Mining area exploration system based on artificial intelligence

Abstract

The invention discloses a mining area exploration system based on artificial intelligence. The mining area exploration system comprises a multi-source data acquisition module, a data optimization module, a mining area candidate area potential identification module and a mining area intelligent exploration module. The invention relates to the technical field of artificial intelligence data analysis and computer vision, in particular to a mining area exploration system based on artificial intelligence, according to the scheme, a mining area candidate area potential identification module and a mining area intelligent exploration module are innovatively combined, and the accuracy of mining area identification and the pertinence of exploration are improved; a weighted graph structure is constructed by adopting a K nearest neighbor algorithm, and an improved graph convolutional neural network with a parallel updating mechanism is introduced, so that high-precision automatic potential classification of a large-range candidate mining area is realized; and a composite chaotic mapping initialization method and a sine index inertia weight improved particle swarm optimization algorithm are introduced, so that the accuracy and stability of yield prediction are improved, and high-precision yield prediction of each sub-region of the mining area is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of artificial intelligence data analysis and computer vision, and particularly discloses a mineral area exploration system based on artificial intelligence. BACKGROUND

[0002] The mineral area exploration system based on artificial intelligence is an intelligent information processing and decision support system for mineral resource exploration. It uses artificial intelligence technology to collect, fuse, analyze and model multi-source data, realizes data processing automation, target area intelligent identification and exploration scheme intelligent decision-making in the exploration process through deep feature extraction and pattern recognition, and thus improves the decision-making efficiency of mineral exploration, optimizes resource allocation and reduces the overall operation cost.

[0003] However, the traditional mineral area exploration system has obvious technical problems in determining the exploration range, which leads to waste of exploration resources, prolongation of exploration period, incomplete exploration and insufficient identification of mineralization areas, and thus affects the integrity and accuracy of exploration results. The existing mineral area potential evaluation model fails to fully utilize the spatial adjacency relationship between candidate area features and is difficult to consider multi-order neighborhood information in the feature extraction process, which leads to insufficient classification accuracy of mineral area potential, unclear class boundaries and low differentiation degree of different potential categories. The hyperparameter setting of the existing mineral area yield prediction model is unreasonable, and the fixed initialization method and linear inertia weight adjustment are used in the hyperparameter optimization process, which leads to insufficient search space coverage and easy falling into local optimum, and thus the prediction accuracy of the model is insufficient. SUMMARY

[0004] In view of the above, in order to overcome the defects of the prior art, the present application provides a mine area exploration system based on artificial intelligence. In view of the obvious technical problems in the determination of the exploration range of the traditional mine area exploration system, which leads to waste of exploration resources, prolongation of the exploration period, and incomplete exploration and insufficient identification of the ore-forming area, thereby affecting the integrity and accuracy of the exploration results, the present scheme innovatively combines the mine area candidate region potential identification module with the mine area intelligent exploration module. First, the mine area candidate region potential identification module is used to automatically classify and evaluate the potential of a large range of candidate regions, and high-potential ore-forming areas are preferentially selected. Then, the mine area intelligent exploration module is used to perform partitioned yield prediction and accurate mining area determination in the high-potential ore-forming area. This can effectively prevent resource and time waste caused by an excessively large exploration range, and avoid the risk of omission caused by an excessively small exploration range. It improves the accuracy of ore-forming area identification and the pertinence of exploration, enhances the utilization efficiency of exploration data, improves the integrity of exploration results, and realizes the transition from blind global exploration to accurate partitioned exploration. In view of the technical problems in the existing mine area potential evaluation model that fail to fully utilize the spatial adjacency relationship between candidate region features and that are difficult to simultaneously consider multi-order neighborhood information during feature extraction, thereby leading to insufficient classification accuracy of mine area potential, unclear class boundaries, and low differentiation degree of different potential categories, the present scheme innovatively proposes to use a K-nearest neighbor algorithm to construct a weighted graph structure, calculate node feature similarity using a Gaussian kernel function, establish adjacency relationships, and generate a graph structure that can reflect spatial adjacency and feature similarity. An improved graph convolutional neural network with a parallel update mechanism is introduced to realize synchronous extraction and fusion of multi-order neighborhood features, and a multi-head attention mechanism is combined to enhance feature expression capability. This can effectively improve the integrity and differentiation degree of feature expression, significantly improve the accuracy and robustness of mine area potential identification, enhance the separability of high-potential and low-potential regions, improve the generalization ability of the model under complex geological backgrounds, and realize high-precision automatic potential classification of a large range of candidate mine areas. In view of the technical problems of the existing mine area yield prediction model, such as unreasonable hyperparameter setting and reliance on fixed initialization methods and linear inertia weight adjustment during hyperparameter optimization, which leads to insufficient search space coverage and easy trapping in local optimum, thereby resulting in insufficient prediction accuracy of the model, the present scheme innovatively introduces a composite chaotic mapping initialization method into the particle swarm optimization algorithm to enhance the diversity of particle initial distribution and global search capability. A sinusoidal exponential inertia weight is used to realize adaptive search intensity adjustment with dynamic fluctuations during iteration, balancing global exploration and local development. This significantly improves the efficiency and accuracy of hyperparameter search of the mine area yield prediction model, enhances the adaptability of the model to complex geological and mineralization conditions, improves the accuracy and stability of yield prediction, and realizes high-precision yield prediction of each sub-area of the ore-forming area and optimization of the mining scheme.

[0005] The technical scheme adopted by the present application is as follows: The present application provides a mine area exploration system based on artificial intelligence, which comprises a multi-source data acquisition module, a data optimization module, a mine area candidate region potential identification module and a mine area intelligent exploration module.

[0006] The multi-source data acquisition module specifically acquires mine area exploration original data through data acquisition operations.

[0007] The data optimization module is used to improve the quality of original data and the adaptability of models, and specifically acquires mine area exploration optimized data through image data preprocessing and structured data preprocessing.

[0008] The mine area candidate region potential identification module is used to identify high-potential ore-forming regions in a large range of mine area candidate regions, and specifically comprises the following steps: a mine area potential evaluation model is established, historical data is used to fully train the model, real-time data is input into the trained model, and the potential evaluation results of each candidate mine area are output; the large range of candidate regions is screened according to the potential evaluation results, and the ore-forming region results are obtained.

[0009] The mine area intelligent exploration module is used to implement zonification yield prediction and accurate mining area determination in the ore-forming region, and specifically comprises the following steps: a mine area yield prediction model is established, historical data is used to train the prediction model, an improved particle swarm optimization algorithm is used to search for the optimal super parameter combination of the model, the super parameters of the prediction model are updated, the mine area yield prediction model with the optimal performance is obtained, real-time data of each sub-region is input into the optimal prediction model, and the yield prediction results of each sub-region are output; finally, a mine area zonification yield distribution map is generated according to the yield prediction results, and intelligent exploration planning and efficient operation management of the target mine area are realized.

[0010] Further, the multi-source data acquisition module specifically acquires mine area exploration original data by acquiring mine area related data from an external data platform; the mine area exploration original data comprises historical mine area screening data, historical ore-forming region yield prediction data, real-time mine area screening data and real-time ore-forming region yield prediction data; the historical mine area screening data and the real-time mine area screening data both comprise medium-resolution remote sensing image data and regional structured data; the historical mine area screening data further comprises historical mine area types; the historical ore-forming region yield prediction data and the real-time ore-forming region yield prediction data both comprise high-resolution remote sensing image data, regional environmental field feature data and element distribution feature data; the historical ore-forming region yield prediction data further comprises mine area yield data.

[0011] Further, the data optimization module specifically comprises the following steps:

[0012] The image data preprocessing specifically includes image cloud removal processing, radiation correction processing, image format standardization processing and image quality enhancement, and remote sensing optimized image data is obtained.

[0013] The structured data preprocessing specifically includes data cleaning, data standardization and data encoding processing, and structured optimized data is obtained.

[0014] Further, the mine area candidate region potential identification module specifically includes the following steps:

[0015] The mine area potential evaluation model is established, including the following steps:

[0016] The candidate region identification feature extraction is specifically image feature extraction and structured feature extraction on the image data and the structured data respectively, mine area candidate image features and mine area candidate structured features are generated, and the two types of features are spliced and fused to obtain mine area candidate comprehensive features.

[0017] The discriminant feature matrix acquisition is used to map the mine area candidate comprehensive features to a low-dimensional discriminant subspace, and enhance the class separability between the mine area candidate regions; specifically, the mine area candidate comprehensive features and the corresponding historical mine area type labels are jointly input, a nonlinear kernel mapping relationship between samples is constructed, nonlinear discriminant compression processing is performed on the mine area candidate comprehensive features by kernel Fisher discriminant analysis method, the intra-class and inter-class scatter is calculated, and the generalized eigenvalue problem is solved to construct a projection matrix, and finally a set of candidate low-dimensional discriminant features is obtained, and a candidate mine discriminant feature matrix is obtained.

[0018] The candidate region graph structure establishment is used to model the adjacency relationship and similarity of the discriminant features in space; specifically including the following steps:

[0019] The two-dimensional feature matrix is constructed, specifically linear normalization processing is performed on each dimension feature in the candidate mine discriminant feature matrix, each feature is sorted in descending order based on feature importance, and the sorted feature sequence is mapped in sequence according to column, and a two-dimensional feature matrix is obtained.

[0020] The feature importance is specifically a comprehensive analysis of the inter-class dispersion and the intra-class dispersion of the jth dimension feature of the candidate mine discriminant feature matrix Y, and a single-dimensional Fisher discriminant score is calculated.

[0021] The node set construction is specifically that the two-dimensional feature matrix is cut by sliding according to a set window size and a sliding step, local feature blocks are extracted in sequence, and each feature block is vectorized to form a node feature vector, and all node feature vectors form a node set.

[0022] Edge connection establishment, specifically, the Euclidean distance is calculated for any two node feature vectors in the node set, and the Euclidean distance is mapped into a similarity weight by using a Gaussian kernel function, for each node, the k nodes with the highest similarity are selected to form its neighbor set, a weighted edge is established according to the similarity weight, and the similarity weight is assigned to the adjacent matrix element to form an initial weighted adjacent matrix, then the initial weighted adjacent matrix is symmetrized and a self-loop edge is added to obtain a final weighted adjacent matrix;

[0023] Graph structure output, specifically, first calculate the center coordinates of each node in the two-dimensional feature matrix, and normalize them to After the interval, the spatial position encoding of the node is generated by linear mapping, then the spatial position encoding and the node feature are spliced and fused to obtain a node fusion feature vector, and all node fusion feature vectors are sequentially combined to form an initial node feature matrix, finally, the node set, edge set, final weighted adjacent matrix and initial node feature matrix are combined to obtain a candidate graph structure;

[0024] Mine area potential result output, specifically, the candidate graph structure is input into an improved graph convolutional neural network with a parallel update mechanism to extract mine area potential classification features, the mine area potential classification features are mapped through a fully connected layer and the probability distribution of each potential category of the candidate mine area is calculated through a Softmax function, the category corresponding to the maximum probability is selected as the final prediction result to obtain the potential evaluation result of the candidate mine area;

[0025] The improved graph convolutional neural network includes a parallel multi-order graph convolutional layer and a multi-head attention mechanism; specifically, first set the number of parallel branches to Q, and each branch corresponds to a normalized adjacent matrix of a different order; each branch receives the initial node feature matrix and the normalized adjacent matrix of the corresponding order in the first layer, and simultaneously performs graph convolution operation to generate single-order node features of this layer; in the subsequent lth layer, each branch receives the single-order node features output by the previous layer and the normalized adjacent matrix of the corresponding order and simultaneously performs graph convolution operation to generate single-order node features of this layer, after parallel convolution of each branch, the convolution node features of all branches are spliced according to the feature dimension to generate multi-order neighborhood features, realizing parallel update and layer-by-layer extraction of multi-order neighborhood features; the multi-order neighborhood features are input into the multi-head attention mechanism to obtain mine area potential classification features;

[0026] Mine area potential evaluation model training, specifically, the historical mine area screening data is used as training data for model training, and finally a trained mine area potential evaluation model is obtained;

[0027] The mineralization area screening is to input the real-time mineralization area screening data into the trained mineralization area potential assessment model, output the potential assessment results of each candidate mineralization area, screen according to the potential assessment results of each candidate mineralization area, select the area with high potential mineralization area as the final mineralization area.

[0028] Furthermore, the mining area intelligent exploration module specifically includes the following steps:

[0029] Establishing a mining area output prediction model includes the following steps:

[0030] Extraction of mining area yield prediction features, specifically performing yield prediction image feature extraction and yield prediction structured feature extraction on image data and structured data respectively, generating yield prediction image features and yield prediction structured features, and splicing and fusing the two types of features to obtain comprehensive yield prediction features;

[0031] Output of production forecast results, specifically, nonlinear regression analysis of comprehensive production forecast characteristics based on a feedforward neural network model to obtain the production forecast results of the mining area;

[0032] Training of the mining area production prediction model, specifically using the historical mineralization area production prediction data as training data to train the prediction model, and finally obtaining the trained mining area production prediction model;

[0033] The performance optimization of the prediction model involves obtaining the optimal hyperparameter combination of the model through an improved search algorithm, updating the hyperparameters of the prediction model based on the optimal hyperparameter combination of the model, and obtaining the optimal mining area output prediction model. The optimization includes the following steps:

[0034] Initialize the particle population. Specifically, encode the trained mining area output prediction model hyperparameters into search individual position vectors, and generate N particle individual position vectors through the composite chaotic mapping initialization method. Each individual encoding represents a candidate prediction model hyperparameter combination to obtain the initial particle population. The formula used is as follows:

[0035] ;

[0036] Where, Indicates the The position of each particle, represents the position of the i-th particle individual, Represents a random number in the range [0,1];

[0037] Calculate the particle fitness value, specifically the fitness value of the particles in the population The performance of the mining area production prediction model established based on the individual particle positions is used as the particle fitness value;

[0038] The sine exponential inertia weight is calculated, specifically by adaptively adjusting the search intensity through the iteration number, and a sine term is introduced to realize periodic fluctuation, and the formula is as follows:

[0039] ;

[0040] In the formula, denotes the sine exponential inertia weight, denotes the maximum iteration number, t denotes the current iteration number, denotes a random number in the range of [0, 1];

[0041] Particle updating, specifically particle velocity updating through the sine exponential inertia weight, and particle position updating based on the updated particle velocity;

[0042] Particle optimal position updating, specifically re-evaluating the fitness value of all updated particles, and comparing the fitness value of the current particle with the global optimal position of the current particle, if the fitness value of the current particle is better, updating the global optimal position of the particle;

[0043] Particle search termination, specifically when the particle fitness value is higher than the fitness threshold and the maximum iteration number is reached, terminating the search and obtaining the global optimal position of the particle; the global optimal position of the particle specifically refers to the optimal hyperparameter combination of the model;

[0044] Prediction model hyperparameter updating, specifically adjusting the hyperparameters of the mine area yield prediction model according to the optimal hyperparameter combination of the model, to obtain the optimal mine area yield prediction model;

[0045] Mine area partition yield prediction, specifically dividing the ore-forming area into multiple sub-areas according to the equal-area grid, inputting the real-time ore-forming area yield prediction data of each sub-area into the optimal mine area yield prediction model, and obtaining the mine area yield prediction results of each sub-area;

[0046] Mine exploration area determination, specifically analyzing the mine area yield prediction results of each sub-area, generating a complete mine area partition yield distribution map based on spatial visualization technology, accurately positioning and prioritizing different yield grade areas according to the distribution map, forming a decision support scheme for mine production management and resource allocation, thereby optimizing the mining plan and utilization efficiency of mineral resources, and realizing intelligent exploration planning and efficient operation management of the target mine area.

[0047] The above-mentioned scheme has the following beneficial effects:

[0048] (1) In view of the technical problems that the determination of the exploration range in the traditional mine area exploration system has obvious deficiencies, which leads to waste of exploration resources, prolongation of the exploration period, incomplete exploration and insufficient identification of the mineralization area, thereby affecting the integrity and accuracy of the exploration results, the scheme innovatively combines the mine area candidate region potential identification module with the mine area intelligent exploration module. First, the mine area candidate region potential identification module is used to automatically classify and evaluate the potential of a large range of candidate regions, and high-potential mineralization areas are preferentially selected. Then, the mine area intelligent exploration module is used to conduct partitioned yield prediction and accurate mining area determination in the high-potential mineralization area. This can effectively prevent resource and time waste caused by an excessively large exploration range, and avoid the risk of omission caused by an excessively small exploration range. It improves the accuracy of mineralization area identification and the pertinence of exploration, enhances the utilization efficiency of exploration data, improves the integrity of exploration results, and realizes the transition from blind global exploration to accurate partitioned exploration.

[0049] (2) In view of the technical problems that the existing mine area potential evaluation model fails to fully utilize the spatial adjacency relationship between candidate region features, and it is difficult to consider multi-order neighborhood information during feature extraction, which leads to insufficient classification accuracy of mine area potential, unclear class boundaries, and low differentiation degree of different potential classes, the scheme innovatively proposes to use K-nearest neighbor algorithm to construct a weighted graph structure, use Gaussian kernel function to calculate node feature similarity and establish adjacency relationship, and generate a graph structure that can reflect spatial adjacency and feature similarity. An improved graph convolutional neural network with parallel update mechanism is introduced to realize synchronous extraction and fusion of multi-order neighborhood features, and a multi-head attention mechanism is combined to enhance feature expression capability. This can effectively improve the integrity and differentiation degree of feature expression, significantly improve the accuracy and robustness of mine area potential identification, enhance the separability of high-potential and low-potential regions, improve the generalization ability of the model under complex geological background, and realize high-precision automatic potential classification of large-scale candidate mine areas.

[0050] (3) In view of the technical problems that the hyperparameter setting of the existing mine area yield prediction model is unreasonable, and the hyperparameter optimization process relies on fixed initialization method and linear inertia weight adjustment, which leads to insufficient search space coverage and easy falling into local optimum, thereby leading to insufficient prediction accuracy of the model, the scheme innovatively introduces a compound chaotic mapping initialization method in the particle swarm optimization algorithm to enhance the diversity of particle initial distribution and global search ability. The sine exponential inertia weight is used to realize adaptive search intensity adjustment with dynamic fluctuation of iterations, and to balance global exploration and local development. This significantly improves the efficiency and accuracy of hyperparameter search of the mine area yield prediction model, enhances the adaptability of the model to complex geology and mineralization conditions, improves the accuracy and stability of yield prediction, realizes high-precision yield prediction of each sub-area of the mineralization area and optimization of mining scheme. BRIEF DESCRIPTION OF DRAWINGS

[0051] Figure 1 A module schematic diagram of the mining area exploration system based on artificial intelligence provided by the present application;

[0052] Figure 2 A flowchart schematic diagram of the mining area candidate region potential identification module;

[0053] Figure 3 A flowchart schematic diagram of the mining area candidate region potential identification module for establishing a mining area potential evaluation model;

[0054] Figure 4 A flowchart schematic diagram of the ore-forming area yield prediction module;

[0055] Figure 5 A flowchart schematic diagram of the ore-forming area yield prediction module for model performance optimization;

[0056] The accompanying drawings are used to provide a further understanding of the present application, and constitute a part of the specification, which together with the embodiments of the present application, are used to explain the present application, and do not constitute a limitation of the present application. DETAILED DESCRIPTION

[0057] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0058] In the description of the present application, it should be understood that the terms 'upper', 'lower', 'front', 'back', 'left', 'right', 'top', 'bottom', 'inner', 'outer' and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the systems or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation of the present application.

[0059] Embodiment one, refer to Figure 1 The mining area exploration system based on artificial intelligence provided by the present application comprises a multi-source data acquisition module, a data optimization module, a mining area candidate region potential identification module, a model optimization module and a mining area intelligent exploration module.

[0060] The multi-source data acquisition module specifically acquires mining area related data from an external data platform, obtains mining area exploration original data, and sends the data to the data optimization module.

[0061] The data optimization module receives the data sent by the multi-source data acquisition module, and is used to improve the quality of the original data and the model adaptability, specifically, through image data preprocessing and structured data preprocessing, the mine area exploration optimization data is obtained, and the data is sent to the mine area candidate area potential identification module and the mine area intelligent exploration module;

[0062] The mine area candidate area potential identification module receives the data sent by the data optimization module, and is used to identify high-potential mineralization areas in a large range of mine area candidate areas, specifically, by establishing a mine area potential evaluation model, and using historical data to fully train the model, real-time data is input into the trained model, and the potential evaluation results of each candidate mine area are output, the large range of candidate areas is screened according to the potential evaluation results, and the mineralization area results are obtained, and the data is sent to the mine area intelligent exploration module;

[0063] The mine area intelligent exploration module receives the data sent by the data optimization module and the mine area candidate area potential identification module, and is used for implementing partitioned yield prediction and accurate mining area determination in the mineralization area; Specifically, first, a mine area yield prediction model is established, and historical data is used to train the prediction model, then an improved particle swarm optimization algorithm is used to search for the optimal super parameter combination of the model, the super parameters of the prediction model are updated, and the optimal mine area yield prediction model is obtained, then the real-time data of each sub-area is input into the optimal prediction model, and the yield prediction results of each sub-area are output, finally, the mine area partitioned yield distribution map is generated according to the yield prediction results, and the intelligent exploration planning and efficient operation management of the target mine area are realized.

[0064] By performing the above operation, in view of the obvious technical problems in the determination of the exploration range in the traditional mine area exploration system, which leads to waste of exploration resources, prolongation of exploration period, and incomplete exploration and insufficient mineralization area identification, thereby affecting the integrity and accuracy of the exploration results, the present scheme innovatively combines the mine area candidate area potential identification module and the mine area intelligent exploration module, first, the mine area candidate area potential identification module is used to automatically classify and evaluate the potential of the large range of candidate areas, and the high-potential mineralization area is preferentially screened out; then the mine area intelligent exploration module is used to perform partitioned yield prediction and accurate mining area determination in the high-potential mineralization area; which can effectively prevent the waste of resources and time caused by too large exploration range, and avoid the omission risk caused by too small exploration range, improve the accuracy of mineralization area identification and the pertinence of exploration, enhance the utilization efficiency of exploration data, improve the integrity of exploration results, and realize the change from blind global exploration to accurate partitioned exploration.

[0065] Embodiment two, refer to Figure 1The embodiment is based on the above-mentioned embodiment, and the multi-source data acquisition module specifically acquires the mine area exploration related data on an external platform to obtain mine area exploration original data; the mine area exploration original data includes historical mine area screening data, historical ore-forming area yield prediction data, real-time mine area screening data, and real-time ore-forming area yield prediction data; the historical mine area screening data and the real-time mine area screening data both include medium-resolution remote sensing image data and regional structured data; the historical mine area screening data further includes historical mine area types; the historical ore-forming area yield prediction data and the real-time ore-forming area yield prediction data both include high-resolution remote sensing image data, regional environmental field feature data, and element distribution feature data; the historical ore-forming area yield prediction data further includes mine area yield data; the medium-resolution remote sensing image data is specifically multispectral remote sensing image data, has a spatial resolution of 20-30 meters, has a wide coverage range and a high update frequency, and is suitable for preliminary screening of mine areas in a large area range and potential identification of mine area candidate regions; the regional structured data is used for identifying the ore-forming geological background of a target region; includes lithology distribution map quantitative data, structural line quantitative data, stratum map quantitative data, and contact zone and rock mass boundary quantitative data; the historical mine area types include high-potential mine areas, medium-potential mine areas, low-potential mine areas, and no-potential mine areas; the high-resolution remote sensing image data is specifically multispectral remote sensing image data with a spatial resolution better than 5 meters, and is suitable for mineralization patch identification, alteration feature extraction, and mine body boundary refinement in a small range; the element distribution feature data includes soil composition analysis data, rock sample composition analysis data, and water body sample composition analysis data.

[0066] Embodiment three, refer to Figure 1 The embodiment is based on the above-mentioned embodiment, and the data optimization module specifically includes the following steps:

[0067] The image data preprocessing is used for uniformly optimizing the image data, specifically includes image cloud removal processing, radiation correction processing, image format standardization processing, and image quality enhancement, and remote sensing optimized image data is obtained;

[0068] The image data includes medium-resolution remote sensing image data and high-resolution remote sensing image data;

[0069] The image cloud removal processing is used for removing the ground object shielding interference caused by clouds, cloud shadows, etc. in the remote sensing image, improving the effective information coverage range and analysis accuracy of the image, and specifically adopts a cloud detection algorithm based on a mask to automatically identify the high-light area in the image and generate a cloud mask to shield the shielded area;

[0070] The radiation correction processing is used for eliminating the spectral inconsistency problems of remote sensing images generated under different time, different satellite sensors and atmospheric conditions, and ensuring the comparability of image brightness and reflectivity. Specifically, the digital values of the original image are converted into apparent and ground object reflectivity by a radiation calibration and atmospheric correction method, and the spectral characteristics are unified.

[0071] The image format standardization processing is used for unifying the size and pixel value range of image data. Specifically, the remote sensing image is scaled to a unified size, and the image pixel value is normalized to map it to a standard numerical interval of 0 to 1, thereby eliminating the numerical differences caused by different image sources and imaging conditions.

[0072] The image enhancement is used for expanding sample diversity. Specifically, random geometric transformation of the image and random brightness and contrast adjustment of the image are performed.

[0073] The structured data preprocessing is used for unified optimization processing of structured data, specifically including data cleaning, data standardization and data encoding processing, to obtain structured optimized data.

[0074] The structured data includes regional structured data, regional environmental field feature data and element distribution feature data.

[0075] The data cleaning is used to improve the quality and consistency of the structured data. Specifically, the structured data is subjected to missing value filling, outlier removal and field standardization processing. The missing value filling specifically fills the missing values in the geological field, geophysical detection points and geochemical samples by a mean filling method. The outlier removal specifically detects and removes extreme values and logical outliers in the original data by a Z-Score algorithm. The field standardization processing specifically standardizes the data in different formats by a unified unit conversion rule to ensure the consistency of the model input.

[0076] The data standardization processing is used to unify the dimension of the structured numerical data. Specifically, all continuous variables are mapped to the [0, 1] interval by a min-max normalization method.

[0077] The data encoding processing is used to convert the classification fields or discrete label fields in the original data into recognizable numerical vectors. Specifically, the category fields in the original data are encoded by a one-hot encoding method to convert discrete text and label variables into sparse numerical vectors.

[0078] Embodiment four, see Figure 1 , Figure 2 and Figure 3 This embodiment is based on the above-mentioned embodiments. The mine area candidate region potential identification module specifically includes the following steps:

[0079] The mine area potential evaluation model is established, including the following steps:

[0080] The candidate area identification feature extraction is used to extract the fusion features reflecting the regional metallogenic potential, specifically, the image feature extraction and the structured feature extraction are performed on the image data and the structured data respectively, the mine area candidate image features and the mine area candidate structured features are generated, and the two types of features are spliced and fused to obtain the mine area candidate comprehensive features;

[0081] The image feature extraction specifically adopts a convolutional neural network to learn the spatial structure features of the medium-resolution remote sensing image data in the mine exploration optimization data, and obtains the mine area candidate image features;

[0082] The structured feature extraction specifically adopts a multi-layer perception network to extract deep features from the regional structured data in the mine exploration optimization data, and obtains the mine area candidate structured features;

[0083] The discriminant feature matrix acquisition is used to map the mine area candidate comprehensive features to a low-dimensional discriminant subspace, and enhance the class separability between the mine area candidate regions; specifically, the mine area candidate comprehensive features and the corresponding historical mine area type labels are jointly input, a nonlinear kernel mapping relationship between samples is constructed, the mine area candidate comprehensive features are processed by nonlinear discriminant compression through kernel Fisher discriminant analysis method, the intra-class and inter-class scatter is calculated, and the generalized eigenvalue problem is solved to construct a projection matrix, finally a set of candidate low-dimensional discriminant features is obtained, and a candidate mine discriminant feature matrix is obtained;

[0084] The candidate area graph structure establishment is used to model the adjacency relationship and similarity of the discriminant features in space; specifically including the following steps:

[0085] The two-dimensional feature matrix is constructed, specifically, the linear normalization processing is performed on each dimension feature in the candidate mine discriminant feature matrix, and the features are sorted in descending order based on the feature importance, and the sorted feature sequence is mapped in turn by column to obtain a two-dimensional feature matrix;

[0086] The feature importance is used to measure the discriminant ability of each dimension feature in the candidate mine discriminant feature matrix in distinguishing different potential mine area categories; specifically, the inter-class dispersion and the intra-class dispersion of the jth dimension feature of the candidate mine discriminant feature matrix Y are comprehensively analyzed, and the single-dimensional Fisher discriminant score is calculated; the formula is as follows:

[0087] ;

[0088] In the formula, denotes the number of the cth class of samples, denotes the mean value of the cth class of samples in the jth dimension feature, μj represents the overall mean of all samples in the jth dimension, σjc represents the variance of the jth feature of the cth sample, min represents the minimum integer, preventing the denominator from being zero, and C represents the total number of potential mine area categories, Fj represents the single-dimensional Fisher discriminant score of the jth feature, used to evaluate the importance of the feature;

[0089] Node set construction, which is used to convert a two-dimensional feature matrix into a node set required for a graph structure, specifically, the two-dimensional feature matrix is cut and slid according to a set window size and sliding step, local feature blocks are extracted in turn, and each feature block is vectorized to form a node feature vector, and all node feature vectors form a node set; the formula used is as follows:

[0090] ;

[0091] In the formula, N represents the total number of nodes, and H represents the height of the two-dimensional feature matrix, W represents the width of the two-dimensional feature matrix, h represents the height of the sliding window, and w represents the width of the sliding window, h represents the sliding step of the window in the height direction, w represents the sliding step of the window in the width direction, floor represents the floor operator;

[0092] Edge connection establishment, which is used to construct a weighted edge connection relationship based on feature similarity in the node set to form the adjacency information of the graph structure; specifically, the Euclidean distance is calculated for any two node feature vectors in the node set, and the Euclidean distance is mapped to a similarity weight using a Gaussian kernel function, for each node, the k nodes with the highest similarity are selected to form its neighbor set, a weighted edge is established according to the similarity weight, and the similarity weight is assigned to the adjacency matrix element to form an initial weighted adjacency matrix, then the initial weighted adjacency matrix is symmetrized and a self-loop edge is added to obtain a final weighted adjacency matrix; the formula used is as follows:

[0093] ;

[0094] ;

[0095] ;

[0096] ;

[0097] In the formula, sim(i, j) represents the similarity weight of node i and node j, dis(i, j) represents the Euclidean distance of node i and node j, represents the Gaussian kernel bandwidth parameter, k represents the number of KNN neighbors, which is used to determine the connection density of each node. represents the initial weighted adjacency matrix, represents the transpose of the initial weighted adjacency matrix, Represents the identity matrix, used to add self-loop edges, represents the final weighted adjacency matrix, Represents the information of the edge between node i and node j, represents the neighbor set of node i;

[0098] Graph structure output is used to introduce the spatial position information of nodes in the graph structure construction process and fuse it with the node content features to generate a complete graph structure. Specifically, the center coordinates of each node in the two-dimensional feature matrix are first calculated and normalized to After the interval, the spatial position code of the node is generated by linear mapping, and then the spatial position code is spliced ​​and fused with the node features to obtain the node fusion feature vector. All node fusion feature vectors are sequentially composed into the initial node feature matrix. Finally, the node set, edge set, final weighted adjacency matrix and initial node feature matrix are combined to obtain the candidate area graph structure. The formula used is as follows:

[0099] ;

[0100] ;

[0101] ;

[0102] ;

[0103] Where, represents the spatial position encoding vector of the i-th node, represents the spatial position encoding mapping weight matrix, represents the spatial position encoding bias vector, represents the row index of node i in the two-dimensional feature matrix, represents the column index of node i in the two-dimensional feature matrix, Represents the splicing and fusion operation, Represents the feature transformation weight matrix after splicing and fusion, represents the fusion feature vector of the i-th node, Represents node features, represents the initial node feature matrix, represents the first node fusion feature vector, Indicates the Node fusion feature vector, represents the candidate region graph structure, a set of nodes, a set of edges, constructed by connecting each node with the nodes in its neighborhood set into edges;

[0104] a mine potential result output, used for automatic potential classification evaluation, specifically, a candidate area graph structure is input into an improved graph convolutional neural network adopting a parallel update mechanism, mine potential classification features are extracted, the mine potential classification features are mapped through a full connection layer and the probability distribution of each potential category of the candidate mine area is calculated through a Softmax function, the category corresponding to the maximum probability is selected as the final prediction result, and the potential evaluation result of the candidate mine area is obtained;

[0105] The improved graph convolutional neural network includes a parallel multi-order graph convolutional layer and a multi-head attention mechanism; specifically, the number of parallel branches is first set to Q, and each branch corresponds to a normalized adjacency matrix of a different order; each branch receives an initial node feature matrix and a normalized adjacency matrix of a corresponding order in the first layer, and simultaneously performs graph convolution operation to generate single-order node features of the layer; in the subsequent lth layer, each branch receives the single-order node features output by the previous layer and the normalized adjacency matrix of the corresponding order and simultaneously performs graph convolution operation to generate single-order node features of the layer, after parallel convolution of each branch, the convolution node features of all branches are spliced according to the feature dimension to generate multi-order neighborhood features, realizing parallel update and layer-by-layer extraction of multi-order neighborhood features; the multi-order neighborhood features are input into the multi-head attention mechanism to obtain mine potential classification features;

[0106] The normalized adjacency matrix of different orders is specifically first-order neighborhood information based on the final weighted adjacency matrix for the first-order branch, the second-order branch is expanded to the second-order neighborhood, and so on to the Qth-order neighborhood;

[0107] ;

[0108] ;

[0109] ;

[0110] ;

[0111] ;

[0112] ;

[0113] ;

[0114] ;

[0115] wherein, the normalized adjacency matrix of the qth order, an adjacency matrix of the q-th order, a degree matrix of a single-order node feature of the q-th branch in the first layer, a single-order node feature of the q-th branch in the l+1-th layer, a weight matrix of the q-th branch in the first layer, a weight matrix of the q-th branch in the l+1-th layer, a single-order node feature of the q-th branch in the l-th layer, a single-order node feature of the first branch in the L-th layer, a single-order node feature of the second branch in the L-th layer, a single-order node feature of the Q-th branch in the L-th layer, a multi-order neighborhood feature of the L-th layer, , and represent the query, key and value projection matrices of the k-th head, respectively, , and represent the query, key and value matrices of the k-th head, respectively, represent the output feature of the h-th attention head, represent the query feature vector of node i on the h-th head, represent the key feature vector of neighbor node j on the h-th head, represent the feature dimension of a single attention head, represent the value feature vector of neighbor node j on the h-th head, represent the vector concatenation operation, represent the output feature of the first attention head, represent the output feature of the H-th attention head, H represents the total number of attention heads, represent the multi-head attention fusion weight matrix, represent the weight matrix of the fully connected layer, represent the bias parameter of the fully connected layer, represent the mining area potential classification feature, represent the predicted probability that the candidate mining area belongs to the class c, L represents the total number of convolutional layers;

[0116] The mining area potential evaluation model is trained, specifically, historical class mining area screening data is used as training data, and the model is trained, and finally a trained mining area potential evaluation model is obtained.

[0117] ​The metallogenic area regional screening is specifically screening the candidate metallogenic areas according to the potential evaluation results of the candidate metallogenic areas, and selecting the area with a high potential evaluation result as the final metallogenic area.

[0118] By performing the above operation, the technical problem that the candidate area characteristics cannot be fully utilized in the existing metallogenic area potential evaluation model, and the multi-order neighborhood information is difficult to be considered simultaneously in the feature extraction process, thereby resulting in insufficient classification accuracy of the metallogenic area potential, unclear category boundary, and low distinguishability of different potential categories, is solved. The scheme innovatively proposes to construct a weighted graph structure by using a K-nearest neighbor algorithm, calculate the node feature similarity by using a Gaussian kernel function, and establish an adjacency relationship to generate a graph structure that can reflect the spatial adjacency and feature similarity. An improved graph convolutional neural network with a parallel updating mechanism is introduced to realize the synchronous extraction and fusion of multi-order neighborhood features, and a multi-head attention mechanism is combined to enhance the feature expression capability. The integrity and distinguishability of the feature expression can be effectively improved, the accuracy and robustness of the metallogenic area potential recognition can be significantly improved, the separability of the high-potential and low-potential areas can be enhanced, the generalization ability of the model under a complex geological background can be improved, and high-precision automatic potential classification of a large range of candidate metallogenic areas can be realized.

[0119] Embodiment five, refer to Figure 1 , Figure 4 and Figure 5 This embodiment is based on the above-mentioned embodiments, and the metallogenic area intelligent exploration module specifically includes the following steps:

[0120] The metallogenic area yield prediction model is established, specifically including the following steps:

[0121] The metallogenic area yield prediction feature extraction is used to extract the fusion features reflecting the regional metallogenic area yield prediction. Specifically, the image data and the structured data are subjected to yield prediction image feature extraction and yield prediction structured feature extraction, respectively, to generate yield prediction image features and yield prediction structured features, and the two types of features are spliced and fused to obtain yield prediction comprehensive features.

[0122] The yield prediction image feature extraction is specifically to use a convolutional neural network to learn the spatial structure features of the high-resolution remote sensing image data in the metallogenic area exploration optimization data to obtain the yield prediction image features.

[0123] The yield prediction structured feature extraction is specifically to use a multi-layer perception network to extract deep features of the regional environmental field feature data and the element distribution feature data in the metallogenic area exploration optimization data to obtain the yield prediction structured features.

[0124] Output of production forecast results, specifically, nonlinear regression analysis of comprehensive production forecast characteristics based on a feedforward neural network model to obtain the production forecast results of the mining area;

[0125] Training of the mining area production prediction model, specifically using the historical mineralization area production prediction data as training data to train the prediction model, and finally obtaining the trained mining area production prediction model;

[0126] The performance optimization of the prediction model involves obtaining the optimal hyperparameter combination of the model through an improved search algorithm, updating the hyperparameters of the prediction model based on the optimal hyperparameter combination of the model, and obtaining the optimal mining area output prediction model. The optimization includes the following steps:

[0127] Initialize the particle population. Specifically, encode the trained mining area output prediction model hyperparameters into search individual position vectors, and generate N particle individual position vectors through the composite chaotic mapping initialization method. Each individual encoding represents a candidate prediction model hyperparameter combination to obtain the initial particle population. The formula used is as follows:

[0128] ;

[0129] Where, Indicates the The position of each particle, represents the position of the i-th particle individual, Represents a random number in the range [0,1];

[0130] Calculate the particle fitness value, specifically the fitness value of the particles in the population The performance of the mining area production prediction model established based on the individual particle positions is used as the particle fitness value;

[0131] Calculate the sine exponential inertia weight by adaptively adjusting the search intensity through the number of iterations and introducing a sine term to achieve periodic fluctuations. The formula used is as follows:

[0132] ;

[0133] Where, represents the sinusoidal exponential inertia weight, represents the maximum number of iterations, t represents the current number of iterations, Represents a random number in the range [0,1];

[0134] Particle update, specifically updating particle velocity and position; the formula used is as follows:

[0135] ;

[0136] Where, Indicates that the i-th particle is in the Iteration speed, Indicates that the i-th particle is in the Iteration speed, represents the position of the i-th particle in the t-th iteration, represents the local optimal position of the individual particle, represents the global optimal position of the particle, and represents a random number in the range [0,1], Indicates that the i-th particle is in the Iteration position, and represent individual learning factors and group learning factors respectively;

[0137] The optimal position of the particle is updated. Specifically, the fitness value of all updated particles is re-evaluated, and based on the fitness value of the current particle, it is compared with the global optimal position of the current particle. If the fitness value of the current particle is better, the global optimal position of the particle is updated;

[0138] The particle search is terminated, specifically when the particle fitness value When the fitness threshold is exceeded and the maximum number of iterations is reached, the search is terminated and the global optimal position of the particle is obtained; the global optimal position of the particle specifically refers to the optimal hyperparameter combination of the model;

[0139] Updating the hyperparameters of the prediction model, specifically adjusting the hyperparameters of the mining area output prediction model according to the optimal hyperparameter combination of the model to obtain the optimal mining area output prediction model;

[0140] The production prediction of mining areas is to divide the mineralization area into multiple sub-areas according to equal-area grids, input the real-time mineralization area production prediction data of each sub-area into the optimal mining area production prediction model, and obtain the mining area production prediction results of each sub-area;

[0141] The mining exploration area is determined by summarizing and analyzing the mining area production forecast results of each sub-area, and generating a complete mining area production distribution map based on spatial visualization technology. According to this distribution map, areas with different production levels are accurately located and prioritized to form a decision support plan for mining area production management and resource allocation, thereby optimizing the mining plan and utilization efficiency of mineral resources and realizing intelligent exploration planning and efficient operation management of the target mining area.

[0142] By performing the above operation, in view of the technical problem that the existing mine yield prediction model is not reasonable for the hyperparameter setting, and in the hyperparameter optimization process, it depends on the fixed initialization method and the linear inertia weight adjustment, which leads to insufficient search space coverage and easy to fall into local optimum, resulting in insufficient model prediction accuracy, the scheme innovatively introduces a composite chaotic mapping initialization method in the particle swarm optimization algorithm, which is used to enhance the diversity of particle initial distribution and global search ability; The sine exponential inertia weight realizes the adaptive search intensity adjustment of dynamic fluctuation with iteration, balances global exploration and local development; Significantly improve the efficiency and accuracy of the search of the hyperparameters of the mine yield prediction model, enhance the adaptability of the model to complex geological and mineralization conditions, improve the accuracy and stability of the yield prediction, realize the high-precision yield prediction of each sub-area of the ore-forming area and the optimization of the mining scheme.

[0143] It should be noted that, in this paper, relational terms such as first and second are used merely to distinguish one entity or action from another, without necessarily requiring or implying any such actual relationship or order between such entities or actions. Moreover, the terms "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or apparatus including a series of elements includes not only those elements, but also other elements not explicitly listed or inherent to such a process, method, article or apparatus.

[0144] Although embodiments of the present application have been shown and described, it will be understood by those having ordinary skill in the art that various changes, modifications, alternatives and variations can be made thereto without departing from the principles and spirit of the application.

[0145] The above describes the present application and its embodiments, which is not restrictive, and the drawings shown are only one of the embodiments of the present application, and the actual structure is not limited thereto. In general, if a person skilled in the art is inspired, without departing from the purpose of the present application, without creative design, similar structure and embodiments of the technical solution can be designed, which shall belong to the protection scope of the present application.

Claims

1. A mining exploration system based on artificial intelligence, characterized by: The application relates to a mining area intelligent exploration system, which comprises a multi-source data acquisition module, a data optimization module, a mining area candidate region potential identification module and a mining area intelligent exploration module. The multi-source data acquisition module is specifically configured to obtain mining area exploration original data through data acquisition operations. The data optimization module is configured to improve the quality of the original data and the model adaptability, and specifically configured to obtain mining area exploration optimized data through image data preprocessing and structured data preprocessing. The mining area candidate region potential identification module is configured to identify high-potential mineralization areas in a large-scale mining area candidate region, and specifically configured to establish a mining area potential evaluation model, train the model by using historical data, input real-time data into the trained model, output potential evaluation results of each candidate mining area, screen the large-scale candidate region according to the potential evaluation results, and obtain a mineralization area result. The establishment of the mining area potential evaluation model comprises candidate region identification feature extraction, discriminant feature matrix acquisition, candidate region graph structure establishment and mining area potential result output. The mining area intelligent exploration module is configured to implement zoned yield prediction and accurate mining area determination in the mineralization area, and specifically configured to establish a mining area yield prediction model, train the model by using historical data, search for an optimal hyperparameter combination of the model by using a composite chaotic mapping initialization method and a sine exponential inertia weight improved particle swarm optimization algorithm, update the prediction model according to the optimal hyperparameter combination, obtain an optimal mining area yield prediction model, input real-time data of each subarea into the prediction model, output yield prediction results of each subarea, generate a mining area zoned yield distribution map according to the yield prediction results, and realize intelligent exploration planning and efficient operation management of the target mining area.

2. The artificial intelligence based mineral field exploration system as claimed in claim 1 wherein: The mining area candidate region potential identification module specifically comprises the following steps: The establishment of the mining area potential evaluation model comprises the following steps: The candidate region identification feature extraction is specifically configured to perform image feature extraction and structured feature extraction on the image data and the structured data respectively, generate mining area candidate image features and mining area candidate structured features, and splice and fuse the two types of features to obtain mining area candidate comprehensive features. The discriminant feature matrix acquisition is configured to map the mining area candidate comprehensive features to a low-dimensional discriminant subspace, enhance the class separability between the mining area candidate regions, and specifically configured to input the mining area candidate comprehensive features and corresponding historical mining area type labels together, construct a nonlinear kernel mapping relationship between samples, perform nonlinear discriminant compression processing on the mining area candidate comprehensive features by using a kernel Fisher discriminant analysis method, calculate the intra-class and inter-class scatter, solve a generalized eigenvalue problem to construct a projection matrix, finally obtain a group of candidate low-dimensional discriminant features, and obtain a candidate mining discriminant feature matrix. The candidate region graph structure establishment; The mining area potential result output; The mining area potential evaluation model training is specifically configured to use historical class mining area screening data as training data to train the model, and finally obtain a trained mining area potential evaluation model. The metallogenic area regional screening is specifically screening each candidate ore district according to the potential evaluation result of each candidate ore district.

3. The artificial intelligence based mineral field exploration system as claimed in claim 1 wherein: The candidate area graph structure establishment specifically includes the following steps: The two-dimensional feature matrix is constructed, specifically linear normalization processing is performed on each dimension feature in the candidate ore discrimination feature matrix, and each feature is sorted in descending order based on feature importance, and the sorted feature sequence is mapped in turn by column to obtain a two-dimensional feature matrix; The feature importance is specifically a comprehensive analysis of the inter-class dispersion and the intra-class dispersion of the jth dimension feature of the candidate ore discrimination feature matrix Y, and a single-dimensional Fisher discrimination score is calculated; The node set construction is specifically that the two-dimensional feature matrix is cut by sliding according to the set window size and sliding step, local feature blocks are extracted in turn, and each feature block is vectorized to form a node feature vector, and all node feature vectors form a node set; The edge connection establishment is specifically that the Euclidean distance between any two node feature vectors in the node set is calculated, and the Euclidean distance is mapped to a similarity weight by using a Gaussian kernel function, for each node, the k nodes with the highest similarity are selected to form its neighbor set, and a weighted edge is established according to the similarity weight, and the similarity weight is assigned to the adjacent matrix element to form an initial weighted adjacency matrix, then the initial weighted adjacency matrix is symmetrized and a self-loop edge is added to obtain a final weighted adjacency matrix; The graph structure output, specifically, first calculates the center coordinates of each node in a two-dimensional feature matrix, normalizes them to After the interval, the spatial position encoding of the node is generated by linear mapping, then the spatial position encoding is spliced and fused with the node feature to obtain the node fusion feature vector, and all node fusion feature vectors are sequentially composed into an initial node feature matrix, finally the node set, edge set, final weighted adjacency matrix and initial node feature matrix are combined to obtain a candidate region graph structure.

4. The artificial intelligence based mineral prospecting system according to claim 1, wherein: The mine area potential result output is specifically that the candidate area graph structure is input into an improved graph convolutional neural network with a parallel update mechanism, the mine area potential classification features are extracted, the mine area potential classification features are mapped through a fully connected layer and the probability distribution of each potential category of the candidate ore district is calculated through a Softmax function, the category corresponding to the maximum probability is selected as the final prediction result, and the potential evaluation result of the candidate ore district is obtained; The improved graph convolutional neural network includes a parallel multi-order graph convolutional layer and a multi-head attention mechanism. Specifically, first, the number of parallel branches is set to Q, and each branch corresponds to a normalized adjacency matrix of a different order; each branch receives the initial node feature matrix and the normalized adjacency matrix of the corresponding order in the first layer, and simultaneously performs graph convolution operation to generate single-order node features of this layer; In the lth layer, each branch receives the single-order node features output by the previous layer and the normalized adjacency matrix of the corresponding order and simultaneously performs graph convolution operation to generate single-order node features of this layer, after parallel convolution of each branch, the convolution node features of all branches are spliced according to the feature dimension to generate multi-order neighborhood features, realizing parallel update and layer-by-layer extraction of multi-order neighborhood features; the multi-order neighborhood features are input into the multi-head attention mechanism to obtain mine area potential classification features.

5. The artificial intelligence based mineral prospecting system according to claim 1, wherein: The mine area intelligent exploration module specifically includes the following steps: The mine area production prediction model is established, specifically including the following steps: The mine yield prediction feature extraction specifically includes yield prediction image feature extraction and yield prediction structured feature extraction on image data and structured data respectively, yield prediction image feature and yield prediction structured feature are generated, and the two types of features are spliced and fused to obtain yield prediction comprehensive feature; The yield prediction result output specifically includes nonlinear regression analysis of the yield prediction comprehensive feature based on the feedforward neural network model to obtain the mine yield prediction result; The mine yield prediction model training specifically includes using historical mine yield prediction data as training data to train the prediction model, and finally obtaining the trained mine yield prediction model; The prediction model performance optimization; The mine subarea yield prediction specifically includes dividing the mine area into multiple subareas according to the equal-area grid, inputting the real-time mine yield prediction data of each subarea into the optimal mine yield prediction model to obtain the mine yield prediction result of each subarea; The mine exploration area determination specifically includes analyzing and summarizing the mine yield prediction results of each subarea, generating a complete mine subarea yield distribution map based on spatial visualization technology, accurately positioning and prioritizing different yield grade areas according to the distribution map, and forming a decision support scheme for mine production management and resource allocation, thereby optimizing the mining plan and utilization efficiency of mineral resources, and realizing intelligent exploration planning and efficient operation management of the target mine.

6. The artificial intelligence based mineral prospecting system as claimed in claim 5, wherein: The prediction model performance optimization specifically includes the following steps: Initializing the particle population, specifically encoding the trained mine yield prediction model hyperparameters into search individual position vectors, and generating N particle individual position vectors through a complex chaotic mapping initialization method, each individual code represents a candidate prediction model hyperparameter combination, and an initial particle swarm is obtained; the formula used is as follows: ； wherein represents the position of the i-th particle individual, represents the position of the i-th particle individual, represents the position of the i-th particle individual, represents a random number in the range [0, 1]; calculating the fitness value of the particle, specifically calculating the fitness value of the particle in the population ; the performance of the mine production prediction model established based on the individual position of the particle is taken as the fitness value of the particle Calculating the sine exponential inertia weight, specifically adjusting the search intensity through the iteration number, and introducing a sine term to realize periodic fluctuation; the formula used is as follows: ； wherein denotes the sinusoidal exponential inertia weight, denotes the maximum number of iterations, t denotes the current iteration number, denotes a random number in the range [0, 1]; Particle updating, specifically updating the particle velocity through the sine exponential inertia weight, and updating the particle position based on the updated particle velocity; Particle optimal position updating, specifically re-evaluating the fitness value of all updated particles, and comparing the fitness value of the current particle with the global optimal position of the current particle, if the fitness value of the current particle is better, the global optimal position of the particle is updated; the particle search terminates, specifically when the particle fitness value when the fitness threshold is exceeded and a maximum number of iterations is reached, the search is terminated and a particle global optimum position is obtained; the particle global optimum position specifically refers to a model optimal hyperparameter combination; Prediction model hyperparameter updating, specifically adjusting the hyperparameters of the mine yield prediction model according to the optimal model hyperparameter combination to obtain the optimal mine yield prediction model.

7. The artificial intelligence based mineral prospecting system according to claim 1, wherein: The multi-source data acquisition module, specifically, obtains the mining area exploration original data by obtaining the data related to the mining area from an external data platform; the mining area exploration original data includes historical mining area screening data, historical mineralized area yield prediction data, real-time mining area screening data, and real-time mineralized area yield prediction data; the historical mining area screening data and the real-time mining area screening data both include medium-resolution remote sensing image data and regional structured data; the historical mining area screening data further includes historical mining area types; the historical mineralized area yield prediction data and the real-time mineralized area yield prediction data both include high-resolution remote sensing image data, regional environmental field feature data, and element distribution feature data; and the historical mineralized area yield prediction data further includes mining area yield data.

8. The artificial intelligence based mineral prospecting system according to claim 1, wherein: The data optimization module specifically includes the following steps: image data preprocessing, specifically including image cloud removal processing, radiation correction processing, image format standardization processing, and image quality enhancement, to obtain remote sensing optimized image data; structured data preprocessing, specifically including data cleaning, data standardization, and data encoding processing, to obtain structured optimized data.

Images