Mine prospecting target area resource decision-making method and system based on minimization of influence of environment sensitive area

Abstract

The invention relates to the technical field of geological prospecting, in particular to a prospecting target area resource decision-making method and system based on minimization of influence of an environment sensitive area, and the method comprises the steps: obtaining geological data and environment data of each prospecting target area in a target exploration area, and carrying out the preprocessing; constructing a space-time coupled environment sensitivity dynamic quantification model based on the preprocessed environment data, and extracting environment sensitivity quantification results of the prospecting target areas; constructing a geological potential quantification model based on the preprocessed geological data set, and extracting a geological potential quantification result of each prospecting target area; constructing a prospecting target region difference matrix based on the environmental sensitivity and the geological potential result, and extracting a typical prospecting target region through fuzzy support vector machine classification and density peak clustering; and constructing a three-dimensional decision-making space, and solving a decision-making result of the prospecting target area by adopting a multi-objective optimization algorithm. The objective of the invention is to improve the efficiency and accuracy of large-scale prospecting target area resource decision.

Description

Technical Field

[0001] The present invention relates to the field of geological prospecting technology, and in particular to a resource decision-making method and system for prospecting target areas based on minimizing the impact of environmentally sensitive areas. Background Art

[0002] In the field of geological and mineral exploration, the precise delineation of prospecting targets is the core link of mineral resource exploration. The delineation of prospecting targets relies on the deep integration and comprehensive analysis of multidisciplinary data such as geology, geophysics and geochemistry. Through the careful sorting of the geological background and mineralization conditions in the region and the in-depth exploration of the distribution law of minerals, the prospecting target area can be delineated and become the core focus area of ​​mineral exploration. Although the existing technology can achieve the preliminary delineation of prospecting targets, it lacks an intelligent screening method for large-scale prospecting target area collections.

[0003] At present, traditional prospecting target area assessment methods usually rely on expert experience and judgment. On the one hand, the static weight allocation dominated by subjective experience leads to the evaluation results being limited by expert cognitive bias, especially in ecologically fragile areas, which cannot accurately represent the cumulative effects of environmental impacts and the natural recovery process, which in turn leads to biased assessment decisions for prospecting targets; on the other hand, for large-scale target area collections, the traditional independent target area assessment model requires repeated execution of multidisciplinary data interpretation and potential calculation, resulting in exponential growth in computational complexity with the number of target areas, which in turn causes redundant calculations and decision delays. These problems limit the efficiency of prospecting target area decision-making and the accuracy of results, and thus restrict the efficiency and scientificity of mineral exploration in highly sensitive areas. Summary of the invention

[0004] In order to improve the efficiency and accuracy of large-scale prospecting target area resource decision-making, the present invention provides a prospecting target area resource decision-making method and system based on minimizing the impact of environmentally sensitive areas. The technical solutions adopted are as follows: The technical solution of the first aspect of the present invention provides a resource decision-making method for prospecting target areas based on minimizing the impact of environmentally sensitive areas, the method comprising: Obtain geological data and environmental data of each prospecting target area in the target exploration area and perform preprocessing; Based on the pre-processed environmental data, a dynamic quantitative model of environmental sensitivity coupled in time and space was constructed to extract the quantitative results of environmental sensitivity of each prospecting target area; Construct a geological potential quantitative model based on the preprocessed geological data set and extract the geological potential quantitative results of each prospecting target area; Based on the environmental sensitivity and geological potential results, a prospecting target area difference matrix is ​​constructed, and typical prospecting target areas are extracted through fuzzy support vector machine classification and density peak clustering. Construct a three-dimensional decision space and use a multi-objective optimization algorithm to solve the decision results of the prospecting target area.

[0005] Furthermore, the geological data and environmental data of each prospecting target area in the target exploration area are obtained and pre-processed, including: Obtain the vegetation coverage, slope, distance to the ecological protection area and permafrost stability index of each prospecting target area, construct an environmental data set and perform standardized preprocessing on the environmental data set; The conjugate fracture density, Bouguer gravity anomaly, and vertical concentration gradient of primary halo elements in each prospecting target area are obtained, and a geological data set is constructed and normalized and preprocessed.

[0006] Furthermore, a spatiotemporal coupled environmental sensitivity dynamic quantitative model is constructed based on the preprocessed environmental data to extract the environmental sensitivity quantitative results of each prospecting target area, including: A triangular fuzzy judgment matrix was constructed to conduct fuzzy evaluation on the relative importance of environmental factors, and the fuzzy analytic hierarchy process was used to extract the weights of environmental factors. The environmental factor weights and pre-processed environmental data are coupled in time and space to extract the historical cumulative effects of environmental impacts; Based on the historical cumulative effect of environmental impact and the preset time attenuation coefficient, the quantitative results of environmental sensitivity of each prospecting target area are extracted.

[0007] Furthermore, the expression of the environmental sensitivity quantification result is:

[0008] In the formula, Indicates Quantification results of environmental sensitivity of each prospecting target area; Indicates the start time; Indicates the current time; represents the integral time variable; Indicates the preset time attenuation coefficient, which is used to reflect the ecological recovery rate; Indicates the number of environmental factors; Indicates The weight of environmental factors; Indicates The first A standardized environmental factor.

[0009] Furthermore, a geological potential quantitative model is constructed based on the preprocessed geological data set to extract the geological potential quantitative results of each prospecting target area, including: The pre-processed conjugate fracture density, Bouguer gravity anomaly, and vertical concentration gradient of primary halo elements are dimensionlessly processed. Based on the dimensionless processed conjugate fracture density, Bouguer gravity anomaly and vertical concentration gradient of primary halo elements, multi-source data fusion is carried out to construct a geological potential quantitative model, and the quantitative results of the geological potential of each prospecting target area are extracted.

[0010] Furthermore, a prospecting target area difference matrix was constructed based on the environmental sensitivity and geological potential results, and typical prospecting target areas were extracted through fuzzy support vector machine classification and density peak clustering, including: Construct a prospecting target area difference matrix based on environmental sensitivity, geological potential results and spatial distance differences; Taking the difference matrix of prospecting target area as input, the fuzzy support vector machine is used to preliminarily classify the prospecting target area and assign fuzzy membership degree. The truncated kernel function is used to calculate the local density of the prospecting target area, and the cluster center is determined in combination with the minimum distance; Based on the cluster centers, the density peak clustering algorithm is used to extract typical prospecting target areas.

[0011] Furthermore, a three-dimensional decision space is constructed, and a multi-objective optimization algorithm is used to solve the decision results of the prospecting target area, including: The environmental sensitivity of typical prospecting targets, the quantitative results of geological potential and their vertical gradient modulus are nonlinearly coupled to construct a comprehensive decision-making index. Based on comprehensive decision-making indicators and preset constraints, a dual-objective integer programming model is constructed to maximize resource development value and minimize environmental risks; Solving the Pareto optimal solution set of the bi-objective integer programming model based on genetic algorithm; Dynamic diversity maintenance and elite retention are used to optimize the Pareto optimal solution set and extract the final decision-making results of mineral exploration target areas.

[0012] Furthermore, the expression of the comprehensive decision-making index is:

[0013] In the formula, Indicates Comprehensive decision-making indicators for typical prospecting targets; Indicates Quantification results of geological potential of typical prospecting target areas; Indicates Quantification results of environmental sensitivity of typical prospecting target areas; represents the geological potential enhancement coefficient; represents the environmental sensitivity inhibition coefficient; represents the gradient smoothing factor; represents the smoothing constant; Indicates The vertical gradient modulus of the geological potential of a typical prospecting target area.

[0014] Furthermore, the expression of the bi-objective integer programming model is:

[0015] In the formula, represents the function of the economic value of mining changing with depth, , is the first constant, i.e. the benchmark economic value constant, is the second constant, i.e., the attenuation coefficient of the economic value of mining with depth; Indicates the mining depth; represents the attenuation function of ecological impact with depth, , is the third constant, i.e., the ecological impact attenuation rate constant; represents the total budget constraint; represents the geological potential threshold; represents the environmental sensitivity threshold; represents a 0-1 decision variable; Indicates The cost of a typical prospecting target area; Indicates the number of typical prospecting target areas.

[0016] The technical solution of the second aspect of the present invention provides a prospecting target area resource decision system based on minimizing the impact of environmentally sensitive areas, using the prospecting target area resource decision method based on minimizing the impact of environmentally sensitive areas described in the technical solution of the first aspect of the present invention, and the system includes: A data acquisition module is configured to acquire geological data and environmental data of each prospecting target area in the target exploration area and perform preprocessing; The environmental sensitivity dynamic quantification module is configured to construct a spatiotemporally coupled environmental sensitivity dynamic quantification model based on the preprocessed environmental data, and extract the environmental sensitivity quantification results of each prospecting target area; A geological potential quantification module is configured to construct a geological potential quantification model based on the preprocessed geological data set and extract the geological potential quantification results of each prospecting target area; Typical target area extraction module, configured to construct a prospecting target area difference matrix based on environmental sensitivity and geological potential results, and extract typical target areas through fuzzy support vector machine classification and density peak clustering; The multi-objective optimization solution module is configured to construct a three-dimensional decision space and use a multi-objective optimization algorithm to solve the decision results of the prospecting target area.

[0017] The present invention has the following beneficial effects: The resource decision-making method for prospecting target areas based on minimizing the impact of environmentally sensitive areas provided by the present invention realizes accurate assessment of environmental risks and resource potential of prospecting target areas by establishing a dynamic quantitative model of environmental sensitivity and a quantitative model of geological potential coupled in time and space; and then uses fuzzy support vector machine and density peak clustering to compress the original target area set into a typical target area set, effectively reducing redundant data and retaining key features, thereby improving decision-making efficiency; finally, by constructing a three-dimensional decision space and adopting a multi-objective optimization algorithm, comprehensively considering factors such as resource development value and environmental risks, balancing the relationship between resource development and environmental protection, ensuring effective resource development while minimizing the impact on environmentally sensitive areas, thereby providing a more scientific solution for resource decision-making in prospecting target areas; especially in the face of large-scale target area sets and highly sensitive areas, this method significantly improves the accuracy and efficiency of resource decision-making in prospecting target areas, and helps to improve the efficiency and scientificity of mineral exploration in highly sensitive areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 A method flow chart of a method for resource decision-making in a prospecting target area based on minimizing the impact of environmentally sensitive areas provided by one embodiment of the present invention; Figure 2 A schematic diagram of the structure of a mineral prospecting target area resource decision-making system based on minimizing the impact of environmentally sensitive areas provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation method, structure, features and effects of a resource decision-making method and system for prospecting target areas based on minimizing the impact of environmentally sensitive areas proposed by the present invention in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0021] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0022] The following is a detailed description of a specific solution of a resource decision-making method and system for prospecting target areas based on minimizing the impact of environmentally sensitive areas provided by the present invention in conjunction with the accompanying drawings.

[0023] A prospecting target area is an area with great prospecting potential that is determined based on geological, geophysical, geochemical and other information in geological prospecting work. It is usually delineated after in-depth research on the regional geological background, mineralization conditions, mineral distribution patterns, etc., and is a key target area for mineral exploration. Taking a specific mountain area as an example, the present invention analyzes the strata, structure, rock type and previous mineral discoveries to determine several prospecting target areas and form a prospecting target area set; based on this, the present invention proposes a prospecting target area resource decision-making method and system based on minimizing the impact of environmentally sensitive areas.

[0024] See also Figure 1 , which shows a method flow chart of a method for resource decision-making in a prospecting target area based on minimizing the impact of environmentally sensitive areas provided by an embodiment of the present invention, the method comprising: Step S100: obtaining geological data and environmental data of each prospecting target area in the target exploration area and performing preprocessing; Step S100 specifically includes: Step S110: Obtain the vegetation coverage, slope, distance to the ecological protection area and frozen soil stability index of each prospecting target area, construct an environmental data set and perform standardized preprocessing on the environmental data set; specifically, this embodiment adopts satellite remote sensing technology and selects satellite images with high resolution and appropriate time coverage, such as Landsat 8 OLI or Sentinel - 2 images, using the normalized vegetation index to calculate the NDVI value of each prospecting target area, setting a certain number of ground sample plots in each prospecting target area, and verifying and calibrating the NDVI value calculated by remote sensing by measuring the ratio of vegetation coverage area to sample plot area on the spot, so as to improve the accuracy of vegetation coverage data; for the distance from the ecological protection zone, this embodiment performs spatial analysis through the central coordinates of the prospecting target area and the boundary data of the ecological protection zone, calculates the Euclidean distance from the center point of the target area to the nearest boundary of the ecological protection zone, and obtains the distance of each prospecting target area from the ecological protection zone; the permafrost stability index is specifically used to evaluate the quantitative index of the stability of the permafrost layer in the prospecting target area. This index mainly considers multiple factors that affect the stability of permafrost, such as ground temperature, soil moisture content, vegetation coverage, etc., to reflect the degree of stability of permafrost under natural conditions or external interference. Since the melting or instability of permafrost can directly affect the stability of geological structures, thereby having a significant impact on the safety and effectiveness of prospecting activities, this embodiment incorporates this index into the environmental data of the prospecting target area; Step S120: Obtain the conjugate fracture density, Bouguer gravity anomaly value, and vertical concentration gradient of primary halo elements in each prospecting target area, construct a geological data set and perform normalization preprocessing on the geological data set; wherein, the conjugate fracture density refers to the density of conjugate fractures in a specific direction in the prospecting target area, that is, the number of conjugate fracture intersections per unit area, and its density reflects the complexity of the geological structure and the rock fragmentation of the prospecting target area; the Bouguer gravity anomaly value is the difference value compared with the standard gravity field calculated based on Bouguer gravity measurement, and the Bouguer gravity anomaly value can be used to infer the heterogeneity of underground geological structure and material composition; the vertical concentration gradient of primary halo elements refers to the rate of change of the concentration of primary halo elements closely related to mineralization in geological samples in the prospecting target area in the vertical direction. This parameter is used to determine the location of the ore body and its extension direction, and then evaluate the potential of the ore deposit.

[0025] Specifically, this embodiment needs to obtain the geological data set and environmental data set of each prospecting target area in the prospecting target area set; wherein the prospecting target area set can be expressed as: ,in Indicates prospecting target areas; Indicates the total number of prospecting target areas; Indicates The center coordinates of the prospecting target area; Indicates Geological data of prospecting target areas; Indicates The environmental data of the prospecting target area are obtained by normalization preprocessing of the geological data and standardization preprocessing of the environmental data. The geological data set after normalization preprocessing can be expressed as: , Represents the quantity of geological data; the standardized environmental data set can be expressed as: , Indicates the quantity of environmental data; the data set of the prospecting target area can be expressed as: ; This embodiment can eliminate the dimensional differences between different data indicators by standardizing environmental data and normalizing geological data, making different types of data comparable. This helps to more accurately reflect the impact of various factors on the prospecting target area when constructing the dynamic quantitative model of environmental sensitivity and the quantitative model of geological potential in the future, and avoid model deviations caused by different data dimensions.

[0026] Step S200: constructing a spatiotemporal coupled environmental sensitivity dynamic quantitative model based on the preprocessed environmental data, and extracting the environmental sensitivity quantitative results of each prospecting target area; Step S200 specifically includes: Step S210: Construct a triangular fuzzy judgment matrix, perform a fuzzy evaluation on the relative importance of environmental factors, and use the fuzzy analytic hierarchy process to extract the weights of environmental factors; specifically, the weights of environmental factors in this embodiment are determined by the fuzzy analytic hierarchy process to reflect the importance of each environmental factor in the overall environmental sensitivity assessment; this embodiment selects 5-10 experts in the field of geological ecology, and uses triangular fuzzy numbers to perform a pairwise importance evaluation of environmental factors (vegetation coverage, slope, distance from the protected area, and permafrost stability) to form a triangular fuzzy judgment matrix. For example: Vegetation coverage vs. permafrost stability: Field experts believe that the former is "slightly important", which can be expressed as converted into triangular fuzzy numbers: ; Frozen soil stability vs slope: Field experts believe that the former is "obviously important" and can be expressed by converting it into triangular fuzzy numbers ; The triangular fuzzy judgment matrix can be expressed as: ,

[0027] In the formula, Represents a triangular fuzzy judgment matrix with dimension , used to reflect the relative importance of environmental factors; Represents a triangular fuzzy number, located in the matrix The and The column reflects the experts' The environmental factors relative to Fuzzy judgment of environmental factors; Experts on The environmental factors relative to The lower limit of the importance judgment of each environmental factor; Experts on The environmental factors relative to The most likely value of the importance judgment of each environmental factor; Experts on The environmental factors relative to The upper limit of the importance judgment of each environmental factor; The calculation formula of environmental factor weight is:

[0028] In the formula, Represents triangular fuzzy numbers The fourth root of Represents the sum variable, which is used to traverse each row of the triangular fuzzy judgment matrix. This embodiment solves the subjectivity and uncertainty of expert judgment in the traditional hierarchical analysis method by integrating multi-expert triangular fuzzy judgment and defuzzification calculation; on the other hand, it adopts geometric mean aggregation and centroid method for defuzzification, so that the weight distribution can retain the consensus of the expert group and avoid the influence of the preference of a single expert.

[0029] Step S220: Couple the environmental factor weights and the pre-processed environmental data in time and space to extract the historical cumulative effect of environmental impacts; specifically, this embodiment spatially locates the pre-processed environmental data based on the geographical location information of the prospecting target area obtained in step S100. The environmental data is sorted in time series to generate time series raster data to ensure the consistency of environmental data in time and space. At different time points , weighted calculation is performed on the environmental factor weights and the standardized environmental factors to obtain the contribution value of each environmental factor to the environmental impact at that time point. Then, the contribution values ​​of different environmental factors at the same time point are accumulated to obtain the comprehensive environmental impact value at that time point. As time goes by, these comprehensive environmental impact values ​​are accumulated in chronological order to form a historical cumulative effect data sequence of environmental impact; finally, the spatiotemporal raster data realizes the visualization of the environmental status of the target area, and the spatial resolution can reach 10m; this embodiment realizes the refined spatiotemporal alignment of environmental data by constructing a spatiotemporal raster data model. The dynamic sliding window accumulation mechanism can capture short-term ecological disturbances (such as seasonal vegetation changes) and long-term trends (such as permafrost degradation), and the cumulative effect prediction error is further reduced compared with the static model.

[0030] Step S230: Based on the historical cumulative effect of environmental impact and the preset time attenuation coefficient, the quantification result of the environmental sensitivity of each prospecting target area is extracted, which can be expressed as:

[0031] In the formula, Indicates Quantification results of environmental sensitivity of each prospecting target area; Indicates the start time; Indicates the current time, that is, calculates the cumulative effect of environmental impact from the start time to the current time; Represents the integral time variable, which is used to integrate the cumulative effect of environmental impact over time; Represents the preset time attenuation coefficient, which is used to reflect the ecological recovery rate, that is, the natural recovery process of environmental impact over time; Indicates the number of environmental factors; Indicates The weight of environmental factors; Indicates The first standardized environmental factors; specifically, the time attenuation coefficient can be determined by the ecological and environmental characteristics of the target exploration area and the existing ecological restoration research results to determine the value of the preset time attenuation coefficient; this embodiment introduces a regional adaptive ecological restoration attenuation coefficient and dynamically adjusts it through historical data fitting. The sensitivity quantification model can distinguish the ecological restoration characteristics of frozen soil areas and non-frozen soil areas, and then use exponential decay integral to calculate the environmental sensitivity, which not only reflects the cumulative effect of historical environmental impacts, but also reflects the natural recovery capacity of the ecosystem. Compared with the traditional linear accumulation model, the long-term sensitivity prediction is more accurate.

[0032] In summary, this embodiment determines the weights of environmental factors through the fuzzy hierarchical analysis method, makes full use of expert experience, and comprehensively considers the relative importance of various environmental factors in the environmental sensitivity assessment, so that the weight distribution is more scientific and reasonable. The spatiotemporal coupling calculation method in this embodiment organically combines the weights of environmental factors with environmental data in time and space dimensions, comprehensively reflects the historical cumulative effects of environmental impacts, and can accurately capture the dynamic impact of environmental changes on prospecting targets; at the same time, the quantitative results of environmental sensitivity are calculated based on the preset time attenuation coefficient, taking into account the natural process of ecological restoration, which is more in line with the actual situation and provides a quantitative basis for evaluating the long-term impact of prospecting activities on the environment. Ultimately, these quantitative results provide important environmental risk assessment indicators for subsequent prospecting target screening and resource decision-making, which helps to minimize the impact on environmentally sensitive areas while ensuring resource development, thereby achieving the coordinated development of resource development and environmental protection.

[0033] Step S300: constructing a geological potential quantitative model based on the preprocessed geological data set, and extracting the geological potential quantitative results of each prospecting target area; Step S300 specifically includes: Step S310: non-dimensionalize the pre-processed conjugate fracture density, Bouguer gravity anomaly, and primary halo element vertical concentration gradient; specifically, based on the acquired and pre-processed conjugate fracture density data, this example further analyzes the difference in the impact of conjugate fractures on mineralization under different geological backgrounds, that is, by statistically analyzing the conjugate fracture density and mineralization of known deposits in the study area, the relationship curve between the conjugate fracture density and the mineralization probability is determined, and the dimensionless fracture density can be expressed as:

[0034] In the formula, It represents the dimensionless conjugate fracture density, reflecting the multiple relationship of fracture density to the mineralization threshold; It represents the mineralization threshold of fracture density; represents the original conjugation break density; For the Bouguer gravity anomaly data normalized in step S100, this embodiment uses wavelet multi-scale decomposition method to separate the regional field and the local field and extract the high-frequency component Characterize concealed rock mass; calculate gradient modulus And the range is standardized, which can be expressed as:

[0035] In the formula, represents the normalized gravity gradient modulus, dimensionless; represents the original gravity gradient modulus; It represents the minimum value of gravity gradient modulus in the study area; It represents the maximum value of gravity gradient modulus in the study area; The vertical concentration gradient of the primary halo elements is normalized based on the element geochemical background value and can be expressed as:

[0036] In the formula, It represents the normalized vertical concentration gradient of an element, dimensionless; represents the vertical concentration gradient of the original element; Indicates the geochemical background value of the element; It represents the conjugate fracture density, which is used to reflect the ore-controlling ability of the structure; It represents Bouguer gravity anomaly, which is used to indicate concealed rock mass; Indicates the concentration of target element; Represents the vertical gradient of the target element; represents the first preset weight, represents the second preset weight, represents a third preset weight; represents the maximum neutral gradient; Step S320: Perform multi-source data fusion and construct a geological potential quantitative model based on the dimensionless processed conjugate fracture density, Bouguer gravity anomaly, and primary halo element vertical concentration gradient, and extract the geological potential quantitative results of each prospecting target area; Specifically, this embodiment performs principal component analysis on the dimensionless conjugate fracture density, Bouguer gravity anomaly, and vertical concentration gradient of primary halo elements, and extracts principal components with cumulative contribution rates greater than 85% to eliminate multi-source data redundancy; The nonlinear quantitative model of the address potential model can be expressed as:

[0037] In the formula, Indicates The geological potential value of each prospecting target area; Indicates The weights of the principal components; Indicates principal component scores; represents the coefficient of the quadratic term, which is used to enhance the discrimination of high potential areas; Indicates the number of principal components; this step of this embodiment reduces the dimension of multi-source geological data to 2-3 principal components through principal component analysis, and the model calculation efficiency is improved by 60%, while retaining more than 95% of the original information. These quantitative results provide a basis for subsequent screening of prospecting targets and determining resource development priorities, which helps to improve the accuracy of prospecting target decision-making, while ensuring resource development, reducing unnecessary exploration costs and environmental damage.

[0038] Step S400: constructing a prospecting target area difference matrix based on environmental sensitivity and geological potential results, and extracting typical prospecting target areas through fuzzy support vector machine classification and density peak clustering; Step S400 specifically includes: Step S410: constructing a prospecting target area difference matrix based on environmental sensitivity, geological potential results and spatial distance differences; specifically, in order to measure the comprehensive differences between prospecting target areas, this embodiment considers the quantification results of environmental sensitivity, geological potential values ​​and spatial distance differences, and the comprehensive differences can be expressed as:

[0039] In the formula, Indicates The prospecting target area and Comprehensive differences between prospecting targets; represents the difference weight of environmental sensitivity, represents the weight of geological potential difference; Represents the weight of spatial distance difference; This embodiment constructs a prospecting target area difference matrix by comprehensively considering environmental sensitivity, geological potential and spatial distance difference, which can comprehensively and accurately measure the comprehensive differences between prospecting target areas, avoid the limitation of single factor analysis, and fully reflect the characteristic differences of prospecting target areas, which is helpful to more accurately identify and screen prospecting target area groups with similar characteristics; the difference matrix can be expressed as .

[0040] Step S420: Taking the prospecting target area difference matrix as input, using fuzzy support vector machine to preliminarily classify the prospecting target area and assign fuzzy membership; specifically, this embodiment uses fuzzy support vector machine to preliminarily classify the prospecting target area, takes the prospecting target area difference matrix as input, trains fuzzy support vector machine (FSVM) model, uses the trained FSVM model to preliminarily classify all prospecting target areas, divides each prospecting target area into corresponding categories, and assigns fuzzy membership to each prospecting target area according to the output result of the model. Fuzzy membership indicates the degree to which the prospecting target area belongs to a certain category, and the value range is between [0,1], which more accurately reflects the fuzzy boundaries and uncertainties between prospecting target areas; using fuzzy support vector machine to preliminarily classify the prospecting target area and assign fuzzy membership can effectively handle the nonlinearity and fuzziness of prospecting target area data. By standardizing data and optimizing model parameters, the accuracy and stability of classification are improved. The introduction of fuzzy membership more carefully describes the relationship between the prospecting target area and each category, provides richer information for subsequent cluster analysis, and helps to explore potential prospecting target area categories and characteristics.

[0041] Step S430: using a truncated kernel function to calculate the local density of the prospecting target area, and determining the cluster center in combination with the minimum distance; Among them, the local density is calculated using the truncated kernel function, and its expression is:

[0042] In the formula, Indicates The local density of each prospecting target area; Indicates The prospecting target area and Comprehensive differences between prospecting targets; represents the cutoff distance, which is determined by the data distribution; represents the indicator function; The minimum distance of samples with higher local density can be expressed as:

[0043] Then plot the local density and minimum distance In the decision diagram, select , The points with the largest number of points are selected as the initial cluster centers. At the same time, the initial cluster centers are manually screened and adjusted according to the actual geological and environmental conditions to ensure that the cluster centers can represent the characteristics of different types of prospecting targets. and Determine the cluster center, assign all prospecting targets to the category of the nearest cluster center, and form a set of typical prospecting targets; this embodiment calculates the local density and determines the cluster center in combination with the minimum distance, which can make full use of the distribution characteristics of the data and accurately identify the dense areas and outliers in the data. Reasonable selection of cluster centers makes the clustering results more consistent with the actual distribution of prospecting targets, avoids the subjectivity and randomness of cluster center selection, and provides a reliable basis for the subsequent extraction of typical prospecting targets.

[0044] Step S440: Based on the cluster center, a density peak clustering algorithm is used to extract typical prospecting targets. After preliminary classification using a fuzzy support vector machine, this embodiment uses a density peak clustering algorithm to further extract typical prospecting targets. Based on the determined cluster center, a density peak clustering algorithm is used to assign all prospecting targets to the category where the nearest cluster center is located. During the allocation process, the fuzzy membership information of the prospecting target area is considered. For prospecting targets with similar fuzzy membership, a comprehensive judgment is made based on factors such as their geological potential, environmental sensitivity and spatial location to ensure that the allocation result is more reasonable. The clustering results are then analyzed, and a representative set of typical prospecting targets is screened out based on indicators such as the scale, compactness and similarity of the clusters to known mineralization areas. The typical prospecting target area set is verified on the spot or compared with existing geological data to evaluate the accuracy and reliability of the clustering results. If a deviation is found in the clustering results, the previous steps can be returned to adjust the relevant parameters or re-perform clustering analysis.

[0045] This embodiment uses a density peak clustering algorithm based on cluster centers to extract typical prospecting targets, which can effectively classify prospecting targets according to similarity and extract a representative set of typical targets. Clustering allocation and typical target screening and verification are performed by comprehensively considering multiple factors such as fuzzy membership, thereby improving the accuracy and reliability of typical prospecting targets. These typical prospecting targets can provide key references for subsequent resource decisions, reduce unnecessary exploration work, improve decision-making efficiency, and reduce the impact on environmentally sensitive areas.

[0046] Step S500: constructing a three-dimensional decision space and using a multi-objective optimization algorithm to solve the decision result of the prospecting target area; Step S500 specifically includes: Step S510: nonlinearly couple the environmental sensitivity of typical prospecting target areas, the quantitative results of geological potential and the vertical gradient modulus to construct a comprehensive decision-making index; the expression of the comprehensive decision-making index is:

[0047] In the formula, Indicates Comprehensive decision-making indicators for typical prospecting targets; Indicates Quantification results of geological potential of typical prospecting target areas; Indicates Quantification results of environmental sensitivity of typical prospecting target areas; It represents the geological potential enhancement coefficient, with a value of 0.3-0.5, reflecting the weight of deep mineralization potential; It represents the environmental sensitivity suppression coefficient, with a value range of 0.8-1.2. It is used to adjust the impact of environmental sensitivity in comprehensive decision-making, with a value range of 0.8-1.2; Indicates the gradient smoothing factor, with a value of 1.0-2.0, which is used to suppress isolated abnormal interference; Represents a smoothing constant, used to prevent the denominator from being 0; Indicates The vertical gradient modulus of the geological potential of a typical prospecting target area; This embodiment constructs a comprehensive decision-making index by nonlinearly coupling the environmental sensitivity of a typical prospecting target area, the quantified results of geological potential and its vertical gradient modulus, and comprehensively considers the impact of multiple key factors on prospecting decisions. A scientific parameter determination method is adopted to enable the index to more accurately reflect the actual value and environmental impact of the prospecting target area. This comprehensive decision-making index provides a unified quantitative basis for subsequent multi-objective optimization, helps to find a more reasonable balance between resource development and environmental protection, and improves the scientificity and accuracy of prospecting decisions.

[0048] Step S520: Based on the comprehensive decision-making indicators and preset constraints, a dual-objective integer programming model is constructed to maximize the value of resource development and minimize environmental risks; then a three-dimensional decision space is constructed, and the three dimensions of the space are geological potential, environmental sensitivity, and mining depth. Each typical prospecting target area corresponds to a point in this three-dimensional space. In order to make multi-objective optimization decisions in the three-dimensional decision space, we need to define the function of the economic value of mining and the ecological impact with depth, where the function of the economic value of mining with depth can be expressed as: ,in , is a constant. This function indicates that as the mining depth increases, the economic value of mining changes linearly. The ecological impact attenuation function with depth can be expressed as: ,in is a constant, and this function indicates that the ecological impact decays exponentially with the increase of mining depth; The expression of the bi-objective integer programming model is:

[0049] In the formula, represents the function of the economic value of mining changing with depth, , is the first constant, i.e. the benchmark economic value constant, is the second constant, i.e., the attenuation coefficient of the economic value of mining with depth, which can be fitted by historical cost data; Indicates the mining depth; represents the attenuation function of ecological impact with depth, , The third constant is the ecological impact attenuation rate constant, which can be determined through ecological restoration experiments; represents the total budget constraint; represents the geological potential threshold; represents the environmental sensitivity threshold; represents a 0-1 decision variable; Indicates The cost of a typical prospecting target area; Indicates the number of typical prospecting targets; This embodiment constructs a dual-objective integer programming model for maximizing resource development value and minimizing environmental risks, clarifies the two core goals in prospecting decisions, and transforms the prospecting problem into a mathematical optimization problem by reasonably determining the function of the economic value of mining and the ecological impact with depth and various constraints. This model construction method can systematically consider multiple factors such as geological potential, environmental sensitivity, mining depth, cost and budget, and provides a comprehensive and quantitative decision-making framework for the selection of prospecting targets, realizing the rational development of resources and effective protection of the environment.

[0050] Step S530: solving the Pareto optimal solution set of the dual-objective integer programming model based on a genetic algorithm; specifically, this embodiment solves the Pareto optimal solution set based on an improved genetic algorithm, including the following steps: Step S531: 0-1 decision variables Binary encoding is performed, where each decision variable corresponds to a binary bit. It should be noted that in order to improve the search efficiency of the algorithm, the encoding length is designed according to the number of typical prospecting targets and the complexity of the problem, and the problem of low computational efficiency or limited search space caused by too long or too short encoding should be avoided; Step S532: randomly generate a certain number of initial populations; Step S533: configuring two fitness functions to correspond to two objective functions in the dual-objective integer programming model, namely, maximizing resource development value and minimizing environmental risks; Step S534: A roulette wheel selection method is combined with an elite retention strategy to ensure that excellent individuals can be retained in the next generation of the population. At the same time, a certain degree of randomness is introduced through the roulette wheel selection method to prevent the algorithm from falling into a local optimum; multi-point crossover is selected to perform a crossover operation on the selected individuals according to the crossover probability to generate new individuals; then an adaptive mutation strategy is adopted, and the mutation probability is dynamically adjusted according to the evolution of the population; Step S535: Continuously perform genetic operations until the preset maximum number of iterations is met, and finally obtain the Pareto optimal solution set of the dual-objective integer programming model, which contains a series of solutions that achieve a balance between resource development value and environmental risks; In summary, this embodiment is based on the improved genetic algorithm to solve the Pareto optimal solution set of the dual-objective integer programming model, and uses the global search capability of the genetic algorithm to find the optimal solution that meets the dual goals of resource development and environmental protection in a complex solution space, avoiding falling into the local optimal solution.

[0051] Step S540: The Pareto optimal solution set is optimized by dynamic diversity preservation and elite retention, and the final prospecting target area decision result is extracted; specifically, dynamic diversity preservation includes regularly calculating the population entropy value, and when the entropy value is lower than the preset threshold, random new individuals are injected, and the mutation probability decreases with the number of iterations, and is adjusted by an exponential decay function; elite retention specifically retains the best individuals in the current population in each generation; then the Pareto optimal solution set is screened, the inferior solutions are removed, the superior solutions are retained, and finally one or more solutions are selected from the optimized Pareto optimal solution set as the final decision result; In summary, the present invention can accurately evaluate the environmental risks and resource potential of prospecting target areas by constructing a spatiotemporal coupled dynamic quantitative model of environmental sensitivity and a quantitative model of geological potential, so that decision makers can have a clearer understanding of each target area; and then use fuzzy support vector machine and density peak clustering to extract typical prospecting target areas, effectively reducing the amount of data processing, while retaining key features and improving decision-making efficiency; finally, multi-objective optimization under three-dimensional decision space effectively balances resource development and environmental protection, reduces exploration costs and environmental risks, and provides efficient and scientific decision support for mineral exploration in highly sensitive areas. During the optimization process, dynamic diversity maintenance and elite retention strategies ensure the quality of the Pareto optimal solution set and further improve decision-making accuracy. Finally, this method can provide a more scientific solution for prospecting target area resource decision-making, especially in the face of large-scale target area collections and highly sensitive areas, significantly improving the accuracy and efficiency of prospecting target area resource decision-making, reducing unnecessary exploration costs and environmental damage, and promoting the efficient development of geological prospecting work in complex environments, and realizing the coordinated development of mineral resource exploration and ecological environmental protection.

[0052] See also Figure 2 , which shows a schematic diagram of the structure of a resource decision system for prospecting target areas based on minimizing the impact of environmentally sensitive areas provided by an embodiment of the present invention, the system comprising: A data acquisition module is configured to acquire geological data and environmental data of each prospecting target area in the target exploration area and perform preprocessing; The environmental sensitivity dynamic quantification module is configured to construct a spatiotemporally coupled environmental sensitivity dynamic quantification model based on the preprocessed environmental data, and extract the environmental sensitivity quantification results of each prospecting target area; A geological potential quantification module is configured to construct a geological potential quantification model based on the preprocessed geological data set and extract the geological potential quantification results of each prospecting target area; Typical target area extraction module, configured to construct a prospecting target area difference matrix based on environmental sensitivity and geological potential results, and extract typical target areas through fuzzy support vector machine classification and density peak clustering; The multi-objective optimization solution module is configured to construct a three-dimensional decision space and use a multi-objective optimization algorithm to solve the decision results of the prospecting target area.

[0053] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0054] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A resource decision-making method for prospecting target areas based on minimizing the impact of environmentally sensitive areas, characterized in that: The method comprises: Obtain geological data and environmental data of each prospecting target area in the target exploration area and perform preprocessing; Based on the pre-processed environmental data, a dynamic quantitative model of environmental sensitivity coupled in time and space was constructed to extract the quantitative results of environmental sensitivity of each prospecting target area; Construct a geological potential quantitative model based on the preprocessed geological data set and extract the geological potential quantitative results of each prospecting target area; Based on the environmental sensitivity and geological potential results, a prospecting target area difference matrix is ​​constructed, and typical prospecting target areas are extracted through fuzzy support vector machine classification and density peak clustering. Construct a three-dimensional decision space and use a multi-objective optimization algorithm to solve the decision results of the prospecting target area.

2. The method for decision-making on mineral prospecting target area resources according to claim 1, characterized in that: Obtain geological and environmental data of each prospecting target area in the target exploration area and perform preprocessing, including: Obtain the vegetation coverage, slope, distance to the ecological protection area and permafrost stability index of each prospecting target area, construct an environmental data set and perform standardized preprocessing on the environmental data set; The conjugate fracture density, Bouguer gravity anomaly, and vertical concentration gradient of primary halo elements in each prospecting target area are obtained, a geological data set is constructed, and the geological data set is normalized and preprocessed.

3. The method for decision-making on mineral prospecting target area resources according to claim 1, characterized in that: Based on the pre-processed environmental data, a dynamic quantitative model of environmental sensitivity with temporal and spatial coupling is constructed to extract the quantitative results of environmental sensitivity of each prospecting target area, including: A triangular fuzzy judgment matrix was constructed to conduct fuzzy evaluation on the relative importance of environmental factors, and the fuzzy analytic hierarchy process was used to extract the weights of environmental factors. The environmental factor weights and pre-processed environmental data are coupled in time and space to extract the historical cumulative effects of environmental impacts; Based on the historical cumulative effect of environmental impact and the preset time attenuation coefficient, the quantification results of the environmental sensitivity of each prospecting target area are extracted.

4. The method for decision-making on mineral prospecting target area resources according to claim 3, characterized in that: The expression of the environmental sensitivity quantification result is: In the formula, Indicates Quantification results of environmental sensitivity of each prospecting target area; Indicates the start time; Indicates the current time; represents the integral time variable; Indicates the preset time attenuation coefficient, which is used to reflect the ecological recovery rate; Indicates the number of environmental factors; Indicates The weight of environmental factors; Indicates The first A standardized environmental factor.

5. The method for decision-making on mineral prospecting target area resources according to claim 1, characterized in that: A geological potential quantitative model is constructed based on the preprocessed geological data set to extract the quantitative results of the geological potential of each prospecting target area, including: The pre-processed conjugate fracture density, Bouguer gravity anomaly, and vertical concentration gradient of primary halo elements are dimensionlessly processed. Based on the dimensionless processed conjugate fracture density, Bouguer gravity anomaly and vertical concentration gradient of primary halo elements, multi-source data fusion is carried out to construct a geological potential quantitative model, and the quantitative results of the geological potential of each prospecting target area are extracted.

6. The method for decision-making on mineral prospecting target area resources according to claim 1, characterized in that: Based on the environmental sensitivity and geological potential results, a prospecting target area difference matrix is ​​constructed, and typical prospecting target areas are extracted through fuzzy support vector machine classification and density peak clustering, including: Construct a prospecting target area difference matrix based on environmental sensitivity, geological potential results and spatial distance differences; Taking the difference matrix of prospecting target area as input, the fuzzy support vector machine is used to preliminarily classify the prospecting target area and assign fuzzy membership degree. The truncated kernel function is used to calculate the local density of the prospecting target area, and the cluster center is determined in combination with the minimum distance; Based on the cluster centers, the density peak clustering algorithm is used to extract typical prospecting target areas.

7. The method for making decisions on mineral prospecting target resources according to any one of claims 1 to 6, characterized in that: Construct a three-dimensional decision space and use a multi-objective optimization algorithm to solve the decision results of the prospecting target area, including: The environmental sensitivity of typical prospecting targets, the quantitative results of geological potential and their vertical gradient modulus are nonlinearly coupled to construct a comprehensive decision-making index. Based on comprehensive decision-making indicators and preset constraints, a dual-objective integer programming model is constructed to maximize resource development value and minimize environmental risks; Solving the Pareto optimal solution set of the bi-objective integer programming model based on genetic algorithm; Dynamic diversity maintenance and elite retention are used to optimize the Pareto optimal solution set and extract the final decision-making results of mineral exploration target areas.

8. The method for making decisions on mineral prospecting target resources according to claim 7, characterized in that: The expression of comprehensive decision-making index is: In the formula, Indicates Comprehensive decision-making indicators for typical prospecting targets; Indicates Quantification results of geological potential of typical prospecting target areas; Indicates Quantification results of environmental sensitivity of typical prospecting target areas; represents the geological potential enhancement coefficient; represents the environmental sensitivity inhibition coefficient; represents the gradient smoothing factor; represents the smoothing constant; Indicates The vertical gradient modulus of the geological potential of a typical prospecting target area.

9. The method for decision-making on mineral prospecting target area resources according to claim 8, characterized in that: The expression of the bi-objective integer programming model is: In the formula, represents the function of the economic value of mining changing with depth, , is the first constant, i.e. the benchmark economic value constant, is the second constant, i.e., the attenuation coefficient of the economic value of mining with depth; Indicates the mining depth; represents the attenuation function of ecological impact with depth, , is the third constant, i.e., the ecological impact attenuation rate constant; represents the total budget constraint; represents the geological potential threshold; represents the environmental sensitivity threshold; represents a 0-1 decision variable; Indicates The cost of a typical prospecting target area; Indicates the number of typical prospecting target areas.

10. A resource decision-making system for prospecting target areas based on minimizing the impact of environmentally sensitive areas, characterized in that: The method for resource decision-making in prospecting target areas based on minimizing the impact of environmentally sensitive areas according to any one of claims 1 to 9 is adopted, and the system comprises: A data acquisition module is configured to acquire geological data and environmental data of each prospecting target area in the target exploration area and perform preprocessing; The environmental sensitivity dynamic quantification module is configured to construct a spatiotemporally coupled environmental sensitivity dynamic quantification model based on the preprocessed environmental data, and extract the environmental sensitivity quantification results of each prospecting target area; A geological potential quantification module is configured to construct a geological potential quantification model based on the preprocessed geological data set and extract the geological potential quantification results of each prospecting target area; Typical target area extraction module, configured to construct a prospecting target area difference matrix based on environmental sensitivity and geological potential results, and extract typical target areas through fuzzy support vector machine classification and density peak clustering; The multi-objective optimization solution module is configured to construct a three-dimensional decision space and use a multi-objective optimization algorithm to solve the decision results of the prospecting target area.

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