Mineral exploration information generation method and system based on big data

Abstract

The invention discloses a mineral exploration information generation method and system based on big data. The method relates to the technical field of mineral exploration information data processing, and comprises the following steps: collecting and analyzing mineral exploration information to generate data, and comprehensively analyzing, optimizing and adjusting. According to the method, the mineral exploration information generation data is acquired and processed, the mineral exploration information generation data is analyzed, the mineral exploration information generation data fusion synchronism evaluation value and the mineral exploration information generation data quality evaluation value are obtained, comprehensive analysis and optimization adjustment are performed, the reliability of the mineral exploration information generation method is improved, and the mineral exploration information generation efficiency is improved. The problem that in the prior art, a mineral exploration information generation method is insufficient in reliability is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of mineral exploration information data processing, and in particular to a method and system for generating mineral exploration information based on big data. Background Art

[0002] Traditional mineral exploration methods often rely on ground surveys, drilling, geophysical exploration and other technologies. These methods are costly, long-term, inefficient, and have limited effects under complex geological conditions. With the rapid development of information technology, big data technology has penetrated into all walks of life. Big data has the characteristics of large data volume, diverse types, and fast processing speed. It can provide new perspectives and methods for mineral exploration. Mineral exploration involves a variety of data types, such as geological data, geophysical data, geochemical data, remote sensing data, etc.

[0003] The existing mineral exploration information generation system uses magnetic exploration to detect magnetic ore bodies or geological structures using changes in the geomagnetic field; uses soil geochemical exploration to analyze the distribution of elements in the soil to find clues to mineralization; uses microwave remote sensing to obtain structural information under the surface, especially in vegetation-covered areas; and uses spatial data analysis to perform spatial analysis on geological data to generate mineralization potential maps.

[0004] For example, the invention patent announcement with announcement number: CN112712276B discloses a method and device for constructing a geographically weighted regression model for mineral exploration, including: constructing multiple elliptical screening areas by selecting target sample points in the target work area, and fitting the attribute characteristics and corresponding mineralization processes included in the target sample points using the weighted least squares method to obtain multiple pending geographically weighted regression models corresponding to the target sample points, and then selecting the pending geographically weighted regression model corresponding to the minimum residual sum of squares as the optimal geographically weighted regression model. In this way, taking mineral exploration based on water system sediment geochemical data as an example, the influence of the anisotropic characteristics of various geological elements related to mineralization can be characterized.

[0005] For example, the invention patent with announcement number: CN117876623B discloses a method for generating a digital terrain model for mineral resource exploration, including: S1. Acquire remote sensing data, ground measurement, drilling data and mineral resource distribution data of the mineral resource exploration area respectively; S2. Integrate the remote sensing data, ground measurement and drilling data into a unified spatial reference framework, and construct a digital elevation model #imgabs0#; S3. Perform three-dimensional geological modeling of the mineral exploration area based on remote sensing data, ground measurement and drilling data; S4. Combine the distribution characteristics of mineral resources and superimpose the mineral resource distribution data on the three-dimensional geological model to form a three-dimensional visualized digital terrain model for mineral resource exploration.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0007] This method is applied in the application scenario of geological mapping in mining areas. There is a problem of insufficient reliability assessment of the method for generating mineral exploration information. Since the remote sensing data and geological data do not match in timestamps, it is difficult to synchronize during the data fusion process, affecting the precision and accuracy of the geological map. The collection of mineral exploration remote sensing data is interfered by environmental factors, and there is noise and error in the mineral exploration remote sensing data, resulting in inaccurate data quality. There is a problem of insufficient reliability of the method for generating mineral exploration information. Summary of the invention

[0008] The embodiments of the present application solve the problem of insufficient reliability of the mineral exploration information generation method in the prior art by providing a mineral exploration information generation method and system based on big data, thereby improving the reliability of the mineral exploration information generation method.

[0009] The embodiment of the present application provides a method for generating mineral exploration information based on big data, comprising the following steps: collecting and processing mineral exploration information generation data; analyzing the mineral exploration information generation data to obtain a fusion synchronization evaluation value of the mineral exploration information generation data and a quality evaluation value of the mineral exploration information generation data; comprehensively analyzing to obtain a reliability evaluation value of the mineral exploration information generation; comparing and analyzing the fusion synchronization evaluation value of the mineral exploration information generation data with a first threshold of the fusion synchronization evaluation value of the mineral exploration information generation data, and optimizing the fusion synchronization method of the mineral exploration information generation data; comparing and analyzing the quality evaluation value of the mineral exploration information generation data with a second threshold of the quality evaluation value of the mineral exploration information generation data, and optimizing the quality method of the mineral exploration information generation data; comparing and analyzing the reliability evaluation value of the mineral exploration information generation with a comprehensive threshold of the reliability evaluation value of the mineral exploration information generation, and optimizing and adjusting the reliability method of the mineral exploration information generation.

[0010] Furthermore, the specific steps for obtaining the quality assessment value of mineral exploration information generation data are as follows: obtaining the electromagnetic intensity of a preset mineral exploration information generation data quality time detection point through a time domain electromagnetic instrument; obtaining the reflected light intensity of the mining area at the preset mineral exploration information generation data quality time detection point through an aerial remote sensing sensor; obtaining the incident light intensity of the mining area at the preset mineral exploration information generation data quality time detection point through an aerial remote sensing sensor; obtaining the exploration information generation data noise at the preset mineral exploration information generation data quality time detection point through a signal processing software; obtaining the remote sensing image pixel resolution at the preset mineral exploration information generation data quality time detection point through a satellite remote sensing sensor; obtaining the missing rate of mineral exploration information generation data at the preset mineral exploration information generation data quality time detection point through a GIS and data management software; obtaining the horizontal coordinate of the mineral exploration information generation data at the preset mineral exploration information generation data quality time detection point in space through a GPS receiver; obtaining the vertical coordinate of the mineral exploration information generation data at the preset mineral exploration information generation data quality time detection point through a GPS receiver; and obtaining the quality assessment value of mineral exploration information generation data through analysis.

[0011] Furthermore, the specific steps of obtaining the reliability assessment value of mineral exploration information generation are: obtaining the mineral exploration information generation data compression efficiency of the preset mineral exploration information generation reliability time detection point through data compression software; obtaining the mineral exploration information generation reliability assessment value through the mineral exploration information generation data fusion synchronization assessment value, the mineral exploration information generation data quality assessment value and the mineral exploration information generation data compression efficiency.

[0012] Furthermore, the specific steps of the method for optimizing the synchronization of fusion of mineral exploration information generation data are as follows: if the fusion synchronization evaluation value of mineral exploration information generation data is greater than or equal to the first threshold value of the fusion synchronization evaluation value of mineral exploration information generation data, there is no need to optimize the method for optimizing the fusion synchronization of mineral exploration information generation data; if the fusion synchronization evaluation value of mineral exploration information generation data is lower than the first threshold value of the fusion synchronization evaluation value of mineral exploration information generation data, then the difference between the fusion synchronization evaluation value of mineral exploration information generation data and the first threshold value of the fusion synchronization evaluation value of mineral exploration information generation data is used to match the corresponding adjustment plan in the mineral exploration information generation database.

[0013] Furthermore, the specific steps of the method for optimizing the quality of mineral exploration information generated data are as follows: if the quality assessment value of the mineral exploration information generated data is lower than or equal to the second threshold value of the mineral exploration information generated data quality assessment value, there is no need to optimize the method for optimizing the quality of mineral exploration information generated data; if the quality assessment value of the mineral exploration information generated data is greater than the second threshold value of the mineral exploration information generated data quality assessment value, then the difference between the quality assessment value of the mineral exploration information generated data and the second threshold value of the mineral exploration information generated data quality assessment value is used to match the corresponding adjustment plan in the mineral exploration information generation database.

[0014] Furthermore, the specific steps of the method for optimizing and adjusting the reliability of mineral exploration information generation are: extracting a comprehensive threshold of a mineral exploration information generation reliability assessment value from a mineral exploration information generation database, and comparing the mineral exploration information generation reliability assessment value with the comprehensive threshold of the mineral exploration information generation reliability assessment value; if the mineral exploration information generation reliability assessment value is greater than or equal to the comprehensive threshold of the mineral exploration information generation reliability assessment value, there is no need to optimize and adjust the method for optimizing and adjusting the reliability of mineral exploration information generation.

[0015] Furthermore, the method for optimizing and adjusting the reliability of mineral exploration information generation also includes: if the mineral exploration information generation reliability assessment value is lower than the comprehensive threshold of the mineral exploration information generation reliability assessment value, then matching the corresponding adjustment plan in the mineral exploration information generation database through the difference between the mineral exploration information generation reliability assessment value and the comprehensive threshold of the mineral exploration information generation reliability assessment value.

[0016] The embodiment of the present application provides a mineral exploration information generation system based on big data, including a mineral exploration information generation data acquisition module, a mineral exploration information generation data analysis module, a comprehensive analysis module and an optimization and adjustment module: the mineral exploration information generation data acquisition module: used to collect and process mineral exploration information generation data; the mineral exploration information generation data analysis module: used to analyze the mineral exploration information generation data, obtain the mineral exploration information generation data fusion synchronization evaluation value and obtain the mineral exploration information generation data quality evaluation value; the comprehensive analysis module: used to comprehensively analyze the mineral exploration information Generate reliability assessment value; Optimization and adjustment module: used to compare and analyze the data fusion synchronization assessment value of mineral exploration information generation with the first threshold of the data fusion synchronization assessment value of mineral exploration information generation, and optimize the data fusion synchronization method of mineral exploration information generation; compare and analyze the data quality assessment value of mineral exploration information generation with the second threshold of the data quality assessment value of mineral exploration information generation, and optimize the data quality method of mineral exploration information generation; compare and analyze the reliability assessment value of mineral exploration information generation with the comprehensive threshold of the reliability assessment value of mineral exploration information generation, and optimize and adjust the reliability method of mineral exploration information generation.

[0017] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0018] 1. By collecting and processing the data generated by mineral exploration information, the data generated by mineral exploration information is analyzed to obtain the fusion synchronization evaluation value of the mineral exploration information generated data and the quality evaluation value of the mineral exploration information generated data. Comprehensive analysis and optimization adjustments are made to improve the reliability of the mineral exploration information generation method, thus solving the problem of insufficient reliability of the mineral exploration information generation method in the existing technology.

[0019] 2. By analyzing the data generated by mineral exploration information, the most promising exploration areas can be identified, thereby concentrating resources and efforts, reducing ineffective work, and improving exploration efficiency. The potential and grade of mineral resources can be more accurately assessed, reducing uncertainty and risk in the exploration process.

[0020] 3. Through comprehensive analysis and optimization and adjustment, the reliability of data can be improved, the uncertainty in the exploration process can be reduced, thereby reducing investment risks, and making the exploration technology more adaptable to different geological conditions and environmental requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A flow chart of a method for generating mineral exploration information based on big data provided in an embodiment of the present application;

[0022] Figure 2 A schematic diagram of a function for generating a reliability assessment value for mineral exploration information provided in an embodiment of the present application;

[0023] Figure 3 A schematic diagram of the structure of a mineral exploration information generation system based on big data provided in an embodiment of the present application. DETAILED DESCRIPTION

[0024] The embodiments of the present application solve the problem of insufficient reliability of the mineral exploration information generation method in the prior art by providing a mineral exploration information generation method and system based on big data. The mineral exploration information generation data is collected and processed, and the mineral exploration information generation data is analyzed to obtain a fusion synchronization evaluation value of the mineral exploration information generation data and a quality evaluation value of the mineral exploration information generation data. Comprehensive analysis and optimization adjustment are performed to improve the reliability of the mineral exploration information generation method.

[0025] The technical solution in the embodiment of the present application is to solve the above-mentioned problem that the method for generating mineral exploration information is not reliable enough. The overall idea is as follows:

[0026] By collecting and processing the data generated by mineral exploration information, the mineral exploration information generated data is analyzed to obtain the fusion synchronization assessment value of the mineral exploration information generated data and the quality assessment value of the mineral exploration information generated data. Comprehensive analysis and optimization adjustments are made to improve the reliability of the mineral exploration information generation method.

[0027] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0028] like Figure 1 As shown, it is a flow chart of a mineral exploration information generation method based on big data provided by an embodiment of the present application. The method is applied to a mineral exploration information generation system based on big data, and the method includes the following steps: collecting and processing mineral exploration information generation data; analyzing the mineral exploration information generation data to obtain a fusion synchronization evaluation value of the mineral exploration information generation data and a quality evaluation value of the mineral exploration information generation data; comprehensively analyzing to obtain a mineral exploration information generation reliability evaluation value; comparing and analyzing the fusion synchronization evaluation value of the mineral exploration information generation data with a first threshold of the fusion synchronization evaluation value of the mineral exploration information generation data, and optimizing the mineral exploration information generation data fusion synchronization method; comparing and analyzing the mineral exploration information generation data quality evaluation value with a second threshold of the mineral exploration information generation data quality evaluation value, and optimizing the mineral exploration information generation data quality method; comparing and analyzing the mineral exploration information generation reliability evaluation value with a comprehensive threshold of the mineral exploration information generation reliability evaluation value, and optimizing and adjusting the mineral exploration information generation reliability method.

[0029] Furthermore, the specific steps of collecting and processing the mineral exploration information generation data are: obtaining the mineral exploration information generation raw data through physical exploration equipment; cleaning and denoising the mineral exploration information generation raw data to obtain the mineral exploration information generation data.

[0030] Furthermore, the specific steps for obtaining the synchronization evaluation value of the fusion of mineral exploration information generation data are as follows: obtaining the electromagnetic intensity of the preset mineral exploration information generation data fusion synchronization time detection point through a time domain electromagnetic instrument; obtaining the reflected light intensity of the mining area at the preset mineral exploration information generation data fusion synchronization time detection point through an aerial remote sensing sensor; obtaining the incident light intensity of the mining area at the preset mineral exploration information generation data fusion synchronization time detection point through an aerial remote sensing sensor; obtaining the mineral exploration information generation data entropy of the preset mineral exploration information generation data fusion synchronization time detection point through a data processor; obtaining the mineral exploration information generation data noise of the preset mineral exploration information generation data fusion synchronization time detection point through a signal processing software; obtaining the clock frequency of the preset mineral exploration information generation data fusion synchronization time detection point through a high-precision clock; obtaining the clock source display time of the preset mineral exploration information generation data fusion synchronization time detection point through a GPS time receiver; and obtaining the synchronization evaluation value of the mineral exploration information generation data fusion through analysis.

[0031] In this embodiment, the specific method for analyzing and obtaining the data fusion synchronization evaluation value of mineral exploration information is as follows:

[0032]

[0033] The preset mineral exploration information generation data fusion synchronization time detection points are numbered in sequence, S0 represents the number of the mineral exploration information generation data fusion synchronization time detection point, S0=1,2,...,S, S represents the total number of mineral exploration information generation data fusion synchronization time detection points.

[0034] It represents the mineral exploration information generation data fusion synchronization evaluation value at the S0th mineral exploration information generation data fusion synchronization time detection point.

[0035] It represents the electromagnetic intensity correction coefficient at the S0th mineral exploration information generation data fusion synchronization time detection point.

[0036] It represents the reflectivity correction coefficient of the mining area at the S0th mineral exploration information generation data fusion synchronization time detection point.

[0037] A1 represents the standard value of mineral exploration information generation data entropy, which is a preset standard value of mineral exploration information generation data entropy obtained from the mineral exploration information generation database, and can be the average value of mineral exploration information generation data entropy at the preset mineral exploration information generation data fusion synchronization time detection point from the historical database.

[0038] It represents the entropy of the mineral exploration information generation data at the S0th mineral exploration information generation data fusion synchronization time detection point.

[0039] Among them, in the assessment of the synchronization of data fusion generated by mineral exploration information, if the data entropy of mineral exploration information generation is equal to the standard value of data entropy of mineral exploration information generation, it means that the complexity of the data meets the expected standard, the data entropy is within the expected range, and the assessment value of the synchronization of data fusion generated by mineral exploration information is more accurate; if the data entropy of mineral exploration information generation is greater than the standard value of data entropy of mineral exploration information generation, it means that the data is more complex than expected and contains more noise, resulting in a decrease in the assessment value of the synchronization of data fusion generated by mineral exploration information; if the data entropy of mineral exploration information generation is lower than the standard value of data entropy of mineral exploration information generation, it means that the data is too simple or the amount of information is insufficient, resulting in inaccurate assessment value of the synchronization of data fusion generated by mineral exploration information.

[0040] Specifically, there is a negative correlation between the square of the difference between the entropy of mineral exploration information generated data and the standard value of the entropy of mineral exploration information generated data and the assessment value of the fusion synchronization of mineral exploration information generated data. The larger the square of the difference between the entropy of mineral exploration information generated data and the standard value of the entropy of mineral exploration information generated data, the lower the assessment value of the fusion synchronization of mineral exploration information generated data.

[0041] Represents the clock frequency error coefficient at the S0th mineral exploration information generation data fusion synchronization time detection point.

[0042] It represents the noise of mineral exploration information generation data at the S0th mineral exploration information generation data fusion synchronization time detection point.

[0043] Z1 represents the mineral exploration information generation data noise threshold, which is the preset mineral exploration information generation data noise threshold obtained from the mineral exploration information generation database, and can be the mineral exploration information generation data noise average value at the preset mineral exploration information generation data fusion synchronization time detection point from the historical database.

[0044] Among them, in the synchronization assessment of data fusion generated by mineral exploration information, if the noise of the data generated by mineral exploration information is lower than or equal to the noise threshold of the data generated by mineral exploration information, it indicates that the quality of the data is good, the noise level is within an acceptable range, and the quality of the data just meets the minimum requirements. The data fusion synchronization assessment value will be in a critical state. The noise level at this time will have a certain impact on the synchronization, but it is still within a controllable range; if the noise of the data generated by mineral exploration information is greater than the noise threshold of the data generated by mineral exploration information, the synchronization assessment value of the data fusion generated by mineral exploration information will be reduced, increasing the error risk of data fusion.

[0045] Specifically, there is a negative correlation between the data noise of mineral exploration information generation and the evaluation value of the fusion synchronization of mineral exploration information generation data. The greater the data noise of mineral exploration information generation, the smaller the evaluation value of the fusion synchronization of mineral exploration information generation data.

[0046] It represents the electromagnetic intensity at the S0th mineral exploration information generation data fusion synchronization time detection point.

[0047] D1 represents the standard value of electromagnetic intensity, which is the preset standard value of electromagnetic intensity obtained from the mineral exploration information generation database, and can be the average value of electromagnetic intensity at the synchronous time detection point of the data fusion of the preset mineral exploration information generation in the historical database.

[0048] Among them, the electromagnetic intensity correction coefficient is calculated by the electromagnetic intensity at the preset mineral exploration information generation data fusion synchronization time detection point and the electromagnetic intensity standard value. In the mineral exploration information generation data fusion synchronization assessment, if the electromagnetic intensity is equal to the electromagnetic intensity standard value, it means that the measured electromagnetic intensity meets the expected or specified safety and performance standards, and will not have a negative impact on the mineral exploration information generation data fusion synchronization assessment; if the electromagnetic intensity is greater than the electromagnetic intensity standard value, it indicates that there are abnormal geological structures or mineralization phenomena in the exploration area, which has a negative impact on the mineral exploration information generation data fusion synchronization assessment value. The higher the electromagnetic intensity, the more it affects the quality and reliability of the data, resulting in a decrease in synchronization during the data fusion process; if the electromagnetic intensity is lower than the electromagnetic intensity standard value, it means that the electromagnetic signal is weak and insufficient to provide effective exploration information, which has a negative impact on the mineral exploration information generation data fusion synchronization assessment value. The lower the electromagnetic intensity, the more information is lost or insufficient during the data fusion process, affecting synchronization.

[0049] Specifically, there is a negative correlation between the square of the difference between the electromagnetic intensity and the standard value of the electromagnetic intensity and the evaluation value of the synchronization of the data fusion generated by the mineral exploration information. The larger the square of the difference between the electromagnetic intensity and the standard value of the electromagnetic intensity, the smaller the evaluation value of the synchronization of the data fusion generated by the mineral exploration information.

[0050] It represents the reflected light intensity of the mining area at the S0th mineral exploration information generation data fusion synchronization time detection point.

[0051] μ1 represents the standard value of reflected light intensity in the mining area, which is the preset standard value of reflected light intensity in the mining area obtained from the mineral exploration information generation database, or can be the average value of reflected light intensity in the mining area at the data fusion synchronization time detection point preset from the historical database of mineral exploration information generation.

[0052] It represents the incident light intensity in the mining area at the S0th mineral exploration information generation data fusion synchronization time detection point.

[0053] R1 represents the standard value of incident light intensity in the mining area, which is the preset standard value of incident light intensity in the mining area obtained from the mineral exploration information generation database, and can be the average value of incident light intensity in the mining area at the data fusion synchronization time detection point preset from the historical database of mineral exploration information generation.

[0054] Among them, the reflectivity correction coefficient of the mining area is calculated by presetting the reflected light intensity of the mining area at the synchronization time detection point of the data fusion generated by the mineral exploration information, the standard value of the reflected light intensity of the mining area, the incident light intensity of the mining area and the standard value of the incident light intensity of the mining area. In the synchronization evaluation of the data fusion generated by the mineral exploration information, if the reflected light intensity of the mining area is equal to the standard value of the reflected light intensity of the mining area, it means that the reflection characteristics of the surface of the mining area are in line with expectations, and the impact on the synchronization evaluation value is positive. The data is stable and reliable, which helps to maintain the synchronization of data fusion; if the reflected light intensity of the mining area is greater than the standard value of the reflected light intensity of the mining area, then the mining area Materials with high reflective properties on the surface, such as specific minerals or rocks, have a positive or negative impact on the synchronization evaluation value. The positive impact on the synchronization evaluation value is because the target minerals may be discovered, and the negative impact on the synchronization evaluation value is because the abnormally high reflected light intensity may indicate data inconsistency or noise. If the reflected light intensity in the mining area is lower than the standard value of the reflected light intensity in the mining area, it means that the surface of the mining area absorbs more light and reflects less. Due to dark minerals or coverings, the impact on the synchronization evaluation value is negative. The lower the reflected light intensity in the mining area, the less information is available, which affects the synchronization of data fusion.

[0055] If the incident light intensity in the mining area is equal to the standard value of the incident light intensity in the mining area, it means that the lighting conditions meet expectations and the impact on the synchronization evaluation value is positive, because stable lighting conditions help to improve the consistency and reliability of the data; if the incident light intensity in the mining area is greater than the standard value of the incident light intensity in the mining area, the excessive brightness caused by direct sunlight or reflection will cause the reflected light intensity to be distorted, and the impact on the synchronization evaluation value is negative. The greater the incident light intensity in the mining area, the data will be saturated or distorted, affecting the synchronization; if the incident light intensity in the mining area is lower than the standard value of the incident light intensity in the mining area, it may be due to clouds, shadows or low light conditions, resulting in insufficient reflected light intensity, which has a negative impact on the synchronization evaluation value. The lower the incident light intensity in the mining area, the less available spectral information will be, affecting the synchronization of data fusion.

[0056] Specifically, the square of the difference between the reflected light intensity in the mining area and the standard value of the reflected light intensity in the mining area is negatively correlated with the evaluation value of the fusion synchronization of the mineral exploration information generation data. The larger the square of the difference between the reflected light intensity in the mining area and the standard value of the reflected light intensity in the mining area, the smaller the evaluation value of the fusion synchronization of the mineral exploration information generation data; the square of the difference between the incident light intensity in the mining area and the standard value of the incident light intensity in the mining area is negatively correlated with the evaluation value of the fusion synchronization of the mineral exploration information generation data. The larger the square of the difference between the incident light intensity in the mining area and the standard value of the incident light intensity in the mining area, the smaller the evaluation value of the fusion synchronization of the mineral exploration information generation data.

[0057] It represents the clock frequency at the S0th mineral exploration information generation data fusion synchronization time detection point.

[0058] P1 represents the standard value of clock frequency, which is the preset standard value of clock frequency obtained from the mineral exploration information generation database, and can be the average value of clock frequency at the data fusion synchronization time detection point preset in the historical database for mineral exploration information generation.

[0059] ΔP represents the standard value of clock frequency error, which is a preset standard value of clock frequency error obtained from a mineral exploration information generation database, and may be an average value of clock frequency error at a data fusion synchronization time detection point preset from a historical database for mineral exploration information generation.

[0060] Indicates the clock source display time at the S0th mineral exploration information generation data fusion synchronization time detection point.

[0061] t1 represents the clock source time standard value, which is the preset clock source time standard value obtained from the mineral exploration information generation database, and can be the clock source time average value at the data fusion synchronization time detection point of the preset mineral exploration information generation in the historical database.

[0062] Among them, the clock frequency error coefficient is calculated by clock frequency, clock frequency standard value, clock frequency error standard value, clock source display time and clock source display time standard value. In the synchronization evaluation of data fusion generated by mineral exploration information, if the clock frequency is equal to the clock frequency standard value, it means that the clock is running normally and the time synchronization is good, which has a positive impact on the synchronization evaluation value. The timestamp of data acquisition is accurate, which helps to maintain the synchronization of data fusion; if the clock frequency is greater than or lower than the clock frequency standard value, it means that there is a deviation in the clock, resulting in time synchronization problems, which has a negative impact on the synchronization evaluation value, because the time deviation will affect the accurate alignment and fusion of data; the difference between the clock frequency and the clock frequency standard value is the clock frequency error, and the clock frequency error standard value is the allowable clock frequency deviation range. If the clock frequency error is within the standard value range, it is considered that the clock is synchronized. If the clock frequency error exceeds the standard value, the clock source needs to be adjusted or replaced.

[0063] If the time displayed by the clock source is equal to the standard value of the time displayed by the clock source, it means that the time displayed by the clock source is accurate and has a positive impact on the synchronization evaluation value, because the accuracy of the time information ensures the synchronization of data fusion; if the time displayed by the clock source is greater than or lower than the standard value of the time displayed by the clock source, it means that there is a time error in the clock source, and has a negative impact on the synchronization evaluation value, because the time error will cause the data acquisition timestamp to be inaccurate, affecting the synchronization of data fusion.

[0064] Specifically, the product of the square of the difference between the clock frequency and the standard value of the clock frequency and the difference between the time displayed by the clock source and the standard value of the time displayed by the clock source is negatively correlated with the evaluation value of the synchronization of data fusion generated by mineral exploration information. The larger the product of the square of the difference between the clock frequency and the standard value of the clock frequency and the difference between the time displayed by the clock source and the standard value of the time displayed by the clock source, the smaller the evaluation value of the synchronization of data fusion generated by mineral exploration information.

[0065] Generate a data entropy impact weight factor for preset mineral exploration information obtained from a mineral exploration information generation database; A preset clock frequency error coefficient influencing weighting factor obtained from a mineral exploration information generation database; Generate data noise impact weight factors for preset mineral exploration information obtained from a mineral exploration information generation database.

[0066] ρ1 is the preset electromagnetic intensity correction factor obtained from the mineral exploration information generation database; ρ2 is the preset mining area reflectivity correction factor obtained from the mineral exploration information generation database.

[0067] The preset mineral exploration information generation data entropy influencing weight factor, the preset clock frequency error coefficient influencing weight factor and the preset mineral exploration information generation data noise influencing weight factor are obtained through mapping relationships. For example, a mapping set of mineral exploration information generation data entropy, clock frequency error coefficient and mineral exploration information generation data noise and their corresponding weights are established respectively through the relationship between the mineral exploration information generation data entropy, clock frequency error coefficient and mineral exploration information generation data noise in historical data and the data update frequency. The preset mineral exploration information generation data entropy influencing weight factor, the preset clock frequency error coefficient influencing weight factor and the preset mineral exploration information generation data noise influencing weight factor corresponding to the mapping set are obtained by inputting real-time mineral exploration information generation data entropy, clock frequency error coefficient and mineral exploration information generation data noise.

[0068] The preset electromagnetic intensity correction factor and the preset mining area reflectivity correction factor are obtained through mapping relationships. For example, a mapping set of electromagnetic intensity and mining area reflectivity and their corresponding weights is established respectively through the relationship between the electromagnetic intensity and mining area reflectivity in historical data and the light intensity. The corresponding preset electromagnetic intensity correction factor and preset mining area reflectivity correction factor in the mapping set are obtained by inputting the real-time electromagnetic intensity and mining area reflectivity.

[0069] The increase of electromagnetic intensity will interfere with optical sensors and affect the measurement results of reflected light intensity in the mining area. The lower the reflected light intensity and incident light intensity in the mining area, the higher the noise of the data generated by mineral exploration information will be, which will increase the uncertainty of the data and increase the entropy of the data generated by mineral exploration information. The higher the clock frequency, the greater the timestamp error will be and the longer the time displayed by the clock source will be. The increase of electromagnetic intensity will cause more noise contained in the data generated by mineral exploration information and the greater the noise of the data generated by mineral exploration information.

[0070] Furthermore, the specific steps for obtaining the quality assessment value of mineral exploration information generation data are as follows: obtaining the electromagnetic intensity of a preset mineral exploration information generation data quality time detection point through a time domain electromagnetic instrument; obtaining the reflected light intensity of the mining area at the preset mineral exploration information generation data quality time detection point through an aerial remote sensing sensor; obtaining the incident light intensity of the mining area at the preset mineral exploration information generation data quality time detection point through an aerial remote sensing sensor; obtaining the exploration information generation data noise at the preset mineral exploration information generation data quality time detection point through a signal processing software; obtaining the remote sensing image pixel resolution at the preset mineral exploration information generation data quality time detection point through a satellite remote sensing sensor; obtaining the missing rate of mineral exploration information generation data at the preset mineral exploration information generation data quality time detection point through a GIS and data management software; obtaining the horizontal coordinate of the mineral exploration information generation data at the preset mineral exploration information generation data quality time detection point through a GPS receiver; obtaining the vertical coordinate of the mineral exploration information generation data at the preset mineral exploration information generation data quality time detection point through a GPS receiver; and obtaining the quality assessment value of mineral exploration information generation data through analysis.

[0071] In this embodiment, the specific method for analyzing and obtaining the data quality assessment value of mineral exploration information is as follows:

[0072]

[0073] σ1+σ2+σ3=1; τ1+τ2+τ3=1;

[0074] The preset mineral exploration information generation data quality time detection points are numbered in sequence, N0 represents the number of the mineral exploration information generation data quality time detection point, N0=1,2,...,N, N represents the total number of mineral exploration information generation data quality time detection points.

[0075] The preset mineral exploration information generation image matching detection points are numbered in sequence, K0 represents the number of the mineral exploration information generation image matching detection point under the N0th mineral exploration information generation data quality time detection point, K0 = 1, 2, ..., K, K represents the total number of mineral exploration information generation image matching detection points.

[0076] It represents the quality assessment value of the mineral exploration information generated data at the N0th mineral exploration information generated data quality time detection point.

[0077] It represents the electromagnetic intensity influence coefficient at the N0th mineral exploration information generation data quality time detection point.

[0078] It represents the reflectivity influence coefficient of the mining area at the N0th mineral exploration information generation data quality time detection point.

[0079] It represents the noise influence coefficient of the exploration information generation data at the N0th mineral exploration information generation data quality time detection point.

[0080] It represents the spatial distance error coefficient of the mineral exploration information generation data at the K0th mineral exploration information generation image matching detection point at the N0th mineral exploration information generation data quality time detection point.

[0081] It represents the pixel resolution of the remote sensing image at the N0th mineral exploration information generation data quality time detection point.

[0082] B1 represents the standard value of remote sensing image pixel resolution, which is the preset standard value of remote sensing image pixel resolution obtained from the mineral exploration information generation database, and can be the average value of remote sensing image pixel resolution at the data quality time detection point of the preset mineral exploration information generation in the historical database.

[0083] Among them, in the data quality assessment of mineral exploration information generation, if the remote sensing image pixel resolution is equal to the remote sensing image pixel resolution standard value, the remote sensing image is considered to have sufficient details for effective mineral exploration and to identify small-scale geological features and signs of mineralization; if the remote sensing image pixel resolution is lower than the remote sensing image pixel resolution standard value, the remote sensing image lacks the necessary details, affecting the accuracy of mining area exploration; if the remote sensing image pixel resolution is greater than the remote sensing image pixel resolution standard value, more image stitching is required to cover a larger area, which increases processing time and complexity and is more susceptible to atmospheric conditions, such as clouds, fog, etc., affecting the quality of remote sensing images.

[0084] Specifically, there is a positive correlation between the absolute value of the pixel resolution of the remote sensing image and the difference in pixel resolution of the remote sensing image and the quality assessment value of the data generated by mineral exploration information. The larger the absolute value of the pixel resolution of the remote sensing image and the difference in pixel resolution of the remote sensing image, the larger the quality assessment value of the data generated by mineral exploration information.

[0085] It represents the missing rate of mineral exploration information generation data at the N0th mineral exploration information generation data quality time detection point.

[0086] Q1 represents the threshold value of missing rate of mineral exploration information generation data, which is the preset threshold value of missing rate of mineral exploration information generation data obtained from the mineral exploration information generation database, and can be the average value of missing rate of mineral exploration information generation data at the preset mineral exploration information generation data quality time detection point from the historical database.

[0087] Among them, in the data quality assessment of mineral exploration information generation data, the missing rate of mineral exploration information generation data refers to the proportion of missing data to all data to be collected in the mineral exploration information generation data. The higher the ratio, the lower the data integrity. The missing rate threshold of mineral exploration information generation data is a pre-set standard used to determine whether the data missing is within an acceptable range. If the missing rate of mineral exploration information generation data is lower than the missing rate threshold of mineral exploration information generation data, it is considered that the data quality is acceptable and the data integrity is good; if the missing rate of mineral exploration information generation data is higher than the missing rate threshold of mineral exploration information generation data, the incompleteness of the data increases the uncertainty of the exploration results; if the missing rate of mineral exploration information generation data is equal to the missing rate threshold of mineral exploration information generation data, it is necessary to pay close attention to the risks brought by data missing.

[0088] Specifically, there is a positive correlation between the missing rate of mineral exploration information generated data and the quality assessment value of mineral exploration information generated data. The greater the missing rate of mineral exploration information generated data, the greater the quality assessment value of mineral exploration information generated data.

[0089] It represents the horizontal coordinate in space of the mineral exploration information generation data at the K0th mineral exploration information generation image matching detection point at the N0th mineral exploration information generation data quality time detection point.

[0090] X1 represents the standard value of the horizontal coordinate of the mineral exploration information generation data in space, which is the standard value of the horizontal coordinate of the preset mineral exploration information generation data in space obtained from the mineral exploration information generation database, and can be the average value of the horizontal coordinate of the mineral exploration information generation data in space at the preset mineral exploration information generation data quality time detection point from the historical database.

[0091] It represents the spatial vertical coordinate of the mineral exploration information generation data at the K0th mineral exploration information generation image matching detection point at the N0th mineral exploration information generation data quality time detection point.

[0092] Y1 represents the standard value of the vertical coordinate of the mineral exploration information generation data in space, which is the standard value of the vertical coordinate of the preset mineral exploration information generation data in space obtained from the mineral exploration information generation database, and can be the average vertical coordinate of the mineral exploration information generation data in space at the preset mineral exploration information generation data quality time detection point from the historical database.

[0093] Among them, in the quality assessment of mineral exploration information generated data, the horizontal coordinate of the mineral exploration information generated data in space represents the position of the mineral exploration data in the horizontal direction, and the standard value of the horizontal coordinate of the mineral exploration information generated data in space is the standard of the accuracy or precision that the horizontal coordinate should achieve. If the square of the difference between the horizontal coordinate of the mineral exploration information generated data in space and the standard value of the horizontal coordinate of the mineral exploration information generated data in space is larger, the specific location of the mineral resources cannot be accurately determined, and the spatial analysis results may be inaccurate, affecting the exploration decision; the vertical coordinate of the mineral exploration information generated data in space represents the position of the mineral exploration data in the vertical direction, and the standard value of the vertical coordinate of the mineral exploration information generated data in space is the standard of the accuracy or precision that the vertical coordinate should achieve. If the square of the difference between the vertical coordinate of the mineral exploration information generated data in space and the vertical coordinate of the mineral exploration information generated data in space is larger, it means that the spatial positioning accuracy of the data is low.

[0094] Specifically, there is a positive correlation between the square of the difference between the horizontal coordinate of the mineral exploration information generation data in space and the standard value of the horizontal coordinate of the mineral exploration information generation data in space and the quality assessment value of the mineral exploration information generation data. The larger the square of the difference between the horizontal coordinate of the mineral exploration information generation data in space and the standard value of the horizontal coordinate of the mineral exploration information generation data in space, the greater the quality assessment value of the mineral exploration information generation data; there is a positive correlation between the square of the difference between the vertical coordinate of the mineral exploration information generation data in space and the vertical coordinate of the mineral exploration information generation data in space and the quality assessment value of the mineral exploration information generation data. The larger the square of the difference between the vertical coordinate of the mineral exploration information generation data in space and the vertical coordinate of the mineral exploration information generation data in space, the greater the quality assessment value of the mineral exploration information generation data.

[0095] Represents the electromagnetic intensity at the N0th mineral exploration information generation data quality time detection point.

[0096] F1 represents the standard value of electromagnetic intensity, which is a preset standard value of electromagnetic intensity obtained from a mineral exploration information generation database, and can be an average value of electromagnetic intensity at a preset mineral exploration information generation data quality time detection point from a historical database.

[0097] Among them, in the data quality assessment of mineral exploration information generation, electromagnetic intensity refers to the electromagnetic response value of underground materials actually measured, and the standard value of electromagnetic intensity is a reference value pre-set based on known geological information, theoretical models or empirical data. It is used to evaluate the accuracy and reliability of the actual measured value. If the electromagnetic intensity is close to the standard value of electromagnetic intensity, it means that the measurement result is relatively reliable; if the electromagnetic intensity deviates from the standard value of electromagnetic intensity, it will reduce the data quality and increase uncertainty.

[0098] Specifically, there is a positive correlation between the square of the difference between the electromagnetic intensity and the standard value of the electromagnetic intensity and the quality assessment value of the data generated by mineral exploration information. The larger the square of the difference between the electromagnetic intensity and the standard value of the electromagnetic intensity, the greater the quality assessment value of the data generated by mineral exploration information.

[0099] It represents the reflected light intensity of the mining area at the N0th mineral exploration information generation data quality time detection point.

[0100] M1 represents the standard value of reflected light intensity in the mining area, which is the preset standard value of reflected light intensity in the mining area obtained from the mineral exploration information generation database, and can be the average value of reflected light intensity in the mining area at the data quality time detection point preset in the historical database of mineral exploration information generation.

[0101] It represents the incident light intensity in the mining area at the N0th mineral exploration information generation data quality time detection point.

[0102] ψ1 represents the standard value of incident light intensity in the mining area, which is the preset standard value of incident light intensity in the mining area obtained from the mineral exploration information generation database, and can be the average value of incident light intensity in the mining area at the data quality time detection point preset in the historical database of mineral exploration information generation.

[0103] Among them, in the data quality assessment of mineral exploration information generation, the reflected light intensity of the mining area reflects the amount of light reflected from the surface of the mining area. It is related to the type, roughness and composition of the surface material. The standard value of the reflected light intensity in the mining area is a pre-set reference value of the reflected light intensity, which is used to evaluate whether the measured value of the reflected light intensity is within the expected range. If the square of the difference between the reflected light intensity in the mining area and the standard value of the reflected light intensity in the mining area is larger, it is necessary to recalibrate the sensor or re-collect data; if the square of the difference between the reflected light intensity in the mining area and the standard value of the reflected light intensity in the mining area is smaller, it means that the remote sensing data quality is high and can accurately reflect the real-time remote sensing data. The characteristics of the mining area surface; the incident light intensity in the mining area refers to the amount of light irradiating the surface of the mining area. It is affected by factors such as the position of the sun, atmospheric conditions and the sensor viewing angle. The standard value of the incident light intensity in the mining area is a pre-set reference value of the incident light intensity, which is used to evaluate whether the measured value of the incident light intensity is within the expected range. If the square of the difference between the incident light intensity in the mining area and the standard value of the incident light intensity in the mining area is larger, it will affect the data quality of the mineral exploration information generation; if the square of the difference between the incident light intensity in the mining area and the standard value of the incident light intensity in the mining area is smaller, it means that the acquisition conditions of the remote sensing data meet the requirements and the data is more reliable.

[0104] Specifically, there is a positive correlation between the square of the difference between the reflected light intensity in the mining area and the standard value of the reflected light intensity in the mining area and the quality assessment value of the data generated by mineral exploration information. The larger the square of the difference between the reflected light intensity in the mining area and the standard value of the reflected light intensity in the mining area, the greater the quality assessment value of the data generated by mineral exploration information; there is a positive correlation between the square of the difference between the incident light intensity in the mining area and the standard value of the incident light intensity in the mining area and the quality assessment value of the data generated by mineral exploration information. The larger the square of the difference between the incident light intensity in the mining area and the standard value of the incident light intensity in the mining area, the greater the quality assessment value of the data generated by mineral exploration information.

[0105] Represents the mineral exploration information generation data noise at the N0th mineral exploration information generation data quality time detection point.

[0106] W1 represents the mineral exploration information generation data noise threshold, which is the preset mineral exploration information generation data noise threshold obtained from the mineral exploration information generation database, and can be the mineral exploration information generation data noise average value at the preset mineral exploration information generation data quality time detection point from the historical database.

[0107] Among them, in the quality assessment of mineral exploration information generated data, mineral exploration information generated data noise refers to random errors or outliers existing in mineral exploration data. These noises may conceal the real geological information. The mineral exploration information generated data noise threshold is a pre-set standard used to judge whether the noise level in the data is within an acceptable range. If the mineral exploration information generated data noise is lower than or equal to the mineral exploration information generated data noise threshold, it indicates that the data quality is good and the noise has little impact on geological interpretation; if the mineral exploration information generated data noise is higher than the mineral exploration information generated data noise threshold, it indicates that the data quality is poor and the noise will interfere with the identification and analysis of geological features.

[0108] Specifically, there is a positive correlation between the data noise generated by mineral exploration information and the quality assessment value of the data generated by mineral exploration information. The greater the data noise generated by mineral exploration information, the greater the quality assessment value of the data generated by mineral exploration information.

[0109] σ1 is the weight factor affecting the spatial distance error coefficient of the preset mineral exploration information generation data obtained from the mineral exploration information generation database; σ2 is the weight factor affecting the pixel resolution of the preset remote sensing image obtained from the mineral exploration information generation database; σ3 is the weight factor affecting the missing rate of the preset mineral exploration information generation data obtained from the mineral exploration information generation database.

[0110] τ1 is the preset electromagnetic intensity influence factor obtained from the mineral exploration information generation database; τ2 is the preset mining area reflectivity influence factor obtained from the mineral exploration information generation database; τ3 is the preset mineral exploration information generation data noise influence factor obtained from the mineral exploration information generation database.

[0111] The preset weight factors affecting the spatial distance error coefficient of mineral exploration information generation data, the preset weight factors affecting the pixel resolution of remote sensing images and the preset weight factors affecting the missing rate of mineral exploration information generation data are obtained through mapping relationships. For example, a mapping set of the spatial distance error coefficient of mineral exploration information generation data, the pixel resolution of remote sensing images and the missing rate of mineral exploration information generation data and their corresponding weights is established respectively through the relationship between the spatial distance error coefficient of mineral exploration information generation data, the pixel resolution of remote sensing images and the missing rate of mineral exploration information generation data in historical data and the clarity of remote sensing images. The preset weight factors affecting the spatial distance error coefficient of mineral exploration information generation data, the preset weight factors affecting the pixel resolution of remote sensing images and the preset weight factors affecting the missing rate of mineral exploration information generation data corresponding to the mapping set are obtained by inputting the real-time spatial distance error coefficient of mineral exploration information generation data, the pixel resolution of remote sensing images and the missing rate of mineral exploration information generation data.

[0112] The preset electromagnetic intensity influencing factor, the preset mining area reflectivity influencing factor and the preset mineral exploration information generation data noise influencing factor are obtained through mapping relationships. For example, a mapping set of electromagnetic intensity, mining area reflectivity and mineral exploration information generation data noise and their corresponding weights is established respectively through the relationship between the electromagnetic intensity, mining area reflectivity and mineral exploration information generation data noise in the historical data and the mineral exploration information generation data timestamp. The corresponding preset electromagnetic intensity influencing factor, preset mining area reflectivity influencing factor and preset mineral exploration information generation data noise in the mapping set are obtained by inputting the real-time electromagnetic intensity, mining area reflectivity and mineral exploration information generation data noise.

[0113] The electromagnetic intensity affects the intensity of reflected light. The stronger the electromagnetic intensity, the greater the intensity of incident light and reflected light in the mining area. The electromagnetic intensity, the intensity of reflected light in the mining area and the intensity of incident light in the mining area all introduce noise during measurement and transmission. The greater the electromagnetic intensity, the intensity of reflected light in the mining area and the intensity of incident light in the mining area, the greater the noise of the mineral exploration information generation data. The horizontal coordinate of the mineral exploration information generation data in space and the vertical coordinate of the mineral exploration information generation data in space are used to locate the specific position of the mineral exploration information in space. The accuracy of the coordinates is affected by the pixel resolution of the remote sensing image. The higher the pixel resolution of the remote sensing image, the more accurate the horizontal coordinate of the mineral exploration information generation data in space and the vertical coordinate of the mineral exploration information generation data in space. The higher the noise of the mineral exploration information generation data, the lower the data quality, which in turn increases the missing rate of the mineral exploration information generation data. The pixel resolution of the remote sensing image will also affect the data missing rate. The larger the pixel resolution of the remote sensing image, the greater the missing rate of the mineral exploration information generation data.

[0114] Furthermore, the specific steps for obtaining the reliability assessment value of mineral exploration information generation are: obtaining the mineral exploration information generation data compression efficiency of the preset mineral exploration information generation reliability time detection point through data compression software; obtaining the mineral exploration information generation reliability assessment value through the mineral exploration information generation data fusion synchronization assessment value, the mineral exploration information generation data quality assessment value and the mineral exploration information generation data compression efficiency.

[0115] In this embodiment, the specific method for analyzing and obtaining the reliability evaluation value of mineral exploration information is as follows:

[0116]

[0117] φ1+φ2+φ3=1;

[0118] The preset mineral exploration information generation reliability time detection points are numbered in sequence, L0 represents the number of the mineral exploration information generation reliability time detection point, L0=1,2,...,L, L represents the total number of the mineral exploration information generation reliability time detection points.

[0119] ζ represents the reliability assessment value of mineral exploration information generation.

[0120] It represents the mineral exploration information generation data fusion synchronization evaluation value at the S0th mineral exploration information generation data fusion synchronization time detection point.

[0121] Among them, the synchronization of data fusion generated by mineral exploration information is a prerequisite for the reliability of data generated by mineral exploration information. If the data fusion generated by mineral exploration information is not synchronized, the information after data integration will be inaccurate, thus affecting the reliability of the data.

[0122] Specifically, there is a positive correlation between the data fusion synchronization evaluation value of mineral exploration information generation and the reliability evaluation value of mineral exploration information generation. The greater the data fusion synchronization evaluation value of mineral exploration information generation, the greater the reliability evaluation value of mineral exploration information generation.

[0123] It represents the quality assessment value of the mineral exploration information generated data at the N0th mineral exploration information generated data quality time detection point.

[0124] Among them, the data quality assessment value generated by mineral exploration information refers to an indicator for evaluating the quality of mineral exploration data, including multiple aspects such as data accuracy and completeness. The data quality assessment value generated by mineral exploration information affects the reliability assessment value generated by mineral exploration information.

[0125] Specifically, there is a negative correlation between the data quality assessment value of mineral exploration information generation and the reliability assessment value of mineral exploration information generation. The larger the data quality assessment value of mineral exploration information generation, the smaller the reliability assessment value of mineral exploration information generation.

[0126] It represents the data compression efficiency of mineral exploration information generation at the L0th mineral exploration information generation reliability time detection point.

[0127] It represents the standard value of mineral exploration information generation data compression efficiency, which is a preset standard value of mineral exploration information generation data compression efficiency obtained from the mineral exploration information generation database, and can be the average value of mineral exploration information generation data compression efficiency at a preset mineral exploration information generation reliability time detection point from a historical database.

[0128] Among them, in the reliability assessment of mineral exploration information generation, the data compression efficiency of mineral exploration information generation refers to the degree of compression that can be achieved by the data compression algorithm in actual application, which is usually expressed as the ratio of the data size before and after compression. The standard value of the data compression efficiency of mineral exploration information generation is the expected compression efficiency target set according to the needs of specific exploration projects and industry standards. If the data compression efficiency of mineral exploration information generation is close to or reaches the standard value of the data compression efficiency of mineral exploration information generation, it means that the compression algorithm can effectively reduce the amount of data while maintaining the integrity of the data; if the data compression efficiency of mineral exploration information generation is greater than the standard value of the data compression efficiency of mineral exploration information generation, it means that the data is lost or distorted, and excessive compression makes it impossible to recover the original data; if the data compression efficiency of mineral exploration information generation is lower than the standard value of the data compression efficiency of mineral exploration information generation, it will lead to delays in data processing and analysis, increase costs, and reduce the efficiency of exploration work.

[0129] Specifically, there is a negative correlation between the absolute value of the compression efficiency of mineral exploration information generation data and the difference in compression efficiency of mineral exploration information generation data and the reliability assessment value of mineral exploration information generation. The larger the absolute value of the compression efficiency of mineral exploration information generation data and the difference in compression efficiency of mineral exploration information generation data, the smaller the reliability assessment value of mineral exploration information generation.

[0130] φ1 is the weight factor affecting the fusion synchronization evaluation value of the preset mineral exploration information generation data obtained from the mineral exploration information generation database; φ2 is the weight factor affecting the quality evaluation value of the preset mineral exploration information generation data obtained from the mineral exploration information generation database; φ3 is the weight factor affecting the compression efficiency of the preset exploration information generation data obtained from the mineral exploration information generation database.

[0131] The preset weight factors affecting the fusion synchronization evaluation value of mineral exploration information generation data, the preset weight factors affecting the quality evaluation value of mineral exploration information generation data and the preset weight factors affecting the compression efficiency of exploration information generation data are obtained through mapping relationships. For example, a mapping set of exploration information generation data fusion synchronization evaluation value, mineral exploration information generation data quality evaluation value and exploration information generation data compression efficiency and their corresponding weights is established respectively through the relationship between the exploration information generation data fusion synchronization evaluation value, mineral exploration information generation data quality evaluation value and exploration information generation data compression efficiency in historical data and data redundancy rate. The preset weight factors affecting the fusion synchronization evaluation value of mineral exploration information generation data, the preset weight factors affecting the quality evaluation value of mineral exploration information generation data and the preset weight factors affecting the compression efficiency of exploration information generation data corresponding to the mapping set are obtained by inputting the real-time exploration information generation data fusion synchronization evaluation value, mineral exploration information generation data quality evaluation value and exploration information generation data compression efficiency.

[0132] Table 1 is an example table of reliability evaluation values ​​for mineral exploration information generation. The example parameters in Table 1 only take the parameters under one mineral exploration information generation reliability detection point for example. The weight factor φ1 is set to 0.4, the weight factor φ2 is set to 0.3, and the weight factor φ3 is set to 0.3. The standard value of data compression efficiency for mineral exploration information generation is 2, as shown in Table 1.

[0133] Table 1 Example of reliability evaluation value for mineral exploration information generation

[0134]

[0135] A high assessment value of the synchronization of data fusion generated by mineral exploration information means that data from different sources and types can be integrated in a timely and consistent manner, which helps to improve the overall quality of the data. The lower the assessment value of the data quality generated by mineral exploration information is; a high assessment value of the synchronization of data fusion generated by mineral exploration information ensures that the data are up to date and consistent when compressed after data fusion, which helps to improve the compression efficiency. The higher the assessment value of the data quality generated by mineral exploration information is.

[0136] like Figure 2 As shown, it is a schematic diagram of the reliability evaluation value function of the mineral exploration information generation provided in the embodiment of the present application; x is the positive semi-axis of the horizontal coordinate, y is the positive semi-axis of the vertical coordinate, the weight factor φ1 is set to 0.4, the weight factor φ2 is set to 0.3, and the weight factor φ3 is set to 0.3. The standard value of data compression efficiency for mineral exploration information generation is 2.

[0137] Curve a indicates that if the quality assessment value of the data generated by mineral exploration information is set to a fixed value of 1, the compression efficiency of the data generated by mineral exploration information is set to a fixed value of 1, the fusion synchronization assessment value of the data generated by mineral exploration information is x, and the reliability assessment value of the data generated by mineral exploration information is y, the reliability assessment value of the data generated by mineral exploration information increases with the increase of the fusion synchronization assessment value of the data generated by mineral exploration information.

[0138] Furthermore, the specific steps for optimizing the method for fusion synchronization of mineral exploration information generation data are as follows: if the fusion synchronization evaluation value of mineral exploration information generation data is greater than or equal to the first threshold value of the fusion synchronization evaluation value of mineral exploration information generation data, there is no need to optimize the method for fusion synchronization of mineral exploration information generation data; if the fusion synchronization evaluation value of mineral exploration information generation data is lower than the first threshold value of the fusion synchronization evaluation value of mineral exploration information generation data, then the difference between the fusion synchronization evaluation value of mineral exploration information generation data and the first threshold value of the fusion synchronization evaluation value of mineral exploration information generation data is used to match the corresponding adjustment plan in the mineral exploration information generation database.

[0139] In this embodiment, assuming that the mineral exploration information generation data fusion synchronization evaluation value is 4, the mineral exploration information generation data fusion synchronization evaluation value first threshold value obtained from the mineral exploration information generation database is 5, and the corresponding difference value is -1, then the mineral exploration information generation data fusion synchronization evaluation value obtained from the mineral exploration information generation database and the mineral exploration information generation data fusion synchronization evaluation value first threshold value The adjustment scheme corresponding to the difference is -1, and the adjustment scheme is: by using a low-pass filter to reduce high-frequency noise, analyze the signal frequency component, and determine the main frequency range of the noise, for example, applying a low-pass filter Filter out high-frequency interference in seismic data and retain low-frequency effective signals. High-frequency noise caused by exploration equipment or environmental factors will be effectively suppressed. Apply high-pass filters to remove low-frequency noise, such as ground vibration, to further purify the data and make it closer to the real signal. Enhance the signal by using adaptive filters or wavelet transforms. For example, by adjusting the contrast of seismic data, the effective signal is more prominent, thereby improving the identifiability of the signal. According to the local characteristics of the data, the gain is automatically adjusted to keep the signal consistent as a whole, further improving the signal quality. This is to improve the evaluation value of the synchronization of data fusion generated by mineral exploration information.

[0140] Furthermore, the specific steps for optimizing the method for quality of mineral exploration information generation data are as follows: if the quality assessment value of mineral exploration information generation data is lower than or equal to the second threshold value of mineral exploration information generation data quality assessment value, there is no need to optimize the method for quality of mineral exploration information generation data; if the quality assessment value of mineral exploration information generation data is greater than the second threshold value of mineral exploration information generation data quality assessment value, then the difference between the quality assessment value of mineral exploration information generation data and the second threshold value of mineral exploration information generation data quality assessment value is used to match the corresponding adjustment plan in the mineral exploration information generation database.

[0141] In this embodiment, assuming that the mineral exploration information generation data quality assessment value is 5, the mineral exploration information generation data quality assessment value second threshold value obtained from the mineral exploration information generation database is 4, and its corresponding difference value is 1, then the mineral exploration information generation data quality assessment value obtained from the mineral exploration information generation database and the mineral exploration information generation data quality assessment value second threshold value The corresponding adjustment scheme is 1. The adjustment scheme is: by using image super-resolution technology to improve data resolution, for example, pre-processing the existing mining area exploration remote sensing images, including denoising, alignment and standardization, to ensure that the image quality meets the requirements of super-resolution processing; through timestamp accuracy, avoid Time alignment errors are introduced. For example, during the data collection process, the timestamp of each data point is accurately recorded, including the mining area exploration time, equipment collection time, etc. During data analysis, a series of mineral exploration information is being analyzed, and the source location needs to be determined based on the time difference between the mineral exploration information generation data reaching different sensors. When processing data, the timestamp recorded by each sensor is checked to ensure that there are no errors introduced by equipment delays or data processing. The arrival time of the mineral exploration information generation data recorded by different sensors is compared, the time difference is calculated, and the precise time difference data is used to apply triangulation to determine the mineral location; so as to reduce the quality assessment value of the mineral exploration information generation data.

[0142] Furthermore, the specific steps for optimizing and adjusting the method for reliability of mineral exploration information generation are as follows: extracting a comprehensive threshold of a reliability assessment value for mineral exploration information generation in a mineral exploration information generation database, and comparing the reliability assessment value for mineral exploration information generation with the comprehensive threshold of a reliability assessment value for mineral exploration information generation; if the reliability assessment value for mineral exploration information generation is greater than or equal to the comprehensive threshold of a reliability assessment value for mineral exploration information generation, then there is no need to optimize and adjust the method for reliability of mineral exploration information generation.

[0143] Furthermore, the method for optimizing and adjusting the reliability of mineral exploration information generation also includes: if the mineral exploration information generation reliability assessment value is lower than the comprehensive threshold of the mineral exploration information generation reliability assessment value, then matching the corresponding adjustment plan in the mineral exploration information generation database through the difference between the mineral exploration information generation reliability assessment value and the comprehensive threshold of the mineral exploration information generation reliability assessment value.

[0144] In this embodiment, assuming that the reliability assessment value of mineral exploration information generation is 5, the comprehensive threshold value of the reliability assessment value of mineral exploration information generation obtained from the mineral exploration information generation database is 9, and the corresponding difference is -4, then the adjustment scheme corresponding to the difference between the reliability assessment value of mineral exploration information generation and the comprehensive threshold value of the reliability assessment value of mineral exploration information generation matched from the mineral exploration information generation database is -4 is as follows: by implementing an iterative process, gradually adjusting the parameters, finding the best balance between the data compression ratio and the data quality, avoiding excessive compression and causing information loss, for example, through the first iteration, keeping the data acquisition parameters unchanged, adjusting the parameters of the data processing algorithm, and observing the change in the reliability assessment value; through the second iteration, according to the result of the first iteration, adjusting the data compression ratio, for example, from 90% compression to The rate is reduced to 80%, and the reliability is evaluated again; through subsequent iterations, the parameter combination is continued to be adjusted, and reliability evaluation is performed after each iteration, the evaluation value after each adjustment is recorded, and the change of the reliability evaluation value is monitored; in each iteration, the parameter adjustment situation and the corresponding reliability evaluation value are recorded in detail; the changing trend of the reliability evaluation value during the iteration process is analyzed, and after multiple iterations, the reliability evaluation values ​​under different parameter combinations and the corresponding data compression ratio are compared. It is assumed that in the fifth iteration, the data compression ratio is adjusted to 85%, and after a parameter in the data processing algorithm is optimized, the reliability evaluation value reaches above the comprehensive threshold, and the data quality is maintained at an acceptable level to ensure that no important information is lost after data compression, and the data quality meets the exploration needs; so as to improve the reliability evaluation value of mineral exploration information generation.

[0145] like Figure 3As shown, it is a structural schematic diagram of a mineral exploration information generation system based on big data provided by an embodiment of the present application. The mineral exploration information generation system based on big data provided by an embodiment of the present application includes: a mineral exploration information generation data acquisition module, a mineral exploration information generation data analysis module, a comprehensive analysis module and an optimization and adjustment module: the mineral exploration information generation data acquisition module: used to collect and process the mineral exploration information generation data; the mineral exploration information generation data analysis module: used to analyze the mineral exploration information generation data, obtain the mineral exploration information generation data fusion synchronization evaluation value and obtain the mineral exploration information generation data quality evaluation value; comprehensive Analysis module: used for comprehensive analysis to obtain the reliability assessment value of mineral exploration information generation; Optimization and adjustment module: used for comparing and analyzing the data fusion synchronization assessment value of mineral exploration information generation with the first threshold of the data fusion synchronization assessment value of mineral exploration information generation, and optimizing the data fusion synchronization method of mineral exploration information generation; comparing and analyzing the data quality assessment value of mineral exploration information generation with the second threshold of the data quality assessment value of mineral exploration information generation, and optimizing the data quality method of mineral exploration information generation; comparing and analyzing the reliability assessment value of mineral exploration information generation with the comprehensive threshold of the reliability assessment value of mineral exploration information generation, and optimizing and adjusting the reliability method of mineral exploration information generation.

[0146] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0147] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0148] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0149] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0150] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0151] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for generating mineral exploration information based on big data, characterized in that: The following steps are involved: Collect and process mineral exploration information to generate data; Analyze the data generated by mineral exploration information to obtain the fusion synchronization evaluation value of the data generated by mineral exploration information and obtain the quality evaluation value of the data generated by mineral exploration information; Comprehensive analysis obtains the reliability assessment value of mineral exploration information generation; Compare and analyze the data fusion synchronization evaluation value of mineral exploration information generation with the first threshold of the data fusion synchronization evaluation value of mineral exploration information generation, and optimize the data fusion synchronization method of mineral exploration information generation; Compare and analyze the quality assessment value of the data generated by the mineral exploration information with the second threshold value of the quality assessment value of the data generated by the mineral exploration information, and optimize the quality method of the data generated by the mineral exploration information; Compare and analyze the reliability assessment value of mineral exploration information generation with the comprehensive threshold of the reliability assessment value of mineral exploration information generation, and optimize and adjust the reliability method of mineral exploration information generation.

2. A method for generating mineral exploration information based on big data as claimed in claim 1, characterized in that: The specific steps of collecting and processing mineral exploration information to generate data are: Generate raw data by obtaining mineral exploration information through physical exploration equipment; The raw data generated by mineral exploration information is cleaned and denoised to obtain the data generated by mineral exploration information.

3. A method for generating mineral exploration information based on big data as claimed in claim 1, characterized in that: The specific steps of obtaining the data fusion synchronization evaluation value generated by mineral exploration information are as follows: The electromagnetic intensity of the preset mineral exploration information generation data fusion synchronization time detection point is obtained through the time domain electromagnetic instrument; The reflected light intensity of the mining area at the preset mineral exploration information generation data fusion synchronization time detection point is obtained through the aerial remote sensing sensor; The incident light intensity of the mining area at the preset mineral exploration information generation data fusion synchronization time detection point is obtained through the aerial remote sensing sensor; The data processor obtains the data entropy of the mineral exploration information generation data fusion synchronization time detection point at the preset mineral exploration information generation data; The mineral exploration information generation data noise of the preset mineral exploration information generation data fusion synchronization time detection point is obtained through signal processing software; The clock frequency of the preset mineral exploration information generation data fusion synchronization time detection point is obtained through a high-precision clock; Obtain the clock source display time of the preset mineral exploration information generation data fusion synchronization time detection point through the GPS time receiver; The analysis results in the evaluation value of data fusion synchronization generated by mineral exploration information.

4. A method for generating mineral exploration information based on big data as claimed in claim 1, characterized in that: The specific steps of obtaining the data quality assessment value generated by mineral exploration information are as follows: The electromagnetic intensity of the preset mineral exploration information generation data quality time detection point is obtained through the time domain electromagnetic instrument; The reflected light intensity of the mining area at the preset mineral exploration information generation data quality time detection point is obtained through the aerial remote sensing sensor; The incident light intensity of the mining area at the preset mineral exploration information generation data quality time detection point is obtained through the aerial remote sensing sensor; The exploration information generation data noise of the preset mineral exploration information generation data quality time detection point is obtained through the signal processing software; The pixel resolution of remote sensing images at preset mineral exploration information generation data quality time detection points is obtained through satellite remote sensing sensors; The missing rate of mineral exploration information generation data at the preset mineral exploration information generation data quality time detection point is obtained through GIS and data management software; Obtaining the spatial horizontal coordinate of the mineral exploration information generation data of the preset mineral exploration information generation data quality time detection point through the GPS receiver; Obtaining the spatial vertical coordinate of the mineral exploration information generation data of the preset mineral exploration information generation data quality time detection point through the GPS receiver; The data quality assessment value generated by mineral exploration information is obtained through analysis.

5. The method for generating mineral exploration information based on big data as claimed in claim 1, characterized in that: The specific steps of obtaining the mineral exploration information to generate the reliability assessment value are as follows: The data compression efficiency of the mineral exploration information generation data at the preset mineral exploration information generation reliability time detection point is obtained through the data compression software; The reliability assessment value of mineral exploration information generation is obtained through the data fusion synchronization assessment value, data quality assessment value and data compression efficiency of mineral exploration information generation.

6. A method for generating mineral exploration information based on big data as claimed in claim 1, characterized in that: The specific steps of the method for optimizing the synchronization of mineral exploration information generation data fusion are as follows: If the mineral exploration information generation data fusion synchronization evaluation value is greater than or equal to the mineral exploration information generation data fusion synchronization evaluation value first threshold value, then there is no need to optimize the mineral exploration information generation data fusion synchronization method; If the data fusion synchronization assessment value of mineral exploration information generation is lower than the first threshold value of the data fusion synchronization assessment value of mineral exploration information generation, the corresponding adjustment plan in the mineral exploration information generation database is matched through the difference between the data fusion synchronization assessment value of mineral exploration information generation and the first threshold value of the data fusion synchronization assessment value of mineral exploration information generation.

7. The method for generating mineral exploration information based on big data as claimed in claim 1, characterized in that: The specific steps of the method for optimizing the quality of mineral exploration information generation data are as follows: If the quality assessment value of the data generated by the mineral exploration information is lower than or equal to the second threshold value of the quality assessment value of the data generated by the mineral exploration information, it is not necessary to optimize the quality method of the data generated by the mineral exploration information; If the mineral exploration information generation data quality assessment value is greater than the mineral exploration information generation data quality assessment value second threshold, the corresponding adjustment plan in the mineral exploration information generation database is matched through the difference between the mineral exploration information generation data quality assessment value and the mineral exploration information generation data quality assessment value second threshold.

8. The method for generating mineral exploration information based on big data as claimed in claim 1, characterized in that: The specific steps of the method for optimizing and adjusting the reliability of mineral exploration information generation are as follows: extracting a comprehensive threshold value of a reliability assessment value of mineral exploration information generation from a mineral exploration information generation database, and comparing the reliability assessment value of mineral exploration information generation with the comprehensive threshold value of a reliability assessment value of mineral exploration information generation; If the reliability assessment value of mineral exploration information generation is greater than or equal to the comprehensive threshold of the reliability assessment value of mineral exploration information generation, there is no need to optimize and adjust the reliability method of mineral exploration information generation.

9. The method for generating mineral exploration information based on big data as claimed in claim 1, characterized in that: The method for optimizing and adjusting the reliability of mineral exploration information generation also includes: If the reliability assessment value of mineral exploration information generation is lower than the comprehensive threshold of the reliability assessment value of mineral exploration information generation, the corresponding adjustment plan in the mineral exploration information generation database is matched through the difference between the reliability assessment value of mineral exploration information generation and the comprehensive threshold of the reliability assessment value of mineral exploration information generation.

10. A mineral exploration information generation system based on big data, characterized in that: It includes the mineral exploration information generation data acquisition module, the mineral exploration information generation data analysis module, the comprehensive analysis module and the optimization and adjustment module: Mineral exploration information generation data acquisition module: used to collect and process mineral exploration information generation data; Mineral exploration information generation data analysis module: used to analyze the mineral exploration information generation data, obtain the mineral exploration information generation data fusion synchronization evaluation value and obtain the mineral exploration information generation data quality evaluation value; Comprehensive analysis module: used to comprehensively analyze mineral exploration information to generate reliability assessment values; Optimization and adjustment module: used for comparing and analyzing the data fusion synchronization evaluation value generated by mineral exploration information with the first threshold value of the data fusion synchronization evaluation value generated by mineral exploration information, and optimizing the data fusion synchronization method generated by mineral exploration information; Compare and analyze the data quality assessment value of mineral exploration information generation with the second threshold of the data quality assessment value of mineral exploration information generation, and optimize the data quality method of mineral exploration information generation; compare and analyze the reliability assessment value of mineral exploration information generation with the comprehensive threshold of the reliability assessment value of mineral exploration information generation, and optimize and adjust the reliability method of mineral exploration information generation.

Images