Method for monitoring dynamic change of suspended sediment and optimizing deep sea operation scheduling

Abstract

The present application relates to the technical field of suspended sediment dynamic monitoring, and particularly relates to a suspended sediment dynamic change monitoring and deep sea operation scheduling optimization method, comprising multi-source fusion data acquisition: synchronously acquiring suspended sediment concentration distribution data, three-dimensional sea current vector data and seabed microtopography slope data of a target area; plume flow dynamic diffusion simulation: inputting the acquired data into a microtopography coupled adaptive plume flow diffusion model to generate a plume flow diffusion three-dimensional space-time cloud atlas for the next 6 hours; dynamic risk zoning generation: performing space-time superposition analysis on the plume flow diffusion three-dimensional space-time cloud atlas and a preset ecological sensitive area dynamic electronic fence to generate an operation risk heat map; intelligent zoning scheduling execution: dynamically dividing a forbidden mining area, a buffer area and a safe area, and sending zoning interval rotation scheduling instructions to a mining equipment group. The present application improves the spatial resolution and time response capability of sediment diffusion behavior prediction, and provides a basis for environmental impact assessment and ecological protection.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of suspended sediment dynamic monitoring, and particularly relates to a suspended sediment dynamic change monitoring and deep sea operation scheduling optimization method. BACKGROUND

[0002] With the continuous development of marine mineral resources development technology, deep sea mining operations are becoming more frequent, however, the disturbance of seabed strata during mining will cause a large amount of fine-grained sediments to be suspended, forming a "plume" structure that spreads with the sea current in the water body; such plume has strong spatiotemporal dynamics and is easy to adversely affect the ecological sensitive area, photosynthetic organism zone and adjacent operation units; due to the problems of time asynchronization, spatial coordinate inconsistency and uneven coverage area of multi-source data, the traditional method is difficult to realize high-precision dynamic modeling and continuity tracking of the plume, which limits the quantitative evaluation ability of the sediment diffusion risk.

[0003] On the basis of the existing sensing system, the plume dynamic simulation model becomes a key tool for understanding the sediment diffusion mechanism; the introduction of microtopography coupling, sea current driving and concentration transport mechanism into the three-dimensional plume diffusion prediction model is helpful to generate a three-dimensional spatiotemporal cloud of suspended sediment within the next few hours; however, the current scheduling response mechanism mostly relies on manual judgment, lacks intelligent strategies based on risk data automatic classification, regional priority evaluation and rotation path optimization, and is difficult to realize dynamic risk avoidance and efficient use of operation equipment. SUMMARY

[0004] Based on the above purpose, the present application provides a suspended sediment dynamic change monitoring and deep sea operation scheduling optimization method.

[0005] The suspended sediment dynamic change monitoring and deep sea operation scheduling optimization method comprises the following steps:

[0006] S1, multi-source fusion data acquisition: through the acoustic Doppler current profiler, laser turbidity sensor and multi-beam topographic scanner deployed on the mining vehicle, seabed base station and monitoring buoy, the suspended sediment concentration distribution data, three-dimensional sea current vector data and seabed microtopography slope data of the target area are synchronously acquired;

[0007] S2, plume dynamic diffusion simulation: the acquired suspended sediment concentration distribution data, three-dimensional sea current vector data and seabed microtopography slope data are input into the microtopography-coupled adaptive plume diffusion model to generate a three-dimensional spatiotemporal cloud of plume diffusion within the next 6 hours;

[0008] S3, dynamic risk zoning generation: the generated three-dimensional spatiotemporal cloud of plume diffusion is spatiotemporally superimposed and analyzed with the preset dynamic electronic fence of the ecological sensitive area to output an operation risk heat map including red / yellow / green three-level risk identification;

[0009] S4: Intelligent partition scheduling execution: according to the risk level of each sub-region in the output job risk heat map, dynamically divide the forbidden mining area, buffer area and safety area, and send partition rotation scheduling instructions to the mining equipment group.

[0010] Further, the S1 comprises:

[0011] S11, multi-node device deployment and initialization: laser turbidity sensors, multi-beam terrain scanners and acoustic Doppler current profilers are respectively deployed on mining vehicles, seabed base stations and monitoring buoys in the target sea area, and a unified time synchronization and spatial coordinate alignment mechanism is established through the seabed positioning system;

[0012] S12, multi-modal data acquisition and estimation: the echo signal intensity is obtained by using the laser turbidity sensor, the suspended sediment concentration of each measurement point is estimated by using the logarithmic conversion model, the three-dimensional flow velocity vector field of multi-layer seawater is obtained by using the acoustic Doppler current profiler, the seabed elevation data is obtained by using the multi-beam scanner, and the micro-topographic slope information is calculated based on the gradient method;

[0013] S13, multi-source data fusion and interpolation reconstruction: the sediment concentration, flow velocity vector and terrain slope data are mapped to a unified reference coordinate system, and the spatial interpolation and time alignment strategy is used to complete the space-time resampling to form a unified four-dimensional data tensor, including concentration distribution, flow field information and terrain attributes.

[0014] Further, the S11 comprises:

[0015] S111, device deployment strategy design and execution: the laser turbidity sensor and ADCP are integrated on the mining vehicle to obtain the near-bottom sediment concentration and local flow velocity at the working point, the multi-beam terrain scanner is deployed on the seabed base station to periodically obtain high-precision seabed micro-topographic data in the working area, and the ADCP and synchronous beacon module are deployed on the monitoring buoy;

[0016] S112, spatial coordinate alignment mechanism establishment: the spatial positions of all devices are three-dimensionally positioned by the seabed acoustic positioning system and unified to the global geocentric coordinate system;

[0017] S113, time synchronization mechanism establishment: all sensing devices realize cross-platform unified timestamp alignment through underwater acoustic synchronization beacon and GPS time reference.

[0018] Further, the S12 comprises:

[0019] S121, suspended sediment concentration distribution data acquisition: the relationship between incident laser and echo intensity is collected by the laser turbidity sensor, and the suspended sediment concentration is calculated by using the turbidity conversion model;

[0020] S122, three-dimensional current vector data acquisition: using acoustic Doppler current profiler to obtain the current velocity and direction information of different water layers, output vector field;

[0021] S123, submarine micro-topographic slope data acquisition: collecting submarine surface elevation data through multi-beam topographic scanner, and calculating micro-topographic slope based on local gradient.

[0022] Further, the S13 multi-source heterogeneous data spatio-temporal fusion and interpolation resampling is based on unified reference coordinate system and unified sampling frequency, adopts weighted interpolation and time interpolation strategy, and maps the data obtained by different nodes to a unified four-dimensional data tensor.

[0023] Further, the S2 comprises:

[0024] S21, initial field reconstruction and boundary condition setting: based on the collected suspended sediment concentration distribution, current vector field and micro-topographic slope, constructing the concentration field of the plume in the initial state, and determining the model boundary condition and diffusion region boundary;

[0025] S22, plume diffusion simulation coupled with micro-topography: using three-dimensional convection-diffusion equation to model the plume diffusion process, and introducing slope coupling term to adjust the local diffusion rate;

[0026] S23, spatio-temporal cloud output and dynamic regionalization expression: setting the future 6 hours as the model running time window, and the step interval as , outputting the concentration field snapshot at each time, and converting the result into standard three-dimensional thermal cloud format NetCDF.

[0027] Further, the S3 comprises:

[0028] S31, superposition analysis and risk calculation: superimposing the plume concentration prediction results and the dynamic electronic fence of the ecological sensitive area, identifying whether the sediment diffusion enters the ecological area, and calculating the risk intensity of different grids according to the ratio of concentration to ecological threshold value;

[0029] S32, risk grading and thermal map generation: dividing the risk intensity values into red, yellow and green three risk levels, respectively representing high, medium and low risk, and converting them into color labels to generate three-dimensional risk thermal map for visualization, and forming dynamic risk map according to time sequence.

[0030] Further, the S31 comprises:

[0031] S311, ecological fence superposition: loading the preset dynamic electronic fence of the ecological sensitive area, constructing a three-dimensional Boolean mask function for indicating whether each spatial voxel belongs to the ecological sensitive area at a specific time;

[0032] S312, risk intensity calculation: the plume three-dimensional concentration field is superimposed with the three-dimensional Boolean mask function in the time and space dimensions, and the relative risk intensity of each grid is calculated.

[0033] Further, the S32 generates a three-dimensional operation risk heat map based on the numerical range of the relative risk intensity of each grid obtained, and outputs a four-dimensional dynamic heat data body in time series.

[0034] Further, the S4 comprises:

[0035] S41, risk level clustering and partition calibration: according to the data in the risk heat map, the operation area is spatially partitioned, and a three-dimensional regional clustering method is used to divide the contiguous area of the same risk level into several sub-areas;

[0036] S42, scheduling priority evaluation and rotation path planning: the scheduling priority is given to each area according to the type at the current time, and the operation time window, the number of devices and the current operation state are combined to generate a rotation scheduling scheme;

[0037] S43, scheduling instruction issuing and feedback updating: a standardized scheduling instruction format is generated according to the optimization result, the rotation plan is issued to the device group, the state information is uploaded after the completion of each task, the system receives the feedback in real time and updates the regional risk assessment and scheduling plan, and the dynamic closed-loop control is realized.

[0038] The beneficial effects of the present application are:

[0039] The present application realizes efficient and collaborative collection of multi-source key environmental data such as three-dimensional ocean current, sediment concentration and microtopography by deploying acoustic Doppler current profiler, laser turbidity sensor and multi-beam terrain scanner on multi-node devices such as mining vehicles, seabed base stations and monitoring buoys, and establishing a unified time synchronization and spatial alignment mechanism combined with seabed positioning system; the six-hour prediction simulation of the sediment transport process is realized by using the adaptive plume diffusion model coupled with microtopography, the four-dimensional dynamic distribution cloud of suspended sediment concentration is constructed, and the spatial resolution and timeliness response capability of sediment diffusion behavior prediction are improved, which provides basis for environmental impact assessment and ecological protection.

[0040] The present application constructs the operation risk heat map of red / yellow / green three-level risk identification by spatiotemporal superposition analysis of the sediment diffusion cloud and the electronic fence of the ecological sensitive area, and automatically completes the dynamic division of the forbidden mining area, the buffer area and the safety area accordingly, which breaks through the limitations of traditional manual zoning lag and coarse granularity; based on the scheduling priority and the risk penalty mechanism, a rotation path optimization model is constructed, an efficient scheduling path is generated according to the device state and block level, and a standardized control instruction is issued, realizing the regional rotation cooperation and risk avoidance operation among operation devices. BRIEF DESCRIPTION OF DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced as follows. Obviously, the drawings in the following description only illustrate the present application, and other drawings can also be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0042] Fig. 1 The method flowchart of the embodiment of the present application is shown in the following table.

[0043] Fig. 2 The intelligent partition map of the embodiment of the present application is shown in the following table. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solutions and advantages of the present application more clear and explicit, the present application will be further described in detail in combination with specific embodiments.

[0045] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present application should be understood as the usual meanings understood by those skilled in the art to which the present application belongs. The terms "first", "second" and similar terms used in the present application do not represent any order, number or importance, but are only used to distinguish different components. The terms "include" or "contain" and similar terms mean that the elements or objects before the terms cover the elements or objects listed after the terms and their equivalents, and do not exclude other elements or objects. The terms "connect" or "connected" and similar terms are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. The terms "up", "down", "left", "right" and the like are only used to represent relative positional relationships, and when the absolute positions of the described objects change, the relative positional relationships may also change accordingly.

[0046] As shown in Figs. 1-2 The method for monitoring dynamic changes of suspended sediment and optimizing deep-sea operation scheduling comprises the following steps:

[0047] S1, multi-source fusion data acquisition: through the acoustic Doppler current profiler, laser turbidity sensor and multi-beam topographic scanner deployed on the mining vehicle, seabed base station and monitoring buoy, the suspended sediment concentration distribution data, three-dimensional current vector data and seabed micro-topographic slope data of the target area are synchronously acquired;

[0048] S2, dynamic diffusion simulation of plume flow: the acquired suspended sediment concentration distribution data, three-dimensional current vector data and seabed micro-topographic slope data are input into the self-adaptive plume diffusion model coupled with micro-topography to generate a three-dimensional space-time cloud map of plume diffusion in the next 6 hours;

[0049] S3, dynamic risk zoning generation: the generated plume diffusion three-dimensional space-time cloud map is superimposed with the preset ecological sensitive area dynamic electronic fence in space-time, and a work risk heat map including red / yellow / green three-level risk identification is output;

[0050] S4: intelligent zoning scheduling execution: according to the risk level of each sub-region in the output work risk heat map, the forbidden mining area, the buffer area and the safety area are dynamically divided, and the zoning rotation scheduling instructions are sent to the mining equipment group.

[0051] S1 includes:

[0052] S11, multi-node device deployment and initialization: laser turbidity sensors, multi-beam terrain scanners and acoustic Doppler current profilers (ADCP) are respectively deployed on mining vehicles, seabed base stations and monitoring buoys in the target sea area, and a unified time synchronization and space coordinate alignment mechanism is established through the seabed positioning system, laying a foundation for synchronous collection of multi-source data;

[0053] S12, multi-modal data acquisition and estimation: the echo signal intensity is obtained by using the laser turbidity sensor, the concentration of suspended sediment at each measurement point is estimated by using the logarithmic conversion model, the three-dimensional flow velocity vector field of multi-layer seawater is obtained by using the acoustic Doppler current profiler (ADCP), the seabed elevation data is obtained by using the multi-beam scanner, and the micro-topographic slope information is calculated based on the gradient method;

[0054] S13, multi-source data fusion and interpolation reconstruction: the sediment concentration, flow velocity vector and terrain slope data are mapped to a unified reference coordinate system, and the space-time resampling is completed by using the weighted spatial interpolation and time alignment strategy to form a unified four-dimensional data tensor including concentration distribution, flow field information and terrain attributes.

[0055] S11 includes:

[0056] S111, device deployment strategy design and execution: the laser turbidity sensor and the ADCP are integrated on the mining vehicle to obtain the near-bottom sediment concentration and local flow velocity at the work point, the multi-beam terrain scanner is deployed on the seabed base station to periodically obtain high-precision seabed micro-topographic data in the work area, and the ADCP and the synchronous beacon module are deployed on the monitoring buoy to continuously collect far-field flow field information and provide time synchronization broadcast signals;

[0057] S112, space coordinate alignment mechanism establishment: the spatial positions of all devices are three-dimensionally positioned by the seabed acoustic positioning system, and are unified to the global geocentric coordinate system The coordinate alignment is realized by inverting the geometric relationship between nodes through ranging information, and the real-time spatial position of each sensor node is represented as:

[0058] ;

[0059] ;

[0060] wherein, is the spatial position of the th node at time (m), is the position coordinate in WGS84 geocentric coordinate or projected coordinate, is the distance between node and node , is the sound speed in local water area , is the sound signal propagation time difference between nodes;

[0061] S113, time synchronization mechanism establishment: all sensing devices achieve cross-platform unified timestamp alignment through underwater acoustic synchronization beacon and GPS time reference, and the local alignment time is represented as:

[0062] ;

[0063] wherein, is the master node time, i.e. the reference time, is the aligned timestamp of the th device, is the local time recorded by the device itself, is the synchronization correction value (unit: s), if there is a clock drift trend between devices, linear regression model is used for drift prediction and real-time update.

[0064] S12 includes:

[0065] S121, suspended sediment concentration distribution data acquisition: the relationship between incident laser and echo intensity is collected by a laser turbidity sensor, and the suspended sediment concentration is calculated by using a turbidity conversion model, which is represented as:

[0066] ;

[0067] wherein, is the three-dimensional space and time suspended sediment concentration , is the incident laser intensity (unitized), is the echo receiving light intensity, is the sensor characteristic coefficient, which is obtained by real ship calibration;

[0068] S122, three-dimensional current vector data acquisition: the acoustic Doppler current profiler is used to obtain the current velocity and direction information of different water layers, and the output vector field is represented as:

[0069] ;

[0070] wherein, is the ocean current velocity vector at the spatiotemporal point , respectively represent , , the flow velocity component in the direction of the slope;

[0071] S123, seabed microtopography slope data acquisition: collect seabed surface elevation data through a multi-beam topographic scanner , calculate the microtopography slope based on the local gradient, denoted as:

[0072] ;

[0073] wherein, is the seabed surface elevation , is the topographic slope angle at the point , is the elevation gradient.

[0074] S13 spatiotemporal fusion and interpolation resampling of multi-source heterogeneous data Based on a unified reference coordinate system and a unified sampling frequency, adopt a weighted interpolation and time interpolation strategy to map the data obtained by different nodes to a unified four-dimensional data tensor, denoted as:

[0075] ;

[0076] wherein, is the spatiotemporal data tensor after multi-source fusion, used for subsequent plume diffusion simulation.

[0077] S2 includes:

[0078] S21, initial field reconstruction and boundary condition setting: based on the collected suspended sediment concentration distribution , the ocean current vector field and the microtopography slope , construct the plume concentration field in the initial state, and determine the model boundary condition and the diffusion region boundary, wherein the initial concentration , the initial flow field , the diffusion domain is derived according to the correlation boundary between the local slope and the diffusion direction;

[0079] S22, plume diffusion simulation coupled with microtopography: adopt a three-dimensional convection-diffusion equation to model the plume diffusion process, and introduce a slope coupling term to adjust the local diffusion rate, the model is denoted as:

[0080] ;

[0081] ;

[0082] where, is the vector differential operator, is the convection transport of sediment carried by the current, is the diffusion term of sediment affected by diffusion in the water body, is the suspended sediment concentration distribution to be predicted , is the current velocity vector , is the effective diffusion coefficient under micro-topographic modulation , is the basic diffusion coefficient (empirical constant), the value range is , is the slope modulation coefficient, the value range is 0.5-3.0, is the seabed slope angle , the value range is 0-1, is the settling or adsorption loss coefficient , the value range is ;

[0083] S23, spatiotemporal cloud output and dynamic regionalization expression: set the future 6 hours as the model running time window, the step interval is , output the concentration field snapshot at each time, and convert the results into the standard three-dimensional thermal cloud chart format NetCDF, which is expressed as:

[0084] ;

[0085] where, is the time prediction step index, is the total number of time steps.

[0086] S3 includes:

[0087] S31, superposition analysis and risk calculation: superimpose the plume concentration prediction results and the dynamic electronic fence of the ecological sensitive area, identify whether the sediment diffusion enters the ecological area, and calculate the risk intensity of different grids according to the ratio of the concentration to the ecological threshold value;

[0088] S32, risk grading and thermal map generation: divide the risk intensity values into three risk levels of red, yellow and green, respectively, representing high, medium and low risk, and convert them into color labels to generate three-dimensional risk thermal maps for visualization, forming a dynamic risk map in time sequence for scheduling decision support.

[0089] S31 includes:

[0090] S311, ecological fence superposition: load the preset ecological sensitive area dynamic electronic fence, construct a three-dimensional Boolean mask function for indicating whether each spatial voxel belongs to the ecological sensitive area at a specific time, represented as:

[0091] ;

[0092] wherein, represents the ecological sensitive area mask (value is 1 or 0);

[0093] S312, risk intensity calculation: point-by-point superposition of the plume three-dimensional concentration field and the three-dimensional Boolean mask function in the space-time dimension, to calculate the relative risk intensity of each grid , represented as:

[0094] ;

[0095] wherein, is the sediment concentration ( ), is the preset maximum acceptable concentration threshold of the ecological area ( ), the value range , if , the risk intensity of the corresponding area .

[0096] S32, based on the numerical range of the relative risk intensity of each grid obtained, the three-level risk grades of red / yellow / green are divided, and a three-dimensional operation risk heat map is generated, and a four-dimensional dynamic heat data body is output in time sequence , represented as:

[0097] .

[0098] S4 includes:

[0099] S41, risk level clustering and partition calibration: according to the data in the risk heat map , the operation area is spatially partitioned, and a three-dimensional region clustering method is used to divide the contiguous regions of the same risk level into several sub-regions , and the region type is calibrated according to the rules, represented as:

[0100] ;

[0101] wherein, is the spatial range of the th connected region at time , is the region type identifier, is the risk level, the value range ;

[0102] S42, Scheduling Priority Evaluation and Rotation Path Planning: Assign scheduling priorities to each region at the current time according to its type. Based on the job time window, number of devices, and current job status, a rotation scheduling scheme is generated, represented as follows:

[0103] ;

[0104] ;

[0105] in, It is the first The area access sequence for the mining equipment. The device moves from the previous sub-area to... The cost, yes The risk level of the area It is the risk penalty weight, in between, It is the total number of devices. It is the first The number of scheduling steps for each mining equipment. It is an optimization model for the rotation scheduling path planning problem;

[0106] S43, Dispatch instruction issuance and feedback update: Based on the optimization results, a standardized dispatch instruction format is generated and a rotation plan is issued to the equipment group. After the equipment completes each round of tasks, it uploads status information. The system receives feedback in real time and updates the regional risk assessment and dispatch plan to achieve dynamic closed-loop control.

[0107] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of the invention is limited to these examples; within the framework of the invention, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of the different aspects of the invention as described above, which are not provided in detail for the sake of brevity.

Claims

1. A method for monitoring dynamic changes of suspended sediment and optimizing deep-sea operation scheduling, characterized in that, The method comprises the following steps: S1, multi-source fusion data acquisition: through the acoustic Doppler current profiler, laser turbidity sensor and multi-beam terrain scanner deployed on the mining vehicle, seabed base station and monitoring buoy, the suspended sediment concentration distribution data, three-dimensional current vector data and seabed micro-topographic slope data of the target area are synchronously acquired; S2, plume flow dynamic diffusion simulation: the acquired suspended sediment concentration distribution data, three-dimensional current vector data and seabed micro-topographic slope data are input into the adaptive plume diffusion model coupled with micro-topography to generate a plume diffusion three-dimensional space-time cloud atlas in the next 6 hours; specifically comprising: S21, initial field reconstruction and boundary condition setting: based on the acquired suspended sediment concentration distribution, current vector field and micro-topographic slope, a plume concentration field in the initial state is constructed, and the model boundary condition and diffusion region boundary are determined; S22, plume diffusion simulation coupled with micro-topography: a three-dimensional convection-diffusion equation is used to model the plume diffusion process, and a slope coupling term is introduced to adjust the local diffusion rate; expressed as: ； ； wherein, is the vector differential operator, denotes the sea current velocity vector, denotes the suspended sediment concentration, is the convective transport of the sediment carried by the sea current, is the diffusion term of the sediment influenced by diffusion in the water body, is the effective diffusion coefficient under the micro-topography modulation, is the basic diffusion coefficient, is the slope modulation coefficient, is the micro-topography slope, is the settling or adsorption loss coefficient; S23, spatiotemporal cloud output and dynamic regionalization: Set the future 6 hours as the model running time window, and the step interval is At each time, output the concentration field snapshot, and convert the results into the standard three-dimensional thermal cloud chart format NetCDF; S3, dynamic risk zoning generation: the generated plume diffusion three-dimensional space-time cloud atlas is spatiotemporally superimposed and analyzed with the preset dynamic electronic fence of the ecological sensitive area, and an operation risk heat map including red / yellow / green three-level risk labels is output; S4, intelligent zoning scheduling execution: according to the risk level of each sub-region in the output operation risk heat map, the forbidden mining area, buffer area and safety area are dynamically divided, and zoning rotation scheduling instructions are sent to the mining equipment group.

2. The method according to claim 1, wherein, The S1 comprises: S11, multi-node device deployment and initialization: the laser turbidity sensor, multi-beam terrain scanner and acoustic Doppler current profiler are respectively deployed on the mining vehicle, seabed base station and monitoring buoy in the target sea area, and a unified time synchronization and space coordinate alignment mechanism is established through the seabed positioning system; S12, multi-modal data acquisition and estimation: the echo signal intensity is acquired by the laser turbidity sensor, the suspended sediment concentration of each measuring point is estimated by the logarithmic conversion model, the three-dimensional flow velocity vector field of multi-layer seawater is obtained by inversion through the acoustic Doppler current profiler, the seabed elevation data is acquired by the multi-beam scanner, and the micro-topographic slope information is calculated based on the gradient method; S13, multi-source data fusion and interpolation reconstruction: the sediment concentration, flow velocity vector and terrain slope data are mapped to a unified reference coordinate system, and the space-time resampling is completed by using the weighted space interpolation and time alignment strategy to form a unified four-dimensional data tensor including concentration distribution, flow field information and terrain attribute.

3. The method according to claim 2, wherein, The S11 comprises: S111, device deployment strategy design and execution: the laser turbidity sensor and ADCP are integrated on the mining vehicle to acquire the near-bottom sediment concentration and local flow velocity at the operation point, the multi-beam terrain scanner is deployed on the seabed base station to periodically acquire high-precision seabed micro-topographic data of the operation area, and the ADCP and synchronous beacon module are deployed on the monitoring buoy; S112, space coordinate alignment mechanism establishment: the spatial positions of all devices are three-dimensionally positioned through the seabed acoustic positioning system and unified to the global geocentric coordinate system; S113, time synchronization mechanism establishment: all sensing devices achieve cross-platform unified timestamp alignment through underwater acoustic synchronization beacon and GPS time reference.

4. The method according to claim 2, wherein, The S12 comprises: S121, suspended sediment concentration distribution data acquisition: the relationship between incident laser and echo intensity is collected by a laser turbidity sensor, and the suspended sediment concentration is calculated by using a turbidity conversion model; S122, three-dimensional current vector data acquisition: the current velocity and direction information of different water layers are obtained by using an acoustic Doppler current profiler, and a vector field is output; S123, seabed microtopography slope data acquisition: seabed surface elevation data are collected by a multi-beam terrain scanner, and the microtopography slope is calculated based on local gradient.

5. The method according to claim 2, wherein, The S13 multi-source heterogeneous data spatio-temporal fusion and interpolation resampling is based on unified reference coordinate system and unified sampling frequency, adopts weighted interpolation and time interpolation strategy, and uniformly maps the data obtained by different nodes to a unified four-dimensional data tensor.

6. The method according to claim 1, wherein, The S3 comprises: S31, superposition analysis and risk calculation: the plume concentration prediction result is superposed with the dynamic electronic fence of the ecological sensitive area, it is identified whether the sediment diffusion enters the ecological area, and the risk intensity of different grids is calculated according to the ratio of concentration to ecological threshold value; S32, risk grading and heat map generation: according to the risk intensity value, three risk levels of red, yellow and green are divided, respectively representing high, medium and low risk, and the three risk levels are converted into color labels to generate a three-dimensional risk heat map for visualization, and a dynamic risk map is formed according to the time sequence.

7. The method according to claim 6, wherein, The S31 comprises: S311, ecological fence superposition: a preset dynamic electronic fence of the ecological sensitive area is loaded, and a three-dimensional Boolean mask function is constructed to indicate whether each spatial voxel belongs to the ecological sensitive area at a specific time; S312, risk intensity calculation: the plume three-dimensional concentration field and the three-dimensional Boolean mask function are superposed point by point in the time and space dimensions to calculate the relative risk intensity of each grid.

8. The method according to claim 6, wherein, The S32 divides the three risk levels of red / yellow / green according to the numerical range of the relative risk intensity of each grid, generates a three-dimensional operation risk heat map, and outputs a four-dimensional dynamic heat data body according to the time sequence.

9. The method according to claim 8, wherein, The S4 comprises: S41, risk level clustering and partition calibration: according to the data in the risk heat map, the operation area is spatially partitioned, and a three-dimensional region clustering method is used to divide the contiguous regions with the same risk level into several sub-regions; S42, scheduling priority evaluation and rotation path planning: each region at the current time is given a scheduling priority according to the type, and a rotation scheduling scheme is generated by combining the operation time window, the number of devices and the current operation state; S43, scheduling instruction issuing and feedback updating: a standardized scheduling instruction format is generated according to the optimization result, the rotation plan is issued to the device group, the device uploads the state information after completing each task, the system receives the feedback in real time and updates the regional risk assessment and scheduling plan, and dynamic closed-loop control is realized.

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