Method of submarine mining plume diffusion monitoring device based on acoustic-optical-electric technology

Abstract

The invention provides a method of a seabed mining plume diffusion monitoring device based on an acoustic-optical-electric technology, and multi-dimensional and high-precision monitoring of a deep sea mining plume diffusion process is realized by combining multiple monitoring technologies of acoustics, optics and electricity. The specific target comprises multi-source data fusion: acoustic, optical and electrical data are effectively fused, and the accuracy and comprehensiveness of plume diffusion monitoring are improved. And the concentration conversion precision is improved: through multi-source data calibration, high-precision conversion from acoustic data to particle concentration data is realized. And real-time monitoring capability: providing real-time monitoring and data analysis capability, and reflecting the dynamic change of plume diffusion in time. And environment impact assessment: providing reliable data support for deep-sea mining environment impact assessment, and promoting sustainable development.

Description

Technical Field

[0001] The present invention relates to the field of marine engineering and environmental monitoring technology, and in particular to a method for monitoring the diffusion of seabed mining plumes based on acoustic-optical-electrical technology. Background Art

[0002] With the development of deep-sea mining technology, seabed mining activities are becoming more frequent, and the resulting environmental impact issues have attracted much attention. The existing technology mainly uses a single monitoring method to monitor mining plumes. For example, patent document CN117491218 B discloses a method for monitoring the diffusion of deep-sea mining plumes using an acoustic Doppler current profiler (ADCP).

[0003] Existing technologies usually use a single monitoring device, such as: Acoustic Doppler Current Profiler (ADCP): monitors the diffusion of plumes by emitting sound waves and receiving echo intensity, and can also obtain data on the speed and direction of water flow. Turbidimeter: used to measure the concentration of suspended particles in water bodies, usually single-point measurement. Electrical probe: identifies the interface between sediment and seawater by monitoring the natural potential difference between sediment and seawater. These devices work independently, providing hydrodynamic data or particle concentration data respectively, but lack a monitoring system that integrates multi-source data.

[0004] Existing technologies for monitoring the spread of deep-sea mining plumes have the following shortcomings: In terms of data acquisition: Although ADCP can simultaneously obtain sound wave echo intensity and water flow speed and direction data, it is easily interfered by noise in complex marine environments, resulting in the inability to accurately identify concentration changes at the plume boundary layer. Electrical probes can effectively identify concentration changes at the plume boundary layer, but their accuracy is not as good as that of acoustic equipment, and they are mainly used to identify the interface between sediments and seawater. Turbidimeters are usually single-point measurements, which make it difficult to cover a wide area of ​​plume diffusion, resulting in insufficient spatial distribution information. Traditional sampling methods require frequent manual intervention and equipment maintenance, consume a lot of manpower and material resources, and cannot achieve real-time monitoring.

[0005] In terms of data accuracy: In a high-noise environment, the echo intensity of the ADCP acoustic signal may be disturbed, affecting the accurate identification of the plume boundary. Although the electrical probe can monitor the potential difference, its sensitivity is low and it is difficult to provide high-precision particle concentration data.

[0006] In terms of data interpretation and processing: the existing technology lacks a mechanism to effectively integrate multi-source data (acoustic, optical, and electrical), resulting in the inability to verify and supplement the data between various monitoring methods, affecting the accuracy and reliability of the overall monitoring results.

[0007] In summary, the deployment location and monitoring range of a single device are limited, making it difficult to achieve comprehensive monitoring of the three-dimensional diffusion of the plume, especially in the boundary layer and long-distance diffusion area. The existing technology has significant deficiencies in data acquisition diversity, monitoring accuracy, data fusion and real-time performance. There is an urgent need for a plume diffusion monitoring method and device that can integrate multi-source data, improve monitoring accuracy and achieve real-time monitoring. Summary of the invention

[0008] In order to make up for the shortcomings of the prior art, the present invention provides a method for a seabed mining plume diffusion monitoring device based on acoustic-optical-electrical technology, which realizes multi-dimensional and high-precision monitoring of the deep-sea mining plume diffusion process by combining acoustic, optical and electrical monitoring technologies. Specific goals include: Multi-source data fusion: Effectively integrate acoustic, optical and electrical data to improve the accuracy and comprehensiveness of plume diffusion monitoring. Improved concentration conversion accuracy: Through multi-source data calibration, high-precision conversion of acoustic data to particle concentration data is achieved. Real-time monitoring capability: Provide real-time monitoring and data analysis capabilities to promptly reflect the dynamic changes of plume diffusion. Environmental impact assessment: Provide reliable data support for deep-sea mining environmental impact assessment and promote sustainable development.

[0009] The present invention is achieved through the following technical solutions: a method of a subsea mining plume diffusion monitoring device based on acoustic-optical-electrical technology, the subsea mining plume diffusion monitoring device comprising an acoustic monitoring module, an acoustic monitoring module, and an optical monitoring module; The acoustic monitoring module includes a downward-looking acoustic Doppler current profiler ADCP, a downward-looking ADCP connecting rod and an acoustic transducer transmitting and receiving head, wherein the acoustic transducer transmitting and receiving head is installed at the end of the acoustic Doppler current profiler ADCP; The acoustic monitoring module includes a device control terminal, a natural potential probe rod and a conical head connected from top to bottom. The acoustic Doppler flow profiler ADCP is installed on the device control terminal through the downward-looking ADCP connecting rod. An electrode ring is set every 2 cm on the natural potential probe rod; the device control terminal 2-1 is used to set the working status of the device and locally store monitoring data to ensure that data is not lost, and can be connected to an external power supply.

[0010] The optical monitoring module includes an optical turbidity meter and a double clamp, and the optical turbidity meter is installed on the natural potential probe rod through the double clamp; The specific steps include: Step S1: Deployment of monitoring equipment Deploy seabed mining plume dispersion monitoring devices on the seabed according to predetermined locations to ensure that each device can cover areas where the plume may spread; Step S2: Data collection The acoustic Doppler current profiler ADCP continuously transmits sound waves and receives echo intensity, recording the acoustic signals of plume diffusion and water flow data; Optical turbidity meters measure the concentration of suspended particles in water in real time and record changes in particle concentration; The natural potential probe regularly records the natural potential difference between sediment and seawater and monitors changes in sediment distribution; Step S3: Data preprocessing Filter and denoise the collected acoustic, optical and electrical data; Step S4: Multi-source data fusion A multi-source data fusion model is established to fuse the data of ADCP, optical turbidity meter and natural potential probe to improve the accuracy of monitoring; the specific steps include: Step S4.1: Data synchronization and alignment Align the data from ADCP, optical turbidity meter and spontaneous potential probe in time and space to ensure that the data at the same time point corresponds to the same spatial position; Step S4.2: Feature extraction Extract key features from each source data: ADCP data: acoustic wave echo intensity 𝐴, flow velocity 𝑉, flow direction 𝜃; Electrical probe data: potential difference 𝐸; Step S4.3: Construct training dataset The training dataset is constructed using the laboratory-calibrated turbidity meter data as the label 𝐶, and the ADCP and electrical probe data as the input features 𝐴, 𝑉, 𝜃, 𝐸; Specifically expressed as: Where: Xi = [Ai, Vi, θi, Ei] is the input feature vector of the i-th sample; Ci is the particle concentration label of the i-th sample; N is the number of samples; Step S4.4: Selecting a machine learning algorithm Select the random forest algorithm to build a model for converting acoustic data to concentration data; Step S4.5: Model training and optimization Train the random forest model using the training dataset 𝐷 constructed in step S4.3; Step S4.6: Model Validation An independent validation dataset is used to validate the trained random forest model and evaluate its prediction performance under different conditions; validation indicators include mean square error (MSE) and determination coefficient (𝑅) 2 ; Step S4.7: Concentration conversion formula Based on the trained random forest model, a conversion relationship from multi-source monitoring data to particle concentration is established; the specific formula is: C=f(A,V,θ,E) Where 𝐶 is the predicted particle concentration vector, 𝐴 is the ADCP acoustic wave echo intensity data, 𝑉 is the water flow velocity data, 𝜃 is the water flow direction data, and 𝐸 is the electrical probe potential difference data; Random forest prediction process: Input feature vector: X=[A,V,θ,E] For each decision tree 𝑡 in the random forest, the prediction result is: C t =DecisionTreet t (X) The final particle concentration prediction value 𝐶 is the average of all decision tree prediction results: Where 𝑇 is the number of decision trees in the random forest; Step S4.8: Application of multi-source data fusion model The trained random forest model is applied to the monitoring data to predict the particle concentration of the current plume by inputting the data of the current ADCP and natural potential probe.

[0011] As a preferred solution, the double clamp includes an optical turbidity meter clamp and a natural potential probe rod clamp. The optical turbidity meter clamp is fixedly mounted on the outer wall of the natural potential probe rod clamp. The optical turbidity meter clamp and the natural potential probe rod clamp are parallel to each other. The optical turbidity meter is installed in the optical turbidity meter clamp, and the natural potential probe rod clamp is mounted on the natural potential probe rod.

[0012] As a preferred solution, step S4.5 specifically includes the following steps: Step S4.5.1 Decision tree construction: Multiple subsamples are randomly selected from the training data set with replacement, i.e. Bootstrap sampling; a decision tree is constructed on each subsample, and some features are randomly selected to select the best split point when the node is split; Step S4.5.2 Model parameter optimization: Determine the parameters of the random forest, including the number of trees 𝑇, the maximum depth of each tree 𝐷, and the maximum number of features considered at each split 𝐹; optimize these parameters through cross-validation and select the parameter combination that optimizes the model performance.

[0013] Due to the adoption of the above technical solution, the present invention has the following beneficial effects compared with the prior art: Multi-source data fusion: Combining acoustic, optical and electrical monitoring methods to achieve comprehensive monitoring of plume diffusion, overcoming the limitations of single device monitoring.

[0014] High-precision concentration conversion: Through machine learning models, ADCP acoustic data is accurately converted into particle concentration data, significantly improving monitoring accuracy.

[0015] Real-time monitoring capability: The system can collect, process and transmit data in real time, promptly reflecting the dynamic changes of plume diffusion, and is suitable for rapid response environmental monitoring needs.

[0016] Data verification and reliability improvement: Use the data from the turbidity meter and electrical probe to calibrate and verify the ADCP data to ensure the reliability and accuracy of the monitoring results.

[0017] Broad application prospects: It is not only suitable for monitoring deep-sea mining plumes, but can also be extended to monitoring the diffusion of suspended particles in other marine engineering environments, and has broad market potential.

[0018] Reduce manpower and material costs: Compared with traditional sampling methods, this device realizes automation and real-time monitoring, reduces manual intervention and equipment maintenance, and reduces manpower and material costs.

[0019] Additional aspects and advantages of the present invention will become apparent from the following description or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which: Figure 1 It is a schematic diagram of the three-dimensional structure of the device of the present invention; Figure 2 It is a schematic diagram of the top view of the structure of the present invention; Figure 3 It is a bottom view structural schematic diagram of the present invention; Figure 4 It is a schematic diagram of the three-dimensional structure of the upper part of the device of the present invention; Figure 5 It is a schematic diagram of the three-dimensional structure of the middle part of the device of the present invention; Figure 6 This is a time series diagram of vertical diffusion of sediments in the polymetallic manganese nodule mining area in the western Pacific Ocean obtained by acoustic inversion; Figure 7 The concentration change of suspended particles at a distance of 0.74m from the bottom was obtained by an electrical probe. DETAILED DESCRIPTION

[0021] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0022] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0023] Combine the following Figures 1 to 7 The method of the seabed mining plume diffusion monitoring device based on the acoustic-optical-electrical technology according to the embodiment of the present invention is specifically described.

[0024] like Figure 1 As shown, the present invention proposes a method for a seabed mining plume diffusion monitoring device based on acoustic-optical-electrical technology, and the seabed mining plume diffusion monitoring device includes an acoustic monitoring module, an acoustic monitoring module, and an optical monitoring module; The acoustic monitoring module includes a downward-looking acoustic Doppler current profiler ADCP1-1, a downward-looking ADCP connecting rod 1-2 and an acoustic transducer transmitting and receiving head 1-3. The acoustic transducer transmitting and receiving head 1-3 is installed at the end of the acoustic Doppler current profiler ADCP1-1; the transducer transmitting and receiving head is used to transmit sound waves and receive echo intensity, monitor the diffusion process of the plume in real time, and obtain water flow velocity and direction data at the same time.

[0025] The acoustic monitoring module includes a device control terminal 2-1, a natural potential probe rod 2-2 and a conical head 2-3 connected from top to bottom. The acoustic Doppler current profiler ADCP1-1 is installed on the device control terminal 2-1 through the downward-looking ADCP connecting rod 1-2. An electrode ring is set every 2 cm on the natural potential probe rod 2-2 to monitor the natural potential difference between seawater and sediment and identify the interface between sediment and seawater. The device control terminal 2-1 is used to set the working status of the device and locally store monitoring data to ensure that data is not lost, and an external power supply can be connected.

[0026] The optical monitoring module includes an optical turbidity meter 3-1 and a double clamp. The optical turbidity meter 3-1 is installed on the natural potential probe rod 2-2 through the double clamp. The optical turbidity meter 3-1 and the acoustic Doppler current profiler ADCP1-1 are deployed synchronously to measure the concentration of suspended particles in the water body as standard data; the double clamp includes an optical turbidity meter clamp 3-2 and a natural potential probe rod clamp 3-3. The optical turbidity meter clamp 3-2 is fixedly installed on the outer wall of the natural potential probe rod clamp 3-3. The optical turbidity meter clamp 3-2 and the natural potential probe rod clamp 3-3 are parallel to each other. The optical turbidity meter 3-1 is installed in the optical turbidity meter clamp 3-2, and the natural potential probe rod clamp 3-3 is mounted on the natural potential probe rod 2-2.

[0027] The specific steps include: Step S1: Deployment of monitoring equipment according to Figures 1 to 5 As shown, the seabed mining plume diffusion monitoring devices are deployed on the seabed according to predetermined positions to ensure that each device can cover the area where the plume may spread; Step S2: Data collection The acoustic Doppler current profiler ADCP1-1 continuously transmits sound waves and receives echo intensity, recording the acoustic signals of plume diffusion and water flow data; The optical turbidity meter 3-1 measures the concentration of suspended particles in the water in real time and records the changes in particle concentration; The natural potential probe 2-2 regularly records the natural potential difference between sediment and seawater and monitors the changes in sediment distribution; Step S3: Data preprocessing Filter and denoise the collected acoustic, optical and electrical data to ensure data quality; Step S4: Multi-source data fusion A multi-source data fusion model is established to fuse the data of ADCP1-1, optical turbidity meter 3-1 and natural potential probe 2-2 to improve the accuracy of monitoring; specifically, the following steps are included: Step S4.1: Data synchronization and alignment The data from ADCP1-1, optical turbidity meter 3-1 and natural potential probe 2-2 are synchronized in time and space to ensure that the data at the same time point corresponds to the same spatial position; Step S4.2: Feature extraction Extract key features from each source data: ADCP1-1 data: acoustic echo intensity 𝐴, flow velocity 𝑉, flow direction 𝜃; Electrical probe data: potential difference 𝐸; Step S4.3: Construct training dataset The training dataset is constructed using the laboratory-calibrated turbidity meter data as the label 𝐶, and the ADCP and electrical probe data as the input features 𝐴, 𝑉, 𝜃, 𝐸; Specifically expressed as: Where: Xi = [Ai, Vi, θi, Ei] is the input feature vector of the i-th sample; Ci is the particle concentration label of the i-th sample; N is the number of samples; Step S4.4: Selecting a machine learning algorithm The random forest algorithm was selected to establish a conversion model from acoustic data to concentration data; random forest can effectively process high-dimensional data and improve the accuracy and robustness of predictions by constructing multiple decision trees and integrating their prediction results.

[0028] Step S4.5: Model training and optimization The random forest model is trained using the training dataset 𝐷 constructed in step S4.3; specifically, the following steps are included: Step S4.5.1 Decision tree construction: Multiple subsamples are randomly selected from the training data set with replacement, i.e. Bootstrap sampling; a decision tree is constructed on each subsample, and some features are randomly selected to select the best split point when the node is split, so as to increase the diversity of the model; Step S4.5.2 Model parameter optimization: Determine the parameters of the random forest, including the number of trees 𝑇, the maximum depth of each tree 𝐷, and the maximum number of features considered at each split 𝐹; optimize these parameters through cross-validation and select the parameter combination that optimizes the model performance.

[0029] Step S4.6: Model Validation An independent validation dataset is used to validate the trained random forest model and evaluate its prediction performance under different conditions; validation indicators include mean square error (MSE) and determination coefficient (𝑅) 2 , to ensure the generalization ability and prediction accuracy of the model; Step S4.7: Concentration conversion formula Based on the trained random forest model, a conversion relationship from multi-source monitoring data to particle concentration is established; the specific formula is: C=f(A,V,θ,E) Where 𝐶 is the predicted particle concentration vector, 𝐴 is the ADCP acoustic wave echo intensity data, 𝑉 is the water flow velocity data, 𝜃 is the water flow direction data, and 𝐸 is the electrical probe potential difference data; Random forest prediction process: Input feature vector: X=[A,V,θ,E] For each decision tree 𝑡 in the random forest, the prediction result is: C t =DecisionTreet t (X) The final particle concentration prediction value 𝐶 is the average of all decision tree prediction results: Where 𝑇 is the number of decision trees in the random forest. Through the above method, the random forest model can comprehensively consider the characteristics of multi-source data and improve the accuracy and reliability of particle concentration prediction.

[0030] Step S4.8: Application of multi-source data fusion model The trained random forest model is applied to the monitoring data. By inputting the data of the current ADCP (1-1) and the natural potential probe (2-2), the particle concentration of the current plume is predicted to achieve high-precision real-time monitoring.

[0031] The acoustic signal data obtained by the acoustic nodes installed at different heights were inverted to obtain the time series diagram of vertical diffusion of sediments. At 09:00, the external force disturbed the seabed, and at 09:01, the external force disturbance stopped. It can be seen that after the disturbance ended, the suspended sediment diffusion height could reach 5 meters. As time went by, the sediment gradually settled. After about 5 minutes, most of the sediment was redeposited on the seabed.

[0032] In the description of the present invention, the term "plurality" refers to two or more than two. Unless otherwise clearly defined, the orientation or positional relationship indicated by the terms "upper" and "lower" is based on the orientation or positional relationship shown in the drawings. 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 operated in a specific orientation. Therefore, it cannot be understood as a limitation of the present invention; the terms "connection", "installation", "fixation", etc. should be understood in a broad sense. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be directly connected or indirectly connected through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0033] In the description of this specification, the description of the terms "one embodiment", "some embodiments", "specific embodiments", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0034] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for monitoring the spread of seabed mining plumes based on acoustic-optical-electrical technology, characterized in that , the seabed mining plume diffusion monitoring device includes an acoustic monitoring module, an acoustic monitoring module, and an optical monitoring module; The acoustic monitoring module comprises a downward-looking acoustic Doppler current profiler ADCP (1-1), a downward-looking ADCP connecting rod (1-2) and an acoustic transducer transmitting and receiving head (1-3), wherein the acoustic transducer transmitting and receiving head (1-3) is installed at the end of the acoustic Doppler current profiler ADCP (1-1); The acoustic monitoring module comprises a device control terminal (2-1), a natural potential probe rod (2-2) and a conical head (2-3) connected from top to bottom, an acoustic Doppler current profiler ADCP (1-1) is installed on the device control terminal (2-1) through a downward ADCP connecting rod (1-2), and an electrode ring is arranged every 2 centimeters on the natural potential probe rod (2-2); the device control terminal 2-1 is used to set the working state of the device and locally store monitoring data to ensure that the data is not lost, and can be connected to an external power supply; The optical monitoring module comprises an optical turbidity meter (3-1) and a double clamp, and the optical turbidity meter (3-1) is installed on the natural potential probe rod (2-2) via the double clamp; The specific steps include: Step S1: Deployment of monitoring equipment Deploy seabed mining plume dispersion monitoring devices on the seabed according to predetermined locations to ensure that each device can cover areas where the plume may spread; Step S2: Data collection The acoustic Doppler current profiler ADCP (1-1) continuously transmits sound waves and receives echo intensity, recording the acoustic signals of plume diffusion and water flow data; Optical turbidity meter (3-1) measures the concentration of suspended particles in water in real time and records the changes in particle concentration; The natural potential probe (2-2) regularly records the natural potential difference between sediment and seawater and monitors changes in sediment distribution; Step S3: Data preprocessing Filter and denoise the collected acoustic, optical and electrical data; Step S4: Multi-source data fusion A multi-source data fusion model is established to fuse the data of ADCP (1-1), optical turbidity meter (3-1) and natural potential probe (2-2) to improve the accuracy of monitoring; specifically, the following steps are included: Step S4.1: Data synchronization and alignment The data from ADCP (1-1), optical turbidity meter (3-1) and natural potential probe (2-2) are synchronized in time and space to ensure that the data at the same time point correspond to the same spatial position; Step S4.2: Feature extraction Extract key features from each source data: ADCP (1-1) data: acoustic wave echo intensity 𝐴, flow velocity 𝑉, flow direction 𝜃; Electrical probe data: potential difference 𝐸; Step S4.3: Construct training dataset The training dataset is constructed using the laboratory-calibrated turbidity meter data as the label 𝐶, and the ADCP and electrical probe data as the input features 𝐴, 𝑉, 𝜃, 𝐸; Specifically expressed as: Where: Xi = [Ai, Vi, θi, Ei] is the input feature vector of the i-th sample; Ci is the particle concentration label of the i-th sample; N is the number of samples; Step S4.4: Selecting a machine learning algorithm Select the random forest algorithm to build a model for converting acoustic data to concentration data; Step S4.5: Model training and optimization Train the random forest model using the training dataset 𝐷 constructed in step S4.3; Step S4.6: Model Validation An independent validation dataset is used to validate the trained random forest model and evaluate its prediction performance under different conditions; validation indicators include mean square error (MSE) and determination coefficient (𝑅) 2 ; Step S4.7: Concentration conversion formula Based on the trained random forest model, a conversion relationship from multi-source monitoring data to particle concentration is established; the specific formula is: C=f(A,V,θ,E) Where 𝐶 is the predicted particle concentration vector, 𝐴 is the ADCP acoustic wave echo intensity data, 𝑉 is the water flow velocity data, 𝜃 is the water flow direction data, and 𝐸 is the electrical probe potential difference data; Random forest prediction process: Input feature vector: X=[A,V,θ,E] For each decision tree 𝑡 in the random forest, the prediction result is: C t =DecisionTreet t (X) The final particle concentration prediction value 𝐶 is the average of all decision tree prediction results: Where 𝑇 is the number of decision trees in the random forest; Step S4.8: Application of multi-source data fusion model The trained random forest model is applied to the monitoring data to predict the particle concentration of the current plume by inputting the data of the current ADCP (1-1) and the natural potential probe (2-2).

2. A method for monitoring the spread of seabed mining plumes based on acoustic-optical-electrical technology according to claim 1, characterized in that The double clamp comprises an optical turbidity meter clamp (3-2) and a natural potential probe rod clamp (3-3); the optical turbidity meter clamp (3-2) is fixedly mounted on the outer wall of the natural potential probe rod clamp (3-3); the optical turbidity meter clamp (3-2) and the natural potential probe rod clamp (3-3) are parallel to each other; the optical turbidity meter (3-1) is mounted in the optical turbidity meter clamp (3-2); and the natural potential probe rod clamp (3-3) is sleeved on the natural potential probe rod (2-2).

3. A method for monitoring the spread of seabed mining plumes based on acoustic-optical-electrical technology according to claim 1, characterized in that , the step S4.5 specifically includes the following steps: Step S4.5.1 Decision tree construction: Multiple subsamples are randomly selected from the training data set with replacement, i.e. Bootstrap sampling; a decision tree is constructed on each subsample, and some features are randomly selected to select the best split point when the node is split; Step S4.5.2 Model parameter optimization: Determine the parameters of the random forest, including the number of trees 𝑇, the maximum depth of each tree 𝐷, and the maximum number of features considered at each split 𝐹; optimize these parameters through cross-validation and select the parameter combination that optimizes the model performance.

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