A method and system for three-dimensional modular design and construction of a dredging vessel

Abstract

The present invention relates to the technical field of dredging management, and in particular to a method and system for the three-dimensional modular design and construction of a dredging vessel. The method comprises the following steps: acquiring multi-source lake and reservoir data, and performing three-dimensional environmental data modeling to obtain a three-dimensional data model of the lake and reservoir environment; optimizing the hull structure topology based on the three-dimensional data model of the lake and reservoir environment, thereby obtaining modular hull structure design data; acquiring hull sensor data and performing sensor data fusion to obtain a lake and reservoir environment sensor network; optimizing the coordinated control of the pump group through the lake and reservoir environment sensor network to obtain optimal pump group control parameter data; dynamically optimizing the actual dredging construction parameters of the dredging vessel based on the lake and reservoir environment sensor network and the modular hull structure design data to obtain dynamic construction parameter data; integrating the hull module transportation strategy based on the lake and reservoir environment sensor network to obtain the hull module transportation strategy. The present invention can improve the dredging efficiency of dredging vessels.

Description

Technical Field

[0001] The present invention relates to the technical field of dredging management, and in particular to a method and system for three-dimensional modular design and construction of a dredging vessel. Background Art

[0002] With the continuous development of infrastructure construction and maintenance, such as water conservancy projects, waterways, and ports, dredging plays a vital role in the management and maintenance of waterways. Traditional dredging methods rely primarily on manual or single mechanical equipment to remove silt. However, due to the complexity and variability of water environments such as rivers, ports, and waterways, a single dredging method often fails to meet the needs of diverse water environments. Traditional dredging vessels, as one of the core equipment for large-scale dredging operations, have played a role to a certain extent, but their technology and design still have significant limitations.

[0003] Although there are many lake and reservoir dredging assembled ships on the market, the technology is relatively mature and can complete a certain scale of dredging tasks, usually the clean water flow does not exceed 7000m 3 / h. If standard equipment on the market is used to carry out large-scale projects, it is often necessary to frequently dispatch ships, resulting in high scheduling costs and personnel coordination costs. In addition, although offshore dredging ships have high dredging efficiency, for example, the maximum clear water flow of the mud pump of ship No. 8527 can reach 13,000m 3 / h, but the high cost of dispatching these ships and the fact that some waters (such as the Yellow River and inland reservoirs) are not navigable make them impossible to transport by water, which limits their application in lake and reservoir dredging projects.

[0004] Furthermore, traditional dredging vessels typically employ a single operating mode and lack comprehensive adaptability to complex water environments. For example, when water flow, silt properties, and environmental conditions change, traditional vessels are often unable to make timely adjustments, resulting in limited operational efficiency and poor dredging results. Summary of the Invention

[0005] Based on this, it is necessary for the present invention to provide a method and system for three-dimensional modular design and construction of a dredging vessel to solve at least one of the above technical problems.

[0006] To achieve the above objectives, a method for three-dimensional modular design and construction of a dredging vessel comprises the following steps:

[0007] Step S1: Acquire multi-source lake and reservoir data, and perform lake and reservoir environment analysis based on the multi-source lake and reservoir data, thereby obtaining lake and reservoir water environment data and lake and reservoir transportation environment data; perform three-dimensional environmental data modeling based on the lake and reservoir water environment data and the lake and reservoir transportation environment data, thereby obtaining a three-dimensional data model of the lake and reservoir environment;

[0008] Step S2: Designing a hull module based on the three-dimensional data model of the lake environment to obtain hull module design data, and performing hull structure topology optimization on the hull module design data to obtain modular hull structure design data, which is uploaded to the dredging vessel production management platform to execute the hull structure production task;

[0009] Step S3: acquiring hull sensor data, and performing sensor data fusion based on multi-source lake and reservoir data and hull sensor data, thereby obtaining a lake and reservoir environment sensor network;

[0010] Step S4: The working status of the hull relay pump is collected through the lake environment sensor network to obtain the working status data of the hull relay pump; the working status data of the hull relay pump is optimized by pump group collaborative control to obtain the optimal pump group control parameter data, and the data is uploaded to the dredging vessel control management platform to execute the pump group control task;

[0011] Step S5: Simulating the dredging construction of the dredging vessel based on the lake and reservoir environment sensor network and the modular hull structure design data, thereby obtaining dredging construction simulation data of the dredging vessel; dynamically optimizing the actual dredging construction parameters of the dredging vessel based on the dredging construction simulation data, thereby obtaining dynamic construction parameter data, and uploading the data to the dredging vessel control and management platform to execute the construction parameter adjustment task;

[0012] Step S6: Real-time environmental status data is collected based on the lake and reservoir environmental sensor network to obtain real-time dredging environmental status data; hull module disassembly strategy analysis is performed based on the real-time dredging environmental status data and the modular hull structure design data to obtain the hull module disassembly strategy; hull module transportation strategy integration is performed based on the lake and reservoir transportation environmental data and the hull module disassembly strategy to obtain the hull module transportation strategy.

[0013] Optionally, step S1 specifically includes:

[0014] Step S11: acquiring multi-source lake and reservoir data, and performing data preprocessing on the multi-source lake and reservoir data, thereby obtaining the multi-source lake and reservoir data to be analyzed;

[0015] Step S12: performing multi-source data classification on the multi-source lake and reservoir data to be analyzed, thereby obtaining lake and reservoir hydrological data and lake and reservoir topographic data;

[0016] Step S13: performing a lake / reservoir surrounding terrain analysis based on the lake / reservoir terrain data to obtain a lake / reservoir surrounding terrain depth map, and performing a lake / reservoir transportation environment analysis based on the lake / reservoir surrounding terrain depth map to obtain lake / reservoir transportation environment data;

[0017] Step S14: performing lake and reservoir water environment analysis based on lake and reservoir hydrological data and a topographic depth map of the lake and reservoir surroundings, thereby obtaining lake and reservoir water environment data;

[0018] Step S15: Perform three-dimensional environmental data modeling based on the lake and reservoir water environment data and the lake and reservoir transportation environment data, thereby obtaining a three-dimensional data model of the lake and reservoir environment.

[0019] Optionally, step S13 is specifically as follows:

[0020] Step S131: performing multi-source terrain data classification on lake and reservoir terrain data to obtain lake bottom sonar sensor data and lake and reservoir lidar data;

[0021] Step S132: performing sonar inverse distance weighted terrain point cloud generation on the lake bottom sonar sensor data, thereby obtaining lake bottom terrain point cloud data; performing laser reflection signal terrain point cloud conversion on the lake lidar data, thereby obtaining lake surface terrain point cloud data;

[0022] Step S133: spatially merging the lake bottom terrain point cloud data and the lake surface terrain point cloud data to obtain lake surrounding terrain point cloud data, and performing rasterization depth map conversion on the lake surrounding terrain point cloud data to obtain a lake surrounding terrain depth map;

[0023] Step S134: performing lake-reservoir land surface terrain zoning on the lake-reservoir surrounding terrain depth map, thereby obtaining lake-reservoir land surface terrain zoning data, and calculating the proportion of gentle terrain based on the lake-reservoir land surface terrain zoning data, thereby obtaining lake-reservoir land surface gentle terrain proportion data;

[0024] Step S135: Obtaining a set of drone images of the lake and reservoir area, and performing edge detection on the drone images of the lake and reservoir area to obtain lake and reservoir area contour data; performing lake and reservoir area road identification based on the lake and reservoir area contour data to obtain lake and reservoir area road data;

[0025] Step S136: identifying lake and reservoir land transportation bottlenecks based on the lake and reservoir land surface flat terrain proportion data and lake and reservoir area road data, thereby obtaining lake and reservoir land transportation bottleneck data;

[0026] Step S137: Perform spatial integration of the lake and reservoir land transportation environment based on lake and reservoir land surface terrain zoning data, lake and reservoir land surface flat terrain proportion data, lake and reservoir area road data, and lake and reservoir land transportation bottleneck data, thereby obtaining lake and reservoir transportation environment data.

[0027] Optionally, step S14 is specifically as follows:

[0028] The depth map of the terrain around the lake is used to divide the lake into different depth zones, thereby obtaining the depth zone data of the lake water area;

[0029] Perform spatial alignment of lake and reservoir water depth zoning data and lake and reservoir hydrological data to obtain hydrological-topographic fusion data;

[0030] Based on the hydrological-topographic fusion data, the water flow velocity distribution statistics in the deep water area are carried out to obtain the water flow velocity distribution data in the deep water area;

[0031] The temperature change trend of the deep water area is predicted based on the hydrological-topographic fusion data, thereby obtaining the temperature change trend data of the deep water area;

[0032] The sediment concentration in the deep water area of ​​lakes and reservoirs is counted based on the hydrological-topographic fusion data to obtain the sediment concentration data in the deep water area;

[0033] Based on the water velocity distribution data, temperature change trend data and sediment concentration data in the deep water area, the silt deposition hotspot area is identified to obtain the silt deposition hotspot area data;

[0034] The lake and reservoir water environment data are spatially integrated by integrating the silt deposition hotspot area data, the water velocity distribution data in the deep water area, the temperature change trend data in the deep water area, and the sediment concentration data in the deep water area to obtain the lake and reservoir water environment data.

[0035] Optionally, step S2 is specifically:

[0036] Step S21: performing environmental integration of the silt deposition hotspot area based on the three-dimensional data model of the lake and reservoir environment, thereby obtaining environmental data of the silt deposition hotspot area, wherein the environmental data of the silt deposition hotspot area includes water depth data of the silt deposition hotspot area, flow velocity data of the silt deposition hotspot area, and sediment data of the silt deposition hotspot area;

[0037] Step S22: obtaining the dredging ship model structure data, and performing modular box structure decomposition on the dredging ship model structure data, thereby obtaining the model hull module structure data and the model function module structure data;

[0038] Step S23: Designing the dredging vessel hull module size and weight based on the sample hull module structure data according to the lake and reservoir transportation environment data and the silt deposition hotspot area environment data, thereby obtaining the dredging vessel hull module design structure data;

[0039] Step S24: Designing a dredging vessel functional module based on the sample functional module structure data according to the silt deposition hotspot area environmental data, thereby obtaining dredging vessel functional module design structure data;

[0040] Step S25: performing module connection design on the dredging ship hull module design structure data and the dredging ship functional module design structure data, thereby obtaining dredging ship module connection design data; performing modular structural connection on the dredging ship hull module design structure data and the dredging ship functional module design structure data according to the dredging ship module connection design data, thereby obtaining initial modular hull structure design data;

[0041] Step S26: Perform lightweight topology optimization on the initial modular hull structure design data to obtain modular hull structure design data, and upload the data to the dredging vessel production management platform to execute the hull structure production task.

[0042] Optionally, step S24 is specifically as follows:

[0043] According to the dredging vessel hull module design structure data, the functional module structure size of the sample functional module structure data is adapted to obtain the functional module structure data to be designed;

[0044] Adapting the functional module dual drive mode to the functional module structure data to be designed by a preset shore power drive system and a preset diesel drive system, thereby obtaining drive adaptation functional module structure data;

[0045] Based on the environmental data of the silt deposition hotspot area, the shipboard sensor deployment design is carried out based on the structural data of the drive adaptation function module, thereby obtaining the modular sensor deployment design data;

[0046] According to the environmental data of the silt deposition hotspot area, the modular dredge suction pump group is designed based on the structural data of the drive adaptation function module, thereby obtaining the modular dredge suction pump group design data;

[0047] The modular sludge suction pump group design data is used to design the pump group collaborative working module through the preset ACC automatic mud control system, thereby obtaining the modular sludge suction pump group module design data;

[0048] Based on the sediment data of the silt deposition hotspot area, the modular structure design of the multi-stage sediment filtration and the modular structure design of the sediment storage are carried out on the drive adaptation function module structure data, thereby obtaining the modular sediment separation module design data;

[0049] According to the water depth data and flow velocity data of the silt deposition hotspot area, the power module design and propulsion module design are carried out based on the drive adaptation function module structure data, thereby obtaining modular power module design data;

[0050] The modular sensor deployment design data, modular dredging pump module design data, modular sediment separation module design data and modular shore power module design data are connected to the drive module circuit to obtain the dredging ship functional module design structure data.

[0051] Optionally, step S4 is specifically:

[0052] Step S41: collecting the working status of the hull relay pump and the real-time construction environment status of the lake and reservoir through the lake and reservoir environment sensor network, thereby obtaining the working status data of the hull relay pump and the real-time construction environment status data of the lake and reservoir;

[0053] Step S42: evaluating the hull pump group efficiency based on the real-time construction environment status data of the lake and reservoir and the working status data of the hull relay pump, thereby obtaining the actual construction efficiency data of the dredging vessel;

[0054] Step S43: obtaining the construction target of the dredging vessel through the dredging vessel control and management platform, and estimating the actual construction efficiency target completion degree of the dredging vessel actual construction efficiency data according to the construction target, thereby obtaining the actual construction efficiency target completion degree data;

[0055] Step S44: performing pump group coordinated control optimization on the hull relay pump working status data according to the actual construction work efficiency target completion data, thereby obtaining pump group control optimization parameter data;

[0056] Step S45: uploading the pump group control optimization parameter data to the dredging vessel control management platform to execute the pump group control task; obtaining the real-time relay pump working status data and the real-time construction environment status data after the pump group control;

[0057] Step S46: performing a construction efficiency review on the real-time relay pump working status data after the pump group is controlled and the real-time construction environment status data after the pump group is controlled according to the construction target of the dredging vessel, thereby obtaining a construction efficiency review report;

[0058] Step S47: Iteratively optimize and adjust the pump group control optimization parameter data according to the construction efficiency review report to obtain the optimal pump group control parameter data, and upload it to the dredging vessel control management platform to execute the pump group control task.

[0059] Optionally, step S5 is specifically as follows:

[0060] Step S51: collecting real-time sensor data through the lake environment sensor network to obtain real-time environmental status data and real-time hull status data; simulating the actual dredging vessel construction environment based on the real-time environmental status data to obtain dredging vessel construction environment simulation data;

[0061] Step S52: performing 3D modeling of the hull structure based on the modular hull structure design data, thereby obtaining a modular hull 3D model; and performing a dredging construction simulation of the dredging vessel on the modular hull 3D model according to the dredging vessel construction environment simulation data, thereby obtaining dredging construction simulation data of the dredging vessel;

[0062] Step S53: integrating the simulated hull state of the dredging ship according to the dredging construction simulation data of the dredging ship, thereby obtaining the simulated hull state data;

[0063] Step S54: performing a working state comparison on the real-time state data of the hull and the simulated state data of the hull, thereby obtaining actual working state error data of the dredging vessel;

[0064] Step S55: obtaining a dredging vessel construction parameter set through the dredging vessel control and management platform, and dynamically optimizing the actual dredging construction parameters of the dredging vessel construction parameter set according to the actual working state error data of the dredging vessel, thereby obtaining dynamic construction parameter data;

[0065] Step S56: Upload the dynamic construction parameter data to the dredging vessel control and management platform to perform the construction parameter adjustment task.

[0066] Optionally, step S6 specifically includes:

[0067] Step S61: Real-time environmental status data is collected based on the lake environment sensor network to obtain real-time dredging environmental status data, and dredging construction progress data is calculated based on the real-time dredging environmental status data according to the dredging vessel construction target to obtain dredging construction progress data;

[0068] Step S62: Classify the dredging construction progress according to the dredging construction progress data. If the dredging construction progress reaches a preset construction progress threshold, mark the corresponding real-time dredging environmental status data as dredging completion environmental status data. If the dredging construction progress does not reach the preset construction progress, return to step S61 to continue collecting real-time environmental status until the dredging construction progress reaches the preset construction progress threshold.

[0069] Step S63: obtaining the hull status data when the dredging is completed through the lake environment sensor network, and marking the hull status data as the hull status data to be dismantled;

[0070] Step S64: performing a hull module disassembly sequence analysis based on the hull state data to be disassembled and the modular hull structure design data, thereby obtaining hull module disassembly sequence data;

[0071] Step S65: performing environmental state change prediction on the environmental state data after dredging is completed, thereby obtaining environmental state change prediction data, and performing a stable hull module disassembly strategy analysis on the hull module disassembly sequence data based on the environmental state change prediction data, thereby obtaining a hull module disassembly strategy;

[0072] Step S66: Integrate the hull module transportation strategy according to the lake transport environment data and the hull module disassembly strategy to obtain the hull module transportation strategy.

[0073] By incorporating multi-source lake and reservoir data, three-dimensional environmental modeling, modular hull design, and intelligent control, the present invention significantly improves the adaptability, operational efficiency, and flexibility of dredging vessels in complex water environments. Based on multi-source lake and reservoir data and lake and reservoir environmental analysis, a comprehensive modeling of the lake and reservoir water and transportation environment is first created, providing a precise basis for subsequent hull design, modular hull production, and module disassembly and transportation strategies. This data-driven environmental modeling enables dredging vessels to perceive and adapt to changes in hydrological topography in real time under varying lake and reservoir conditions, effectively avoiding the poor adaptability of traditional dredging vessels in complex water environments. By topologically optimizing the hull structure and implementing modular design, the hull structure can be rapidly customized to meet diverse operational requirements, while also reducing hull weight and improving operational flexibility and efficiency. The modular design not only helps optimize the hull structure itself but also facilitates transportation, maintenance, and disassembly, effectively reducing transportation and dispatch costs. Furthermore, the modular design ensures the detachable structure, enabling the vessel to adapt to diverse water environments and operate efficiently, avoiding the limitations of traditional vessels that require rapid adjustment. During the vessel's operation, by integrating lake and reservoir environmental data with hull sensor data, the vessel's operating status and changes in the dredging environment are monitored in real time, achieving precise coordinated pump control optimization. This ensures maximum pump efficiency in diverse water conditions while avoiding the inefficiencies often associated with traditional dredging vessels due to poor pump coordination. Furthermore, real-time construction environment simulation and dynamic construction parameter optimization enable the dredging vessel to adjust operating parameters based on real-time operating conditions, further improving operational efficiency and quality. Through intelligent optimization of hull module disassembly and transportation strategies, this solution not only enhances the dredging vessel's adaptability but also ensures efficient and safe disassembly through effective modular disassembly strategies, reducing labor and time costs during disassembly and transportation. After dredging is completed, analysis of the hull disassembly sequence and stability minimizes risks during the disassembly process. The disassembly sequence can be rationally planned based on the progress of the dredging operation, significantly improving operational continuity and controllability. To sum up, after adopting this technical solution, dredging ships can achieve higher operating efficiency, lower costs, and stronger adaptability when facing complex water environments, and can flexibly respond to different operational needs, thereby improving the comprehensive performance and market competitiveness of dredging ships.

[0074] Optionally, this specification also provides a system for three-dimensional modular design and construction of a dredging vessel, which is used to perform the above-mentioned method for three-dimensional modular design and construction of a dredging vessel. The system for three-dimensional modular design and construction of a dredging vessel includes:

[0075] The three-dimensional environmental data modeling module is used to obtain multi-source lake and reservoir data and perform lake and reservoir environmental analysis based on the multi-source lake and reservoir data, thereby obtaining lake and reservoir water environment data and lake and reservoir transportation environment data; and perform three-dimensional environmental data modeling based on the lake and reservoir water environment data and the lake and reservoir transportation environment data, thereby obtaining a three-dimensional data model of the lake and reservoir environment;

[0076] The hull module design module is used to design the hull module according to the three-dimensional data model of the lake environment, thereby obtaining the hull module design data, and performing hull structure topology optimization on the hull module design data to obtain modular hull structure design data, which is then uploaded to the dredging vessel production management platform to execute the hull structure production task;

[0077] The sensor data fusion module is used to obtain hull sensor data and perform sensor data fusion based on multi-source lake and reservoir data and hull sensor data to obtain a lake and reservoir environment sensor network; real-time sensor data is collected through the lake and reservoir environment sensor network to obtain real-time environmental status data and real-time hull status data;

[0078] The construction parameter dynamic optimization module is used to simulate the dredging construction of the dredging vessel based on the real-time status data of the environment and the modular hull structure design data, thereby obtaining the dredging construction simulation data of the dredging vessel; based on the dredging construction simulation data of the dredging vessel, the actual dredging construction parameters of the dredging vessel are dynamically optimized to obtain dynamic construction parameter data, and the data is uploaded to the dredging vessel control and management platform to execute the construction parameter adjustment task;

[0079] The pump group parameter iterative optimization module is used to collect the working status of the hull relay pump through the lake environment sensor network to obtain the working status data of the hull relay pump; the hull relay pump working status data is used to optimize the pump group collaborative control to obtain the optimal pump group control parameter data, and then upload it to the dredging vessel control and management platform to execute the pump group control task;

[0080] The hull evacuation strategy analysis module is used to collect real-time environmental status data based on the lake and reservoir environmental sensor network, thereby obtaining real-time dredging environmental status data; perform hull module disassembly strategy analysis based on real-time dredging environmental status data and modular hull structure design data, thereby obtaining a hull module disassembly strategy; and integrate hull module transportation strategies based on lake and reservoir transportation environmental data and hull module disassembly strategies, thereby obtaining a hull module transportation strategy.

[0081] The system for three-dimensional modular design and construction of a dredging vessel of the present invention can implement any one of the three-dimensional modular design and construction methods of a dredging vessel of the present invention, and is used to combine the operation and signal transmission medium between each module to complete the three-dimensional modular design and construction method of a dredging vessel. The internal modules of the system cooperate with each other, thereby improving the overall efficiency of the dredging work. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0083] Figure 1 A schematic flow chart of the steps of the method for three-dimensional modular design and construction of a dredging vessel according to the present invention;

[0084] Figure 2 Detailed flowchart of step S1 in the present invention.

[0085] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0086] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0087] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0088] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0089] To achieve this, please refer to Figures 1 to 2 The present invention provides a method for three-dimensional modular design and construction of a dredging vessel, the method comprising the following steps:

[0090] Step S1: Acquire multi-source lake and reservoir data, and perform lake and reservoir environment analysis based on the multi-source lake and reservoir data, thereby obtaining lake and reservoir water environment data and lake and reservoir transportation environment data; perform three-dimensional environmental data modeling based on the lake and reservoir water environment data and the lake and reservoir transportation environment data, thereby obtaining a three-dimensional data model of the lake and reservoir environment;

[0091] In this embodiment, detailed hydrological data for the target lake or reservoir is acquired through a variety of sensors and data acquisition devices. This data includes hydrological and meteorological data such as water velocity, lake level, temperature, salinity, sediment concentration, flow direction, precipitation, air pressure, and temperature, as well as environmental factors such as bottom sediment type, lake topography, and transportation routes. Furthermore, long-term data related to the ecological environment surrounding the lake or reservoir, such as water quality, vegetation cover, and lake biomes, is collected to comprehensively assess the lake or reservoir environment. Hydrological data is collected through various channels, including publicly available online resources, established hydrological monitoring stations, remote sensing satellite data, drone aerial photography, and networks of fixed and floating sensors. This collected hydrological data is then fused with other environmental data. Data fusion techniques, such as Kalman filtering and particle filtering, are used to process sensor data from various sources, remove noise, and correct errors to ensure data accuracy and timeliness. This data can be used to analyze the changing trends in the lake or reservoir's hydrological environment and their impact on vessel operations, such as the impact of changes in water velocity on vessel propulsion and the impact of changes in sediment concentration on pump efficiency. Based on this multi-source data, a three-dimensional spatial environment model of the lake and reservoir was constructed using Geographic Information System (GIS) technology and 3D modeling software (such as AutoCAD and ArcGIS). This model not only incorporates information on water depth and variations in lake bottom sediments, but also includes fluid dynamics such as current, temperature, and sediment in the flowing water. Comprehensive analysis of this data yields a 3D environmental data model that will provide foundational data support for subsequent steps such as hull module design, construction simulation, and construction parameter optimization.

[0092] Step S2: Designing a hull module based on the three-dimensional data model of the lake environment to obtain hull module design data, and performing hull structure topology optimization on the hull module design data to obtain modular hull structure design data, which is uploaded to the dredging vessel production management platform to execute the hull structure production task;

[0093] In this embodiment, a modular hull design is performed for a dredging vessel based on a three-dimensional data model of the lake environment (including data on water flow, sediment concentration, bottom sediment type, and lake water level). Using this environmental data, computational fluid dynamics (CFD) simulation and finite element analysis (FEA) techniques are used to simulate the vessel's performance in different hydrological environments. The shape, size, and structural layout of each hull module are determined by analyzing the stress conditions, stability, and operational efficiency of each hull module. For example, simulation results show that in waters with high sediment concentrations, the pump module of the hull needs to increase the pump diameter and pump blade area to improve sediment extraction efficiency; while in waters with faster water flow, the propeller module of the hull needs to increase the power of the thrust system and optimize the hull's streamlined design to reduce resistance. In modular hull design, particular consideration is given to the interfaces and connection methods between hull modules to ensure the modules' detachability and reassembly, allowing the hull structure to be flexibly adjusted to adapt to changing operating conditions under different construction environments. Next, the hull module design data is subjected to structural topology optimization through a topology optimization algorithm (such as one based on a genetic algorithm or a simulated annealing algorithm). This process not only optimizes the material usage of each hull module and reduces redundant parts, but also ensures the stability and efficiency of the hull in complex water conditions. For example, the hull structure is optimized to make it more adaptable to changes in the hull's center of gravity and fluctuations in the pump group load, thereby maximizing structural strength and operational efficiency. The optimized modular hull structure design data, including detailed design drawings of each module component, material selection, assembly process and other information, will be uploaded to the dredging vessel production management platform and used as production task input to guide the manufacture and assembly of each hull module. The production management platform will arrange manufacturing tasks based on these design data, allocate relevant resources, and ensure that the hull structure is efficiently produced in accordance with design requirements.

[0094] Step S3: acquiring hull sensor data, and performing sensor data fusion based on multi-source lake and reservoir data and hull sensor data, thereby obtaining a lake and reservoir environment sensor network;

[0095] In this embodiment, data from various sensors on the dredging vessel are obtained. These sensors include hull structure monitoring sensors, pump group operating status monitoring sensors, propeller performance monitoring sensors, and environmental sensors (such as water flow rate, temperature, humidity, sediment concentration, etc.). These sensors are installed at different locations on the hull to collect real-time data on the hull's operating status and construction environment. Hull structure sensors (such as accelerometers and pressure sensors) monitor the forces and strains on various parts of the hull to ensure that the hull can maintain structural safety in complex environments. Pump group sensors (such as flow meters and pressure sensors) record the operating status of the pump group, ensuring that the pump group can operate efficiently and detect faults in a timely manner. Propeller performance monitoring sensors monitor the propeller's speed, power, and efficiency in real time to ensure that the hull's travel speed is consistent with expectations. At the same time, lake and reservoir environmental sensors (such as water quality monitors and flow rate sensors) included in the multi-source lake and reservoir data continuously monitor the lake and reservoir's hydrological data, such as water flow direction, flow rate, water level, sediment concentration, etc. These data can reflect environmental changes in different construction areas and affect the construction efficiency and safety of the hull. All of this sensor data is aggregated into a central data acquisition system via the Internet of Things (IoT). Next, multi-source data fusion is used to integrate the vessel sensor data with lake and reservoir environmental data. Data fusion algorithms, such as Kalman filtering or particle filtering, are employed to perform real-time calibration and denoising of the various sensor data, ensuring accuracy and consistency. Data fusion combines feedback from multiple sensors to provide a more accurate and comprehensive perception network of the lake and reservoir environment and vessel operating status.

[0096] Step S4: The working status of the hull relay pump is collected through the lake environment sensor network to obtain the working status data of the hull relay pump; the working status data of the hull relay pump is optimized by pump group collaborative control to obtain the optimal pump group control parameter data, and the data is uploaded to the dredging vessel control management platform to execute the pump group control task;

[0097] In this embodiment, real-time operating status data of the hull relay pumps is acquired through a lake and reservoir environmental sensor network. This data includes the pump unit's operating pressure, flow rate, power consumption, mud discharge volume, and pump body temperature. Sensors installed at key locations on the pump unit and hull (such as flow sensors, pressure sensors, and power meters) monitor the pump unit's operation in real time to ensure proper operation within preset operating conditions. Based on the lake and reservoir environmental and hull status data, a pump unit collaborative control optimization algorithm is employed to optimize the pump unit's operating status. The goal of pump unit collaborative control optimization is to ensure load balancing and optimal efficiency across different operating conditions, avoiding equipment loss and energy efficiency degradation caused by excessive loading or uneven operation. For example, if multiple pump units are unevenly loaded, this may result in excessive energy consumption or reduced mud discharge efficiency for some pump units. The optimization algorithm automatically adjusts the operating pressure, flow rate, and speed of each pump unit to evenly distribute the load across all pump units, improving overall operating efficiency. A dynamic optimization control algorithm (such as a genetic algorithm or particle swarm optimization) is employed to adjust the operating parameters of each pump unit. This algorithm calculates the optimal operating parameter combination based on real-time feedback from the pump unit status data. The optimization results generate optimal pump control parameter data, including operating pressure, flow rate, and speed for each pump group. These optimized pump control parameter data is uploaded to the dredging vessel control and management platform. The platform uses these control parameters to schedule pump groups and execute tasks, ensuring maximum efficiency during dredging operations. The platform also monitors the pump group's operating status in real time, enabling timely adjustments and feedback. This control mechanism improves vessel operating efficiency, reduces energy consumption, and ensures operational safety.

[0098] Step S5: Simulating the dredging construction of the dredging vessel based on the lake and reservoir environment sensor network and the modular hull structure design data, thereby obtaining dredging construction simulation data of the dredging vessel; dynamically optimizing the actual dredging construction parameters of the dredging vessel based on the dredging construction simulation data, thereby obtaining dynamic construction parameter data, and uploading the data to the dredging vessel control and management platform to execute the construction parameter adjustment task;

[0099] In this embodiment, based on the real-time environmental data (such as water flow rate, sediment concentration, ambient temperature, etc.) collected from the lake environment sensor network and the modular hull structure design data (such as hull size, weight, module configuration, etc.), the construction simulation of the dredging vessel is carried out using multi-physics field coupling simulation technology. The simulation uses computational fluid dynamics (CFD) and finite element analysis (FEA) to simulate the dynamic behavior of the hull during dredging operations and its interaction with the surrounding environment. During the simulation process, the different structural characteristics of the hull modules, environmental impacts, construction objectives and other factors are taken into account to predict the working state of the dredging vessel under different environmental conditions. For example, the simulation results may show that in areas with higher sediment concentrations, the efficiency of the hull's pump group module may decrease due to the inhalation of more sediment; or in areas with faster water flow, the thrust of the hull propeller is insufficient to maintain the stability of the hull. These all require adjusting the construction parameters to optimize the working state of the hull. Based on the dredging vessel construction simulation data, the actual dredging construction parameters are dynamically optimized through dynamic optimization algorithms (such as adaptive control algorithms or fuzzy control algorithms). Specifically, the algorithm automatically adjusts key parameters, such as pump operating pressure, ship speed, and pump flow rate, based on construction progress, environmental changes, and simulation data to ensure optimal construction efficiency. The optimized construction parameters (such as pump operating parameters, propeller power settings, and ship attitude adjustments) are uploaded to the dredging vessel control and management platform, which adjusts the construction parameters and executes the task. The dredging vessel control system adjusts the operating status of systems such as the pump group and propeller in real time based on the optimized parameters to achieve the best construction results and ensure the smooth progress of dredging operations.

[0100] Step S6: Real-time environmental status data is collected based on the lake and reservoir environmental sensor network to obtain real-time dredging environmental status data; hull module disassembly strategy analysis is performed based on the real-time dredging environmental status data and the modular hull structure design data to obtain the hull module disassembly strategy; hull module transportation strategy integration is performed based on the lake and reservoir transportation environmental data and the hull module disassembly strategy to obtain the hull module transportation strategy.

[0101] In this embodiment, the dredging vessel collects environmental status data in real time through the lake environment sensor network, including information such as water flow rate, water quality, sediment concentration, and temperature. These data provide important references for the subsequent disassembly and transportation of the hull modules. By combining real-time environmental status data with modular hull structure design data, the difficulty of disassembling and transporting the hull modules is analyzed. For example, some modules may require longer time or more sophisticated equipment support during disassembly due to their heavy weight or complex connection with other modules. The hull module disassembly strategy analysis uses a decision support system (DSS) to determine the optimal disassembly sequence and disassembly method for each module based on real-time data and design data. Assuming that the pump group module of the hull is heavier and occupies more space than the propeller module, the pump group module needs to be disassembled first during the disassembly process to make room for the disassembly of other modules. Then, the transportation strategy is integrated based on the lake transportation environment data (such as water flow, lake surface conditions, etc.) and the disassembly strategy. Through transportation planning algorithms (such as the Dijkstra algorithm), factors such as the path, speed, and risk of module transportation are evaluated to formulate a transportation plan. For example, if the water flow is high during transportation, a safer channel may be selected to avoid the vessel tilting or equipment damage during transportation. The integrated hull module transportation strategy will be implemented in the dredging vessel control and management platform. The platform schedules the transportation of hull modules based on the transportation strategy and monitors environmental changes and hull status in real time during transportation to ensure that the disassembled modules can be safely and smoothly transported to the designated location.

[0102] Optionally, step S1 specifically includes:

[0103] Step S11: acquiring multi-source lake and reservoir data, and performing data preprocessing on the multi-source lake and reservoir data, thereby obtaining the multi-source lake and reservoir data to be analyzed;

[0104] In this embodiment, detailed hydrological data, including that of the target lake or reservoir, is acquired through a variety of sensors and data acquisition equipment. This data includes hydrological and meteorological data such as water velocity, lake level, temperature, salinity, sediment concentration, flow direction, precipitation, air pressure, and air temperature, as well as environmental factors such as bottom sediment type, lake topography, and transportation routes. Furthermore, long-term data related to the ecological environment surrounding the lake or reservoir, such as water quality, vegetation cover, and lake biomes, is collected to facilitate a comprehensive assessment of the lake or reservoir environment. Hydrological data is collected through a variety of channels, including publicly available online resources, established hydrological monitoring stations, remote sensing satellite data, drone aerial photography, and networks of fixed and floating sensors. Data preprocessing includes steps such as data denoising, missing value supplementation, and time synchronization to ensure data consistency and accuracy. Specifically, for hydrological data collected by sensors, a Kalman filter algorithm is used to remove noise and supplement intermittent data from water level sensors to obtain high-quality data for analysis. The processed data is stored in a standardized format to facilitate subsequent classification and analysis.

[0105] Step S12: performing multi-source data classification on the multi-source lake and reservoir data to be analyzed, thereby obtaining lake and reservoir hydrological data and lake and reservoir topographic data;

[0106] In this embodiment, the pre-processed multi-source lake and reservoir data are classified, and the data are mainly divided into lake and reservoir hydrological data and lake and reservoir topographic data. Lake and reservoir hydrological data include dynamic changing data such as water flow velocity, temperature, salinity, and water level, which are mainly used to reflect the hydrological characteristics of lakes and reservoirs and their impact on dredging operations; lake and reservoir topographic data include static data such as lake bottom depth, slope, and topographic changes, which are used to describe the structural characteristics of the lake and reservoir bottom. In order to ensure the effective classification of data, data clustering analysis technology (such as K-means algorithm or DBSCAN clustering) is used to group different types of sensor data and different types of information, and the data is marked and stored through automated classification tools. For example, data with large dynamic changes such as water flow velocity, sediment concentration and temperature will be classified as lake and reservoir hydrological data, while relatively static data such as lake bottom type, lake bottom depth and topographic changes will be classified as lake and reservoir topographic data. The classified data will be further input into the analysis system to provide necessary information support for subsequent lake and reservoir environmental modeling and construction simulation.

[0107] Step S13: performing a lake / reservoir surrounding terrain analysis based on the lake / reservoir terrain data to obtain a lake / reservoir surrounding terrain depth map, and performing a lake / reservoir transportation environment analysis based on the lake / reservoir surrounding terrain depth map to obtain lake / reservoir transportation environment data;

[0108] In this embodiment, a surrounding terrain analysis is performed based on lake and reservoir topography data, using digital elevation model (DEM) technology to extract specific topographic features from lake bottom depth data. By analyzing the topographic changes at and around the lake bottom, a topographic depth map of the lake's perimeter is generated. This depth map can provide a detailed description of the lake's topographic undulations and slope variations, providing a precise analysis of the lake bottom. Next, based on the topographic depth map of the lake's perimeter, a lake and reservoir transportation environment analysis is performed. This analysis focuses on the impact of factors such as water flow, sediment concentration, lake depth, and road surface topography and slope on transportation channels. For example, if certain areas have rapid water flow or a steep lake bottom slope, navigation will be difficult for ships, requiring special operation plans. By comprehensively considering environmental factors such as the topographic characteristics of the lake's perimeter, transportation channels, flow rate, and sediment concentration, lake and reservoir transportation environment data is generated. This transportation environment data will include assessments of key parameters such as transportation safety, waterway accessibility, and land connectivity, providing important decision support for subsequent construction and transportation operations.

[0109] Step S14: performing lake and reservoir water environment analysis based on lake and reservoir hydrological data and a topographic depth map of the lake and reservoir surroundings, thereby obtaining lake and reservoir water environment data;

[0110] In this embodiment, the lake and reservoir water environment analysis is carried out by combining the lake and reservoir hydrological data with the lake and reservoir surrounding terrain depth map. The hydrological data reflects the dynamic changes of the water flow velocity, sediment concentration, temperature, precipitation, etc. of the lake and reservoir, while the terrain depth map provides the static characteristics of the lake bottom. By comprehensively analyzing these two types of data, multivariate regression analysis or machine learning algorithms (such as decision trees, support vector machines, etc.) are used to model the environment of the lake and reservoir water area, and calculate the water environment data of different areas of the lake and reservoir. These environmental data include information such as water flow rate, sediment deposition, temperature gradient, etc. in different areas, which can reflect the changes in the lake and reservoir water area and its impact on dredging operations. For example, in areas with higher water flow velocity, sediment is more easily washed away, while in areas with slower water flow, sediments will accumulate, affecting dredging efficiency. Through this comprehensive analysis, a comprehensive set of lake and reservoir water environment data is finally obtained, providing accurate environmental support for subsequent construction.

[0111] Step S15: Perform three-dimensional environmental data modeling based on the lake and reservoir water environment data and the lake and reservoir transportation environment data, thereby obtaining a three-dimensional data model of the lake and reservoir environment.

[0112] In this embodiment, the lake and reservoir water environment data and the lake and reservoir transportation environment data are combined, and three-dimensional modeling of the lake and reservoir environment is performed through three-dimensional modeling technology (such as OpenGL, Unity3D, etc.). The model includes factors such as the terrain, water area, and transportation channel of the lake and reservoir, which can fully reflect the environmental characteristics of the lake and reservoir. During the modeling process, factors such as the water depth, bottom type, water flow changes, and transportation routes of the lake and reservoir are taken into consideration to create a dynamic, interactive three-dimensional lake and reservoir environment model. This three-dimensional model can not only display the static terrain and water environment of the lake and reservoir, but also dynamically simulate the impact of factors such as water flow, sediment deposition, and environmental changes on dredging operations. For example, the model will simulate the running trajectory and operating efficiency of the dredging ship under different water flow rates and temperature conditions. These data will provide a scientific basis for the operation optimization of the dredging ship, construction path planning, etc., and provide precise guidance for the formulation of subsequent construction tasks.

[0113] Optionally, step S13 is specifically as follows:

[0114] Step S131: performing multi-source terrain data classification on lake and reservoir terrain data to obtain lake bottom sonar sensor data and lake and reservoir lidar data;

[0115] In this embodiment, raw data is obtained from multi-source lake and reservoir terrain data, including lake bottom sonar sensor data and lake and reservoir lidar data. In order to effectively classify these data, classification algorithms (such as metadata-based classification, data identifiers, or sensor locations) are used to classify the collected data according to the type of sensor and acquisition method. Lake bottom sonar sensor data usually contains information on the depth and terrain characteristics of the bottom of the water, while lidar data provides high-precision three-dimensional information about the land surface, coastline, and surrounding terrain of the lake area. These two types of data are stored separately and prepared for subsequent analysis and processing.

[0116] Step S132: performing sonar inverse distance weighted terrain point cloud generation on the lake bottom sonar sensor data, thereby obtaining lake bottom terrain point cloud data; performing laser reflection signal terrain point cloud conversion on the lake lidar data, thereby obtaining lake surface terrain point cloud data;

[0117] In this embodiment, for the lake bottom sonar sensor data, the sonar inverse distance weighting method is used to convert the depth data measured by the sonar into a lake bottom topography point cloud. Specifically, the intensity of the sonar reflection signal is inversely proportional to the distance. Therefore, this characteristic is utilized to generate accurate lake bottom topography point cloud data through weighted processing. These point cloud data show the undulations and topographic features of the lake bottom. For the lake lidar data, the time delay and intensity of the laser reflection signal are analyzed to perform point cloud conversion to generate lake surface topography point cloud data. These point cloud data reflect the details of the terrain around the lake area, such as changes in the shoreline, vegetation cover, etc. After processing, the lake surface topography point cloud data will provide high-precision geographic information for subsequent spatial analysis.

[0118] Step S133: spatially merging the lake bottom terrain point cloud data and the lake surface terrain point cloud data to obtain lake surrounding terrain point cloud data, and performing rasterization depth map conversion on the lake surrounding terrain point cloud data to obtain a lake surrounding terrain depth map;

[0119] In this embodiment, spatial merging is performed on the basis of the generated lake bottom terrain point cloud data and lake land surface terrain point cloud data. Through geographic coordinate alignment and point cloud registration, combined with different data sources of lidar and sonar sensors, complete lake and reservoir surrounding terrain point cloud data is generated. These point clouds include the depth information of the lake bottom and the undulations of the shoreline and land surface. Afterwards, the merged point cloud data is rasterized, that is, the irregular point cloud data is converted into a regular grid form to generate a depth map of the terrain around the lake. This conversion converts the point cloud data into an isobath map, which is convenient for analyzing the terrain features around the lake, such as water depth distribution, slope, etc. The rasterized depth map can provide effective numerical support for subsequent environmental simulation and construction path planning.

[0120] Step S134: performing lake-reservoir land surface terrain zoning on the lake-reservoir surrounding terrain depth map, thereby obtaining lake-reservoir land surface terrain zoning data, and calculating the proportion of gentle terrain based on the lake-reservoir land surface terrain zoning data, thereby obtaining lake-reservoir land surface gentle terrain proportion data;

[0121] In this embodiment, terrain zoning is performed based on the terrain depth map around the lake. By setting a threshold (such as an area with a slope greater than a certain standard is considered as steep terrain, and an area with a slope less than a certain standard is considered as gentle terrain), the land terrain is divided into different areas and marked as lake and reservoir land surface terrain zoning data. This step can help identify different terrain features around the lake area, such as gentle areas, areas with larger slopes, and relatively steep terrain areas. Based on these terrain zoning data, the proportion of gentle terrain on the lake and reservoir land surface is further calculated, that is, the proportion of gentle areas in the total land area is calculated. This data is of great significance for subsequent road planning and traffic flow analysis, because gentle terrain areas are more suitable for building transport roads or carrying heavy equipment.

[0122] Step S135: Obtaining a set of drone images of the lake and reservoir area, and performing edge detection on the drone images of the lake and reservoir area to obtain lake and reservoir area contour data; performing lake and reservoir area road identification based on the lake and reservoir area contour data to obtain lake and reservoir area road data;

[0123] In this embodiment, aerial photography of the lake and reservoir area is performed by using a drone to obtain a high-resolution image data set. Then, the edge detection algorithm in computer vision technology (such as Canny edge detection) is used to extract the contour data of the lake and reservoir area from the drone image. These edge data can accurately identify important geographical features such as the boundary and shoreline of the lake and reservoir area. After obtaining the contour data of the lake and reservoir area, image processing technology is used to further perform road recognition to identify transportation facilities such as roads and traffic paths in the area. Through edge detection and morphological processing and other technologies, different road types (such as dirt roads, gravel roads, hardened roads, etc.) are identified to generate lake and reservoir area road data. These road data are of great reference value for planning the transportation routes of dredging ships and analyzing land transportation bottlenecks.

[0124] Step S136: identifying lake and reservoir land transportation bottlenecks based on the lake and reservoir land surface flat terrain proportion data and lake and reservoir area road data, thereby obtaining lake and reservoir land transportation bottleneck data;

[0125] In this embodiment, the lake and reservoir land transportation bottlenecks are identified based on the data on the proportion of flat terrain on the lake and reservoir land surface and the road data in the lake and reservoir area. The terrain proportion data is used to determine which areas have a higher flatness, and these areas are suitable for land transportation. Then, combined with the road data, it is analyzed which roads have poor traffic capacity and may become bottlenecks that limit efficiency during transportation. For example, it can be identified that some roads have too large slopes or too rugged road surfaces, and these roads may become obstacles to transportation. By identifying these bottleneck points, lake and reservoir land transportation bottleneck data is generated, potential problem areas in the transportation route are pointed out, and optimization suggestions (such as detours or road repairs) are provided to provide decision support for subsequent transportation planning.

[0126] Step S137: Perform spatial integration of the lake and reservoir land transportation environment based on lake and reservoir land surface terrain zoning data, lake and reservoir land surface flat terrain proportion data, lake and reservoir area road data, and lake and reservoir land transportation bottleneck data, thereby obtaining lake and reservoir transportation environment data.

[0127] In this embodiment, lake and reservoir land surface terrain zoning data, lake and reservoir land surface flat terrain proportion data, lake and reservoir regional road data, and lake and reservoir land transportation bottleneck data are obtained respectively. These data are spatially integrated to form lake and reservoir land transportation environment data. Through the GIS system, combined with real-time information on terrain, roads, and transportation bottlenecks, a comprehensive lake and reservoir transportation environment model can be established. This integrated data model can provide information such as transportation routes, road condition assessments, and transportation bottleneck locations in the surrounding areas of the lake, which will help plan efficient dredging ship transportation routes, reduce stagnation time in transportation, and improve operational efficiency. These integrated data will be used to formulate more accurate transportation task allocation and scheduling strategies to ensure the smooth progress of lake and reservoir dredging operations.

[0128] Optionally, step S14 is specifically as follows:

[0129] The depth map of the terrain around the lake is used to divide the lake into different depth zones, thereby obtaining the depth zone data of the lake water area;

[0130] In this embodiment, after obtaining the depth map of the terrain around the lake, the lake water area is partitioned by the set depth threshold (such as less than 2 meters for shallow water, 2-5 meters for medium water, and greater than 5 meters for deep water). For example, the water depth is spatially divided by the digital elevation model (DEM). For each water depth section, the spatial analysis tool will be used to calibrate the area and generate water depth partition data. This data provides a detailed regional basis for the subsequent analysis of water flow velocity, temperature and sediment concentration, ensuring accurate analysis of environmental changes in each depth area.

[0131] Perform spatial alignment of lake and reservoir water depth zoning data and lake and reservoir hydrological data to obtain hydrological-topographic fusion data;

[0132] In this example, spatial registration technology is used to align hydrological data with water depth partitioning data. For example, hydrological sensor data is paired with water depth partitioning data using spatial interpolation methods to ensure that each hydrological data point corresponds to the correct water depth segment. This aligned data is then aggregated into fused hydrological and topographic data to support further analysis of the water environment.

[0133] Based on the hydrological-topographic fusion data, the water flow velocity distribution statistics in the deep water area are carried out to obtain the water flow velocity distribution data in the deep water area;

[0134] In this example, water velocity distribution data is obtained by statistically analyzing flow velocities in different water depth sections using fused hydrological and topographic data. For example, the mean, standard deviation, maximum, and minimum values ​​of flow velocity data within different water depth sections are calculated. For areas with a water depth of 2-5 meters, assuming a flow velocity range of 0.2-0.8 m / s, a spatial distribution map of water velocity is created based on this data, further analyzing which areas have higher and lower flow velocities. This data can help predict sediment transport routes and the optimal operating areas for dredging vessels.

[0135] The temperature change trend of the deep water area is predicted based on the hydrological-topographic fusion data, thereby obtaining the temperature change trend data of the deep water area;

[0136] In this example, temperature data from the fused hydrological-topographic data is combined with water depth zoning data, and prediction algorithms (such as ARIMA, regression analysis, or neural networks) are used to predict temperature trends in deep water areas. For example, the temperature fluctuation trends in a shallow water area over the past few months are analyzed, and factors such as seasonal changes, sunlight exposure, and water flow velocity are combined to predict temperature changes over the next period of time. The prediction results can provide data support for temperature-sensitive operations (such as ecological restoration and dredging).

[0137] The sediment concentration in the deep water area of ​​lakes and reservoirs is counted based on the hydrological-topographic fusion data to obtain the sediment concentration data in the deep water area;

[0138] In this example, sediment concentration data from the fused hydrological and topographic data is combined with water depth zoning data to calculate sediment concentrations in areas of varying water depths. For example, for an area with a water depth of 2 meters, sediment concentrations are calculated and their changing trends over time are analyzed. Assuming the sediment concentration in that area reaches 300 mg / L during a certain period, the mean, maximum, and minimum values ​​of this data are calculated. Based on the changing sediment concentration trends, a risk assessment of silt deposition is then provided. This data helps identify key operational areas for dredging vessels.

[0139] Based on the water velocity distribution data, temperature change trend data and sediment concentration data in the deep water area, the silt deposition hotspot area is identified to obtain the silt deposition hotspot area data;

[0140] In this embodiment, silt deposition hotspots are identified using multivariate analysis (e.g., weighted average analysis, cluster analysis, etc.) based on water velocity distribution data, temperature trend data, and sediment concentration data across depths. For example, by combining regions with low flow velocity, high sediment concentration, and relatively constant temperature, hotspots where sediment deposition is more likely to occur under these conditions can be identified. By combining these data, silt deposition hotspot data is generated, enabling dredging vessels to prioritize operations in these hotspots, thereby improving dredging efficiency.

[0141] The lake and reservoir water environment data are spatially integrated by integrating the silt deposition hotspot area data, the water velocity distribution data in the deep water area, the temperature change trend data in the deep water area, and the sediment concentration data in the deep water area to obtain the lake and reservoir water environment data.

[0142] In this example, after obtaining data on silt deposition hotspots, this data is spatially integrated with data on flow velocity distribution, temperature trends, and sediment concentration to form comprehensive lake and reservoir environmental data. For example, using a GIS platform, these data are spatially matched and overlaid to create a comprehensive environmental model. This model can display flow velocity, temperature, sediment concentration, and the distribution of silt deposition hotspots at different water depths, helping decision-makers optimize dredging operations, improve efficiency, and minimize environmental impact.

[0143] Optionally, step S2 is specifically:

[0144] Step S21: performing environmental integration of the silt deposition hotspot area based on the three-dimensional data model of the lake and reservoir environment, thereby obtaining environmental data of the silt deposition hotspot area, wherein the environmental data of the silt deposition hotspot area includes water depth data of the silt deposition hotspot area, flow velocity data of the silt deposition hotspot area, and sediment data of the silt deposition hotspot area;

[0145] In this embodiment, a multi-dimensional silt deposition hotspot area environmental model is constructed by integrating the lake and reservoir water area environmental data (including water depth, flow velocity, sediment concentration, etc.) in the three-dimensional data model of the lake and reservoir environment with the silt deposition hotspot area data. Specifically, the water depth, flow velocity and sediment concentration of each hotspot area are statistically integrated through spatial analysis technology (such as Kriging interpolation or rasterization processing) to generate comprehensive environmental data of the silt deposition area. For example, in an area with a depth of 5 meters and a slow flow rate, the sediment concentration is high, and this area is identified as a key area for silt deposition. The data generated in this step will provide an environmental basis for subsequent hull design, dredging operations, etc.

[0146] Step S22: obtaining the dredging ship model structure data, and performing modular box structure decomposition on the dredging ship model structure data, thereby obtaining the model hull module structure data and the model function module structure data;

[0147] In this example, the existing dredging vessel prototype structural data (such as the hull, functional modules, and power system) is modularized, allowing the hull structure and functional modules to be designed separately. For example, the hull structure may include the support structure and cabin, while the functional modules may include sediment treatment, pumping systems, and sensor arrays. After modularization, detailed hull module structure data and functional module structure data are generated, including the dimensions, weight, connection method, and functional description of each module, to facilitate subsequent design and optimization.

[0148] Step S23: Designing the dredging vessel hull module size and weight based on the sample hull module structure data according to the lake and reservoir transportation environment data and the silt deposition hotspot area environment data, thereby obtaining the dredging vessel hull module design structure data;

[0149] In this embodiment, the size and weight of the hull module are designed in combination with the transportation environment data of the lake and reservoir (such as road width, ground slope, transportation channel, etc.) and the environmental data of the silt deposition hotspot area (such as water depth, flow rate, etc.). Through data analysis, the specific requirements for the size, weight and carrying capacity of the dredging ship in different transportation environments are obtained. For example, if it is necessary to pass through a narrow road or bridge during transportation, a smaller hull module will be designed for transportation. At the same time, if it is necessary to operate in deep water areas, the hull design will take into account higher stability and greater load-bearing capacity. Ultimately, the hull module design data of the dredging ship that meets both transportation and operation requirements is generated, including the specific size, weight and special structural requirements of each module (such as reinforcement points or weight reduction design).

[0150] Step S24: Designing a dredging vessel functional module based on the sample functional module structure data according to the silt deposition hotspot area environmental data, thereby obtaining dredging vessel functional module design structure data;

[0151] In this embodiment, the functional modules of the dredging vessel are designed and optimized based on the environmental data of the silt deposition hotspot area (such as water depth, flow rate, sediment concentration, etc.). For example, for areas with high silt concentration, a module with an efficient sediment suction function is designed, and a high-flow suction pump and filtration system are used to improve the dredging efficiency. For areas with faster flow rates, a more powerful propulsion system is designed to enhance the stability and power output of the hull to ensure that it can still work efficiently in a strong current environment. In addition, the size, power, adjustability, etc. of the functional modules will be optimized according to the specific environment. Ultimately, functional module design data that meets the requirements of the silt deposition environment is generated, including the size, power, connection method, etc. of the module.

[0152] Step S25: performing module connection design on the dredging ship hull module design structure data and the dredging ship functional module design structure data, thereby obtaining dredging ship module connection design data; performing modular structural connection on the dredging ship hull module design structure data and the dredging ship functional module design structure data according to the dredging ship module connection design data, thereby obtaining initial modular hull structure design data;

[0153] In this embodiment, the connection design between the dredging vessel's hull modules and functional modules is designed to ensure reliable connection and good coordination between the modules. Using modular design principles, the module connections are designed using methods such as bolts, snap-fit ​​connections, or chute connections. Specifically, the hull modules and functional modules are connected through interface design. These interfaces are precisely calculated and optimized to ensure a tight fit between the modules and ease of disassembly and reassembly. For example, when designing the connection between the sediment extraction system module and the hull, the connection points must be able to withstand the enormous power generated by the suction pump and be conveniently located for transportation and assembly. This process generates module connection design data and prepares detailed design documents for subsequent modular structural connection. The specific modular structural connection operations are performed based on this design data. The hull modules and functional modules are rationally allocated and combined. For example, the power system and dredging pump modules can be connected to the hull modules via sliding rails, facilitating quick replacement and maintenance in different operating environments. Furthermore, the interface design of the connection points must be sealed to prevent moisture from penetrating the hull and affecting the strength and stability of the hull structure. After the module connection design is completed, preliminary modular hull structure design data is generated, providing specific installation instructions, connection drawings, and connection requirements for each module. This design data includes the specific dimensions of the inter-module connections, required materials, and connection load-bearing analysis. Ultimately, this preliminary design data is combined with the module connection data to form the initial modular hull structure design data for the dredging vessel.

[0154] Step S26: Perform lightweight topology optimization on the initial modular hull structure design data to obtain modular hull structure design data, and upload the data to the dredging vessel production management platform to execute the hull structure production task.

[0155] In this embodiment, the initial modular hull structure design data is topologically optimized to achieve a lightweight design of the hull. A topological optimization algorithm (such as a structural optimization method based on finite element analysis) is used to analyze the stress conditions of various parts of the hull, identify and remove excess materials, and optimize the structure of the hull. Under the premise of ensuring the strength and stability of the hull, unnecessary materials are reduced to reduce the weight of the hull. For example, the structural wall thickness of certain cabin areas can be reduced during the optimization process, or the efficiency of material utilization can be improved by adjusting the cabin distribution. Finally, the optimized modular hull structure design data is generated and uploaded to the dredging vessel production management platform. The platform starts the production process based on these data and begins to produce hull modules that meet the optimization requirements.

[0156] Optionally, step S24 is specifically as follows:

[0157] According to the dredging vessel hull module design structure data, the functional module structure size of the sample functional module structure data is adapted to obtain the functional module structure data to be designed;

[0158] In this embodiment, the design structure data of the hull module of the dredging ship is used as the basis, and the functional modules are adaptively designed in combination with the environmental requirements of the target area (such as water depth, flow rate, silt type, etc.). During the specific implementation, the size and structure of the model functional module are dynamically adjusted to make it perfectly match the hull module. For example, if the hull design needs to adapt to shallower waters, the functional modules (such as dredging pumps and sensor modules) will be designed as compact, low-center-of-gravity structures to ensure the stability of the hull in narrow waters; in deeper waters, they will be designed as modules with higher mud pumping capacity and stronger power support. During the design process, simulation software based on finite element analysis (FEA) technology is used to perform force and dynamic response calculations to ensure that the strength and stability of the module meet the hull load-bearing requirements.

[0159] Adapting the functional module dual drive mode to the functional module structure data to be designed by a preset shore power drive system and a preset diesel drive system, thereby obtaining drive adaptation functional module structure data;

[0160] In this embodiment, the adaptation of the dual drive system (shore power and diesel drive) ensures that the functional modules can operate efficiently in different operating environments. Assume that in the operating environment of a mountain reservoir, shore power can provide stable power support, while in remote areas where shore power is lacking, diesel drive provides backup power. According to the structural data of the functional module to be designed, the power requirements under the two drive modes are calculated, the drive mode switching rules are formulated according to environmental changes, and the power and fuel consumption are optimized. Under shore power drive, the drive system of the functional module will control the current and power distribution through the power management module to ensure that all modules can work efficiently; in diesel drive mode, the diesel engine will automatically adjust the speed according to the load to ensure sufficient power output. By using sensors to monitor the battery power and fuel level in the two power modes in real time, the drive mode can be automatically switched to ensure continuous operation.

[0161] Based on the environmental data of the silt deposition hotspot area, the shipboard sensor deployment design is carried out based on the structural data of the drive adaptation function module, thereby obtaining the modular sensor deployment design data;

[0162] In this embodiment, the layout design of the shipboard sensors is formulated using environmental data of the silt deposition hotspot area (such as water depth, flow rate, sediment concentration, etc.). According to the requirements of different functional modules (such as dredge pump module, propulsion module, etc.), the sensor deployment on the hull will be optimized separately. For example, in areas with high sediment concentration, the sensor module of the dredging vessel needs to monitor the water flow and sediment content in real time to ensure that the dredging pump can work under optimal conditions. When deploying sensors, data such as water depth, flow rate, sediment concentration, and meteorology are transmitted to the central control system in real time through multi-source data fusion technology to provide comprehensive environmental perception for the hull. The sensors will include temperature and humidity sensors, water quality sensors, flow rate sensors, etc., which will be reasonably distributed and equipped according to the structure and functional requirements of the hull modules to ensure that each module can receive the most accurate environmental data.

[0163] According to the environmental data of the silt deposition hotspot area, the modular dredge suction pump group is designed based on the structural data of the drive adaptation function module, thereby obtaining the modular dredge suction pump group design data;

[0164] In this embodiment, the dredge suction pump group is designed based on the specific environmental data of the silt deposition hotspot area (such as silt type, sediment thickness, etc.). Assuming that there is a deep silt deposit in the water area, a modular dredge suction pump with high suction and high efficiency will be designed. During the design, the sludge suction and discharge forces of the pump group will be optimized based on the actual silt concentration and water flow velocity. According to the available space and load capacity of the hull module, the dredge suction pump will be designed as a detachable module for easy maintenance and replacement. During the design process of the pump group, fluid dynamics simulation technology is used to optimize the efficiency and durability of the pump to ensure its stable operation in harsh water conditions.

[0165] The modular sludge suction pump group design data is used to design the pump group collaborative working module through the preset ACC automatic mud control system, thereby obtaining the modular sludge suction pump group module design data;

[0166] In this embodiment, the ACC automatic mud control system calculates the optimal operating parameters for the modular dredge pumps working together based on their design data. The ACC system dynamically adjusts the operating rate and mud discharge pressure of each pump to ensure coordinated operation under varying operating conditions. For example, during dredging operations, if silt concentration in a particular area is high, the system automatically increases the suction power of the corresponding dredge pump while reducing the load on other pumps, thereby achieving energy savings and emissions reductions. The control system also monitors the operating status of the pumps in real time. If any pump is detected to be operating abnormally, the system automatically adjusts or shuts down that pump to prevent damage to other pumps or system instability.

[0167] Based on the sediment data of the silt deposition hotspot area, the modular structure design of the multi-stage sediment filtration and the modular structure design of the sediment storage are carried out on the drive adaptation function module structure data, thereby obtaining the modular sediment separation module design data;

[0168] In this embodiment, a multi-stage filtration module and a sediment storage module are designed based on the sediment data of the silt deposition hotspot area. The sediment filtration module adopts a multi-stage filtration method, using coarse filtration, medium filtration and fine filtration units respectively to remove sediment of different particle sizes from the water. During the design process, a suitable filter medium (such as metal mesh, filter paper, activated carbon, etc.) is selected according to the sediment particle size distribution data, and a suitable filtration rate and mud discharge method are set for each level of filtration unit (the first stage is coarse filtration, the second stage is medium efficiency filtration, and the third stage is fine filtration). For the sediment storage module, a detachable mud storage bin is designed to facilitate regular cleaning and maintenance. The sediment multi-stage filtration module and the sediment storage module are then combined to obtain a modular sediment separation module.

[0169] According to the water depth data and flow velocity data of the silt deposition hotspot area, the power module design and propulsion module design are carried out based on the drive adaptation function module structure data, thereby obtaining modular power module design data;

[0170] In this embodiment, the most suitable power module and propulsion module are designed by analyzing the water depth and flow velocity data of the silt deposition hotspot area. Based on the water depth data, the power module will be designed to be adjustable to adapt to different water depth environments, ensuring that the hull can obtain sufficient thrust in both deep and shallow water. The propulsion module is designed with thrusters and propellers of different specifications based on the flow velocity data to meet the operational requirements under different flow rates. A computational fluid dynamics model is used to simulate the propulsion efficiency of the hull in different water conditions and optimize the operating parameters of the propulsion system. The power module and propulsion module are then combined to form a modular power module.

[0171] The modular sensor deployment design data, modular dredging pump module design data, modular sediment separation module design data and modular shore power module design data are connected to the drive module circuit to obtain the dredging ship functional module design structure data.

[0172] In this embodiment, the wiring connections for each module (sensor, dredge suction pump, sediment separation, and shore power module) are designed. First, appropriate power transmission and data communication lines are designed based on the power and communication requirements of each module to ensure efficient interconnection between modules during operation. The power line design takes into account power loss and current safety, utilizing high-efficiency, low-loss cables. Data communication lines are connected via optical fiber or wireless communication systems to ensure real-time data transmission. All lines are designed with ease of modular disassembly and maintenance in mind to ensure stable system operation.

[0173] Optionally, step S4 is specifically:

[0174] Step S41: collecting the working status of the hull relay pump and the real-time construction environment status of the lake and reservoir through the lake and reservoir environment sensor network, thereby obtaining the working status data of the hull relay pump and the real-time construction environment status data of the lake and reservoir;

[0175] In this embodiment, a network of environmental sensors deployed throughout the lake and reservoir collects real-time data on the operating status of the hull relay pump and the construction environment. Sensors monitor the hull relay pump's operating status, including key data such as pump speed, suction volume, discharge pressure, and flow rate, to determine whether it is operating within its intended operating range. Simultaneously, real-time construction environment data for the lake and reservoir includes environmental variables such as water depth, flow rate, and sediment concentration. Data from the environmental sensors is collected synchronously with that from the hull sensors and transmitted in real time to the data processing platform via a wireless communication network.

[0176] Step S42: evaluating the hull pump group efficiency based on the real-time construction environment status data of the lake and reservoir and the working status data of the hull relay pump, thereby obtaining the actual construction efficiency data of the dredging vessel;

[0177] In this embodiment, the work efficiency evaluation is performed by combining the working status data of the hull relay pump (such as flow, pressure, temperature, etc.) with the real-time construction environment data of the lake (such as water flow velocity, silt concentration, water depth, etc.). Assuming that the target work efficiency is to clear a certain volume of silt per hour, the actual silt removal capacity of the current hull pump group is calculated by comparing the real-time pump group flow data with environmental data such as water flow and silt concentration. If there is a high concentration of silt in the environment, it may cause the silt discharge capacity of the pump group to decrease, and a work efficiency report will be generated to evaluate the actual construction efficiency. If the work efficiency is lower than the target, it will prompt that the construction strategy needs to be adjusted, such as increasing the working pressure of the pump group or changing the load distribution of the pump group.

[0178] Step S43: obtaining the construction target of the dredging vessel through the dredging vessel control and management platform, and estimating the actual construction efficiency target completion degree of the dredging vessel actual construction efficiency data according to the construction target, thereby obtaining the actual construction efficiency target completion degree data;

[0179] In this embodiment, the dredging vessel control and management platform receives construction targets set by the construction management team (such as clearing 50 cubic meters of silt per hour) and compares these targets with real-time work efficiency data. For example, if the current hull's work efficiency is calculated to be 40 cubic meters of silt per hour, the actual completion rate will be 80%. At this point, the platform will calculate whether the construction task can be completed on time based on the completion rate of the work efficiency target. If the target completion rate is lower than expected, feedback will be provided to indicate whether it is necessary to adjust the pump group operating parameters, optimize the pump group load, or modify the construction area strategy.

[0180] Step S44: performing pump group coordinated control optimization on the hull relay pump working status data according to the actual construction work efficiency target completion data, thereby obtaining pump group control optimization parameter data;

[0181] In this embodiment, the hull relay pump is collaboratively controlled and optimized based on the actual construction efficiency target completion data. The current working status of the hull relay pump is analyzed, including parameters such as flow, pressure, temperature, and load, to determine whether the load of the pump group is balanced. If it is detected that the load of one pump is too high and the load of the other pump is too low, the working parameters of the pump group (such as pump speed, pressure adjustment, etc.) will be dynamically adjusted according to the degree of load imbalance and the rules of load balancing. On the basis of load balancing, the pump group collaborative working strategy will be further optimized according to real-time environmental data (such as water flow velocity and sediment concentration) through optimization algorithms such as annealing algorithms to ensure that the cooperation of multiple pump groups is more efficient. Through this optimization process, an optimal pump group control parameter data is eventually generated, and these data will be uploaded to the control platform.

[0182] Step S45: uploading the pump group control optimization parameter data to the dredging vessel control management platform to execute the pump group control task; obtaining the real-time relay pump working status data and the real-time construction environment status data after the pump group control;

[0183] In this embodiment, the optimized pump group control parameter data is uploaded to the dredging vessel control and management platform via a wireless network, and the platform immediately executes the pump group control task. The control platform adjusts the pump speed, mud discharge pressure, flow rate, and other parameters based on the optimized parameters. Afterwards, it continues to monitor the operating status of the pump group and the construction environment in real time. Based on the real-time data obtained by sensors (such as pump flow rate, suction pressure, etc.), the platform determines whether the pump is operating as expected, ensuring that the construction progress is not affected.

[0184] Step S46: performing a construction efficiency review on the real-time relay pump working status data after the pump group is controlled and the real-time construction environment status data after the pump group is controlled according to the construction target of the dredging vessel, thereby obtaining a construction efficiency review report;

[0185] In this embodiment, the dredging vessel control and management platform evaluates the working status of the pump group after control and the construction environment data based on the construction objectives of the dredging vessel. The gap between the actual construction progress and the target will be calculated, and a construction efficiency review report will be generated through gap analysis and target comparison methods. The report will list in detail the impact of the working status of the pump group (such as efficiency, energy consumption, mud discharge volume, etc.) and the construction environment (such as water flow, sediment concentration) on the construction efficiency. If the actual efficiency is lower than the target, the report will provide improvement suggestions to help optimize the working status of the pump group. Calculate the efficiency of the current construction and generate a construction efficiency review report. If the efficiency meets the standard, the report will show that the target has been successfully completed; if the efficiency is insufficient, the report will list the possible reasons in detail and provide improvement measures. For example, if the increase in water flow velocity causes the efficiency of the pump group to decrease, the report will recommend adjusting the pump speed or changing the construction area.

[0186] Step S47: Iteratively optimize and adjust the pump group control optimization parameter data according to the construction efficiency review report to obtain the optimal pump group control parameter data, and upload it to the dredging vessel control management platform to execute the pump group control task.

[0187] In this embodiment, based on the construction efficiency review report, the control platform iteratively optimizes the pump group control optimization parameters. If the report indicates that the pump group flow rate does not meet expectations, the pump control parameters will be iteratively adjusted based on real-time environmental changes (such as water flow and sediment concentration). For example, if the pump group is operating inefficiently in an area with high sediment concentration, the pump suction pressure will be increased and the speed will be adjusted. The new optimized parameters will be uploaded to the control management platform via wireless transmission, and the new pump group control task will be executed to ensure that the pump group continues to operate in the optimized state.

[0188] Optionally, step S5 is specifically as follows:

[0189] Step S51: collecting real-time sensor data through the lake environment sensor network to obtain real-time environmental status data and real-time hull status data; simulating the actual dredging vessel construction environment based on the real-time environmental status data to obtain dredging vessel construction environment simulation data;

[0190] In this embodiment, a plurality of environmental sensors are arranged around the dredging vessel, including sensors for water depth, flow velocity, sediment concentration, temperature, turbidity, meteorological conditions, etc. At the same time, sensors are installed on the hull to monitor the hull's posture, pressure, speed and power system status in real time. These sensors transmit real-time data to the hull control and management platform via a wireless network. Taking flow velocity as an example, sensors are arranged in front of and behind the hull, and by acquiring water flow velocity data in real time, the working performance of the hull under different flow velocity environments is simulated. Using real-time environmental data (such as water flow velocity, sediment concentration, etc.) and hull status data (such as pump group pressure, mud discharge flow, etc.), construction environment simulation data of the dredging vessel is generated based on digital simulation technology to provide a basis for subsequent simulations.

[0191] Step S52: performing 3D modeling of the hull structure based on the modular hull structure design data, thereby obtaining a modular hull 3D model; and performing a dredging construction simulation of the dredging vessel on the modular hull 3D model according to the dredging vessel construction environment simulation data, thereby obtaining dredging construction simulation data of the dredging vessel;

[0192] In this embodiment, CAD (computer-aided design) software and BIM (building information modeling) technology are used to convert the design data of the modular hull structure into a three-dimensional digital model. The model contains information such as the size, shape, and material of the hull, and can perform dynamic simulation. During the construction simulation, combined with environmental simulation data, such as the water flow velocity and sediment concentration of the lake, the working state of the hull under these environments is simulated through fluid dynamics simulation (CFD). Assuming that the dredging ship is working in an area with faster water flow, the interaction between the hull and the water flow is simulated, and key factors such as changes in the pump group load and changes in the hull posture are predicted. Ultimately, the dredging construction simulation data of the dredging ship in a specific environment is generated to provide a basis for subsequent parameter adjustments.

[0193] Step S53: integrating the simulated hull state of the dredging ship according to the dredging construction simulation data of the dredging ship, thereby obtaining the simulated hull state data;

[0194] In this embodiment, the construction simulation data of the dredging vessel includes multiple variables, such as pump group flow rate, pressure, hull speed, and sediment concentration in the construction area. By integrating these simulation data, simulated hull state data of the dredging vessel is generated, reflecting the comprehensive working state of the hull in the simulated construction environment. For example, the simulation data shows that in areas with high sediment concentration, the mud discharge flow of the pump group may decrease, and the power demand of the hull will increase. At this time, all relevant data (such as the working status of the pump group, hull position, environmental changes, etc.) are integrated to generate simulated state data of the hull, which will be used for error assessment and parameter optimization in subsequent steps.

[0195] Step S54: performing a working state comparison on the real-time state data of the hull and the simulated state data of the hull, thereby obtaining actual working state error data of the dredging vessel;

[0196] In this embodiment, real-time hull status data (such as pump group flow rate, mud discharge pressure, hull speed, etc.) is compared with hull status data obtained through simulation. For example, if the actual operating flow rate of the hull in a specific construction area is lower than the expected simulated value, this difference is calculated through error comparison to generate operating status error data. This error data can reflect system performance deviations, such as uneven pump group loading and the influence of environmental factors. The error data is uploaded to the management platform in real time and provides guidance for subsequent optimization of construction parameters.

[0197] Step S55: obtaining a dredging vessel construction parameter set through the dredging vessel control and management platform, and dynamically optimizing the actual dredging construction parameters of the dredging vessel construction parameter set according to the actual working state error data of the dredging vessel, thereby obtaining dynamic construction parameter data;

[0198] In this embodiment, the management platform obtains the current set of construction parameters based on the construction goals and real-time status data, including the working pressure, flow rate, and ship speed of the pump group. Based on the obtained working status error data, the construction parameters are dynamically adjusted through a fuzzy control method or an adaptive control method. For example, if the error data indicates that the mud discharge flow rate of the pump group is lower than expected, the working pressure or flow rate of the pump group will be automatically adjusted to balance the load of the pump group and optimize construction efficiency. If the error data indicates that the ship's speed is too slow, the power of the propeller will be increased or the posture of the ship will be adjusted to improve work efficiency. These dynamically adjusted construction parameters will be continuously optimized under real-time monitoring to ensure that the construction goals are achieved.

[0199] Step S56: Upload the dynamic construction parameter data to the dredging vessel control and management platform to perform the construction parameter adjustment task.

[0200] In this embodiment, the optimized dynamic construction parameters (such as the working pressure of the pump group, the mud discharge flow rate, the hull speed, etc.) will be uploaded to the control and management platform of the dredging vessel. The control platform automatically adjusts the working state of the hull according to these optimized parameters, and directs the operation of equipment such as the pump group, the propeller, and the power system. For example, the pump group can be instructed to adjust its mud suction pressure, or the speed of the propeller can be adjusted according to real-time data, so that the hull can complete the dredging task in the optimal working state. The adjustment of these parameters will be reflected in the working state of the hull in real time, ensuring the efficient completion of the construction task. At the same time, the crew can also view the adjusted construction parameters through the platform and perform necessary manual intervention or verification.

[0201] Optionally, step S6 specifically includes:

[0202] Step S61: Real-time environmental status data is collected based on the lake environment sensor network to obtain real-time dredging environmental status data, and dredging construction progress data is calculated based on the real-time dredging environmental status data according to the dredging vessel construction target to obtain dredging construction progress data;

[0203] In this embodiment, a network of environmental sensors deployed in the lake and reservoir area is used to monitor various environmental parameters such as water flow, sediment concentration, water depth, and temperature in real time. The data collected by the sensors is transmitted to the control and management platform of the ship through a wireless network. The platform combines real-time environmental data with the construction objectives of the dredging ship (such as the predetermined dredging area or depth) to calculate the progress. For example, assuming that the current dredging ship's goal is to clean a 50-meter × 50-meter area in the lake area, the construction efficiency will be calculated based on the actual collected sediment concentration and water flow rate, and the current construction progress will be calculated. The progress calculation model will take into account the impact of various external environmental factors on construction efficiency. For example, a faster water flow rate will lead to reduced dredging efficiency, thereby affecting the calculation of the construction progress. Ultimately, the output dredging construction progress data will serve as the basis for subsequent decision-making and adjustments.

[0204] Step S62: Classify the dredging construction progress according to the dredging construction progress data. If the dredging construction progress reaches a preset construction progress threshold, mark the corresponding real-time dredging environmental status data as dredging completion environmental status data. If the dredging construction progress does not reach the preset construction progress, return to step S61 to continue collecting real-time environmental status until the dredging construction progress reaches the preset construction progress threshold.

[0205] In this embodiment, the obtained dredging construction progress data is compared with the preset construction progress threshold. Assuming that the preset construction progress threshold is 80%, when the real-time progress reaches or exceeds the threshold, the relevant environmental data (such as water flow, sediment concentration, etc.) will be marked as "dredging completion environmental status data". If the dredging progress does not reach the threshold, real-time environmental data will continue to be collected from the lake environment sensor network, and the construction progress will be updated until the progress reaches 80%, and the environmental data at this time will be marked as "dredging completion environmental status data". This process ensures that all environmental status data are continuously collected and evaluated before the dredging task is completed, and the work of the dredging ship is always optimized based on the latest data.

[0206] Step S63: obtaining the hull status data when the dredging is completed through the lake environment sensor network, and marking the hull status data as the hull status data to be dismantled;

[0207] In this embodiment, once the dredging vessel completes the scheduled dredging task, the hull status data at the time of dredging completion is collected through the sensor network. These data include information such as the posture of the hull, the working status of the pump group, and the power output of the propeller. The hull status data is transmitted to the hull control platform in real time through the sensor and marked according to the time point when the dredging is completed. The record marked as "hull status data to be disassembled" ensures that the subsequent disassembly process can be reasonably planned according to the current status of the dredging vessel, avoiding unnecessary failures or safety problems during the hull disassembly process. For example, the hull's pump group is subjected to a certain load during the dredging process, and it is necessary to evaluate whether its status is suitable for continued use or disassembly.

[0208] Step S64: performing a hull module disassembly sequence analysis based on the hull state data to be disassembled and the modular hull structure design data, thereby obtaining hull module disassembly sequence data;

[0209] In this embodiment, analysis is performed based on the hull modular design data (such as the module installation sequence, the difficulty of disassembly, the module weight, the connection method, etc.) and the status data of the hull to be disassembled. It is assumed that some hull modules must be disassembled first because they are heavy or occupy a large space to avoid safety hazards or difficulties during subsequent disassembly. Through heuristic algorithm or integer programming algorithm analysis, a disassembly sequence is generated based on the structural characteristics, workload and other data of each hull module. For example, the pump group module is disassembled first, then the propeller module, and finally the structural support module, to ensure the rationality and safety of the disassembly sequence. This data is used to guide the actual disassembly process of the hull to ensure the efficiency and safety of the disassembly operation.

[0210] Step S65: performing environmental state change prediction on the environmental state data after dredging is completed, thereby obtaining environmental state change prediction data, and performing a stable hull module disassembly strategy analysis on the hull module disassembly sequence data based on the environmental state change prediction data, thereby obtaining a hull module disassembly strategy;

[0211] In this embodiment, the environmental data after the construction of the dredging ship is completed is used to predict the changes in environmental conditions using historical data and prediction models. For example, factors such as water flow velocity, temperature, and sediment concentration may change after the dredging is completed, affecting the subsequent disassembly operations. The disassembly order of the hull modules is adjusted according to the predicted data (such as the predicted trend of water flow changes) to ensure that the hull remains stable under different environmental conditions. For example, if it is predicted that the water flow velocity will increase during the disassembly process, the disassembly order will be adjusted to give priority to disassembling modules that have a greater impact on the stability of the hull (such as pump groups and heavy modules). Through this strategy, the stability and safety of the hull disassembly under different environmental changes are ensured.

[0212] Step S66: Integrate the hull module transportation strategy according to the lake transport environment data and the hull module disassembly strategy to obtain the hull module transportation strategy.

[0213] In this embodiment, the module transportation strategy is integrated based on the transportation environment data of the lake area (such as water depth, transportation channel width, water flow speed, etc.) and the hull module disassembly strategy. For example, if the disassembled module needs to be transported through a narrow waterway, the appropriate transportation route and ship configuration will be selected based on data such as water flow speed and water depth. The transportation path will also be optimized to avoid areas with strong water flow or many obstacles to ensure the safety and efficiency of the module transportation process. By integrating all this data, the final hull module transportation strategy is generated to ensure that the disassembled modules can be transported to the designated location safely and efficiently, completing the entire dredging operation.

[0214] Optionally, this specification also provides a system for three-dimensional modular design and construction of a dredging vessel, which is used to perform the above-mentioned method for three-dimensional modular design and construction of a dredging vessel. The system for three-dimensional modular design and construction of a dredging vessel includes:

[0215] The three-dimensional environmental data modeling module is used to obtain multi-source lake and reservoir data and perform lake and reservoir environmental analysis based on the multi-source lake and reservoir data, thereby obtaining lake and reservoir water environment data and lake and reservoir transportation environment data; and perform three-dimensional environmental data modeling based on the lake and reservoir water environment data and the lake and reservoir transportation environment data, thereby obtaining a three-dimensional data model of the lake and reservoir environment;

[0216] The hull module design module is used to design the hull module according to the three-dimensional data model of the lake environment, thereby obtaining the hull module design data, and performing hull structure topology optimization on the hull module design data to obtain modular hull structure design data, which is then uploaded to the dredging vessel production management platform to execute the hull structure production task;

[0217] The sensor data fusion module is used to obtain hull sensor data and perform sensor data fusion based on multi-source lake and reservoir data and hull sensor data to obtain a lake and reservoir environment sensor network; real-time sensor data is collected through the lake and reservoir environment sensor network to obtain real-time environmental status data and real-time hull status data;

[0218] The construction parameter dynamic optimization module is used to simulate the dredging construction of the dredging vessel based on the real-time status data of the environment and the modular hull structure design data, thereby obtaining the dredging construction simulation data of the dredging vessel; based on the dredging construction simulation data of the dredging vessel, the actual dredging construction parameters of the dredging vessel are dynamically optimized to obtain dynamic construction parameter data, and the data is uploaded to the dredging vessel control and management platform to execute the construction parameter adjustment task;

[0219] The pump group parameter iterative optimization module is used to collect the working status of the hull relay pump through the lake environment sensor network to obtain the working status data of the hull relay pump; the hull relay pump working status data is used to optimize the pump group collaborative control to obtain the optimal pump group control parameter data, and then upload it to the dredging vessel control and management platform to execute the pump group control task;

[0220] The hull evacuation strategy analysis module is used to collect real-time environmental status data based on the lake and reservoir environmental sensor network, thereby obtaining real-time dredging environmental status data; perform hull module disassembly strategy analysis based on real-time dredging environmental status data and modular hull structure design data, thereby obtaining a hull module disassembly strategy; and integrate hull module transportation strategies based on lake and reservoir transportation environmental data and hull module disassembly strategies, thereby obtaining a hull module transportation strategy.

Claims

1. A method for three-dimensional modular design and construction of a dredging vessel, characterized in that: The following steps are involved: Step S1: Acquire multi-source lake and reservoir data, and perform lake and reservoir environment analysis based on the multi-source lake and reservoir data, thereby obtaining lake and reservoir water environment data and lake and reservoir transportation environment data; perform three-dimensional environmental data modeling based on the lake and reservoir water environment data and the lake and reservoir transportation environment data, thereby obtaining a three-dimensional data model of the lake and reservoir environment; Step S2: Designing a hull module based on the three-dimensional data model of the lake environment to obtain hull module design data, and performing hull structure topology optimization on the hull module design data to obtain modular hull structure design data, which is uploaded to the dredging vessel production management platform to execute the hull structure production task; Step S3: acquiring hull sensor data, and performing sensor data fusion based on multi-source lake and reservoir data and hull sensor data, thereby obtaining a lake and reservoir environment sensor network; Step S4: The working status of the hull relay pump is collected through the lake environment sensor network to obtain the working status data of the hull relay pump; the working status data of the hull relay pump is optimized by pump group collaborative control to obtain the optimal pump group control parameter data, and the data is uploaded to the dredging vessel control management platform to execute the pump group control task; Step S5: Simulating the dredging construction of the dredging vessel based on the lake and reservoir environment sensor network and the modular hull structure design data, thereby obtaining dredging construction simulation data of the dredging vessel; dynamically optimizing the actual dredging construction parameters of the dredging vessel based on the dredging construction simulation data, thereby obtaining dynamic construction parameter data, and uploading the data to the dredging vessel control and management platform to execute the construction parameter adjustment task; Step S6: Real-time environmental status data is collected based on the lake and reservoir environmental sensor network to obtain real-time dredging environmental status data; hull module disassembly strategy analysis is performed based on the real-time dredging environmental status data and the modular hull structure design data to obtain the hull module disassembly strategy; hull module transportation strategy integration is performed based on the lake and reservoir transportation environmental data and the hull module disassembly strategy to obtain the hull module transportation strategy.

2. The method for three-dimensional modular design and construction of a dredging vessel according to claim 1 is characterized in that: Step S1 is specifically as follows: Step S11: acquiring multi-source lake and reservoir data, and performing data preprocessing on the multi-source lake and reservoir data, thereby obtaining the multi-source lake and reservoir data to be analyzed; Step S12: performing multi-source data classification on the multi-source lake and reservoir data to be analyzed, thereby obtaining lake and reservoir hydrological data and lake and reservoir topographic data; Step S13: performing a lake / reservoir surrounding terrain analysis based on the lake / reservoir terrain data to obtain a lake / reservoir surrounding terrain depth map, and performing a lake / reservoir transportation environment analysis based on the lake / reservoir surrounding terrain depth map to obtain lake / reservoir transportation environment data; Step S14: performing lake and reservoir water environment analysis based on lake and reservoir hydrological data and a topographic depth map of the lake and reservoir surroundings, thereby obtaining lake and reservoir water environment data; Step S15: Perform three-dimensional environmental data modeling based on the lake and reservoir water environment data and the lake and reservoir transportation environment data, thereby obtaining a three-dimensional data model of the lake and reservoir environment.

3. The method for three-dimensional modular design and construction of a dredging vessel according to claim 2, characterized in that: Step S13 is specifically as follows: Step S131: performing multi-source terrain data classification on lake and reservoir terrain data to obtain lake bottom sonar sensor data and lake and reservoir lidar data; Step S132: performing sonar inverse distance weighted terrain point cloud generation on the lake bottom sonar sensor data, thereby obtaining lake bottom terrain point cloud data; performing laser reflection signal terrain point cloud conversion on the lake lidar data, thereby obtaining lake surface terrain point cloud data; Step S133: spatially merging the lake bottom terrain point cloud data and the lake surface terrain point cloud data to obtain lake surrounding terrain point cloud data, and performing rasterization depth map conversion on the lake surrounding terrain point cloud data to obtain a lake surrounding terrain depth map; Step S134: performing lake-reservoir land surface terrain zoning on the lake-reservoir surrounding terrain depth map, thereby obtaining lake-reservoir land surface terrain zoning data, and calculating the proportion of gentle terrain based on the lake-reservoir land surface terrain zoning data, thereby obtaining lake-reservoir land surface gentle terrain proportion data; Step S135: obtaining a set of drone images of the lake and reservoir area, and performing edge detection on the set of drone images of the lake and reservoir area, thereby obtaining lake and reservoir area contour data; Identify the roads in the lake and reservoir area based on the lake and reservoir area contour data, thereby obtaining the lake and reservoir area road data; Step S136: identifying lake and reservoir land transportation bottlenecks based on the lake and reservoir land surface flat terrain proportion data and lake and reservoir area road data, thereby obtaining lake and reservoir land transportation bottleneck data; Step S137: Perform spatial integration of the lake and reservoir land transportation environment based on lake and reservoir land surface terrain zoning data, lake and reservoir land surface flat terrain proportion data, lake and reservoir area road data, and lake and reservoir land transportation bottleneck data, thereby obtaining lake and reservoir transportation environment data.

4. The method for three-dimensional modular design and construction of a dredging vessel according to claim 2, characterized in that: Step S14 is specifically as follows: The depth map of the terrain around the lake is used to divide the lake into different depth zones, thereby obtaining the depth zone data of the lake water area; Perform spatial alignment of lake and reservoir water depth zoning data and lake and reservoir hydrological data to obtain hydrological-topographic fusion data; Based on the hydrological-topographic fusion data, the water flow velocity distribution statistics in the deep water area are carried out to obtain the water flow velocity distribution data in the deep water area; The temperature change trend of the deep water area is predicted based on the hydrological-topographic fusion data, thereby obtaining the temperature change trend data of the deep water area; The sediment concentration in the deep water area of ​​lakes and reservoirs is counted based on the hydrological-topographic fusion data to obtain the sediment concentration data in the deep water area; Based on the water velocity distribution data, temperature change trend data and sediment concentration data in the deep water area, the silt deposition hotspot area is identified to obtain the silt deposition hotspot area data; The lake and reservoir water environment data are spatially integrated by integrating the silt deposition hotspot area data, the water velocity distribution data in the deep water area, the temperature change trend data in the deep water area, and the sediment concentration data in the deep water area to obtain the lake and reservoir water environment data.

5. The method for three-dimensional modular design and construction of a dredging vessel according to claim 1 is characterized in that: Step S2 is specifically as follows: Step S21: performing environmental integration of the silt deposition hotspot area based on the three-dimensional data model of the lake and reservoir environment, thereby obtaining environmental data of the silt deposition hotspot area, wherein the environmental data of the silt deposition hotspot area includes water depth data of the silt deposition hotspot area, flow velocity data of the silt deposition hotspot area, and sediment data of the silt deposition hotspot area; Step S22: obtaining the dredging ship model structure data, and performing modular box structure decomposition on the dredging ship model structure data, thereby obtaining the model hull module structure data and the model function module structure data; Step S23: Designing the dredging vessel hull module size and weight based on the sample hull module structure data according to the lake and reservoir transportation environment data and the silt deposition hotspot area environment data, thereby obtaining the dredging vessel hull module design structure data; Step S24: Designing a dredging vessel functional module based on the sample functional module structure data according to the silt deposition hotspot area environmental data, thereby obtaining dredging vessel functional module design structure data; Step S25: performing module connection design on the dredging vessel hull module design structure data and the dredging vessel function module design structure data, thereby obtaining dredging vessel module connection design data; Performing modular structural connection on the dredging vessel hull module design structure data and the dredging vessel function module design structure data according to the dredging vessel module connection design data, thereby obtaining initial modular hull structure design data; Step S26: Perform lightweight topology optimization on the initial modular hull structure design data to obtain modular hull structure design data, and upload the data to the dredging vessel production management platform to execute the hull structure production task.

6. The method for three-dimensional modular design and construction of a dredging vessel according to claim 5, characterized in that: Step S24 is specifically as follows: According to the dredging vessel hull module design structure data, the functional module structure size of the sample functional module structure data is adapted to obtain the functional module structure data to be designed; Adapting the functional module dual drive mode to the functional module structure data to be designed by a preset shore power drive system and a preset diesel drive system, thereby obtaining drive adaptation functional module structure data; Based on the environmental data of the silt deposition hotspot area, the shipboard sensor deployment design is carried out based on the structural data of the drive adaptation function module, thereby obtaining the modular sensor deployment design data; According to the environmental data of the silt deposition hotspot area, the modular dredge suction pump group is designed based on the structural data of the drive adaptation function module, thereby obtaining the modular dredge suction pump group design data; The modular sludge suction pump group design data is used to design the pump group collaborative working module through the preset ACC automatic mud control system, thereby obtaining the modular sludge suction pump group module design data; Based on the sediment data of the silt deposition hotspot area, the modular structure design of the multi-stage sediment filtration and the modular structure design of the sediment storage are carried out on the drive adaptation function module structure data, thereby obtaining the modular sediment separation module design data; According to the water depth data and flow velocity data of the silt deposition hotspot area, the power module design and propulsion module design are carried out based on the drive adaptation function module structure data, thereby obtaining modular power module design data; The modular sensor deployment design data, modular dredging pump module design data, modular sediment separation module design data and modular shore power module design data are connected to the drive module circuit to obtain the dredging ship functional module design structure data.

7. The method for three-dimensional modular design and construction of a dredging vessel according to claim 1, characterized in that: Step S4 is specifically as follows: Step S41: collecting the working status of the hull relay pump and the real-time construction environment status of the lake and reservoir through the lake and reservoir environment sensor network, thereby obtaining the working status data of the hull relay pump and the real-time construction environment status data of the lake and reservoir; Step S42: evaluating the hull pump group efficiency based on the real-time construction environment status data of the lake and reservoir and the working status data of the hull relay pump, thereby obtaining the actual construction efficiency data of the dredging vessel; Step S43: obtaining the construction target of the dredging vessel through the dredging vessel control and management platform, and estimating the actual construction efficiency target completion degree of the dredging vessel actual construction efficiency data according to the construction target, thereby obtaining the actual construction efficiency target completion degree data; Step S44: performing pump group coordinated control optimization on the hull relay pump working status data according to the actual construction work efficiency target completion data, thereby obtaining pump group control optimization parameter data; Step S45: uploading the pump group control optimization parameter data to the dredging vessel control management platform to execute the pump group control task; obtaining the real-time relay pump working status data and the real-time construction environment status data after the pump group control; Step S46: performing a construction efficiency review on the real-time relay pump working status data after the pump group is controlled and the real-time construction environment status data after the pump group is controlled according to the construction target of the dredging vessel, thereby obtaining a construction efficiency review report; Step S47: Iteratively optimize and adjust the pump group control optimization parameter data according to the construction efficiency review report to obtain the optimal pump group control parameter data, and upload it to the dredging vessel control management platform to execute the pump group control task.

8. The method for three-dimensional modular design and construction of a dredging vessel according to claim 1, characterized in that: Step S5 is specifically as follows: Step S51: collecting real-time sensor data through the lake environment sensor network to obtain real-time environmental status data and real-time hull status data; simulating the actual dredging vessel construction environment based on the real-time environmental status data to obtain dredging vessel construction environment simulation data; Step S52: performing 3D modeling of the hull structure based on the modular hull structure design data, thereby obtaining a modular hull 3D model; and performing a dredging construction simulation of the dredging vessel on the modular hull 3D model according to the dredging vessel construction environment simulation data, thereby obtaining dredging construction simulation data of the dredging vessel; Step S53: integrating the simulated hull state of the dredging ship according to the dredging construction simulation data of the dredging ship, thereby obtaining the simulated hull state data; Step S54: performing a working state comparison on the real-time state data of the hull and the simulated state data of the hull, thereby obtaining actual working state error data of the dredging vessel; Step S55: obtaining a dredging vessel construction parameter set through the dredging vessel control and management platform, and dynamically optimizing the actual dredging construction parameters of the dredging vessel construction parameter set according to the actual working state error data of the dredging vessel, thereby obtaining dynamic construction parameter data; Step S56: Upload the dynamic construction parameter data to the dredging vessel control and management platform to perform the construction parameter adjustment task.

9. The method for three-dimensional modular design and construction of a dredging vessel according to claim 1, characterized in that: Step S6 is specifically as follows: Step S61: Real-time environmental status data is collected based on the lake environment sensor network to obtain real-time dredging environmental status data, and dredging construction progress data is calculated based on the real-time dredging environmental status data according to the dredging vessel construction target to obtain dredging construction progress data; Step S62: Classify the dredging construction progress according to the dredging construction progress data. If the dredging construction progress reaches a preset construction progress threshold, mark the corresponding real-time dredging environmental status data as dredging completion environmental status data. If the dredging construction progress does not reach the preset construction progress, return to step S61 to continue collecting real-time environmental status until the dredging construction progress reaches the preset construction progress threshold. Step S63: obtaining the hull status data when the dredging is completed through the lake environment sensor network, and marking the hull status data as the hull status data to be dismantled; Step S64: performing a hull module disassembly sequence analysis based on the hull state data to be disassembled and the modular hull structure design data, thereby obtaining hull module disassembly sequence data; Step S65: performing environmental state change prediction on the environmental state data after dredging is completed, thereby obtaining environmental state change prediction data, and performing a stable hull module disassembly strategy analysis on the hull module disassembly sequence data based on the environmental state change prediction data, thereby obtaining a hull module disassembly strategy; Step S66: Integrate the hull module transportation strategy according to the lake transport environment data and the hull module disassembly strategy to obtain the hull module transportation strategy.

10. A system for three-dimensional modular design and construction of dredging vessels, characterized in that: A method for executing the three-dimensional modular design and construction of a dredging vessel according to claim 1, wherein the system comprises: The three-dimensional environmental data modeling module is used to obtain multi-source lake and reservoir data and perform lake and reservoir environmental analysis based on the multi-source lake and reservoir data, thereby obtaining lake and reservoir water environment data and lake and reservoir transportation environment data; and perform three-dimensional environmental data modeling based on the lake and reservoir water environment data and the lake and reservoir transportation environment data, thereby obtaining a three-dimensional data model of the lake and reservoir environment; The hull module design module is used to design the hull module according to the three-dimensional data model of the lake environment, thereby obtaining the hull module design data, and performing hull structure topology optimization on the hull module design data to obtain modular hull structure design data, which is then uploaded to the dredging vessel production management platform to execute the hull structure production task; The sensor data fusion module is used to obtain hull sensor data and perform sensor data fusion based on multi-source lake and reservoir data and hull sensor data to obtain a lake and reservoir environment sensor network; real-time sensor data is collected through the lake and reservoir environment sensor network to obtain real-time environmental status data and real-time hull status data; The construction parameter dynamic optimization module is used to simulate the dredging construction of the dredging vessel based on the real-time status data of the environment and the modular hull structure design data, thereby obtaining the dredging construction simulation data of the dredging vessel; based on the dredging construction simulation data of the dredging vessel, the actual dredging construction parameters of the dredging vessel are dynamically optimized to obtain dynamic construction parameter data, and the data is uploaded to the dredging vessel control and management platform to execute the construction parameter adjustment task; The pump group parameter iterative optimization module is used to collect the working status of the hull relay pump through the lake environment sensor network to obtain the working status data of the hull relay pump; the hull relay pump working status data is used to optimize the pump group collaborative control to obtain the optimal pump group control parameter data, and then upload it to the dredging vessel control and management platform to execute the pump group control task; The hull evacuation strategy analysis module is used to collect real-time environmental status data based on the lake and reservoir environmental sensor network, thereby obtaining real-time dredging environmental status data; perform hull module disassembly strategy analysis based on real-time dredging environmental status data and modular hull structure design data, thereby obtaining a hull module disassembly strategy; and integrate hull module transportation strategies based on lake and reservoir transportation environmental data and hull module disassembly strategies, thereby obtaining a hull module transportation strategy.

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