, comprising: a processor, a memory storing a computer program, wherein when the computer program is executed by the processor, the method described above is executed. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0073] The embodiment of the present invention also provides a computer-readable storage medium storing instructions, which, when executed on a computer, enable the computer to execute the method described above. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0074] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for extracting geological surveying and mapping information based on multi-source data fusion, characterized in that: The method comprises: Step 1: Collect multi-source geological data, including satellite remote sensing data, geological exploration data, geophysical data, geochemical data and ground measured data; Step 2, preprocessing the multi-source geological data to obtain preprocessed multi-source geological data; Step 3, based on the amount of information provided by each data source, the frequency of data updates, and the accuracy of historical data, calculate the dynamic adjustment factor of each data source; Step 4, using the dynamic adjustment factor, fusing the preprocessed multi-source geological data to obtain fused geological data; Step 5, based on the fused geological data, using a geological information extraction algorithm to extract target geological mapping information; Step 6: Screen the extracted target geological surveying and mapping information to form the final geological surveying and mapping information extraction result.

2. The method for extracting geological surveying and mapping information based on multi-source data fusion according to claim 1, characterized in that: Collect geological data from multiple sources, including: Determine the data sources and types to form the space for data collection; randomly generate a set of combinations of data sources as the initial population, each combination represents a solution, and a solution represents a set of selected data sources; Construct a fitness function to evaluate the quality of each data source combination; based on the fitness function, select the corresponding individuals from the current population as the parent generation to generate the next generation; Combine the selected parent individuals to generate new offspring individuals; perform random mutation on the newly generated offspring individuals, replace part of the old population with the newly generated offspring individuals to form a new generation population, and repeat the process of selection, crossover, mutation and generation of new population until the preset number of iterations is reached to obtain the optimized data source combination; According to the optimized data source combination, the corresponding multi-source geological data are collected.

3. The method for extracting geological surveying and mapping information based on multi-source data fusion according to claim 2 is characterized in that: Fitness function The calculation formula is: ； in, Indicates The number of valid, non-missing data entries in each data source; Indicates The total number of data entries in the data source; , and is the weight parameter; Indicates the total number of data sources; and It is the index of the data source, which is used to traverse and identify different data sources; represents the complementarity score; Representative data sources and Redundancy score between data sources; It is The correlation score between each data source and the target geological mapping information; ； in, Indicates Data source The value of a data item; Indicates The average value of all data in a data source; Indicates the target geological mapping information The value of a data item; It represents the average value of all data in the target geological mapping information; Indicates the total number of data entries; It is the index of the data entry, which is used to traverse the specific data points in the data source.

4. The method for extracting geological surveying and mapping information based on multi-source data fusion according to claim 3 is characterized in that: No. data sources and Redundancy score between data sources The calculation formula is: ； in, Indicates Data source The value of the data item; complementarity score The calculation formula is: ； in, Indicates data sources and The amount of information contained in the data sources; Indicates data sources and The total amount of information after merging the data sources.

5. The method for extracting geological surveying and mapping information based on multi-source data fusion according to claim 4, characterized in that: Dynamic Adjustment Factor The calculation formula is: ； in, , and represents the weight parameter; Indicates The amount of information in each data source; Indicates The data update frequency of each data source; Indicates Data source Historical data points; Indicates True value; Represents the total number of data points, that is, when calculating the The number of data points to consider for historical data accuracy of a data source; represents the total number of data sources, i.e., the number of different data sources considered during the dynamic adjustment process; It is used to traverse all data sources to calculate the total amount; Represents a method used to iterate over all data points of a particular data source to calculate accuracy.

6. The method for extracting geological surveying and mapping information based on multi-source data fusion according to claim 5, characterized in that: The pre-processed multi-source geological data are fused using dynamic adjustment factors to obtain fused geological data, including: Multiply the data in each data source by the corresponding dynamic adjustment factor to obtain weighted data source data; Determine the unit for average calculation, and for each unit for average calculation, traverse the data source and find the data point matching the current unit, so as to extract the corresponding data value from all weighted data source data; For each unit, all extracted weighted data values ​​were averaged; By extracting the corresponding data values ​​and averaging, a comprehensive geological data value integrating information from multiple data sources is calculated for each unit. The comprehensive geological data value is the integrated geological data.

7. The method for extracting geological surveying and mapping information based on multi-source data fusion according to claim 6, characterized in that: Based on the fused geological data, the target geological mapping information is extracted using the geological information extraction algorithm, including: Conduct statistical analysis on the fused geological data to determine the numerical range and distribution characteristics of the data; A threshold is set based on the specific representation of the target geological feature in the data; Traverse the fused geological data and judge each data point to see whether it exceeds the set threshold. If the value of the data point exceeds the set threshold, the data point is marked as a target geological feature point; otherwise, it is marked as a non-target point; Create a new data table to store the extracted target geological mapping information. For each data point marked as a target geological feature, add its location information and attribute value to the data table to obtain the target geological mapping information.

8. A geological surveying and mapping information extraction system based on multi-source data fusion, characterized in that: Applied to the method according to any one of claims 1 to 7, comprising: The collection module is used to collect multi-source geological data, including satellite remote sensing data, geological exploration data, geophysical data, geochemical data and ground measured data; A preprocessing module, used for preprocessing multi-source geological data to obtain preprocessed multi-source geological data; A calculation module, used to calculate the dynamic adjustment factor of each data source based on the amount of information provided by each data source, the frequency of data update and the accuracy of historical data; A fusion module is used to fuse the pre-processed multi-source geological data using a dynamic adjustment factor to obtain fused geological data; An extraction module is used to extract target geological surveying and mapping information based on the fused geological data using a geological information extraction algorithm; The screening module is used to screen the extracted target geological surveying and mapping information to form the final geological surveying and mapping information extraction result.

9. A computing device, characterized in that include: one or more processors; A storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program, which, when executed by a processor, implements the method according to any one of claims 1 to 7.

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