Underwater geological exploration and mineral resource acquisition system for promoting ecological protection of water body

Abstract

The invention discloses an underwater geological detection and mineral resource acquisition system for promoting water body ecological protection, and relates to the technical field of underwater detection, and the system comprises an underwater detection module which is used for collecting water body and underwater geological data through an underwater detection technology; the underwater data processing module is used for generating an underwater geological environment model, evaluating water quality, obtaining mineral resource distribution information and establishing a mineral resource quantity prediction model based on the mineral resource distribution information; the mineral resource positioning module is used for positioning mineral resource positions based on the underwater geological environment model and mineral resource distribution information; and the mineral resource acquisition module is used for setting a mineral resource quantity error range, comparing the acquired mineral resource quantity with the predicted mineral resource quantity, continuing acquisition if the acquired mineral resource quantity exceeds the error range, and stopping acquisition if the acquired mineral resource quantity is within the error range. According to the invention, by combining a multi-modal detection technology, advanced data processing and a machine learning algorithm, collaborative optimization of water body ecological protection and mineral resource collection is effectively realized, and the precision of mineral resource collection is improved.

Description

Technical Field

[0001] The present invention relates to the field of underwater detection technology, and in particular to an underwater geological detection and mineral collection system for promoting water ecological protection. Background Art

[0002] With the increasing demand for water ecological protection and resource utilization, underwater geological exploration and mineral collection have become an important technical field. The development of underwater detection technology, especially the application of multimodal detection methods such as underwater sonar, laser scanning, seismic wave detection and magnetic detection, has significantly improved the survey capability of underwater environment. In this context, combined with modern data processing technology, based on in-depth analysis of underwater geological data, it can effectively provide accurate information for the positioning and collection of mineral deposits.

[0003] At present, most underwater exploration and mineral collection technologies rely on a single detection method or predetermined collection strategy, which makes it difficult to cope with the complex and changeable underwater environment. The mutual cooperation between underwater geological exploration systems and mineral collection systems can achieve more efficient mineral collection and further promote the protection of the water ecological environment. Summary of the Invention

[0004] In order to solve the above technical problems, an underwater geological exploration and mineral collection system is provided to promote the protection of water ecology. This technical solution solves the above problems.

[0005] In order to achieve the above objects, the technical solution adopted by the present invention is:

[0006] Underwater geological exploration and mineral collection system to promote water ecological protection,

[0007] Including: underwater detection module, underwater data processing module, mineral location module, mineral collection module;

[0008] The underwater detection module is electrically connected to the underwater data processing module, the underwater data processing module is electrically connected to the mineral deposit positioning module, and the mineral deposit positioning module is electrically connected to the mineral deposit collection module;

[0009] The underwater detection module is used to collect water body and underwater geological data through underwater detection technology;

[0010] The underwater data processing module is used to analyze and process the collected underwater geological data, generate an underwater geological environment model, evaluate water quality, obtain mineral distribution information, establish a mineral quantity prediction model based on the mineral distribution information, and predict the mineral quantity;

[0011] The mineral deposit positioning module is used to locate the mineral deposit location based on the underwater geological environment model and mineral deposit distribution information;

[0012] The mineral collection module is used to collect minerals in the located mineral area through collection equipment, set the error range of the mineral quantity, and compare the collected mineral quantity with the predicted mineral quantity. If it exceeds the error range, collection continues; if it is within the error range, collection stops.

[0013] Preferably, the underwater detection module specifically includes:

[0014] A multimodal detection unit is used to obtain geological information on water depth, bottom type, and potential mineral distribution using underwater detection methods such as sonar detection, laser scanning, seismic wave detection, and magnetic detection;

[0015] The data synchronization unit is used to perform time synchronization processing on the underwater data acquired by the multi-modal detection unit and transmit it to the underwater data processing module in a unified manner.

[0016] Preferably, the underwater data processing module includes:

[0017] Data pre-processing unit, used to perform denoising and normalization processing on the collected underwater geological data to eliminate environmental interference in the data;

[0018] Water quality assessment unit, used to conduct real-time assessment of water quality based on data collected by underwater detection, including temperature, pH value, dissolved oxygen and turbidity water quality parameters;

[0019] The environmental modeling unit generates an underwater geological environment model based on the processed data, which includes information on water depth, bottom type, and mineral distribution;

[0020] The mineral deposit prediction unit, based on the underwater geological environment model, combines historical mineral deposit data with machine learning algorithms to establish a mineral deposit quantity prediction model, predict the mineral deposit quantity in the potential mineral deposit area, and output the prediction results.

[0021] Preferably, the underwater geological environment model generated based on the processed data, which includes water depth, bottom type and mineral distribution information, specifically includes:

[0022] The geological environment model is established by collecting water depth data, and the Kriging interpolation method is used to estimate the water depth and bottom type at any location. The geological environment model expression is:

[0023]

[0024] Where Z(x,y) is the water depth at position (x,y), z i is the water depth at the known position i, z i is the interpolation weight of position i, C(x,y) is the geological type at position (x,y), C k is the kth type of substrate, P(Ck |D) is the predicted bottom type C based on the collected data D k The conditional probability of , M(x,y) is the mineral distribution at the location (x,y), H(x,y) is the historical mineral data, and f is the neural network model.

[0025] Preferably, the method of establishing a mineral quantity prediction model based on the underwater geological environment model, combining historical mineral data with a machine learning algorithm, predicting the mineral quantity in the potential mineral deposit area, and outputting the prediction results specifically includes:

[0026] Based on the output of the geological environment model, water depth, geological type and mineral distribution data are obtained. Water depth, geological type and mineral distribution data are used as characteristic variables of the mineral quantity prediction model. A mineral quantity prediction model is established based on the linear regression equation to predict the mineral quantity in the potential mineral deposit area. The mineral quantity prediction model formula is as follows:

[0027]

[0028] Where N p is the predicted number of mineral deposits, α i is the variable X related to the i-th underwater geological characteristic i The relevant regression coefficient, X i is the i-th underwater geological characteristic variable, β is the intercept term of the regression model, and m is the number of underwater geological characteristic variables used.

[0029] Preferably, the mineral deposit positioning module includes:

[0030] Data input and pre-processing unit: receives the underwater geological environment model and mineral distribution information from the underwater data processing module, and performs normalization processing on the underwater geological environment model;

[0031] Mineral deposit location calculation unit: identifies possible mineral deposit clusters based on water, bottom sediment type, and mineral deposit distribution information in the underwater geological model;

[0032] The location and density of mineral deposits are estimated based on the Kriging interpolation method, and the location coordinates of mineral deposits are generated based on the mineral distribution prediction.

[0033] Preferably, estimating the location and density of the mineral deposits based on the Kriging interpolation method and generating the location coordinates of the mineral deposits based on the mineral deposit distribution prediction specifically include:

[0034] The formula for estimating the location and density of mineral deposits based on the Kriging interpolation method is:

[0035] P(M|x,y)=f(Z(x,y),C(x,y),Q(x,y))

[0036] Where f is the estimation function of mineral distribution, Z(x,y) is the water depth data, C(x,y) is the bottom type, and Q(x,y) is the water quality data;

[0037] Generates the coordinates for the locations of mineral deposits based on the output of Kriging interpolation formulas for estimating the location and density of mineral deposits.

[0038] Preferably, the mineral collection module includes:

[0039] Intelligent collection unit, which collects mineral deposits through collection equipment, optimizes the collection path based on genetic algorithms, and dynamically adjusts the collection strategy according to changes in mineral distribution;

[0040] The mineral quantity comparison unit compares the collected mineral quantity with the predicted mineral quantity. If the difference between the collected mineral quantity and the predicted mineral quantity exceeds the set error range, the collection will continue; if it is within the error range, the collection will stop.

[0041] Automatic feedback control unit, which automatically adjusts the acquisition depth and speed during the acquisition process based on the feedback information from the acquisition error monitoring unit;

[0042] The acquisition data storage unit stores the mineral data in the acquisition process into the database.

[0043] Preferably, the collection path optimization of the intelligent collection unit is performed using a genetic algorithm, and the specific optimization formula is:

[0044]

[0045] In the formula, P represents the path, γ is the penalty coefficient, and P e (P) is the penalty term for paths that do not meet the target.

[0046] Preferably, the automatic adjustment of the acquisition depth and speed during the acquisition process based on the feedback information from the acquisition error monitoring unit specifically includes:

[0047] Monitor the errors during the collection process, obtain the difference between the amount of mineral deposits collected and the predicted amount of mineral deposits, and use PID control algorithms to adjust the depth and speed of collection;

[0048] Among them, the PID control algorithm formula is:

[0049]

[0050] Where ΔD is the adjustment of acquisition depth, ΔS is the adjustment of acquisition speed, and E is the error at the current moment. is the integral term of the error, is the differential term of the error, K p , K i , Kd are the proportional, integral and differential coefficients of the PID controller respectively.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] The underwater geological exploration and mineral collection system of the present invention effectively achieves the coordinated optimization of water ecological protection and mineral collection by combining multimodal detection technology, advanced data processing and machine learning algorithms. The system uses sonar, laser scanning, seismic waves and magnetic detection to efficiently obtain underwater data, evaluate water quality in real time and generate accurate geological environment models. It uses Kriging interpolation method and linear regression model to accurately predict and locate mineral deposits to ensure collection accuracy and efficiency. The genetic algorithm optimization of the mineral collection path and the PID control automatic adjustment mechanism effectively reduce errors and improve the intelligence and controllability of the collection process. This system not only improves the accuracy of mineral collection, but also has important significance in protecting the water ecological environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 A framework diagram of the underwater geological exploration and mineral collection system to promote water ecological protection;

[0054] Figure 2 This is the internal framework diagram of the underwater detection module;

[0055] Figure 3 This is the internal framework diagram of the underwater data processing module;

[0056] Figure 4 This is the internal framework diagram of the mineral location module;

[0057] Figure 5 This is the internal framework diagram of the mineral collection module. DETAILED DESCRIPTION

[0058] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0059] Reference Figure 1 As shown, the underwater geological exploration and mineral collection system for promoting water ecological protection includes: an underwater detection module, an underwater data processing module, a mineral location module, and a mineral collection module;

[0060] The underwater detection module is electrically connected to the underwater data processing module, the underwater data processing module is electrically connected to the mineral deposit positioning module, and the mineral deposit positioning module is electrically connected to the mineral deposit collection module;

[0061] The underwater detection module is used to collect water body and underwater geological data through underwater detection technology;

[0062] The underwater data processing module is used to analyze and process the collected underwater geological data, generate an underwater geological environment model, evaluate water quality, obtain mineral distribution information, establish a mineral quantity prediction model based on the mineral distribution information, and predict the mineral quantity;

[0063] The mineral deposit positioning module is used to locate the mineral deposit location based on the underwater geological environment model and mineral deposit distribution information;

[0064] The mineral collection module is used to collect minerals in the located mineral area through collection equipment, set the error range of the mineral quantity, and compare the collected mineral quantity with the predicted mineral quantity. If it exceeds the error range, collection continues; if it is within the error range, collection stops.

[0065] Preferably, the underwater detection module specifically includes:

[0066] A multimodal detection unit is used to obtain geological information on water depth, bottom type, and potential mineral distribution using underwater detection methods such as sonar detection, laser scanning, seismic wave detection, and magnetic detection;

[0067] The data synchronization unit is used to perform time synchronization processing on the underwater data acquired by the multi-modal detection unit and transmit it to the underwater data processing module;

[0068] The system achieves efficient and accurate underwater geological exploration and mineral collection through multi-module linkage, which can effectively reduce the risk of environmental damage, protect the water ecology, ensure the accuracy of mineral collection, and avoid waste of resources.

[0069] Preferably, the underwater data processing module includes:

[0070] Data pre-processing unit, used to perform denoising and normalization processing on the collected underwater geological data to eliminate environmental interference in the data;

[0071] Water quality assessment unit, used to conduct real-time assessment of water quality based on data collected by underwater detection, including temperature, pH value, dissolved oxygen and turbidity water quality parameters;

[0072] The environmental modeling unit generates an underwater geological environment model based on the processed data, which includes information on water depth, bottom type, and mineral distribution;

[0073] The mineral deposit prediction unit, based on the underwater geological environment model, combines historical mineral deposit data with machine learning algorithms to establish a mineral deposit quantity prediction model, predict the mineral deposit quantity in the potential mineral deposit area, and output the prediction results;

[0074] Multimodal detection methods can comprehensively utilize different technologies such as sonar and laser scanning to obtain various types of underwater geological information, improve the accuracy and coverage of detection, and provide more comprehensive data support for subsequent data analysis and mineral location.

[0075] Preferably, the underwater geological environment model generated based on the processed data, which includes water depth, bottom type and mineral distribution information, specifically includes:

[0076] The geological environment model is established by collecting water depth data, and the Kriging interpolation method is used to estimate the water depth and bottom type at any location. The geological environment model expression is:

[0077]

[0078] Where Z(x,y) is the water depth at position (x,y), z i is the water depth at the known position i, z i is the interpolation weight of position i, C(x,y) is the geological type at position (x,y), C k is the kth type of substrate, P(C k |D) is the predicted bottom type C based on the collected data D k The conditional probability of M(x,y) is the mineral distribution at the location (x,y), H(x,y) is the historical mineral data, and f is the neural network model;

[0079] Through data preprocessing and water quality assessment, the system can monitor the water environment in real time to ensure that mineral extraction activities do not cause irreversible pollution to the water body. In addition, the mineral prediction unit combined with machine learning algorithms can efficiently predict the distribution and quantity of minerals, improving the efficiency and accuracy of resource collection.

[0080] Preferably, the method of establishing a mineral quantity prediction model based on the underwater geological environment model, combining historical mineral data with a machine learning algorithm, predicting the mineral quantity in the potential mineral deposit area, and outputting the prediction results specifically includes:

[0081] Based on the output of the geological environment model, water depth, geological type and mineral distribution data are obtained. Water depth, geological type and mineral distribution data are used as characteristic variables of the mineral quantity prediction model. A mineral quantity prediction model is established based on the linear regression equation to predict the mineral quantity in the potential mineral deposit area. The mineral quantity prediction model formula is as follows:

[0082]

[0083] Where N p is the predicted number of mineral deposits, α i is the variable X related to the i-th underwater geological characteristici The relevant regression coefficient, X i is the i-th underwater geological characteristic variable, β is the intercept term of the regression model, and m is the number of underwater geological characteristic variables used;

[0084] By combining historical mineral data with machine learning algorithms, the mineral quantity prediction model can be continuously optimized, making mineral predictions more accurate. This method improves the reliability of resource forecasts, helps to rationally plan mining operations, and reduce environmental damage.

[0085] Preferably, the mineral deposit positioning module includes:

[0086] Data input and pre-processing unit: receives the underwater geological environment model and mineral distribution information from the underwater data processing module, and performs normalization processing on the underwater geological environment model;

[0087] Mineral deposit location calculation unit: identifies possible mineral deposit clusters based on water, bottom sediment type, and mineral deposit distribution information in the underwater geological model;

[0088] The location and density of mineral deposits are estimated based on the Kriging interpolation method, and the location coordinates of mineral deposits are generated based on the mineral distribution prediction.

[0089] Preferably, estimating the location and density of the mineral deposits based on the Kriging interpolation method and generating the location coordinates of the mineral deposits based on the mineral deposit distribution prediction specifically include:

[0090] The formula for estimating the location and density of mineral deposits based on the Kriging interpolation method is:

[0091] P(M|x,y)=f(Z(x,y),C(x,y),Q(x,y))

[0092] Where f is the estimation function of mineral distribution, Z(x,y) is the water depth data, C(x,y) is the bottom type, and Q(x,y) is the water quality data;

[0093] generating the location coordinates of the mineral deposits based on the output of the formula for estimating the location and density of the mineral deposits using the Kriging interpolation method;

[0094] Through the application of Kriging interpolation method, the mineral location module can accurately estimate the location and density of mineral deposits, ensure the accuracy of mineral location, and reduce interference with the environment during the collection process.

[0095] Preferably, the mineral collection module includes:

[0096] Intelligent collection unit, which collects mineral deposits through collection equipment, optimizes the collection path based on genetic algorithms, and dynamically adjusts the collection strategy according to changes in mineral distribution;

[0097] The mineral quantity comparison unit compares the collected mineral quantity with the predicted mineral quantity. If the difference between the collected mineral quantity and the predicted mineral quantity exceeds the set error range, the collection will continue; if it is within the error range, the collection will stop.

[0098] Automatic feedback control unit, which automatically adjusts the acquisition depth and speed during the acquisition process based on the feedback information from the acquisition error monitoring unit;

[0099] The acquisition data storage unit stores the mineral data in the acquisition process into the database.

[0100] Preferably, the collection path optimization of the intelligent collection unit is performed using a genetic algorithm, and the specific optimization formula is:

[0101]

[0102] In the formula, P represents the path, γ is the penalty coefficient, and P e (P) is the penalty term for the path not meeting the target;

[0103] The intelligent collection unit can dynamically adjust the collection path, optimize collection efficiency, and avoid waste of resources. Through linkage with the mineral quantity comparison unit, the collection activities can be completed within the precise control error range, ensuring the matching of collection quality and quantity.

[0104] Preferably, the automatic adjustment of the acquisition depth and speed during the acquisition process based on the feedback information from the acquisition error monitoring unit specifically includes:

[0105] Monitor the errors during the collection process, obtain the difference between the amount of mineral deposits collected and the predicted amount of mineral deposits, and use PID control algorithms to adjust the depth and speed of collection;

[0106] Among them, the PID control algorithm formula is:

[0107]

[0108] Where ΔD is the adjustment of acquisition depth, ΔS is the adjustment of acquisition speed, and E is the error at the current moment. is the integral term of the error, is the differential term of the error, K p , K i , K d are the proportional, integral and differential coefficients of the PID controller respectively.

[0109] The PID control algorithm can accurately adjust the working state of the acquisition equipment according to real-time feedback information, ensuring the optimization of acquisition depth and speed, reducing errors, and improving the stability and accuracy of the acquisition process.

[0110] The use process of the present invention is:

[0111] Step 1: Start the underwater geological exploration and mineral collection system and check the connection and power supply of each module;

[0112] Step 2: Set the working mode of the underwater detection module and select the appropriate detection method;

[0113] Step 3: Collect preliminary data on water depth, bottom type, and mineral distribution through the underwater detection module;

[0114] Step 4: Time-synchronize the underwater data acquired by the multi-modal detection unit to ensure the consistency of various detection data;

[0115] Step 5: Transmitting the synchronized underwater data to the underwater data processing module;

[0116] Step 6: The underwater data processing module performs denoising and normalization processing on the collected underwater geological data through the data preprocessing unit;

[0117] Step 7: The underwater data processing module conducts real-time assessment of water quality;

[0118] Step 8: The underwater data processing module generates an underwater geological environment model based on the processed data, including water depth, bottom type and mineral distribution information;

[0119] Step 9: Using historical mineral deposit data and machine learning algorithms, a mineral quantity prediction model is established based on the geological environment model to predict the mineral quantity in the potential mineral deposit area;

[0120] Step 10: Monitor the water environment in real time to ensure that mining activities do not cause pollution to the water body;

[0121] Step 11: Transmitting the generated mineral distribution information to the mineral location module;

[0122] Step 12: The mineral deposit positioning module uses the Kriging interpolation method to locate the mineral deposit based on the underwater geological environment model and mineral deposit distribution information;

[0123] Step 13: The mineral location module uses Kriging interpolation to estimate the location and density of the mineral deposits and outputs the location coordinates of the mineral deposits;

[0124] Step 14: Based on the mineral distribution information, use genetic algorithm to optimize the mineral collection path;

[0125] Step 15: Based on the mineral location coordinates provided by the mineral location module, start the mineral collection module to collect minerals;

[0126] Step 16: Compare the collected mineral quantity with the predicted mineral quantity in real time to check whether it is within the set error range;

[0127] Step 17: If the difference between the collected mineral quantity and the predicted quantity exceeds the error range, continue collecting; if the error is within the range, stop collecting;

[0128] Step 18: Based on the feedback information from the acquisition error monitoring unit, the PID control algorithm is used to adjust the depth and speed during the acquisition process;

[0129] Step 19: Store the collected mineral data into the database to ensure long-term archiving and management of the data;

[0130] Step 20: Monitor all system operations in real time to ensure stable system operation and handle any anomalies that may occur in a timely manner.

[0131] In summary, the advantages of the present invention are:

[0132] Multimodal detection and synchronous data processing:

[0133] The water quality assessment unit in the underwater data processing module can evaluate water quality changes in real time and provide support for environmental modeling and mineral deposit prediction. By combining historical mineral deposit data with machine learning algorithms, it can efficiently predict mineral deposit distribution and provide an accurate basis for mineral deposit location based on the mineral deposit quantity prediction model;

[0134] The mineral location module accurately estimates the location and density of mineral deposits through Kriging interpolation-based mineral distribution prediction. This technology improves the accuracy of mineral location and helps to more accurately identify mineral concentration areas. Combined with the setting of the mineral quantity error range, it can effectively control the quality of mineral collection.

[0135] By optimizing the collection path through genetic algorithms, the mining operation becomes more efficient. This technology can dynamically adjust the collection strategy based on the distribution of underwater mineral deposits, reducing errors in the mining process and further minimizing interference with the water ecosystem.

[0136] The automatic feedback control unit can adjust the acquisition depth and speed in real time based on acquisition error monitoring to ensure that the error during the acquisition process is within an acceptable range. The PID control algorithm is used to adjust the acquisition depth and speed, which can flexibly respond to dynamic underwater environments and improve acquisition efficiency.

[0137] The data generated during the collection process is systematically managed and stored through the data storage unit, which facilitates subsequent analysis and backtracking, ensuring the integrity and traceability of the data.

[0138] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions merely illustrate the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. An underwater geological exploration and mineral collection system that promotes water ecological protection, characterized by: include: Underwater detection module, underwater data processing module, mineral location module, mineral collection module; The underwater detection module is electrically connected to the underwater data processing module, the underwater data processing module is electrically connected to the mineral deposit positioning module, and the mineral deposit positioning module is electrically connected to the mineral deposit collection module; The underwater detection module is used to collect water body and underwater geological data through underwater detection technology; The underwater data processing module is used to analyze and process the collected underwater geological data, generate an underwater geological environment model, evaluate water quality, obtain mineral distribution information, establish a mineral quantity prediction model based on the mineral distribution information, and predict the mineral quantity; The mineral deposit positioning module is used to locate the mineral deposit location based on the underwater geological environment model and mineral deposit distribution information; The mineral collection module is used to collect minerals in the located mineral area through collection equipment, set the error range of the mineral quantity, and compare the collected mineral quantity with the predicted mineral quantity. If it exceeds the error range, collection continues; if it is within the error range, collection stops.

2. The underwater geological exploration and mineral collection system for promoting water ecological protection according to claim 1 is characterized in that: The underwater detection module specifically includes: A multimodal detection unit is used to obtain geological information on water depth, bottom type, and potential mineral distribution using underwater detection methods such as sonar detection, laser scanning, seismic wave detection, and magnetic detection; The data synchronization unit is used to perform time synchronization processing on the underwater data acquired by the multi-modal detection unit and transmit it to the underwater data processing module in a unified manner.

3. The underwater geological exploration and mineral collection system for promoting water ecological protection according to claim 2 is characterized in that: The underwater data processing module includes: Data pre-processing unit, used to perform denoising and normalization processing on the collected underwater geological data to eliminate environmental interference in the data; Water quality assessment unit, used to conduct real-time assessment of water quality based on data collected by underwater detection, including temperature, pH value, dissolved oxygen and turbidity water quality parameters; The environmental modeling unit generates an underwater geological environment model based on the processed data, which includes information on water depth, bottom type, and mineral distribution; The mineral deposit prediction unit, based on the underwater geological environment model, combines historical mineral deposit data with machine learning algorithms to establish a mineral deposit quantity prediction model, predict the mineral deposit quantity in the potential mineral deposit area, and output the prediction results.

4. The underwater geological exploration and mineral collection system for promoting water ecological protection according to claim 3 is characterized in that: The underwater geological environment model generated based on the processed data includes water depth, bottom type and mineral distribution information, specifically including: The geological environment model is established by collecting water depth data, and the Kriging interpolation method is used to estimate the water depth and bottom type at any location. The geological environment model expression is: Where Z(x,y) is the water depth at position (x,y), z i is the water depth at the known position i, z i is the interpolation weight of position i, C(x,y) is the geological type at position (x,y), C k is the kth type of substrate, P(C k |D) is the predicted bottom type C based on the collected data D k The conditional probability of , M(x,y) is the mineral distribution at the location (x,y), H(x,y) is the historical mineral data, and f is the neural network model.

5. The underwater geological exploration and mineral collection system for promoting water ecological protection according to claim 4 is characterized in that: Based on the underwater geological environment model, combined with historical mineral data and machine learning algorithms, a mineral quantity prediction model is established to predict the mineral quantity in the potential mineral deposit area, and the prediction results are output, specifically including: Based on the output of the geological environment model, water depth, geological type and mineral distribution data are obtained. Water depth, geological type and mineral distribution data are used as characteristic variables of the mineral quantity prediction model. A mineral quantity prediction model is established based on the linear regression equation to predict the mineral quantity in the potential mineral deposit area. The mineral quantity prediction model formula is as follows: Where N p is the predicted number of mineral deposits, α i is the variable X related to the i-th underwater geological characteristic i The relevant regression coefficient, X i is the i-th underwater geological characteristic variable, β is the intercept term of the regression model, and m is the number of underwater geological characteristic variables used.

6. The underwater geological exploration and mineral collection system for promoting water ecological protection according to claim 5 is characterized in that: The mineral deposit positioning module includes: Data input and pre-processing unit: receives the underwater geological environment model and mineral distribution information from the underwater data processing module, and performs normalization processing on the underwater geological environment model; Mineral deposit location calculation unit: identifies possible mineral deposit clusters based on water, bottom sediment type, and mineral deposit distribution information in the underwater geological model; The location and density of mineral deposits are estimated based on the Kriging interpolation method, and the location coordinates of mineral deposits are generated based on the mineral distribution prediction.

7. The underwater geological exploration and mineral collection system for promoting water ecological protection according to claim 6 is characterized in that: The location and density of the mineral deposits are estimated based on the Kriging interpolation method, and the location coordinates of the mineral deposits are generated based on the mineral deposit distribution prediction. include: The formula for estimating the location and density of mineral deposits based on the Kriging interpolation method is: P(M|x,y)=f(Z(x,y),C(x,y),Q(x,y)) Where f is the estimation function of mineral distribution, Z(x,y) is the water depth data, C(x,y) is the bottom type, and Q(x,y) is the water quality data; Generates the coordinates for the locations of mineral deposits based on the output of Kriging interpolation formulas for estimating the location and density of mineral deposits.

8. The underwater geological exploration and mineral collection system for promoting water ecological protection according to claim 7 is characterized in that: The mineral collection module includes: Intelligent collection unit, which collects mineral deposits through collection equipment, optimizes the collection path based on genetic algorithms, and dynamically adjusts the collection strategy according to changes in mineral distribution; The mineral quantity comparison unit compares the collected mineral quantity with the predicted mineral quantity. If the difference between the collected mineral quantity and the predicted mineral quantity exceeds the set error range, the collection will continue; if it is within the error range, the collection will stop. Automatic feedback control unit, which automatically adjusts the acquisition depth and speed during the acquisition process based on the feedback information from the acquisition error monitoring unit; The acquisition data storage unit stores the mineral data in the acquisition process into the database.

9. The underwater geological exploration and mineral collection system for promoting water ecological protection according to claim 8 is characterized in that: The acquisition path optimization of the intelligent acquisition unit is performed using a genetic algorithm. The specific optimization formula is: In the formula, P represents the path, γ is the penalty coefficient, and P e (P) is the penalty term for paths that do not meet the target.

10. The underwater geological exploration and mineral collection system for promoting water ecological protection according to claim 9 is characterized in that: The automatic adjustment of the acquisition depth and speed during the acquisition process based on the feedback information of the acquisition error monitoring unit specifically includes: Monitor the errors during the collection process, obtain the difference between the amount of mineral deposits collected and the predicted amount of mineral deposits, and use PID control algorithms to adjust the depth and speed of collection; Among them, the PID control algorithm formula is: Where ΔD is the adjustment of acquisition depth, ΔS is the adjustment of acquisition speed, and E is the error at the current moment. is the integral term of the error, is the differential term of the error, K p , K i , K d are the proportional, integral and differential coefficients of the PID controller respectively.

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