Niobium-tantalum metal ore metallogenic evaluation system based on multi-source information analysis

Abstract

The invention relates to the technical field of multi-source information analysis, in particular to a niobium-tantalum metal ore metallogenic evaluation system based on multi-source information analysis, which comprises a geologic structure association module, a mineral distribution interpretation module, a metallogenic environment adaptation module, a spatial feature analysis module and a potential region judgment module. According to the method, by introducing a multi-source data analysis and fusion method, the evaluation precision and comprehensive capability of resource exploration are effectively improved, analysis after information fusion is combined with relevance and spatial modes of different data types, it is ensured that mineralization potential evaluation is more accurate, the limitation of a single data source and manual analysis is overcome, and the method is suitable for popularization and application. According to the method, the identification precision and reliability of the mineralization law are improved, the potential area can be identified more accurately, resource potential analysis is optimized in combination with the adaptability of the mineralization environment, the efficiency and accuracy of determining the mineralization area are remarkably improved, the problems of information isolation and analysis deviation in a traditional method are avoided, and the scientificity and the practical application value of the exploration process are enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of multi-source information analysis, in particular to a niobium-tantalum metal mine mineralization evaluation system based on multi-source information analysis. BACKGROUND

[0002] The technical field of multi-source information analysis belongs to the category of information processing and comprehensive analysis, mainly studies how to extract, integrate and correlate information from different sources, different structures and different types of data, and identifies, judges and reasons the target object through unified modeling and analysis means. The core of this technical field includes multi-source data acquisition, data preprocessing, feature extraction, data fusion and correlation analysis, etc. It covers the integrated use of multi-type information such as image data, geological data, remote sensing data and sensor data, and is widely used in resource exploration, environmental monitoring, safety warning and other scenarios. This technical field emphasizes the diversity of data sources, the complementarity between data, and the depth of analysis after information fusion. The overall technical path presents the structured integration of multi-dimensional data, the unified extraction of cross-domain features, and the improvement of comprehensive judgment ability.

[0003] Among them, the traditional niobium-tantalum and other rare metal mine mineralization evaluation system refers to the process of mineral exploration. Through qualitative judgment and empirical analysis of geological exploration data, relying on single source data such as drilling profile information, mineral assemblage distribution rule and rock geochemical index, combined with artificial recognition and two-dimensional map interpretation method, the comprehensive evaluation of mineralization geological conditions is carried out. In the traditional way, geological exploration sample analysis, artificial interpretation of remote sensing images and rock and mineral experimental detection are used to identify ore body position and predict resource potential. There are problems such as single data, low information utilization rate and limited identification accuracy of mineralization regularity.

[0004] The existing technology relies on single data source and empirical judgment, and relies too much on traditional data such as drilling profile, mineral assemblage rule and geochemical index. The information acquisition in the analysis process is limited, and it is difficult to fully evaluate the potential area in the complex geological environment. The traditional method relies too much on artificial interpretation of remote sensing images and sample analysis, resulting in low efficiency of data integration and limited analysis accuracy. It cannot fully tap the complementarity of multi-source information, and thus affects the accurate identification of mineralization regularity. In the process of geological exploration, too much reliance on empirical judgment and two-dimensional map interpretation method, ignoring the depth correlation and cross-domain analysis between data, making it difficult to meet the multi-dimensional information demand in complex geological environment, thus limiting the accuracy and reliability of potential area evaluation, leading to misjudgment and resource waste. SUMMARY

[0005] In order to solve the technical problems existing in the prior art, the present application provides a niobium-tantalum metal mine mineralization evaluation system based on multi-source information analysis. The technical solution is as follows: In one aspect, a niobium-tantalum metal ore mineralization evaluation system based on multi-source information analysis is provided, and the system comprises: The geological structure correlation module obtains geological structure information, including stratum distribution, fault position and fold shape, analyzes the influence degree of tectonic stress field on mineralization, and generates a tectonic stress influence index; The mineral distribution interpretation module extracts mineral combination rules and chemical composition distribution characteristics based on the tectonic stress influence index, analyzes the corresponding relationship between mineral enrichment area and tectonic stress field, and obtains a mineral enrichment degree value; The mineralization environment adaptation module extracts stratum lithology and structural characteristics in the mineralization environment according to the mineral enrichment degree value, analyzes the relevance of lithology combination and mineralization potential, and generates an environment adaptation index; The spatial feature analysis module calls the environment adaptation index, extracts the spatial distribution pattern and boundary characteristics of the mineralization area, analyzes the relationship between spatial continuity and mineralization potential, and generates a spatial distribution optimization set.

[0006] As a further scheme of the present application, the tectonic stress influence index includes stress distribution uniformity, tectonic disturbance intensity and stress action directionality, the mineral enrichment degree value includes enrichment intensity level, mineral concentration coefficient and spatial aggregation distribution degree, the environment adaptation index includes lithology adaptability score, tectonic coordination degree and mineralization environment consistency, and the spatial distribution optimization set includes distribution continuity score, boundary integrity index and regional form regularity.

[0007] As a further scheme of the present application, the geological structure correlation module comprises: The stratum distribution extraction submodule obtains geological structure information, extracts stratum thickness variation and lithology interface characteristics, analyzes the influence degree of stratum fluctuation on mineralization, and generates fault activity intensity; The fold shape analysis submodule extracts the axial plane inclination and wavelength change value in the fold shape according to the fault activity intensity, analyzes the action degree of fold deformation on mineral migration and enrichment, and generates a fold deformation degree; The stress field evaluation submodule extracts tectonic stress field distribution information based on the fold deformation degree, analyzes the influence range of tectonic stress concentration on mineralization, and generates a tectonic stress influence index.

[0008] As a further scheme of the present application, the mineral distribution interpretation module comprises: The mineral combination extraction submodule collects mineral combination sample data based on the tectonic stress influence index, decomposes the mineral particle characteristics in the sample, determines the mineral species content ratio, extracts the chemical composition numerical value in the mineral particle, compares the distribution of adjacent particles in the differential mineral combination, identifies the paragenetic mineral combination block, and generates mineral species diversity; The chemical composition analysis submodule filters key elements in the differential mineral combination according to the mineral variety, calculates an element stable fluctuation interval, extracts a stable element proportion in the sample, extracts an easily variable element content change section, discriminates a differential element combination relationship, marks an influence degree of the differential combination on sample distribution, and generates chemical composition stability. The enrichment area distribution scoring submodule calls the chemical composition stability, segments a mineral enrichment area sample unit, determines a key mineral content density in the enrichment unit, superimposes the unit and a tectonic stress block corresponding range, identifies a high-density enrichment block boundary, quantizes a demarcation point set of the enrichment block and a low-density area, and obtains a mineral enrichment degree value.

[0009] As a further scheme of the present application, the mineral enrichment degree value adopts a formula: ; Wherein, represents the mineral enrichment degree value, represents a content density of an i-th mineral sample, represents a distribution density of the i-th mineral sample, represents a unit volume mineral quality of the i-th mineral sample, represents an average value of the unit volume mineral quality of all units, represents a total number of mineral samples.

[0010] As a further scheme of the present application, the metallogenic environment adaptation module comprises: The lithology combination extraction submodule extracts a stratum lithology combination type in a metallogenic environment according to the mineral enrichment degree value, analyzes a support degree of the lithology combination on mineral enrichment, and generates a lithology combination matching degree; The tectonic feature matching submodule calls the lithology combination matching degree, extracts a fault strike and a fold shape in a tectonic feature, analyzes a contribution degree of the tectonic feature to mineralization, and generates a tectonic feature contribution rate; The potential weight calculation submodule extracts a metallogenic potential ranking table based on the tectonic feature contribution rate, analyzes an adaptation degree of a metallogenic potential weight to a metallogenic environment, and generates an environment adaptation index.

[0011] As a further scheme of the present application, the spatial feature analysis module comprises: The spatial continuity analysis submodule calls the environment adaptation index, collects multi-source spatial data in a metallogenic area, interprets a target area spatial distribution unit, identifies a continuity strength of adjacent units, divides a continuity level block, analyzes a differential level block combination mode, and generates a spatial continuity index; The boundary feature extraction submodule locates a boundary trend line segment of a metallogenic region according to the spatial continuity index, measures a boundary definition value range, calibrates a boundary transition width interval, decomposes a differentiated interval boundary point set, analyzes a position relationship of the boundary point set corresponding to a main body area, and generates a boundary feature definition; The distribution optimization scoring submodule combs a spatial distribution unit combination sequence based on the boundary feature definition, compares adjacent unit boundary fitting states, screens a distribution continuity mutation area, marks a high-difference distribution unit, evaluates an influence of the high-difference unit on overall distribution coupling degree, and generates a spatial distribution optimization set.

[0012] As a further scheme of the present application, the multi-source spatial data refers to a geographic spatial information set from differentiated sensors or data platforms; The spatial continuity index refers to an index of strength of distribution continuity between adjacent spatial units; The boundary feature definition refers to an index of a degree of definition of a regional boundary form and structure certainty.

[0013] As a further scheme of the present application, the system further comprises a potential region determination module: The potential region determination module analyzes a regional metallogenic potential grade based on the spatial distribution optimization set, in combination with original exploration data and specification requirements, and obtains a rare metal ore metallogenic potential region evaluation result; The rare metal ore metallogenic potential region evaluation result comprises a potential grade division, a region reliability score, and an exploration consistency label.

[0014] As a further scheme of the present application, the potential region determination module comprises: A potential probability analysis submodule extracts a metallogenic potential index and a spatial distribution feature in original exploration data based on the spatial distribution optimization set, analyzes a frequency and regularity of metallogenic potential occurrence, and generates a potential probability interval; A metallogenic intensity evaluation submodule calls the potential probability interval, extracts an influence degree of a metallogenic event on resource reserves and quality, analyzes a distribution feature of metallogenic intensity, and generates a metallogenic intensity grade; A potential scoring submodule analyzes a distribution law of a regional potential score based on the metallogenic intensity grade in combination with a potential grade division standard in a specification requirement, and generates a rare metal ore metallogenic potential region evaluation result.

[0015] The technical scheme provided by the embodiments of the present application has at least the following beneficial effects: By introducing multi-source data analysis and fusion method, the geological structure, mineral distribution, ore-forming environment and spatial characteristics can be systematically associated and optimized, the metallogenic potential area is extracted by using a new idea, the comprehensive evaluation ability and precision of resource exploration are effectively improved, the analysis after information fusion is more profound, the correlation analysis and spatial pattern extraction of different types of data are combined, the metallogenic potential evaluation in the mineral exploration process is more accurate, the limitations of single data source and manual analysis are avoided, and the precision and reliability of metallogenic regularity identification are improved. By using the correlation between geological structure and mineral enrichment degree, more effective potential area can be identified, and the analysis of resource potential is optimized by combining the adaptability of ore-forming environment, the determination efficiency and accuracy of metallogenic area are greatly improved, the problems such as information isolation and analysis deviation in the traditional method are avoided, and the scientificity and practical application value in the exploration process are enhanced. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0017] Figure 1 is a schematic diagram of the niobium-tantalum metal ore metallogenic evaluation system provided by the embodiment of the present application based on multi-source information analysis; Figure 2 is a system framework schematic diagram of the present application; Figure 3 is a geological structure correlation module flow chart in the present application; Figure 4 is a mineral distribution interpretation module flow chart in the present application; Figure 5 is a metallogenic environment adaptation module flow chart in the present application; Figure 6 is a spatial feature analysis module flow chart in the present application; Figure 7 is a potential area determination module flow chart in the present application. DETAILED DESCRIPTION

[0018] The technical solutions in the present application will be described below in combination with the drawings.

[0019] In the embodiments of the present application, the words such as "example", "for example" are used to represent an example, illustration, or description. Any embodiment or design scheme described as "example" in the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. Rather, the word "example" is intended to present the concept in a specific manner. In addition, in the embodiments of the present application, the meaning expressed by "and / or" can be both, or can be one of the two.

[0020] In the embodiments of the present application, "image" and "picture" can be used interchangeably at times, and it should be pointed out that the meanings expressed are consistent when the distinction is not emphasized. "Of", "corresponding" and "corresponding" can be used interchangeably at times, and it should be pointed out that the meanings expressed are consistent when the distinction is not emphasized.

[0021] In the embodiments of the present application, sometimes the subscript such as W1 can be written in the form of non-subscript such as W1, and the meanings expressed are consistent when the distinction is not emphasized.

[0022] In order to make the technical problems, technical schemes and advantages to be solved by the present application more clear, the following will be described in detail in combination with the drawings and specific embodiments.

[0023] The embodiments of the present application provide a niobium-tantalum metal ore mineralization evaluation system based on multi-source information analysis, as shown in Figures 1-2 The schematic diagram of the niobium-tantalum metal ore mineralization evaluation system based on multi-source information analysis, the system comprises: The geological structure correlation module acquires geological structure information, including stratum distribution, fault position and fold shape, analyzes the influence degree of tectonic stress field on mineralization, and generates a tectonic stress influence index; The mineral distribution interpretation module extracts mineral combination rules and chemical composition distribution characteristics based on the tectonic stress influence index, analyzes the corresponding relationship between mineral enrichment area and tectonic stress field, and obtains a mineral enrichment degree value; The ore-forming environment adaptation module extracts stratum lithology and structural characteristics in the ore-forming environment according to the mineral enrichment degree value, analyzes the relevance of lithology combination and mineralization potential, and generates an environment adaptation index; The spatial feature analysis module calls the environment adaptation index, extracts the spatial distribution pattern and boundary characteristics of the ore-forming area, analyzes the relationship between spatial continuity and mineralization potential, and generates a spatial distribution optimization set; The potential area determination module analyzes the regional mineralization potential grade based on the spatial distribution optimization set, combines the original exploration data and the specification requirements, and obtains a rare metal ore mineralization potential area evaluation result; The tectonic stress influence index includes stress distribution uniformity, tectonic disturbance intensity, and stress action directionality, the mineral enrichment degree value includes enrichment intensity level, mineral concentration coefficient, and spatial aggregation distribution degree, the environment adaptation index includes lithology adaptability score, tectonic coordination degree, and consistency of ore-forming environment, the spatial distribution optimization set includes distribution continuity score, boundary integrity index, and regional form regularity, and the rare metal ore-forming potential region evaluation result includes potential level division, regional reliability score, and exploration consistency label.

[0024] Specifically, as shown in Figure 2 、 3 The geological structure correlation module includes: The stratum distribution extraction submodule obtains geological structure information, extracts stratum thickness variation and lithology interface characteristics, analyzes the influence degree of stratum relief on mineralization, and generates fault activity intensity; Obtain geological structure information, integrate and process seismic exploration data, drilling core data and ground geological survey data to obtain a three-dimensional geological structure model of the target region, extract stratum thickness variation and lithology interface characteristics, stratum thickness data revealed by drilling hole ZK01 in a certain mining area shows that the thickness of Silurian sandstone is 350 meters, the underlying Ordovician limestone is 200 meters thick, and the lithology interface is an unconformity surface between sandstone and limestone, which shows erosion and truncation characteristics, analyze the influence degree of stratum relief on mineralization, and determine the area where the stratum thickness is less than 100 meters and the lithology interface inclination is greater than 15 degrees as the area with strong stratum relief, the area with strong stratum relief causes significant hindrance and diversion effect on the migration of ore fluid, affecting the precipitation and enrichment of ore, and generates fault activity intensity, which is defined as the weighted average of fault offset stratum number and fault displacement, wherein the weight of fault offset stratum number is set to 0.6, and the weight of displacement is set to 0.4, for example, a F1 fault offsets 3 main strata, and the maximum vertical displacement is 50 meters, then the fault activity intensity is calculated as 3x0.6+50x0.4=21.8, the greater the fault activity intensity value, the stronger the control of the fault on mineralization.

[0025] The fold shape analysis submodule extracts the axial plane inclination and wavelength variation value in the fold shape according to the fault activity intensity, analyzes the influence degree of fold deformation on mineral migration and enrichment, and generates fold deformation degree; According to the fault activity intensity, the relevant fold region data is extracted based on the fault activity intensity value, for example, when the fault activity intensity is greater than 15, the fold structure in the region is taken as the analysis object, the axial plane dip angle and the wavelength change value in the fold shape are extracted, through the analysis of the geological profile and the three-dimensional geological model of the target region, in a certain fold structure, the axial plane dip angle gradually changes from 65 degrees in the north wing to 45 degrees in the south wing, and the wavelength changes from 800 meters in the east to 600 meters in the west, the degree of influence of fold deformation on mineral migration and enrichment is analyzed, and the region where the axial plane dip angle is greater than 40 degrees and the wavelength is less than 700 meters is evaluated as a region with high fold deformation intensity. The fold deformation in this region leads to changes in rock porosity and permeability, affecting the migration path of hydrothermal minerals. In the fold core or hinge part, fluid is easy to gather, and ore is enriched here to generate fold deformation degree. The fold deformation degree is calculated by the product of the axial plane dip angle and the wavelength change rate, where the axial plane dip angle is 55 degrees, the wavelength change rate is (800-600) / 800=0.25, and the fold deformation degree is calculated as 55x0.25=13.75. This value is used to quantify the potential impact of folds on mineral enrichment.

[0026] The stress field evaluation submodule extracts structural stress field distribution information based on the fold deformation degree, analyzes the influence range of structural stress concentration on mineralization, and generates a structural stress influence index. Based on the fold deformation degree, the fold deformation degree value is retrieved, and when the fold deformation degree is greater than 10, the structural stress field distribution information corresponding to the deformation degree is extracted. The stress tensor data of the study area is obtained through finite element simulation, including the maximum principal stress, the minimum principal stress direction and size. For example, in a certain area, the maximum principal stress is 80 MPa, the minimum principal stress is 20 MPa, and the directions are 30 degrees and 60 degrees with the fold axis, respectively. The influence range of structural stress concentration on mineralization is analyzed. According to the Mohr-Coulomb failure criterion, the rock failure probability under the current stress field is calculated. The area with a failure probability greater than 0.7 is determined as a stress concentration area. The stress concentration in this area causes rock fractures, providing channels for ore fluid circulation, thereby affecting the precipitation and distribution of ore. A structural stress influence index is generated, which is calculated by the product of the area ratio of the stress concentration area and the stress intensity difference value. The stress intensity difference value is the difference between the maximum principal stress and the minimum principal stress. For example, the stress concentration area is 10 square kilometers, the total area of the study area is 100 square kilometers, and the area ratio is 0.1. The stress intensity difference value is , and the structural stress influence index is calculated as This index reflects the comprehensive influence of structural stress on mineralization.

[0027] Specifically, as shown in Figure 2 , 4 , the mineral distribution interpretation module includes: The mineral assemblage extraction submodule collects mineral assemblage sample data based on the tectonic stress influence index, decomposes the characteristics of mineral particles within the sample, determines the content proportion of mineral species, extracts the chemical composition values within the mineral particles, compares the distribution of adjacent particles in the differential mineral assemblage, identifies the paragenetic mineral assemblage block, and generates mineral species diversity. Based on the tectonic stress influence index, when the tectonic stress influence index is greater than 5, collect mineral assemblage sample data, obtain rock samples of the target area through field outcrop sampling and drill core sampling, for example, collect 10 hand specimens and 50 cm drill cores in a certain mineralized alteration zone, decompose the characteristics of mineral particles within the sample, observe the sample thin section using an optical microscope and a scanning electron microscope to obtain the size, shape, and crystal structure characteristics of the mineral particles, for example, identify pyrite euhedral crystals and galena granular aggregates, determine the content proportion of mineral species, quantitatively analyze the volume percentage of main minerals in the sample through image analysis and X-ray diffraction (XRD) technology, for example, the quartz content in a certain sample is 30%, the pyrite content is 20%, and the galena content is 15%, extract the chemical composition values within the mineral particles, use an electron probe (EPMA) to perform micro-area analysis on individual mineral particles to obtain the content of major elements and trace elements, for example, the Fe content in pyrite is 46.5%, the S content is 53.2%, and the trace Ag content is 100 ppm, compare the distribution of adjacent particles in the differential mineral assemblage, identify the spatial relationship between different mineral particles through element surface scanning and mineral distribution maps, for example, pyrite particles are often associated with galena particles around them, and their boundaries are straight, identify the paragenetic mineral assemblage block, divide the area with similar mineral assemblage proportion and spatial distribution pattern into a paragenetic mineral assemblage block based on similar mineral assemblage characteristics, for example, divide the pyrite-galena-quartz assemblage into a block, generate mineral species diversity, which is calculated by the weighted average of the number of mineral species and the uniformity of the content of the main minerals in each block, for example, a block contains 5 kinds of minerals, and the uniformity of the content of the main minerals (measured by Shannon index) is 0.8, then the mineral species diversity is 5x0.5+0.8x0.5=2.9, and the weight is 0.5.

[0028] The chemical composition analysis submodule selects key elements within the differential mineral assemblage based on the mineral species diversity, calculates the stable fluctuation interval of the elements, extracts the proportion of stable elements within the sample, extracts the content variation section of the variable elements, discriminates the relationship of the differential element assemblage, marks the influence degree of the differential assemblage on the sample distribution, and generates chemical composition stability. According to the mineral species diversity, the key elements in the differential mineral combination with a mineral species diversity greater than 2.5 are screened, and the elements with a content greater than 5% in each mineral combination block are selected as the main analysis objects. For example, in the pyrite-galena-quartz combination, Fe, S, Pb, Si, and O are selected as the key elements, the element stable fluctuation interval is calculated, the normal fluctuation range of the content of each key element is determined through statistical analysis of a large amount of sample data, for example, the stable fluctuation interval of the content of Fe is set to 40% to 50%, and the fluctuation within the interval is considered to be stable, the proportion of stable elements in the sample is extracted, and the total content of the key elements within the stable fluctuation interval in the sample accounts for the proportion of the total content of all key elements, for example, the content of Fe in a sample is 45%, the content of S is 52%, the content of Pb is 12%, the content of Si is 25%, and the content of O is 30%, among which Fe, Si, and O are within the stable fluctuation interval, and the proportion is , the variable element content change section is extracted, elements with a content exceeding the stable fluctuation interval are identified, and the percentage of the excess is recorded, for example, the content of S is 52%, which exceeds the stable interval (assuming 45%-50%) by 2%, and the content of Pb is 12%, which also exceeds the stable interval (assuming 8%-10%) by 2%, the differential element combination relationship is distinguished, the paragenesis, association or antagonism between elements is identified through principal component analysis and cluster analysis, for example, Fe and S show a significant positive correlation, and Pb and Ag show an association relationship, the influence degree of the differential combination on the sample distribution is marked, the influence of the element combination relationship and the content change section on mineralization enrichment or depletion is evaluated, for example, the stable paragenetic relationship of Fe-S indicates primary sulfide mineralization, and the abnormal enrichment of Pb-Ag indicates later superimposed modification or high-grade mineralization, the chemical composition stability is calculated by weighted average of the stable element proportion and the variable element content change section, the weight of the stable element proportion is set to 0.7, and the weight of the reciprocal of the variable element content change section is set to 0.3, for example, the stable element proportion is 0.61, the total variable element change percentage is 4% (2% of S plus 2% of Pb), and the reciprocal of the variable element content change section is , the chemical composition stability is .

[0029] The enrichment zone distribution scoring sub-module calls the chemical composition stability, divides the mineral enrichment zone sample unit, measures the key mineral content density in the enrichment unit, superimposes the unit with the corresponding range of tectonic stress blocks, identifies the boundary of the high-density enrichment block, quantifies the demarcation point set of the enrichment block and the low-density area, and obtains the mineral enrichment degree value; The mineral enrichment degree value is calculated by the formula: ; wherein, represents the mineral enrichment degree value, This represents the content density of the i-th mineral sample. This represents the distribution density of the i-th mineral sample. This represents the amount of mineral per unit volume of the i-th mineral sample. This represents the average mineral content per unit volume across all units. Represents the total number of mineral samples; The chemical composition stability is used. When the chemical composition stability is greater than 7.5, the sample units of the mineral enrichment area are divided. Based on information such as mineralization alteration characteristics, mineral assemblage, and chemical composition stability, the study area is divided into square sample units with a side length of 50 meters. For example, a mining area is divided into 100 grid units of 50m × 50m. The content density of key minerals in the enrichment unit is measured. Through borehole core analysis, geophysical anomaly interpretation, and surface sampling results, the average content of the target rare metal mineral in each unit is obtained. For example, the average content of tantalum (Ta) in a certain unit is 300 ppm, which is converted to a content density of... (Assuming the rock density is 3) The superimposed units correspond to the tectonic stress blocks. Each sample unit is spatially superimposed with the previously generated tectonic stress blocks to determine whether each unit is located in a high-stress concentration area. For example, unit A is located in a stress concentration area, and unit B is located in a stress dispersion area. The boundaries of high-density enrichment blocks are identified, and mineral content densities exceeding a preset threshold are identified (e.g., tantalum content density greater than a certain threshold). The adjacent units of the high-density enrichment area are connected to form a high-density enrichment block, and its spatial boundary line is drawn. The set of boundary points between the enrichment block and the low-density area is quantified. Through the boundary detection algorithm, the high-density enrichment block and the surrounding low-density area (e.g., tantalum content density less than 1.5%) are identified. The specific points between these points form a clear boundary line, from which the mineral enrichment value is obtained, and the formula is used. In the calculation, the parameters in the formula... This value represents the mineral enrichment level, a comprehensive indicator used to quantify the spatial concentration of mineral deposits. Representing the The content density of each mineral sample, in units of This represents the mass of the target mineral per unit volume. Representing the Distribution density of each mineral sample, in units of This indicates the number of mineral particles per unit volume or the density at proven depths. These two parameters together reflect the actual occurrence of the mineral. Representing the The amount of mineral per unit volume of a mineral sample, in units of It is obtained by weighing a mineral sample and dividing by its volume. Total number of mineral samples, i.e. total number of discrete mineral sample points involved in the calculation, Average mineral mass per unit volume representing all cells, is the arithmetic mean of all values, which measures the average mineral mass level of the entire area, represents the average content density of all from 1 to is summed up; First part of the formula represents the weighted average content density, where is the content density, is the distribution density, this part calculates the average content of the mineral considering the distribution density, the higher the distribution density, the greater the contribution to the average value, reflecting the concentration of the mineral in space, the second part of the formula represents the standard deviation of the mineral mass per unit volume, where is the mass of a single sample, is the average mass of all samples, is the number of samples, this part quantifies the degree of dispersion or uniformity of the mineral mass in the spatial distribution, the greater the standard deviation, the greater the fluctuation of the mineral mass, the higher the enrichment or the more uneven, the formula multiplies these two parts, so that the mineral enrichment value takes into account both the average content level of the mineral and the concentration or fluctuation degree of its distribution, three representative mineral sample points are taken in a certain mineral area; Sample 1: ; Sample 2: ; Sample 3: ; First, calculate ; Then calculate the first part : ; Next, calculate the second part : ; Finally, ; This formula more comprehensively reflects the spatial enrichment characteristics of the mineral by combining the content density, distribution density and dispersion of the mass per unit volume, avoiding the problem of ignoring the uniformity of the distribution only by high content, the result shows that the mineral enrichment value is 107.57, indicating that the mineral in this area exists in a moderate degree of enrichment, this value is an important indicator to measure the occurrence characteristics of the mineral.

[0030] Specifically, as Figure 2 , 5As shown, the ore-forming environment adaptation module includes: The lithological combination extraction submodule extracts the stratum lithological combination type in the ore-forming environment according to the mineral enrichment degree value, analyzes the support degree of the lithological combination to the mineral enrichment, and generates a lithological combination matching degree; According to the mineral enrichment degree value, when the mineral enrichment degree value is greater than 100, the stratum lithological combination type in the ore-forming environment is extracted, the rock types related to mineralization and their spatial combination relationship are identified through comprehensive interpretation of the geological map, drill columnar section and well logging curve, for example, the granite-walling rock contact zone, the interbedded combination of metamorphic sandstone and schist, the support degree of the lithological combination to the mineral enrichment is analyzed, the geochemical background, physical properties (such as porosity, permeability) and affinity with the paragenetic mineral of each lithological combination are evaluated, and the favorable or unfavorable influence of the lithological combination on the enrichment of the target mineral is quantified, for example, the felsic intrusive rock has a high support degree to the enrichment of rare metal ore because it is rich in rare metal elements and easy to form fissures, and the carbonate rock has a strong reactivity and adsorbs mineral matter to generate a lithological combination matching degree, which is calculated by the quantified score of the support degree of the lithological combination to the mineral enrichment, for example, the matching degree of the granite-walling rock contact zone is scored as 0.8, and the matching degree of the interbedded combination of metamorphic sandstone and schist is scored as 0.6, the higher the matching degree, the more favorable the lithological combination to the mineral enrichment.

[0031] The structural feature matching submodule calls the lithological combination matching degree, extracts the fault strike and fold shape in the structural feature, analyzes the contribution degree of the structural feature to the ore-forming process, and generates a structural feature contribution rate; When the lithological combination matching degree is greater than 0.7, the structural feature matching submodule extracts the fault strike and fold shape in the structural feature, obtains the three-dimensional spatial distribution direction and dip angle of the faults and the geometric elements such as the axial direction, plunge direction and wing dip angle of the folds in the study area by analyzing the high-precision gravity and magnetic data, aerial remote sensing images and geological surveying and mapping data, for example, the strike of a certain fault is northeast 45 degrees and the dip angle is 70 degrees, and the axial direction of a certain fold is northeast 30 degrees, analyzes the contribution degree of the structural feature to the ore-forming process, evaluates the effectiveness of the fault as a mineral liquid migration channel, calculates the control ability of the fold hinge and wing to the mineral liquid accumulation and precipitation, for example, the ore body controlled by the north-east fault zone generally has a higher grade than the north-west fault, and the turning end of the fold is the preferred position for ore body enrichment, and generates a structural feature contribution rate, which is calculated by the weighted average of the fault ore-transporting ability and the fold ore-controlling ability, wherein the weight of the fault ore-transporting ability is set to 0.6 and the weight of the fold ore-controlling ability is set to 0.4, for example, the ore-transporting ability of a certain fault is scored as 0.75 and the ore-controlling ability of a certain fold is scored as 0.65, then the structural feature contribution rate is 0.75x0.6+0.65x0.4=0.71, the higher the contribution rate, the greater the contribution of the structural feature to the ore-forming.

[0032] The potential weight calculation submodule extracts a metallogenic potential ranking table based on the structural feature contribution rate, analyzes the adaptation degree of the metallogenic potential weight to the metallogenic environment, and generates an environment adaptation index; When the structural feature contribution rate is greater than 0.7, the metallogenic potential ranking table is extracted, the geological expert experience, historical exploration data and regional metallogenic regularity analysis are used to establish a metallogenic potential grade division standard according to different geological, geochemical and geophysical feature combinations, and a potential weight distribution table is formed, for example, containing "high potential area: weight 0.9, medium potential area: weight 0.6, low potential area: weight 0.3" and the like, the adaptation degree of the metallogenic potential weight to the metallogenic environment is analyzed, the geological features of the current analysis area are compared with each index in the potential ranking table, and the corresponding potential weight is distributed according to the matching degree, for example, if a region has similar fault structure and lithological combination as the high potential area, it is given a potential weight of 0.9, an environment adaptation index is generated, the index is calculated by the product of the potential weight and the analyzed geological features (such as lithological matching degree, structural contribution rate), for example, the potential weight of a certain region is 0.9, the lithological combination matching degree is 0.8, and the structural feature contribution rate is 0.71 (from the previous step), the environment adaptation index is 0.9x0.8x0.71=0.5112, which comprehensively reflects the comprehensive favorable degree of the metallogenic environment.

[0033] Specifically, as shown in Figure 2 , 6 the spatial feature analysis module includes: The spatial continuity analysis submodule calls the environment adaptation index, collects multi-source spatial data of the metallogenic area, interprets the spatial distribution unit of the target area, identifies the strength of the continuity of adjacent units, divides the continuity level blocks, analyzes the differential level block combination mode, and generates a spatial continuity index; Multi-source spatial data refers to a set of geographic spatial information from differential sensors or data platforms; The spatial continuity index refers to an index indicating the strength of the continuity between adjacent spatial units; When the environmental adaptation index is greater than 0.5, the multi-source spatial data of the ore-forming area is collected, the satellite remote sensing image (for example, Sentinel 2 multispectral image), terrain elevation data (DEM) and drill hole distribution data are obtained, the spatial data of different sources are geographically registered and fused, the spatial distribution unit of the target area is interpreted, based on the fused multi-source data, the research area is divided into regular grid units with a side length of 200 meters through image segmentation and geological unit demarcation, each unit represents a spatial distribution unit, for example, in a 100 square kilometer research area, 2500 200mx200m grid units are divided, the continuity of adjacent units is identified, the difference value of the environmental adaptation index between adjacent grid units is calculated, the difference value less than 0.1 is determined as strong continuity, the difference value between 0.1 and 0.3 is determined as medium continuity, and the difference value greater than 0.3 is determined as weak continuity, for example, the environmental adaptation index of unit A is 0.55, the adjacent unit B is 0.53, the difference is 0.02, which is strong continuity, unit C is 0.48, the difference is 0.07, which is strong continuity, and unit D is 0.35, the difference is 0.20, which is medium continuity, the continuity level blocks are divided, the adjacent units with similar continuity strength are aggregated to form the continuity level blocks, for example, all the strong continuity units are aggregated into a high continuity block, the differentiated block combination mode is analyzed, the spatial adjacency relationship, geometric shape and scale between different continuity level blocks are analyzed, for example, the high continuity block is distributed in a strip shape and consistent with the strike of the fault structure, and the low continuity block is distributed in a sporadic shape, a spatial continuity index is generated, which is calculated by the weighted average of the area proportion of the strong continuity region and the average environmental adaptation index of the block, the weight of the area proportion of the strong continuity region is set to 0.7, and the weight of the average environmental adaptation index of the block is set to 0.3, for example, the area proportion of the strong continuity region in the total area of the research area is 0.6, and the average environmental adaptation index of all blocks is 0.52, then the spatial continuity index is 0.6x0.7+0.52x0.3=0.576, which reflects the uniformity and continuity of the ore-forming environment in the spatial distribution.

[0034] The boundary feature extraction submodule locates the boundary strike line segment of the ore-forming area according to the spatial continuity index, measures the boundary definition distribution value range, calibrates the boundary transition band width interval, decomposes the boundary point set of the differentiated interval, analyzes the position relationship of the boundary point set corresponding to the main block, and generates the boundary feature definition; The boundary feature definition is an index of the definition degree and structure certainty of the regional boundary shape; When the spatial continuity index is greater than 0.5, the boundary trend line segment of the ore-forming area is located, the boundary line segment representing the core of the ore-forming area is extracted by vectorizing the edge of the high continuity block, for example, a 2000-meter-long north-east boundary line is extracted on the edge of a certain long strip-shaped high continuity block, the boundary definition value range is measured, the boundary line segment is sampled at an interval of 10 meters, and the standard deviation of the spatial continuity index within a radius of 50 meters around each sampling point is calculated, the standard deviation is less than 0.05 for high definition, 0.05 to 0.1 for medium definition, and greater than 0.1 for low definition, the length proportion of high definition, medium definition and low definition area is counted, for example, 70% of the length of the boundary line segment is high definition, 20% is medium definition, and 10% is low definition, the boundary transition zone width interval is calibrated, for each boundary sampling point, the distance from the high value to the low value of the spatial continuity index is searched outward and inward, the distance is defined as the transition zone width, and the minimum value, maximum value and average value of all transition zone widths are counted, for example, the transition zone width interval is 50 meters to 150 meters, the differentiated interval boundary point set is decomposed, the boundary line segment is divided into different point sets according to the boundary definition and the transition zone width, for example, the point set of high definition and narrow transition zone, the point set of low definition and wide transition zone, the spatial relationship between the boundary point set and the main area position is analyzed, the spatial relationship between each boundary point set and the known ore body or main mineralization zone in the area is evaluated, for example, the high definition boundary point set is adjacent to the main ore body, and the low definition boundary point set is far away, the boundary feature definition is generated, the definition is calculated by the difference between the high definition boundary length proportion and the low definition boundary length proportion, for example, the high definition boundary length proportion is 0.7, and the low definition boundary length proportion is 0.1, then the boundary feature definition is 0.7-0.1=0.6, the higher the value, the more determined the boundary form of the ore-forming area.

[0035] The distribution optimization score sub-module sorts the spatial distribution unit combination sequence based on the boundary feature definition, compares the fitting state of adjacent unit boundaries, filters the distribution continuity mutation area, marks the high difference unit, evaluates the influence of the high difference unit on the overall distribution coupling degree, and generates a spatial distribution optimization set; Based on the boundary feature clarity, when the boundary feature clarity is greater than 0.5, the spatial distribution unit combination sequence is combed, all grid units are sorted and grouped according to the similarity and spatial proximity of their geological features according to the spatial continuity index and the boundary feature clarity, for example, units with high environmental adaptation index and strong spatial continuity are classified into one category, the fitting state of adjacent unit boundaries is compared, it is checked whether the boundary line between adjacent units is smooth, continuous, and coincides with the actual geological boundary, for example, the boundary of two adjacent units coincides with a fault line by 90%, it is considered that the fitting state is good, the distribution continuity mutation area is screened, the spatial continuity index or the environmental adaptation index suddenly decreases greatly between adjacent units, for example, the boundary of a unit with an environmental adaptation index of 0.8 suddenly decreases to a unit with an environmental adaptation index of 0.2, which is a mutation area, mark the high difference distribution unit, mark those isolated units whose environmental adaptation index is significantly different from the surrounding units and do not belong to any known geological body boundary, for example, an isolated unit with an environmental adaptation index of 0.9 is surrounded by units with an environmental adaptation index of 0.3, evaluate the influence of high difference units on the overall distribution coupling degree, analyze whether the high difference unit represents an unidentified mineralization anomaly or a measurement error, quantify its interference or promotion effect on the whole area metallogenic potential evaluation, generate a spatial distribution optimization set, the optimization set considers the comprehensive evaluation of the number of distribution continuity mutation areas and the influence degree of high difference distribution units, the optimized spatial distribution unit sequence is used for subsequent potential evaluation, for example, the spatial distribution optimization set contains all high continuity and high environmental adaptation areas, and excludes units with uncertain boundaries or high differences.

[0036] Specifically, as shown in Figure 2 , 7 , the potential area determination module includes: The potential probability analysis submodule extracts the metallogenic potential indicators and spatial distribution characteristics in the original exploration data based on the spatial distribution optimization set, analyzes the frequency and regularity of metallogenic potential occurrence, and generates a potential probability interval. Based on the spatial distribution optimization set, the metallogenic potential indicators and spatial distribution characteristics in the original exploration data are extracted. From historical drilling data, geophysical and geochemical anomaly maps, and geological mapping data, the discovered ore point grade, ore body thickness, and anomaly intensity are obtained as metallogenic potential indicators, and the spatial position information of the indicators is extracted, for example, a drill hole reveals that the tantalum (Ta) grade is 500 ppm, the ore body thickness is 10 meters, and it is located at the center of a geochemical anomaly. The frequency and regularity of metallogenic potential occurrence are analyzed, and the number of high-grade mineralization points and their spatial distribution patterns under different geological backgrounds, structural positions, and lithological combinations are counted, for example, 80% of high-grade tantalum mineralization points occur near the northeast-trending faults at the granite-metamorphic rock contact zone. The potential probability interval is generated. Through statistical analysis, the probability range of discovering high-grade rare metal ores in different types of geological units is determined, for example, in the granite-metamorphic rock contact zone, the potential probability interval is 0.6 to 0.8, and in the non-contact zone, the potential probability interval is 0.1 to 0.3. This interval reflects the likelihood of discovering mineral resources under specific geological conditions.

[0037] The metallogenic intensity evaluation submodule calls the potential probability interval, extracts the influence degree of metallogenic events on resource reserves and quality, analyzes the distribution characteristics of metallogenic intensity, and generates metallogenic intensity grades. When the potential probability interval is greater than 0.5, the influence degree of metallogenic events on resource reserves and quality is extracted, the resource quantity (tons) and average grade (ppm or %) of the explored ore body are calculated, and the size and quality of the ore deposit formed by the metallogenic event are quantified, for example, a certain ore body has a proven resource quantity of 1 million tons and an average tantalum grade of 300 ppm. The distribution characteristics of metallogenic intensity are analyzed, and the distribution patterns of high-grade enrichment cores, medium-grade mineralization zones, and low-grade halos are identified based on ore body shape, occurrence, and grade distribution, for example, a high-grade enrichment core is located at the intersection of faults, and the grade decreases outward. The metallogenic intensity grades are generated. According to the resource reserves, average grade, and uniformity of grade distribution, the metallogenic intensity is divided into different grades, for example, a resource quantity greater than 1 million tons and an average grade greater than 200 ppm are classified as first-grade metallogenic intensity, and a resource quantity of 50-100 million tons and a grade of 100-200 ppm are classified as second-grade metallogenic intensity. The higher the metallogenic intensity grade, the higher the size and quality of mineral resources in the region.

[0038] The potential scoring submodule generates rare metal mineralization potential regional evaluation results based on the metallogenic intensity grades and the potential grade division standards in the specification requirements, analyzes the distribution patterns of regional potential scores, and generates rare metal mineralization potential regional evaluation results. Based on the ore-forming intensity grade, combined with the potential grade division standard in the specification requirement, the determined ore-forming intensity grade is compared with the rare metal mineral resource potential evaluation standard stipulated by the state or industry, for example, referring to the rare metal mineral exploration degree grade division standard in the solid mineral exploration specification, the distribution law of the regional potential score is analyzed, the ore-forming intensity grade is combined with the potential probability interval, the final potential score of each evaluation unit is calculated through the geological model and the geostatistics method, and the regional potential score contour map is drawn, for example, the score of the high-intensity ore-forming area combined with the high-potential probability area is significantly higher than the region, the rare metal mineralization potential regional evaluation result is generated, the rare metal mineralization potential distribution map and the potential grade report in the research region are finally determined and output, and the 'extremely high potential area', 'high potential area','medium potential area' and 'low potential area' are divided, for example, a certain area is evaluated as an extremely high potential area, and it is predicted that the future exploration investment will bring significant resource discovery.

[0039] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A niobium-tantalum metal ore mineralization evaluation system based on multi-source information analysis, characterized in that, The system includes: The geological structure association module acquires geological structural information, including stratigraphic distribution, fault location and fold morphology, analyzes the degree of influence of tectonic stress field on mineralization, and generates tectonic stress influence index; Based on the tectonic stress influence index, the mineral distribution interpretation module extracts the mineral assemblage pattern and chemical composition distribution characteristics, analyzes the correspondence between mineral enrichment areas and tectonic stress fields, and obtains mineral enrichment values. The mineralization environment adaptation module extracts the stratigraphic lithology and structural characteristics of the mineralization environment based on the mineral enrichment value, analyzes the correlation between lithological combinations and mineralization potential, and generates an environment adaptation index. The spatial feature analysis module calls the environmental adaptation index to extract the spatial distribution pattern and boundary features of the mineralization area, analyzes the relationship between spatial continuity and mineralization potential, and generates an optimized spatial distribution set.

2. The niobium-tantalum metal mineralization evaluation system based on multi-source information analysis according to claim 1, characterized in that: The tectonic stress influence index includes stress distribution uniformity, tectonic disturbance intensity, and stress directionality; the mineral enrichment value includes enrichment intensity level, mineral concentration coefficient, and spatial aggregation distribution degree; the environmental adaptability index includes lithological adaptability score, tectonic coordination degree, and metallogenic environment consistency; and the spatial distribution optimization set includes distribution continuity score, boundary integrity index, and regional morphological regularity.

3. The niobium-tantalum metal ore mineralization evaluation system based on multi-source information analysis according to claim 1, characterized in that: The geological structure association module includes: The stratigraphic distribution extraction submodule acquires geological structural information, extracts stratigraphic thickness variations and lithological interface characteristics, analyzes the degree of influence of stratigraphic undulations on mineralization, and generates fault activity intensity. The fold morphology analysis submodule extracts the axial dip angle and wavelength variation values ​​in the fold morphology based on the fault activity intensity, analyzes the degree of effect of fold deformation on mineral migration and enrichment, and generates the degree of fold deformation. The stress field assessment submodule extracts the tectonic stress field distribution information based on the degree of fold deformation, analyzes the influence range of tectonic stress concentration on mineralization, and generates a tectonic stress influence index.

4. The niobium-tantalum metal ore mineralization evaluation system based on multi-source information analysis according to claim 3, characterized in that: The mineral distribution interpretation module includes: Based on the structural stress influence index, the mineral assemblage extraction submodule collects mineral assemblage sample data, decomposes the characteristics of mineral particles in the sample, determines the content ratio of mineral types, extracts the chemical composition values ​​of mineral particles, compares the distribution of adjacent particles in differentiated mineral assemblages, identifies symbiotic mineral assemblage blocks, and generates mineral diversity. Based on the diversity of mineral types, the chemical composition analysis submodule screens key elements within differentiated mineral assemblages, calculates the stable fluctuation range of elements, extracts the proportion of stable elements in the sample, extracts the range of changes in the content of volatile elements, identifies the relationship between differentiated element combinations, calibrates the degree of influence of differentiated combinations on the sample distribution, and generates chemical composition stability. The enrichment zone distribution scoring submodule calls the chemical composition stability, divides the mineral enrichment zone sample unit, measures the content density of key minerals in the enrichment unit, determines the range of superimposed units and tectonic stress blocks, identifies the boundary of high-density enrichment blocks, quantifies the boundary point set between enrichment blocks and low-density areas, and obtains the mineral enrichment value.

5. The niobium-tantalum metal ore mineralization evaluation system based on multi-source information analysis according to claim 4, characterized in that: The mineral enrichment value is calculated using the following formula: ； in, Indicates the mineral enrichment value. This represents the content density of the i-th mineral sample. This represents the distribution density of the i-th mineral sample. This represents the amount of mineral per unit volume of the i-th mineral sample. This represents the average mineral content per unit volume across all units. This represents the total number of mineral samples.

6. The niobium-tantalum metal ore mineralization evaluation system based on multi-source information analysis according to claim 5, characterized in that: The mineralization environment adaptation module includes: The lithological assemblage extraction submodule extracts the stratigraphic lithological assemblage type in the mineralization environment based on the mineral enrichment value, analyzes the degree of support of the lithological assemblage for mineral enrichment, and generates the lithological assemblage matching degree. The structural feature matching submodule calls the lithological combination matching degree to extract the fault strike and fold morphology in the structural features, analyzes the contribution of structural features to mineralization, and generates the structural feature contribution rate. The potential weight calculation submodule extracts the mineralization potential ranking table based on the contribution rate of the structural features, analyzes the adaptability of the mineralization potential weights to the mineralization environment, and generates an environment adaptability index.

7. The niobium-tantalum metal mineralization evaluation system based on multi-source information analysis according to claim 6, characterized in that: The spatial feature parsing module includes: The spatial continuity analysis submodule calls the environmental adaptation index to collect multi-source spatial data of the mineralized area, interprets the spatial distribution units of the target area, identifies the continuity strength of adjacent units, divides the continuity level blocks, analyzes the combination pattern of differentiated level blocks, and generates the spatial continuity index. The boundary feature extraction submodule locates the boundary trend line segment of the ore-forming area based on the spatial continuity index, measures the boundary clarity distribution range, calibrates the width range of the boundary transition zone, decomposes the boundary point set of the differentiated interval, analyzes the positional relationship between the boundary point set and the main area, and generates the boundary feature clarity. Based on the clarity of the boundary features, the distribution optimization scoring submodule sorts out the spatial distribution unit combination sequence, compares the boundary fitting state of adjacent units, filters the distribution continuity abrupt change region, marks the high difference distribution units, evaluates the impact of the high difference units on the overall distribution coupling degree, and generates a spatial distribution optimization set.

8. The niobium-tantalum metal mineralization evaluation system based on multi-source information analysis according to claim 7, characterized in that: The multi-source spatial data refers to a collection of geospatial information from differentiated sensors or data platforms; The spatial continuity index refers to an indicator of the strength of the continuity of distribution between adjacent spatial units. The clarity of boundary features refers to an indicator of the degree of clarity of the shape of the regional boundary and the certainty of its structure.

9. The niobium-tantalum metal mineralization evaluation system based on multi-source information analysis according to claim 1, characterized in that: The system also includes a potential area determination module: The potential area determination module, based on the spatial distribution optimization set and combined with the original exploration data and standard requirements, analyzes the metallogenic potential level of the region and obtains the assessment results of the metallogenic potential area of ​​rare metal minerals. The regional assessment results of the rare metal mineralization potential include potential level classification, regional credibility score, and exploration consistency label.

10. The niobium-tantalum metal mineralization evaluation system based on multi-source information analysis according to claim 9, characterized in that: The potential region determination module includes: The potential probability analysis submodule extracts mineralization potential indicators and spatial distribution characteristics from the original exploration data based on the spatial distribution optimization set, analyzes the frequency and regularity of mineralization potential occurrence, and generates potential probability intervals. The mineralization intensity assessment submodule calls the potential probability interval, extracts the degree of impact of mineralization events on resource reserves and quality, analyzes the distribution characteristics of mineralization intensity, and generates mineralization intensity levels; The potential scoring submodule analyzes the distribution pattern of regional potential scores based on the metallogenic intensity level and the potential level classification standards in the specification requirements, and generates regional assessment results of rare metal mineralization potential.

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