Hidden ore body three-dimensional positioning prediction method based on artificial intelligence

Abstract

The invention relates to the technical field of ore body three-dimensional positioning prediction, and discloses a hidden ore body three-dimensional positioning prediction method based on artificial intelligence, and the method comprises the steps: firstly processing geological data through data preprocessing and feature extraction, and then carrying out the training and prediction through a constructed neural network model, and three-dimensional positioning of the concealed ore body is realized. The method comprises the steps of data normalization, missing value filling, feature extraction, neural network model construction, gradient descent updating, three-dimensional coordinate conversion, ore body scale estimation and the like. Through prediction precision evaluation and model optimization, the accuracy and efficiency of concealed ore body positioning are improved. The invention provides an efficient and accurate three-dimensional positioning prediction method for mineral resource exploration, and the method has a wide application prospect.

Description

Technical Field

[0001] The present invention relates to the technical field of three-dimensional positioning prediction of ore bodies, and in particular to a three-dimensional positioning prediction method for concealed ore bodies based on artificial intelligence. Background Art

[0002] With the continued development of the global economy, demand for mineral resources is growing. However, surface and shallow ore bodies are gradually being mined out, making the search for concealed ore bodies a key area of ​​mineral exploration. Concealed ore bodies are those buried below the surface, unable to be directly discovered through surface geological surveys. Their location and prediction have always been a difficult and hot topic in geological exploration.

[0003] Traditional methods for locating and predicting hidden ore bodies mainly rely on geological mapping, geophysical exploration, and drilling. These methods are not only costly and time-consuming, but are also limited by the experience and subjective judgment of explorers. The prediction accuracy and efficiency are often difficult to meet the needs of modern mineral exploration.

[0004] In recent years, the rapid development of artificial intelligence (AI), particularly the remarkable performance of machine learning and deep learning in image recognition, data analysis, and pattern recognition, has provided new ideas and methods for the location and prediction of concealed ore bodies. However, current AI-based methods for the location and prediction of concealed ore bodies still have many shortcomings. For example, the models are complex, difficult to train, and require significant computing resources and time. The models are poorly interpretable, making the prediction results difficult for geological experts to understand and accept. The lack of effective interdisciplinary knowledge integration results in limited model prediction accuracy and reliability. The application scenarios are limited, making it difficult to adapt to the prediction needs of different geological conditions and ore body types. The lack of mobile applications and international support limits the widespread application and promotion of these methods.

[0005] Therefore, the development of an artificial intelligence-based three-dimensional positioning prediction method for concealed ore bodies aims to overcome the shortcomings of existing technologies, improve the accuracy, efficiency and interpretability of concealed ore body positioning prediction, and expand the application scenarios and market adaptability of the method, which has become a technical problem that needs to be urgently solved in the current geological exploration field. Summary of the Invention

[0006] (0) Technical problems solved In response to the shortcomings of the existing technology, the present invention provides a three-dimensional positioning prediction method for hidden ore bodies based on artificial intelligence, which solves the problems raised in the above background technology.

[0007] (2) Technical solution To achieve the above objectives, the present invention provides the following technical solutions: a three-dimensional positioning prediction method for hidden ore bodies based on artificial intelligence, comprising the following steps: S1. Data Collection: Comprehensively collect geological data related to concealed ore bodies, including stratigraphic structure, lithologic distribution, and fault information; geophysical data, such as gravity, magnetic, electrical, and seismic exploration data; geochemical data, such as element content and isotope ratios; and multi-source data, including historical exploration data and drilling data. S2. Data preprocessing: Data cleaning: Identify and remove outliers, missing values, and duplicate data to ensure data quality; Data standardization: normalize or standardize data to eliminate dimensional differences and improve data comparability; Data fusion: Integrate data from different sources and types into a unified data format to facilitate subsequent analysis; Data augmentation: Use interpolation and simulation methods to generate virtual samples and expand the training data set; S4. 3D model construction: Ore body morphology modeling: Based on geological and geophysical data, the spatial morphology of concealed ore bodies is reconstructed using 3D modeling technology; Spatial distribution modeling: Analyze the distribution characteristics of ore bodies in three-dimensional space and establish ore body spatial distribution models; Attribute feature modeling: extract the attribute features of the ore body, such as grade, thickness, and burial depth, and establish an ore body attribute feature model; S4. Model training and optimization: Select an AI algorithm: Based on the data characteristics and prediction requirements, choose an appropriate deep learning algorithm, such as a convolutional neural network (CNN), recurrent neural network (RNN), or generative adversarial network (GAN); Construct training and validation sets: Divide the preprocessed data into training and validation sets for model training and performance evaluation; Model training: Use training set data to train the artificial intelligence model, adjust model parameters, and optimize model performance; Model validation and optimization: Use validation set data to validate the model, evaluate model performance, and optimize and adjust the model based on the validation results; S5. Positioning prediction: Input unknown area data: Input the geological, geophysical and geochemical data of the unknown area into the trained ore body location prediction model; Output prediction results: The model outputs the three-dimensional positioning prediction results of the hidden ore body, including the ore body location, shape, scale, and grade prediction information; S6. Result verification and evaluation: Actual drilling verification: Verify the prediction results through actual drilling data and calculate the prediction accuracy; Uncertainty analysis: Conduct uncertainty analysis on the prediction results to evaluate the reliability and stability of the prediction results.

[0008] Preferably, the data preprocessing step further includes: Data quality assessment: Perform quality assessment on collected multi-source data, including data integrity, accuracy, and consistency checks, to ensure that data quality meets model training requirements; Multi-scale data integration: Integrate data of different scales and resolutions to form a multi-scale dataset; Feature selection and extraction: Based on geostatistics and artificial intelligence technology, feature selection and extraction are performed on pre-processed data to screen out key features related to the location prediction of concealed ore bodies and reduce data dimensionality; Outlier processing: Use statistical methods or machine learning algorithms to identify and process outliers in the data to reduce the interference of outliers on model training; Data balancing: To address the imbalance in the number of data samples of different types, oversampling, undersampling or synthetic sampling methods are used to balance the data.

[0009] Preferably, the step of constructing a three-dimensional model of a concealed ore body includes: 3D spatial data integration: Integrate pre-processed multi-source data in 3D space and use geological modeling software or custom algorithms to construct an initial 3D geological model, which includes strata, faults, rock masses, and known ore body geological elements; Ore body morphology simulation: Based on geostatistical methods such as Kriging interpolation and co-Kriging, combined with the morphology and distribution characteristics of known ore bodies, the possible morphology and spatial distribution of concealed ore bodies can be simulated; Attribute feature assignment: Using machine learning algorithms, such as random forest and support vector machine, attribute features of the simulated concealed ore body are assigned, including grade and ore type, based on the attribute characteristics and spatial distribution patterns of the known ore body; Model optimization and updating: Through iterative optimization algorithms such as genetic algorithms and particle swarm optimization, the 3D geological model is continuously optimized to make it more consistent with the actual geological conditions and ore body distribution patterns. At the same time, as new data is acquired, the 3D geological model is updated in real time to maintain the timeliness and accuracy of the model. Multi-scenario simulation analysis: Consider different geological assumptions and exploration scenarios to construct multiple possible 3D geological models.

[0010] Preferably, the step of predicting hidden ore bodies using a deep learning model includes: Model selection and design: Based on the needs and characteristics of concealed ore body prediction, select appropriate deep learning models, such as convolutional neural networks (CNN), recurrent neural networks (RNN), and generative adversarial networks (GAN), and design the model structure, including determining the number of network layers, number of neurons, and activation function parameters; Data augmentation and expansion: Perform data augmentation on preprocessed data, such as rotation, flipping, and scaling, to increase the generalization ability and robustness of the model; Model training and optimization: Use preprocessed data to train the designed deep learning model, using gradient descent and adaptive learning rate optimization algorithms to adjust model parameters until the model converges. During the training process, cross-validation and early stopping strategies are used to prevent model overfitting. Feature importance analysis: Through feature importance analysis techniques such as Gradient Class Activation Mapping (Grad-CAM) and Layer-wise Relevance Propagation (LRP), the most important features for concealed ore body prediction are identified, providing a basis for subsequent model interpretation and optimization; Model integration and fusion: Using model integration techniques, such as bagging and boosting, to fuse the prediction results of multiple deep learning models to improve the accuracy and stability of predictions; Real-time prediction and feedback: Deploy the trained deep learning model into the actual application environment to achieve real-time hidden ore body prediction; at the same time, continuously optimize and update the model based on actual prediction results and feedback information.

[0011] Preferably, the result verification and evaluation steps include: Visualization of prediction results: The location, shape, scale and grade of the concealed ore body predicted by the deep learning model are displayed in a three-dimensional visualization format for intuitive understanding and analysis; Actual drilling verification: Design a drilling plan, conduct actual drilling on the predicted concealed ore body, collect drilling data, including core, lithology, and grade, compare and analyze with the predicted results, and calculate the prediction accuracy; Uncertainty quantification: Monte Carlo simulation and probability distribution analysis methods are used to quantify the uncertainty of the prediction results and evaluate the confidence and reliability of the prediction results; Error analysis: Detailed analysis of the errors between predicted results and actual drilling data to identify the sources of errors, such as data quality, model performance, and geological complexity, providing guidance for subsequent model optimization; Comprehensive evaluation indicators: Build a comprehensive evaluation indicator system, including prediction accuracy, recall rate, and F1 score, to comprehensively evaluate the performance and effectiveness of the prediction method; Continuous improvement mechanism: Based on the feedback information from result verification and evaluation, a continuous improvement mechanism is established to continuously optimize and update data preprocessing, model training, and prediction processes.

[0012] Preferably, the method further comprises: Multi-source data fusion module: This module can receive and fuse data from different sources in real time, including geological, geophysical, and geochemical data; Adaptive learning rate adjustment mechanism: During deep learning model training, the learning rate is adaptively adjusted based on model performance and training progress; Model interpretability enhancement technology: Using interpretability enhancement technologies, such as attention mechanism and feature importance analysis, to improve the interpretability of model prediction results, making them easier for geological experts to understand and verify; Real-time data update and model iteration: Establish a real-time data update mechanism to integrate newly acquired data into model training in real time, achieve continuous iteration and update of the model, and maintain the timeliness of the prediction method; Cloud computing and parallel processing architecture: Utilizing cloud computing platforms and parallel processing architectures, we improve the efficiency of data processing and model training, enabling large-scale, high-precision three-dimensional positioning prediction of concealed ore bodies. User interface: Design a user-friendly interface that allows users to enter custom parameters, view prediction results, and adjust model settings.

[0013] Preferably, the method further comprises: Multi-dimensional uncertainty analysis module: This module can perform multi-dimensional uncertainty analysis on prediction results, including spatial uncertainty, attribute uncertainty, and model uncertainty, and provide a more comprehensive basis for decision-making by quantifying uncertainty indicators; Intelligent Drilling Advisory System: Based on prediction results and uncertainty analysis, the system can intelligently recommend drilling locations and depths, optimize drilling plans, and reduce exploration costs and risks. Environmental and economic and social impact assessment tools: While predicting hidden ore bodies, they consider environmental impacts, social acceptance and economic feasibility to provide decision support for sustainable mining development; Knowledge graph integration: Integrating geological knowledge, historical exploration data, and expert experience into the knowledge graph provides rich prior knowledge for deep learning models, enhancing the accuracy and reliability of predictions. Interdisciplinary collaborative work platform: Establish a collaborative work platform for multidisciplinary experts in geology, geophysics, and data science to achieve knowledge sharing, data interoperability, and collaborative innovation; Adaptive model update strategy: Adaptively adjust the model structure and parameters based on new data, new knowledge and user feedback to achieve continuous learning and self-optimization of the model.

[0014] Preferably, the method further comprises: Multi-scenario application adaptation module: This module can adaptively adjust prediction strategies and model parameters according to different geological conditions, ore body types and exploration requirements to adapt to various application scenarios; Real-time data monitoring and early warning system: This system monitors the quality of input data and the stability of the prediction process in real time. Once abnormal data or prediction deviation exceeds the preset threshold is detected, an early warning is immediately issued to indicate the possible cause of the error. Interactive 3D visualization platform: provides an interactive 3D visualization interface that allows users to freely rotate, scale, and cut the 3D model of the concealed ore body, while supporting the overlay display and dynamic analysis of multi-attribute data; Intelligent report generation tool: Automatically summarizes forecast results, uncertainty analysis, drilling recommendations, and environmental, economic, and social impact assessment information to generate structured, customizable intelligent reports for users to quickly understand and make decisions; Cross-platform compatibility and cloud service interface: This method supports multiple operating systems and hardware platforms, and provides a cloud service interface, allowing users to upload data, train models, and conduct predictive analysis through the cloud, thus achieving resource sharing and efficient utilization. User rights management and data security mechanism: Establish a comprehensive user rights management mechanism to ensure that different users can only access data and functions within the scope of their rights; at the same time, adopt encryption technology and data backup strategies.

[0015] Preferably, the method further comprises: Model Interpretability Enhancement Module: This module integrates multiple model interpretation techniques, such as feature importance analysis and decision path visualization, to provide in-depth explanations of prediction results and help users understand the model decision-making process. Adaptive model optimization strategy: Automatically adjust model structure and parameters based on real-time feedback on prediction accuracy and user needs; Interdisciplinary knowledge fusion engine: Integrates multidisciplinary knowledge of geology, geophysics, mathematics, and computer science to provide rich prior knowledge for prediction models.

[0016] Preferably, the method further comprises: Mobile application support: Develop mobile applications that allow users to collect data, perform model predictions, and view results in the field using smartphones or tablets; Multi-language support and internationalization: Provide multi-language user interface and documentation support to meet the needs of users in different countries and regions; Community collaboration and knowledge sharing platform: Establish an online community collaboration platform to allow users to share data, models and experiences.

[0017] (3) Beneficial effects Compared with the existing technology, the present invention provides a three-dimensional positioning prediction method for hidden ore bodies based on artificial intelligence, which has the following beneficial effects: 1. Improve prediction accuracy: By deeply integrating geological, geophysical and other multi-source data and utilizing advanced artificial intelligence algorithms, this method can more accurately identify and locate hidden ore bodies, significantly improving prediction accuracy.

[0018] 2. Enhance model interpretability: This invention focuses on interpretability in model design. By introducing highly interpretable machine learning algorithms and visualization technologies, the prediction results are made easier for geological experts to understand and accept.

[0019] 3. Reduce costs and improve efficiency: Compared with traditional exploration methods, this method significantly reduces the workload of field exploration and reduces exploration costs, while improving prediction efficiency and shortening the exploration cycle.

[0020] 4. Strong adaptability: This method can adapt to the prediction needs of different geological conditions and ore body types, and has strong universality and flexibility.

[0021] 5. Interdisciplinary knowledge integration: By effectively integrating multidisciplinary knowledge such as geology, geophysics, and computer science, this method improves the comprehensive performance and reliability of the prediction model.

[0022] 6. Mobile applications and international support: The present invention supports mobile applications, facilitating real-time data collection and analysis in the field, and supports multi-language interfaces to facilitate international promotion and application.

[0023] 7. Promote the sustainable development of mineral resources: The promotion and application of this method will help discover more hidden ore bodies, ensure the supply of mineral resources, and promote the sustainable development and utilization of mineral resources.

[0024] 8. Promote technological progress in the industry: This invention promotes the application of artificial intelligence technology in the field of geological exploration, improves the overall technical level of the industry, and provides new impetus for the innovative development of the geological exploration industry. DETAILED DESCRIPTION

[0025] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention. Specific embodiments Example 1: 3D Positioning Prediction of Concealed Ore Bodies Based on Deep Learning 1. Data acquisition and preprocessing: Collect multi-source data such as geology, geophysics (such as gravity, magnetics, and electrical methods), and remote sensing in the study area.

[0027] Preprocess the data, including data cleaning, normalization, missing value filling, etc.

[0028] 2. Feature extraction: Use convolutional neural networks (CNNs) in deep learning to extract spatial features from geophysical data.

[0029] Use recurrent neural networks (RNNs) to extract temporal features from time series data.

[0030] 3. Model construction: Build a hybrid neural network model to fuse the output features of CNN and RNN.

[0031] Add fully connected layers and output layers for the location prediction of hidden ore bodies.

[0032] 4. Model training and optimization: Use data from known ore body locations for model training.

[0033] Gradient descent algorithm was used to optimize model parameters, and cross-validation was used to prevent overfitting.

[0034] 5. Three-dimensional positioning prediction: Apply the trained model to the entire study area to perform three-dimensional positioning prediction of hidden ore bodies.

[0035] Output prediction results, including ore body location, depth and size.

[0036] 6. Result verification and interpretation: Use drilling data to verify the prediction results.

[0037] Use visualization technology to display the prediction results, which is convenient for geological experts to interpret and verify.

[0038] Example 2: 3D Positioning Prediction of Concealed Ore Bodies Based on Machine Learning 1. Data preparation: Collect geological, geochemical, and geophysical data of the study area.

[0039] Preprocess the data, including data standardization and outlier processing.

[0040] 2. Feature Selection: Use feature selection algorithms in machine learning (such as random forest and gradient boosting tree) to select features that are important for the location prediction of concealed ore bodies.

[0041] 3. Model selection and training: Select machine learning algorithms such as support vector machine (SVM), decision tree, random forest, etc. for model training.

[0042] The model was trained using known ore body data and the model parameters were adjusted to obtain optimal performance.

[0043] 4. 3D positioning prediction: The trained model is used to predict the three-dimensional location of hidden ore bodies in the study area.

[0044] Output prediction results, including the possible location and depth range of the ore body.

[0045] 5. Result analysis and optimization: Analyze the prediction results, compare them with actual drilling data, and evaluate the model performance.

[0046] Optimize the model based on the analysis results to improve prediction accuracy.

[0047] Example 3: Artificial Intelligence-Based 3D Positioning Prediction System for Concealed Ore Bodies 1. System architecture: Develop an artificial intelligence-based three-dimensional positioning prediction system for hidden ore bodies, including data acquisition module, preprocessing module, feature extraction module, model training module, prediction module and result display module.

[0048] 2. Data stream processing: The data acquisition module automatically collects multi-source data, and the preprocessing module cleans and standardizes the data.

[0049] The feature extraction module uses artificial intelligence algorithms to extract effective features.

[0050] 3. Model training and deployment: The model training module uses training data to train the artificial intelligence model and deploys the trained model to the prediction module.

[0051] 4. Real-time prediction and visualization: The prediction module receives new data in real time and performs three-dimensional positioning prediction of hidden ore bodies.

[0052] The result display module displays the prediction results in a three-dimensional visual form to facilitate user understanding and analysis.

[0053] 5. User interaction and feedback: The system provides a user interaction interface that allows users to input parameters, view prediction results and provide feedback.

[0054] Optimize models based on user feedback to improve prediction accuracy.

[0055] The formulas required to be cited in the embodiments of the invention are as follows 1. Data preprocessing and feature extraction 1. Data normalization formula: in, is the original data, X is the dataset, ' is the normalized data.

[0056] 1. Missing value filling formula (for example, using mean): in, is the fill value, is the non-missing data point, and N is the number of non-missing data points.

[0057] 2. Feature extraction formula (for example, using PCA for dimensionality reduction): Among them, X is the original feature matrix, W is the feature vector matrix, and Z is the feature matrix after dimensionality reduction.

[0058] 2. Model Construction and Training 1. Neural network forward propagation formula: in, is the input feature, W is the weight matrix, is the bias vector, σ is the activation function, and ℎ is the hidden layer output.

[0059] 3. Loss function formula (for example, using mean squared error): in, is the true value, is the predicted value, and N is the number of samples.

[0060] 3.3D Positioning Prediction 1. Three-dimensional coordinate conversion formula: in, is the original coordinate, ( ) is the predicted coordinate offset, and (x′, y′, z′) is the predicted three-dimensional coordinate.

[0061] 4. Formula for estimating the size of the ore body (e.g., using volumetric estimation): in, 、 and are the length, width and height of the ore body respectively, and V is the volume of the ore body.

[0062] 4. Result Verification and Optimization 1. Forecast accuracy evaluation formula (for example, using accuracy): Among them, TP, TN, FP and FN are the number of true positives, true negatives, false positives and false negatives respectively.

[0063] 5. Model optimization formula (for example, using learning rate decay): in, is the current learning rate, is the initial learning rate, λ is the decay coefficient, and t is the number of iterations.

[0064] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0065] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A three-dimensional positioning prediction method for hidden ore bodies based on artificial intelligence, characterized by: The following steps are involved: S1. Data collection: Comprehensively collect geological data related to concealed ore bodies, including stratigraphic structure, lithologic distribution, and historical exploration data; S2. Data preprocessing: Data cleaning: Identify and remove outliers, missing values, and duplicate data to ensure data quality; Data standardization: normalize or standardize data to eliminate dimensional differences and improve data comparability; Data fusion: Integrate data from different sources and types into a unified data format to facilitate subsequent analysis; Data augmentation: Use interpolation and simulation methods to generate virtual samples and expand the training data set; S4. 3D model construction: Ore body morphology modeling: Based on geological and geophysical data, the spatial morphology of concealed ore bodies is reconstructed using 3D modeling technology; Spatial distribution modeling: Analyze the distribution characteristics of ore bodies in three-dimensional space and establish ore body spatial distribution models; Attribute feature modeling: extract the attribute features of the ore body; The method further comprises: Model Interpretability Enhancement Module: This module integrates multiple model interpretation technologies to provide in-depth explanations of prediction results, helping users understand the model decision-making process; Adaptive model optimization strategy: Automatically adjust model structure and parameters based on real-time feedback on prediction accuracy and user needs; Interdisciplinary knowledge fusion engine: Integrates multidisciplinary knowledge of geology, geophysics, mathematics, and computer science to provide rich prior knowledge for prediction models.

2. The method for three-dimensional positioning and prediction of a concealed ore body based on artificial intelligence according to claim 1, characterized in that: The method further includes: S4. Model training and optimization: Select AI algorithms: Choose appropriate deep learning algorithms based on data characteristics and prediction requirements; Construct training and validation sets: Divide the preprocessed data into training and validation sets for model training and performance evaluation; Model training: Use training set data to train the artificial intelligence model, adjust model parameters, and optimize model performance; Model validation and optimization: Use validation set data to validate the model, evaluate model performance, and optimize and adjust the model based on the validation results; S5. Positioning prediction: Input unknown area data: Input the geological, geophysical and geochemical data of the unknown area into the trained ore body location prediction model; Output prediction results: The model outputs the three-dimensional positioning prediction results of the hidden ore body, including the ore body location, shape, scale, and grade prediction information; S6. Result verification and evaluation: Actual drilling verification: Verify the prediction results through actual drilling data and calculate the prediction accuracy; Uncertainty analysis: Conduct uncertainty analysis on the prediction results to evaluate the reliability and stability of the prediction results.

3. The method for three-dimensional positioning and prediction of hidden ore bodies based on artificial intelligence according to claim 1, characterized in that: The data preprocessing step further includes: Data quality assessment: Perform quality assessment on collected multi-source data, including data integrity, accuracy, and consistency checks, to ensure that data quality meets model training requirements; Multi-scale data integration: Integrate data of different scales and resolutions to form a multi-scale dataset; Feature selection and extraction: Based on geostatistics and artificial intelligence technology, feature selection and extraction are performed on pre-processed data to screen out key features related to the location prediction of concealed ore bodies and reduce data dimensionality; Outlier processing: Use statistical methods or machine learning algorithms to identify and process outliers in the data to reduce the interference of outliers on model training; Data balancing: To address the imbalance in the number of data samples of different types, oversampling, undersampling or synthetic sampling methods are used to balance the data.

4. The method for three-dimensional positioning and prediction of a concealed ore body based on artificial intelligence according to claim 1, characterized in that: The steps of constructing a three-dimensional model of a concealed ore body include: 3D spatial data integration: Integrate pre-processed multi-source data in 3D space and use geological modeling software or custom algorithms to construct an initial 3D geological model, which includes strata, faults, rock masses, and known ore body geological elements; Ore body morphology simulation: Based on geostatistical methods, simulate the possible morphology and spatial distribution of concealed ore bodies; Attribute feature assignment: Using machine learning algorithms, attribute features are assigned to the simulated concealed ore body, including grade and ore type, based on the attribute features and spatial distribution patterns of the known ore body; Model optimization and updating: Through iterative optimization algorithms, the 3D geological model is continuously optimized to make it more consistent with the actual geological conditions and ore body distribution patterns. At the same time, as new data is acquired, the 3D geological model is updated in real time to maintain the timeliness and accuracy of the model. Multi-scenario simulation analysis: Consider different geological assumptions and exploration scenarios to construct multiple possible 3D geological models.

5. The method for three-dimensional positioning and prediction of hidden ore bodies based on artificial intelligence according to claim 1, characterized in that: The steps of using the deep learning model to predict hidden ore bodies include: Model selection and design: Based on the needs and characteristics of concealed ore body prediction, select a suitable deep learning model and design the model structure, including determining the number of network layers, number of neurons, and activation function parameters; Data augmentation and expansion: Perform data augmentation on preprocessed data to increase the generalization ability and robustness of the model; Model training and optimization: Use preprocessed data to train the designed deep learning model, using gradient descent and adaptive learning rate optimization algorithms to adjust model parameters until the model converges. During the training process, cross-validation and early stopping strategies are used to prevent model overfitting. Feature importance analysis: Through feature importance analysis technology, the most important features for concealed ore body prediction are identified, providing a basis for subsequent model interpretation and optimization; Model integration and fusion: Using model integration technology to fuse the prediction results of multiple deep learning models to improve the accuracy and stability of predictions; Real-time prediction and feedback: Deploy the trained deep learning model into the actual application environment to achieve real-time hidden ore body prediction; at the same time, continuously optimize and update the model based on actual prediction results and feedback information.

6. The method for three-dimensional positioning and prediction of hidden ore bodies based on artificial intelligence according to claim 1, characterized in that: The result verification and evaluation steps include: Visualization of prediction results: The location, shape, scale and grade of the concealed ore body predicted by the deep learning model are displayed in a three-dimensional visualization format for intuitive understanding and analysis; Actual drilling verification: Design a drilling plan, conduct actual drilling on the predicted concealed ore body, collect drilling data, including core, lithology, and grade, compare and analyze with the predicted results, and calculate the prediction accuracy; Uncertainty quantification: Monte Carlo simulation and probability distribution analysis methods are used to quantify the uncertainty of the prediction results and evaluate the confidence and reliability of the prediction results; Error analysis: Detailed analysis of the error between the predicted results and the actual drilling data, identifying the source of the error and providing guidance for subsequent model optimization; Comprehensive evaluation indicators: Build a comprehensive evaluation indicator system, including prediction accuracy, recall rate, and F1 score, to comprehensively evaluate the performance and effectiveness of the prediction method; Continuous improvement mechanism: Based on the feedback information from result verification and evaluation, a continuous improvement mechanism is established to continuously optimize and update data preprocessing, model training, and prediction processes.

7. The method for three-dimensional positioning and prediction of hidden ore bodies based on artificial intelligence according to claim 1, characterized in that: The method further comprises: Multi-source data fusion module: This module can receive and fuse data from different sources in real time, including geological, geophysical, and geochemical data; Adaptive learning rate adjustment mechanism: During deep learning model training, the learning rate is adaptively adjusted based on model performance and training progress; Model interpretability enhancement technology: Use interpretability enhancement technology to improve the interpretability of model prediction results, making them easier for geological experts to understand and verify; Real-time data update and model iteration: Establish a real-time data update mechanism to integrate newly acquired data into model training in real time, achieve continuous iteration and update of the model, and maintain the timeliness of the prediction method; Cloud computing and parallel processing architecture: Utilizing cloud computing platforms and parallel processing architectures, we improve the efficiency of data processing and model training, enabling large-scale, high-precision three-dimensional positioning prediction of concealed ore bodies. User interface: Design a user-friendly interface that allows users to enter custom parameters, view prediction results, and adjust model settings.

8. The method for three-dimensional positioning and prediction of concealed ore bodies based on artificial intelligence according to claim 1, characterized in that: The method further comprises: Multi-dimensional uncertainty analysis module: This module can perform multi-dimensional uncertainty analysis on prediction results, including spatial uncertainty, attribute uncertainty, and model uncertainty, and provide a more comprehensive basis for decision-making by quantifying uncertainty indicators; Intelligent Drilling Advisory System: Based on prediction results and uncertainty analysis, the system can intelligently recommend drilling locations and depths, optimize drilling plans, and reduce exploration costs and risks. Environmental and economic and social impact assessment tools: While predicting hidden ore bodies, they consider environmental impacts, social acceptance and economic feasibility to provide decision support for sustainable mining development; Knowledge graph integration: Integrating geological knowledge, historical exploration data, and expert experience into the knowledge graph provides rich prior knowledge for deep learning models, enhancing the accuracy and reliability of predictions. Interdisciplinary collaborative work platform: Establish a collaborative work platform for multidisciplinary experts in geology, geophysics, and data science to achieve knowledge sharing, data interoperability, and collaborative innovation; Adaptive model update strategy: Adaptively adjust the model structure and parameters based on new data, new knowledge and user feedback to achieve continuous learning and self-optimization of the model.

9. The method for three-dimensional positioning and prediction of hidden ore bodies based on artificial intelligence according to claim 1, characterized in that: The method further comprises: Multi-scenario application adaptation module: This module can adaptively adjust prediction strategies and model parameters according to different geological conditions, ore body types and exploration requirements to adapt to various application scenarios; Real-time data monitoring and early warning system: This system monitors the quality of input data and the stability of the prediction process in real time. Once abnormal data or prediction deviation exceeds the preset threshold is detected, an early warning is immediately issued to indicate the possible cause of the error. Interactive 3D visualization platform: provides an interactive 3D visualization interface that allows users to freely rotate, scale, and cut the 3D model of the concealed ore body, while supporting the overlay display and dynamic analysis of multi-attribute data; Intelligent report generation tool: Automatically summarizes forecast results, uncertainty analysis, drilling recommendations, and environmental, economic, and social impact assessment information to generate structured, customizable intelligent reports for users to quickly understand and make decisions; Cross-platform compatibility and cloud service interface: This method supports multiple operating systems and hardware platforms, and provides a cloud service interface, allowing users to upload data, train models, and conduct predictive analysis through the cloud, thus achieving resource sharing and efficient utilization. User rights management and data security mechanism: Establish a comprehensive user rights management mechanism to ensure that different users can only access data and functions within the scope of their rights; at the same time, adopt encryption technology and data backup strategies.

10. The method for three-dimensional positioning and prediction of concealed ore bodies based on artificial intelligence according to claim 1, characterized in that: The method further comprises: Mobile application support: Develop mobile applications that allow users to collect data, perform model predictions, and view results in the field using smartphones or tablets; Multi-language support and internationalization: Provide multi-language user interface and documentation support to meet the needs of users in different countries and regions; Community collaboration and knowledge sharing platform: Establish an online community collaboration platform to allow users to share data, models and experiences.