Geological exploration risk assessment method and system based on artificial intelligence

Abstract

The invention discloses a geological exploration risk assessment method and system based on artificial intelligence, and the method and system achieve the extraction and selection of geological features through collecting the multi-source data of a geological exploration region, employing the deep learning, machine learning and other algorithms, and constructing a risk assessment model. The model can accurately reflect a mapping relation between geological features and risk levels, and automatic and intelligent assessment of geological exploration risks is realized; the system comprises a data acquisition module, a preprocessing module and the like, and supports a user to customize risk assessment indexes and adjust model parameters; the method and the system have the characteristics of high efficiency, accuracy, optimizability and visualization, and can be widely applied to the fields of mineral resource exploration, geological disaster early warning, urban planning and the like; through practical application, the method and system can accurately evaluate the risk condition in the geological exploration process, provide scientific decision support for exploration activities, effectively reduce the risk and cost, and provide powerful support for innovation and transformation in the geological exploration field.

Description

Technical Field

[0001] The present invention relates to the field of geological exploration technology, and in particular to a method for geological exploration risk assessment using artificial intelligence technology and an implementation system thereof, aiming to improve the accuracy and efficiency of geological exploration and reduce exploration risks. Background Art

[0002] 1. Existing Technology

[0003] Data processing and analysis technology

[0004] In geological exploration, data processing and analysis are key links. Traditional data processing methods often rely on manual operations and expert experience, which are inefficient and easily affected by subjective factors. With the development of information technology, existing data processing and analysis technologies have made significant progress, such as database management systems and data mining technologies. These technologies can efficiently process and analyze the large amount of data generated in geological exploration, providing strong support for risk assessment.

[0005] Geological Models and Prediction Techniques

[0006] Geological models are important tools for geological exploration risk assessment. Existing geological models and prediction technologies are relatively mature and can build three-dimensional models reflecting geological characteristics based on geological data, and then predict geological phenomena. These technologies provide an important scientific basis for geological exploration risk assessment.

[0007] Artificial Intelligence Technology

[0008] The rapid development of artificial intelligence technology has brought new opportunities for geological exploration risk assessment. Artificial intelligence algorithms, such as machine learning and deep learning, can automatically extract features from data and make predictions, and have powerful data processing and analysis capabilities. In the field of geological exploration, artificial intelligence technology has been widely used in mineral identification, geological structure analysis, geological disaster risk assessment, etc.

[0009] 2. Disadvantages of Existing Technologies

[0010] Data processing efficiency and accuracy issues

[0011] Although the existing data processing and analysis technologies have made significant progress, there are still problems with efficiency and accuracy when processing the large amount of data generated in geological exploration. In particular, when processing complex geological data, traditional data processing methods are often unable to cope with it, and existing advanced data processing technologies may face challenges such as high consumption of computing resources and long processing time.

[0012] Uncertainty in geological model construction and prediction

[0013] Geological model construction and prediction are the core links of geological exploration risk assessment. However, due to the complexity and uncertainty of geological phenomena, existing geological models are often difficult to fully and accurately reflect geological characteristics, resulting in a certain degree of uncertainty in the prediction results. This uncertainty may bring potential risks to geological exploration risk assessment.

[0014] Limitations of AI Technology

[0015] Although artificial intelligence technology has powerful data processing and analysis capabilities, it still has some limitations in geological exploration risk assessment. For example, artificial intelligence algorithms are highly dependent on data. If the data quality is not high or there are missing data, it may affect the performance and accuracy of the algorithm. In addition, the interpretability of artificial intelligence algorithms is relatively weak, and it is difficult to directly give a detailed explanation and basis for risk assessment.

[0016] Lack of interdisciplinary knowledge and talent

[0017] Geological exploration risk assessment involves knowledge from multiple disciplines such as geology, statistics, and computer science. However, the current lack of interdisciplinary knowledge and talent has become an important factor restricting the development of geological exploration risk assessment technology. Many geological survey personnel lack knowledge in computer science and statistics, while computer scientists and statisticians often lack professional knowledge in the field of geology, which makes it difficult to give full play to their respective advantages in the risk assessment process.

[0018] In summary, although the existing geological exploration risk assessment methods and systems have made certain progress, they still have shortcomings such as data processing efficiency and accuracy, uncertainty in geological model construction and prediction, limitations of artificial intelligence technology, and lack of interdisciplinary knowledge and talents. Therefore, the present invention proposes a geological exploration risk assessment method and system based on artificial intelligence, aiming to overcome the shortcomings of the existing technology and improve the accuracy and efficiency of geological exploration risk assessment. Summary of the invention

[0019] The present invention aims to provide a geological exploration risk assessment method and system based on artificial intelligence, which can efficiently integrate and analyze data in geological exploration areas to achieve accurate risk assessment.

[0020] To achieve the above objectives, the present invention is implemented through the following technical solutions.

[0021] A geological exploration risk assessment method based on artificial intelligence comprises the following steps:

[0022] S1: Data collection: Collect historical geological data, remote sensing image data, geophysical and geochemical exploration data in the geological survey area;

[0023] S2: Data preprocessing: Clean and standardize the collected data to ensure data quality;

[0024] S3: Feature extraction: Use deep learning models to extract features from remote sensing images, identify key information on geological structure and lithology distribution, and combine geostatistical methods to extract spatial distribution characteristics of geophysical and geochemical data;

[0025] S4: Risk assessment model construction: Build a risk assessment model based on machine learning algorithm, train the model through training set data, and learn the mapping relationship between geological characteristics and risk levels;

[0026] S5: Risk prediction: Use the trained risk assessment model to predict the risk of the target area and generate a risk level distribution map;

[0027] S6: Result visualization and output: Integrate the geographic information system platform to achieve intuitive display of risk prediction results.

[0028] Furthermore, the deep learning model is a convolutional neural network, which is used to extract geological features from remote sensing images.

[0029] Furthermore, the machine learning algorithm is at least one of a random forest, a support vector machine (SVM) or a deep learning network.

[0030] Furthermore, it also includes feedback and optimization steps, that is, feedback adjustment of the risk assessment model is carried out according to the actual exploration results to improve the prediction accuracy of the model.

[0031] A geological exploration risk assessment system based on artificial intelligence, comprising:

[0032] a) Data acquisition and processing module, used to collect and pre-process the data required for geological exploration;

[0033] b) Feature extraction module, which uses deep learning models to extract geological features from data;

[0034] c) Risk assessment model building module, which builds a risk assessment model based on machine learning algorithms;

[0035] d) Risk prediction module, which uses risk assessment model to predict the risk of target area;

[0036] e) Result visualization module, integrated with Geographic Information System (GIS) platform, used to display risk prediction results;

[0037] f) Feedback and optimization module, which is used to provide feedback and adjust the risk assessment model according to the actual exploration results.

[0038] Furthermore, it also includes a user interaction interface that allows experts to fine-tune the prediction results based on experience and achieve human-computer collaborative evaluation.

[0039] Furthermore, the system further includes a data storage module for storing processed data, model parameters and prediction results.

[0040] Furthermore, the system can exchange and integrate data with other geological exploration software or systems to achieve information sharing and collaborative work.

[0041] Beneficial effects of the present invention:

[0042] 1. Significantly improve the accuracy and refinement of assessments

[0043] Intelligent algorithm optimization: Through advanced artificial intelligence algorithms such as deep learning and neural networks, this invention can deeply mine the implicit information and features in geological survey data to achieve a more accurate assessment of geological disaster risks. Compared with traditional methods, this method based on big data and machine learning can more accurately identify risk areas and potential threats.

[0044] Multi-source data fusion: The system can integrate data from multiple sources such as satellite remote sensing, geological surveys, geophysical exploration, etc., and improve the comprehensiveness and accuracy of risk assessment through data fusion technology. This cross-domain data integration helps to discover risk points that may be missed by a single data source.

[0045] Dynamic update and real-time monitoring: Utilizing real-time data input and the dynamic update mechanism of the algorithm, the system can track changes in geological conditions and adjust risk assessment results in a timely manner to ensure the timeliness and accuracy of the assessment.

[0046] 2. Significantly improve evaluation efficiency and automation level

[0047] Automated data processing: Through automated processes, the system can quickly process and analyze large amounts of geological survey data, significantly reducing manual operations and improving assessment efficiency.

[0048] Intelligent early warning system: The system's built-in intelligent early warning mechanism can immediately issue an alarm when potential risks are discovered, providing valuable time for emergency response and risk management.

[0049] Visual report generation: The assessment results are presented in an intuitive visual form, such as 3D geological models, risk distribution maps, etc., to facilitate decision makers to quickly understand and make decisions.

[0050] 3. Effectively reduce costs and resource consumption

[0051] Reduce labor costs: The automated and intelligent evaluation process reduces dependence on professionals and reduces labor costs.

[0052] Optimize resource allocation: Optimize the allocation of exploration equipment and personnel through intelligent algorithms to avoid unnecessary waste of resources and improve exploration efficiency.

[0053] Long-term economic benefits: Accurate risk assessment helps avoid future economic losses caused by geological disasters and improves the overall economic benefits of the project.

[0054] IV. Enhance the scientific nature and timeliness of decision-making

[0055] Scientific basis: The assessment method based on big data and artificial intelligence provides decision makers with scientific and objective risk assessment results, enhancing the scientific nature of decision-making.

[0056] Rapid response: Real-time updated risk assessment results and intelligent early warning systems enable decision makers to respond quickly to potential risks and take effective measures to prevent or reduce disaster losses.

[0057] V. Promote technological innovation and development in the field of geological exploration

[0058] Technological innovation: The implementation of this invention has promoted technological innovation in the field of geological exploration and promoted the in-depth application of artificial intelligence technology in geological risk assessment.

[0059] Interdisciplinary cooperation: The implementation of the project has strengthened cooperation and communication among multiple disciplines such as geology, computer science, and statistics, and promoted the development of related disciplines.

[0060] Standardization and normalization: With the widespread application of this method, it is expected to promote the standardization and normalization of geological exploration risk assessment and improve the professional level of the entire industry.

[0061] To sum up, the artificial intelligence-based geological exploration risk assessment method and system of the present invention not only performs well in improving assessment accuracy, efficiency and reducing costs, but also enhances the scientificity and timeliness of decision-making, and at the same time promotes technological innovation and development in the field of geological exploration. These beneficial effects are of great significance to improving the level of geological disaster risk management and protecting people’s lives and property. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 This is a system structure diagram of a geological exploration risk assessment method and system based on artificial intelligence of the present invention;

[0063] Figure 2 The present invention is a method flow chart of a geological exploration risk assessment method and system based on artificial intelligence. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0065] In the description of the embodiments of the present invention, it should be noted that if the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. appear, the orientation or position relationship indicated is based on the orientation or position relationship shown in the drawings, or the orientation or position relationship in which the invented product is usually placed when used. It is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0066] In addition, the terms "horizontal", "vertical", "overhanging" and the like do not mean that the components must be absolutely horizontal or overhanging, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0067] In the description of the embodiments of the present invention, "plurality" means at least 2.

[0068] In the description of the embodiments of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal connection of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0069] Example:

[0070] 1. Overview

[0071] The artificial intelligence-based geological exploration risk assessment method and system provided by the present invention aims to utilize artificial intelligence technology to collect multi-source data in the geological exploration area, combined with deep learning, machine learning and other algorithms, to conduct a comprehensive and accurate assessment of geological exploration risks. The system can automatically generate a risk assessment report to provide scientific decision-making support for geological exploration activities.

[0072] 2. Specific implementation steps

[0073] 1. Data collection and preprocessing

[0074] Data collection: Use IoT sensor networks, remote sensing technology, geological surveys and other means to collect multi-source data such as geological structure, climate and meteorology, hydrogeology, etc. in the geological survey area, including historical geological data, remote sensing image data, geophysical and geochemical exploration data;

[0075] Among them, among historical geological data, remote sensing image data, geophysical and geochemical exploration data, each category of data has different characteristics and uses:

[0076] Historical geological data:

[0077] Including but not limited to data on crustal structure, regional geological characteristics, genesis of mineralization, etc.

[0078] Crustal structure data include: geological structure map, crust thickness data, plate boundary data, crust movement speed data;

[0079] Regional geological characteristic data include: stratigraphic data, rock type data, geological structure type data, and geophysical data;

[0080] The mineralization body genesis data include: mineralization type data, mineralization layer data, mineralization condition data, and mineralization body morphology data;

[0081] These data provide geological background information and help understand the geological history and evolution of the target area;

[0082] Remote sensing image data:

[0083] It is mainly obtained through remote sensing platforms such as satellites or aircraft;

[0084] Contains information data on surface morphology, vegetation coverage, and water system distribution.

[0085] Surface morphology data include: topographic maps, altitude data, slope data, and aspect data;

[0086] Vegetation coverage data include: vegetation type data, vegetation coverage rate data, vegetation growth status data, and vegetation remote sensing monitoring data;

[0087] Water system distribution data include: water system map, water flow direction data, water system density data, and water quality monitoring data;

[0088] For geological exploration, remote sensing image data helps to identify potential geological structures and mineralized zones;

[0089] Geophysical and geochemical exploration data:

[0090] Geophysical data include: seismic wave data, gravity field data, magnetic field data, electrical property data, heat flow data, which reflect the physical properties of underground materials;

[0091] Geochemical data include: element content data, isotope data, geochemical anomaly data, gas geochemical data, which provide information on the chemical composition of underground materials;

[0092] These data are important for inferring the type, size and distribution of underground mineral deposits.

[0093] Data preprocessing: Clean, denoise, standardize and other preprocessing operations on the collected data to ensure the quality and consistency of the data. At the same time, complete the missing data to improve the accuracy of subsequent analysis;

[0094] Among them, the criteria for judging missing data can usually be considered from the following aspects:

[0095] (I) Data integrity check

[0096] Missing data entry: directly check whether there are null or missing values ​​in the data set. For example, in a geological survey data table, the measurement value of a geological feature is missing, that is, the data entry is empty;

[0097] Data range check: For some geological features, their measured values ​​are usually within a reasonable range. If the measured value of a data point exceeds this range and cannot be reasonably explained (such as an extreme value caused by an abnormal geological phenomenon), it may be considered as missing data;

[0098] (II) Data consistency check

[0099] Data logic verification: Check the correlation and consistency between data based on common sense and logic of geological exploration. For example, if the measured value of a geological feature is obviously inconsistent with the measured values ​​of other related features, it may indicate that there is a problem with the data point and it needs to be considered as missing data;

[0100] Data distribution check: Analyze the distribution of data. If the measured value of a data point is significantly different from the measured values ​​of other data points in distribution (such as isolated points or outliers), and its source cannot be reasonably explained, it may be regarded as missing data.

[0101] 3. Data quality assessment

[0102] Data accuracy assessment: Evaluate the accuracy of the data by comparing or verifying with other reliable data sources. If the measurement value of a data point is significantly different from the measurement value of other reliable data sources and the reason for the difference cannot be reasonably explained, it may indicate that there is a problem with the data point and it needs to be considered as missing data;

[0103] Data reliability assessment: Consider the reliability of the data source and the data collection process. If the data source is unreliable or there are obvious problems in the data collection process (such as equipment failure, operational errors, etc.), it may lead to low data quality or missing data;

[0104] 2. Feature extraction and selection

[0105] Feature extraction: Use deep learning models (such as convolutional neural networks) to extract features from preprocessed data to identify key factors that affect geological exploration risks. These features may include geological structure, stratum lithology, rainfall, groundwater level, etc.

[0106] Feature selection: Through feature selection technology, key features that have a significant impact on risk assessment are screened out, which helps to reduce the complexity of the model and improve the generalization ability of the model;

[0107] 3. Risk assessment model construction

[0108] Model selection: According to the characteristics of geological exploration risks, select appropriate machine learning algorithms (such as random forest, support vector machine, neural network, etc.) to build risk assessment models;

[0109] Model training: Use the training set data to train the model so that it can accurately reflect the mapping relationship between geological characteristics and risk levels. During the training process, methods such as cross-validation can be used to evaluate the performance of the model and perform parameter tuning;

[0110] Among them, the risk assessment model based on machine learning algorithm is mainly constructed in the following aspects:

[0111] (I) Model structure

[0112] Through machine learning algorithms such as decision trees, support vector machines, and neural networks, a complex model capable of processing geological exploration data was constructed. This model contains multiple layers of nodes and connections. Each node represents a specific calculation or decision-making process, and the whole constitutes a complex nonlinear mapping relationship.

[0113] 2. Model construction method

[0114] Choose appropriate machine learning algorithms, such as decision trees, support vector machines, neural networks, etc.;

[0115] Collect and organize training set data, including known geological features and corresponding risk levels;

[0116] Preprocess the training set data, such as data cleaning, feature selection, etc.

[0117] Input the preprocessed training set data into the machine learning algorithm for model training;

[0118] The final model is:

[0119] After training, a risk assessment model is obtained that can identify the mapping relationship between geological features and risk levels;

[0120] The model can analyze new geological data and predict the corresponding risk level;

[0121] (III) Mapping relationship

[0122] The core result of model construction is to learn the mapping relationship between geological features and risk levels. This mapping relationship is obtained by training a large number of geological features and corresponding risk levels in the training set data. During the training process, the model continuously adjusts its internal parameters to minimize the prediction error, thereby achieving accurate prediction of new data;

[0123] 4. Predictive Ability

[0124] The constructed risk assessment model has strong predictive capabilities. It can quickly process and analyze new geological survey data and automatically predict the corresponding risk level based on geological characteristics. This predictive ability is achieved based on the mapping relationship between geological characteristics and risk levels learned by the model during training;

[0125] (V) Output format

[0126] The results of model construction also include the definition of the output format. In geological exploration risk assessment, the output format is usually a risk level or risk probability value. This value can be obtained through the model's processing and analysis of input data, and is used to guide subsequent geological exploration and disaster prevention work;

[0127] (VI) Model performance

[0128] Finally, the results of model building also include an evaluation of model performance. This is usually done by testing the model using validation set data. During the test, the model's accuracy, recall, F1 score and other indicators can be calculated to evaluate the model's performance in geological exploration risk assessment. These indicators can help us understand the strengths and weaknesses of the model and further optimize and improve it;

[0129] In summary, the risk assessment model based on machine learning algorithm is constructed in S4 with a complex structure, strong prediction ability, clear output format and good performance. This model can learn the mapping relationship between geological characteristics and risk levels, and accurately predict new geological exploration data, providing strong support for geological exploration and disaster prevention work;

[0130] 4. Risk prediction and visualization

[0131] Risk prediction: Input the data of the area to be assessed into the trained risk assessment model to make risk predictions. The model will output a risk level distribution map to indicate the risk levels of different areas.

[0132] The process of using the trained risk assessment model to predict the risk of the target area is as follows:

[0133] (1) Data preparation:

[0134] Collect geological data of the target area, including historical geological data, remote sensing image data, geophysical and geochemical exploration data, etc.;

[0135] Preprocess these data to make them meet the input requirements of the risk assessment model;

[0136] (2) Model application:

[0137] Input the preprocessed geological data of the target area into the trained risk assessment model;

[0138] The model will automatically calculate and output the corresponding risk level based on the input geological data;

[0139] (3) Generate risk level distribution map:

[0140] Based on the risk level output by the model, a risk level distribution map is generated using technologies such as Geographic Information System (GIS);

[0141] This map can intuitively display the risk levels of different locations in the target area, providing an important reference for geological exploration and disaster prevention;

[0142] Visualization: Using platforms such as Geographic Information System (GIS) to visualize risk prediction results, through an intuitive graphical interface, users can clearly see the distribution of risk levels in different areas, facilitating decision-making analysis;

[0143] 5. System architecture and function implementation

[0144] System architecture: The system usually includes data acquisition module, data preprocessing module, feature extraction module, risk assessment module, risk prediction and visualization module, etc. The modules transmit and interact with each other through communication protocols to realize the automation and intelligence of risk assessment.

[0145] Functional realization: The system can realize the whole process of data collection, preprocessing, feature extraction, risk assessment, risk prediction and visualization. At the same time, the system also supports functions such as user-defined risk assessment indicators and adjustment of model parameters to meet the needs of different users;

[0146] 3. Technical features and advantages

[0147] Efficiency: Ability to efficiently process and analyze large amounts of geological data, improving the efficiency and accuracy of risk assessment.

[0148] Accuracy: Through algorithms such as deep learning, complex patterns and correlations in geological data can be accurately identified, providing a scientific basis for risk assessment.

[0149] Optimizability: Ability to adaptively learn and optimize based on new data and feedback, continuously improving the accuracy and generalization ability of the risk assessment model.

[0150] Visualization: Risk assessment results are visualized through platforms such as geographic information systems, making it easier for users to understand and make decisions.

[0151] IV. Application scenarios and effects

[0152] This method and system are widely used in the fields of mineral resource exploration, geological disaster early warning, urban planning, etc. Through practical application, this method and system can accurately assess the risk situation in the geological exploration process, provide scientific and reasonable decision-making suggestions for exploration activities, and effectively reduce the risks and costs of exploration activities; at the same time, this method and system can also provide strong support for geological disaster early warning, and improve the accuracy and timeliness of early warning.

[0153] In summary, the geological exploration risk assessment method and system based on artificial intelligence has broad application prospects and development space. With the continuous advancement and improvement of technology, this method and system will bring more innovations and changes to the field of geological exploration.

[0154] The present invention and its embodiments are described above, and such description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if ordinary technicians in the field are inspired by it, without departing from the purpose of the invention, they can design a structure and embodiment similar to the technical solution without creativity, which should belong to the protection scope of the present invention.

Claims

1. A geological exploration risk assessment method based on artificial intelligence, characterized in that: The following steps are involved: S1: Data collection: Collect historical geological data, remote sensing image data, geophysical and geochemical exploration data in the geological survey area; S2: Data preprocessing: Clean and standardize the collected data to ensure data quality; S3: Feature extraction: Use deep learning models to extract features from remote sensing images, identify key information on geological structure and lithology distribution, and combine geostatistical methods to extract spatial distribution characteristics of geophysical and geochemical data; S4: Risk assessment model construction: Build a risk assessment model based on machine learning algorithm, train the model through training set data, and learn the mapping relationship between geological characteristics and risk levels; S5: Risk prediction: Use the trained risk assessment model to predict the risk of the target area and generate a risk level distribution map; S6: Result visualization and output: Integrate the geographic information system platform to achieve intuitive display of risk prediction results.

2. The method for risk assessment of geological exploration based on artificial intelligence according to claim 1, characterized in that: The deep learning model is a convolutional neural network, which is used to extract geological features from remote sensing images.

3. The method for risk assessment of geological exploration based on artificial intelligence according to claim 1, characterized in that: The machine learning algorithm is at least one of a random forest, a support vector machine or a deep learning network.

4. The method for risk assessment of geological exploration based on artificial intelligence according to claim 1, characterized in that: It also includes feedback and optimization steps, that is, feedback adjustment of the risk assessment model based on the actual exploration results to improve the prediction accuracy of the model.

5. A geological exploration risk assessment system based on artificial intelligence, characterized in that: include: a) Data acquisition and processing module, used to collect and pre-process the data required for geological exploration; b) Feature extraction module, which uses deep learning models to extract geological features from data; c) Risk assessment model building module, which builds a risk assessment model based on machine learning algorithms; d) Risk prediction module, which uses risk assessment model to predict the risk of target area; e) Result visualization module, integrated with geographic information system platform, used to display risk prediction results; f) Feedback and optimization module, which is used to provide feedback and adjust the risk assessment model according to the actual exploration results.

6. The artificial intelligence-based geological exploration risk assessment system according to claim 5, characterized in that: The system also includes a user interaction interface, which allows experts to fine-tune the prediction results based on experience and realize human-computer collaborative evaluation.

7. A geological exploration risk assessment system based on artificial intelligence according to any one of claims 5 or 6, characterized in that: The system further includes a data storage module for storing processed data, model parameters and prediction results.

8. The artificial intelligence-based geological exploration risk assessment system according to claim 7, characterized in that: The system can exchange and integrate data with other geological exploration software or systems to achieve information sharing and collaborative work.

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