Method and system for three-dimensional modular design and construction of dredger

Abstract

The invention relates to the technical field of dredging management, in particular to a dredging ship three-dimensional modular design and construction method and system. The method comprises the following steps: obtaining multi-source lake and reservoir data, and carrying out three-dimensional environment data modeling to obtain a lake and reservoir environment three-dimensional data model; according to the lake and reservoir environment three-dimensional data model, ship structure topological optimization is carried out, so that modular ship structure design data is obtained; acquiring ship body sensor data and performing sensing data fusion to obtain a lake and reservoir environment sensing network; performing pump set cooperative control optimization through the lake and reservoir environment sensing network to obtain optimal pump set control parameter data; according to the lake and reservoir environment sensing network and the modular hull structure design data, dynamic optimization is conducted on actual dredging construction parameters of the dredging ship, and dynamic construction parameter data are obtained; and ship body module transportation strategy integration is carried out according to the lake and reservoir environment sensing network, and a ship body module transportation strategy is obtained. The desilting efficiency of the desilting ship can be improved.

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, waterways, and ports, dredging plays a vital role in the management and maintenance of water areas. Traditional dredging methods mainly rely 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 cannot meet the needs of different water environments. As one of the core equipment for large-scale water dredging operations, traditional dredging ships have played a role to a certain extent, but their technology and design still have obvious 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 implement large projects, it is often necessary to frequently dispatch ships, resulting in high dispatch costs and personnel coordination costs. In addition, although offshore dredging ships have a 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 and cannot be transported by water have limited their application in lake and reservoir desilting projects.

[0004] In addition, traditional dredging vessels usually adopt a single operation 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 operating 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 purpose, 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, so as to obtain 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, so as to obtain a three-dimensional data model of the lake and reservoir environment;

[0008] Step S2: Design a hull module according to the three-dimensional data model of the lake environment to obtain hull module design data, and perform hull structure topology optimization on the hull module design data to obtain modular hull structure design data, and upload it to the dredging ship 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: collecting 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; optimizing the pump group coordinated control of the hull relay pump working status data to obtain the optimal pump group control parameter data, and uploading it to the dredging ship control management platform to execute the pump group control task;

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

[0012] Step S6: Collect real-time environmental status data based on the lake environment sensor network to obtain real-time dredging environmental status data; analyze the hull module disassembly strategy based on the real-time dredging environmental status data and the modular hull structure design data to obtain the hull module disassembly strategy; integrate the hull module transportation strategy based on the lake transportation environment 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 multi-source lake and reservoir data to be analyzed;

[0015] Step S12: classifying 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, thereby obtaining a lake reservoir surrounding terrain depth map, and performing a lake reservoir transportation environment analysis based on the lake reservoir surrounding terrain depth map, thereby obtaining 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, so as to obtain a three-dimensional data model of the lake and reservoir environment.

[0019] Optionally, step S13 is specifically:

[0020] Step S131: performing multi-source terrain data classification on lake and reservoir terrain data, thereby obtaining 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 the lake surrounding terrain point cloud data, and performing rasterization depth map conversion on the lake surrounding terrain point cloud data to obtain the lake surrounding terrain depth map;

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

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

[0025] Step S136: identifying the bottleneck of lake and reservoir land transportation based on the data of the proportion of gentle landforms on the lake and reservoir land surface and the road data in the lake and reservoir area, thereby obtaining the bottleneck data of lake and reservoir land transportation;

[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 to obtain lake and reservoir transportation environment data.

[0027] Optionally, step S14 is specifically:

[0028] The depth map of the terrain around the lake is used to divide the water depth of the lake into different zones, thereby obtaining the water depth division 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, so as to obtain 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, so as 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 specifically includes:

[0036] Step S21: performing environmental integration of the silt deposition hotspot area based on the three-dimensional data model of the lake 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: Acquire the sample structure data of the dredging ship, and perform modular box structure decomposition on the sample structure data of the dredging ship, so as to obtain the sample hull module structure data and the sample function module structure data;

[0038] Step S23: Designing the size and weight of the dredging ship hull module 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 ship hull module design structure data;

[0039] Step S24: Designing a dredging ship functional module based on the sample functional module structure data according to the silt deposition hotspot area environmental data, thereby obtaining the dredging ship 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 function module design structure data, thereby obtaining the dredging ship module connection design data; performing modular structure connection on the dredging ship hull module design structure data and the dredging ship function module design structure data according to the dredging ship module connection design data, thereby obtaining the initial modular hull structure design data;

[0041] Step S26: Perform lightweight topological 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:

[0043] According to the dredging ship 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] The function module dual drive mode is adapted to the function module structure data to be designed by a preset shore power drive system and a preset diesel drive system, thereby obtaining the drive adaptation function module structure data;

[0045] Design the onboard sensor deployment based on the structural data of the drive adaptation function module according to the environmental data of the silt deposition hotspot area, so as to obtain the modular sensor deployment design data;

[0046] According to the environmental data of the silt deposition hotspot area, the modular sludge suction pump group is designed based on the structure data of the drive adaptation function module, so as to obtain the design data of the modular sludge suction pump group;

[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, so as to obtain the modular sludge suction pump group module design data;

[0048] According to the sediment data of the silt deposition hotspot area, the modular structure design of the multi-stage filtration of sediment and the modular structure design of sediment storage are carried out on the structure data of the drive adaptation function module, so as to obtain the design data of the modular sediment separation module;

[0049] According to the water depth data of the silt deposition hotspot area and the flow velocity data of the silt deposition hotspot area, the power module design and the propulsion module design are performed on the drive adaptation function module structure data, so as to obtain the modular power module design data;

[0050] The modular sensor deployment design data, modular dredge suction pump module design data, modular sediment separation module design data and modular shore power module design data are connected to the drive module lines 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 according to the real-time construction environment status data of the lake 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 ship through the dredging ship control management platform, and estimating the actual construction efficiency target completion degree of the actual construction efficiency data of the dredging ship according to the construction target of the dredging ship, 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: Upload the pump group control optimization parameter data to the dredging vessel control management platform to execute the pump group control task; obtain 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;

[0057] Step S46: according to the construction target of the dredging vessel, 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 are reviewed for construction efficiency, 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 specifically includes:

[0060] Step S51: real-time sensor data collection is performed through the lake environment sensor network to obtain real-time environmental status data and real-time hull status data; actual dredging ship construction environment simulation is performed according to the real-time environmental status data to obtain dredging ship 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 dredging construction simulation of the dredging ship on the modular hull 3D model according to the dredging ship construction environment simulation data, thereby obtaining dredging construction simulation data of the dredging ship;

[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 hull simulation 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, so as to obtain actual working state error data of the dredging vessel;

[0064] Step S55: obtaining a dredging vessel construction parameter set through the dredging vessel control management platform, and dynamically optimizing 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 execute the construction parameter adjustment task.

[0066] Optionally, step S6 specifically includes:

[0067] Step S61: Real-time environmental status data is collected according to 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 construction target of the dredging ship 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 real-time environmental status collection until the dredging construction progress reaches the preset construction progress threshold;

[0069] Step S63: acquiring 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 disassembled;

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

[0071] Step S65: predicting the environmental state change of the dredging completion environmental state data, thereby obtaining environmental state change prediction data, and analyzing the hull module disassembly sequence data for a stable hull module disassembly strategy according to 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] The present invention significantly improves the adaptability, operation efficiency and flexibility of dredging ships in complex water environments by introducing multi-source lake and reservoir data, three-dimensional environmental modeling, modular hull design, intelligent control and other means. On the basis of obtaining multi-source lake and reservoir data and conducting lake and reservoir environmental analysis, the lake and reservoir waters and transportation environment are firstly comprehensively modeled, thereby providing an accurate basis for subsequent hull design, hull modular production, and hull module disassembly and transportation strategies. This data-driven environmental modeling enables dredging ships to perceive and adapt to changes in hydrological topography in real time under different lake and reservoir environments, effectively avoiding the problem of poor adaptability of traditional dredging ships in complex water environments. By topologically optimizing and modularizing the hull structure, the hull structure can be quickly customized according to different operating requirements, and the weight of the hull can be reduced, improving the flexibility and efficiency of the operation. The modular design not only helps to optimize the hull structure itself, but also facilitates the transportation, maintenance and disassembly of the hull, effectively reducing the transportation and scheduling costs. In addition, the modular design of the hull ensures the detachability of the structure, so that the hull can adapt to different water environments and perform efficient operations, avoiding the limitation that traditional ships cannot be adjusted quickly. During the working process of the hull, by integrating the lake environment and the hull sensor data, the working status of the hull and the changes in the dredging environment are monitored in real time, and accurate pump group coordinated control optimization is achieved. This can ensure that the pump group of the dredging ship is maximized in different water environments, while avoiding the inefficiency of traditional dredging ships caused by poor pump group coordination. In addition, real-time construction environment simulation and dynamic construction parameter optimization enable the dredging ship to adjust the working parameters according to real-time operating conditions, further improving the operating efficiency and quality. Through the intelligent optimization of the hull module disassembly and transportation strategy, this solution not only improves the adaptability of the dredging ship, but also ensures the efficiency and safety of the hull disassembly process through an effective modular disassembly strategy, reducing the manpower and time costs during the disassembly and transportation process. In particular, after the dredging work is completed, the disassembly sequence and stability analysis of the hull can ensure that the risks in the disassembly process are minimized, and the disassembly sequence of the hull can be reasonably planned according to the progress of the dredging construction, thereby greatly improving the continuity and controllability of the operation. 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 requirements, 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 three-dimensional modular design and construction method of a dredging vessel as described above, and 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, so as to obtain 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, so as to obtain 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, so as to obtain the hull module design data, and to perform hull structure topology optimization on the hull module design data, so as to obtain modular hull structure design data, and upload it to the dredging ship production management platform to execute the hull structure production task;

[0077] The sensor data fusion module is used to obtain the hull sensor data and perform sensor data fusion based on multi-source lake and reservoir data and hull sensor data, so as to obtain the lake and reservoir environment sensor network; real-time sensor data collection is performed through the lake and reservoir environment sensor network, so as to obtain the real-time state data of the environment and the real-time state data of the hull;

[0078] The construction parameter dynamic optimization module is used to simulate the dredging construction of the dredging ship according to the real-time status data of the environment and the modular hull structure design data, so as to obtain the dredging construction simulation data of the dredging ship; dynamically optimize the actual dredging construction parameters of the dredging ship according to the dredging construction simulation data of the dredging ship, so as to obtain dynamic construction parameter data, and upload it to the dredging ship control management platform to execute the construction parameter adjustment task;

[0079] The pump group parameter iteration optimization module is used to collect the working status of the hull relay pump through the lake environment sensor network, so as to obtain the working status data of the hull relay pump; the pump group collaborative control optimization is performed on the working status data of the hull relay pump, so as to obtain the optimal pump group control parameter data, and upload it to the dredging ship control 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 environment sensor network, so as to obtain real-time dredging environmental status data; to analyze the hull module disassembly strategy based on the real-time dredging environmental status data and the modular hull structure design data, so as to obtain the hull module disassembly strategy; to integrate the hull module transportation strategy according to the lake and reservoir transportation environment data and the hull module disassembly strategy, so as to obtain the hull module transportation strategy.

[0081] The system for three-dimensional modular design and construction of a dredging ship of the present invention can realize any one of the three-dimensional modular design and construction methods of a dredging ship of the present invention, and is used to combine the medium for operation and signal transmission between various modules to complete the three-dimensional modular design and construction method of a dredging ship. 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 from 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 of the present invention;

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

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

[0086] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0087] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

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

[0089] To achieve this, please refer to Figure 1 to Figure 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, so as to obtain 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, so as to obtain a three-dimensional data model of the lake and reservoir environment;

[0091] In this embodiment, detailed hydrological data including the target lake and reservoir are obtained through a variety of sensors and data acquisition equipment. These data include hydrological and meteorological data such as water flow velocity, lake water level, temperature, salinity, sediment concentration, flow direction, precipitation, air pressure, temperature, etc., as well as environmental factors such as bottom type, lake topography, and transportation channels. In addition, long-term data related to the ecological environment around the lake and reservoir, such as water quality, vegetation coverage, lake biological community, etc., are also collected to comprehensively evaluate the lake and reservoir environment. The collection of hydrological data is carried out through multiple channels such as online public information, deployed hydrological monitoring stations, remote sensing satellite data, drone aerial photography, and fixed and floating sensor networks. The collected hydrological data is fused with other environmental data, and data fusion technology, such as Kalman filtering, particle filtering and other algorithms, is used to process sensor data from different sources, eliminate noise, and correct errors to ensure the accuracy and timeliness of the data. Through these data, the trend of hydrological environment changes in the lake and reservoir and its impact on the operation of the hull can be analyzed, such as the impact of changes in water flow velocity on the propulsion of the hull, and the impact of changes in sediment concentration on the working efficiency of the pump group. Based on these multi-source data, the three-dimensional spatial environment of the lake is modeled using geographic information system (GIS) technology and three-dimensional modeling software (such as AutoCAD and ArcGIS). This model not only includes the depth information of the water area and the changes in the lake bottom, but also includes the fluid dynamics characteristics of the flowing water body such as water flow, temperature and sediment. Through comprehensive analysis of these data, the generated three-dimensional environmental data model will provide basic data support for subsequent steps such as hull module design, construction simulation, and construction parameter optimization.

[0092] Step S2: Design a hull module according to the three-dimensional data model of the lake environment to obtain hull module design data, and perform hull structure topology optimization on the hull module design data to obtain modular hull structure design data, and upload it to the dredging ship production management platform to execute the hull structure production task;

[0093] In this embodiment, a modular hull design is performed on the dredging vessel according to the acquired three-dimensional data model of the lake environment (including data such as water flow, sediment concentration, bottom type, and lake water level). Using these environmental data, the working performance of the hull in different hydrological environments is simulated by computational fluid dynamics (CFD) simulation and finite element analysis (FEA) technology. The shape, size, and structural layout of each module of the hull are determined by analyzing the stress conditions, stability, and operating efficiency of each module of the hull. For example, the simulation results show that in waters with high sediment concentration, the pump module of the hull needs to increase the pump body diameter and pump blade area to improve the sediment absorption efficiency; while in waters with faster water flow, the propeller module of the hull needs to increase the power of the thrust system, while optimizing the streamlined design of the hull to reduce resistance. In the modular hull design, the interface and connection method between the hull modules should be taken into consideration in particular to ensure the detachability and reorganization of the modules, so that the hull structure can be flexibly adjusted under different construction environments to adapt to changing working conditions. 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 module of the hull and reduces redundant parts, but also ensures the stability and efficiency of the hull under 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 operating 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 ship production management platform and used as a 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 on the basis of meeting the 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, various sensor data on the dredging ship are obtained, including hull structure monitoring sensors, pump group operation status monitoring sensors, propeller performance monitoring sensors and environmental sensors (such as water flow velocity, temperature, humidity, sediment concentration, etc.). These sensors are installed at different positions on the hull to collect the working status data and construction environment data of the hull in real time. Hull structure sensors (such as accelerometers and pressure sensors) monitor the stress and strain of each part of the hull to ensure that the hull can maintain structural safety in a complex environment. Pump group sensors (such as flow meters and pressure sensors) record the working status of the pump group to ensure that the pump group can operate efficiently and detect faults in time. The propeller performance monitoring sensor monitors the speed, power and efficiency of the propeller in real time to ensure that the travel speed of the hull is consistent with expectations. At the same time, the lake and reservoir environmental sensors (such as water quality monitors, flow rate sensors, etc.) contained in the multi-source lake and reservoir data continuously monitor the hydrological data of the lake and reservoir, such as water flow direction, flow rate, water level, sediment concentration, etc. These data can reflect the environmental changes in different construction areas and affect the construction efficiency and safety of the hull. All these sensor data are gathered into a central data acquisition system through the Internet of Things (IoT) technology. Then, based on multi-source data fusion technology, the hull sensor data is fused with the lake environment data, and data fusion algorithms such as Kalman filtering or particle filtering are used to perform real-time calibration and denoising of various sensor data to ensure the accuracy and consistency of sensor data. Data fusion technology can combine the feedback information of multiple sensors to provide a more accurate and comprehensive perception network of the lake environment and hull working status.

[0096] Step S4: collecting 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; optimizing the pump group coordinated control of the hull relay pump working status data to obtain the optimal pump group control parameter data, and uploading it to the dredging ship control management platform to execute the pump group control task;

[0097] In this embodiment, the real-time working status data of the hull relay pump is obtained through the lake environment sensor network. These data include the working pressure, flow rate, power consumption, mud discharge volume, pump body temperature, etc. of the pump group. Sensors (such as flow sensors, pressure sensors, power meters, etc.) installed at important parts of the pump group and the hull are used to monitor the operation of the pump group in real time to ensure that it operates normally under preset working conditions. According to the lake environment and hull status data, the pump group collaborative control optimization algorithm is used to optimize the working state of the pump group. The goal of the pump group collaborative control optimization is to ensure that the pump group achieves load balancing and efficiency optimization under different operating conditions, and avoids equipment loss and energy efficiency reduction caused by excessive load or unbalanced operation. For example, if the loads of multiple pump groups are uneven, it may cause some pump groups to consume too much energy or reduce mud discharge efficiency. The optimization algorithm can automatically adjust the working pressure, flow rate and speed of each pump group so that all pump groups evenly distribute the load and improve the overall work efficiency. A dynamic optimization control algorithm (such as genetic algorithm or particle swarm optimization) is used to adjust the operating parameters of each pump group. The algorithm calculates the optimal working parameter combination based on the real-time feedback of the pump group status data. The optimization results generate the optimal pump group control parameter data, including the working pressure, flow rate and speed of each pump group. The optimized pump group control parameter data will be uploaded to the dredging ship control management platform. The management platform dispatches the pump group and executes the task according to these control parameters to ensure that the pump group can play the maximum efficiency in the dredging operation, and monitors the working status of the pump group in real time for timely adjustment and feedback. Through this control mechanism, the hull operation efficiency can be improved, energy consumption can be reduced, and construction safety can be ensured.

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

[0099] In this embodiment, according to the real-time environmental data (such as water flow rate, sediment concentration, ambient temperature, etc.) collected in the lake environment sensor network and the modular hull structure design data (such as hull size, weight, module configuration, etc.), the multi-physics field coupling simulation technology is used to simulate the construction of the dredging ship. The simulation uses computational fluid dynamics (CFD) and finite element analysis (FEA) to simulate the dynamic behavior of the hull in the dredging operation and its interaction with the surrounding environment. During the simulation process, the working state of the dredging ship under different environmental conditions is predicted taking into account the different structural characteristics, environmental impacts, construction goals and other factors of the hull module. For example, the simulation results may show that in areas with higher sediment concentrations, the pump group module of the hull may cause a decrease in efficiency due to the inhalation of more sediment; or in areas with faster water flow, the thrust of the hull propeller is not enough to keep the hull stable, which requires adjusting the construction parameters to optimize the working state of the hull. Based on the construction simulation data of the dredging ship, the actual dredging construction parameters are dynamically optimized by a dynamic optimization algorithm (such as an adaptive control algorithm or a fuzzy control algorithm). Specifically, the algorithm will automatically adjust key parameters such as pump group working pressure, hull travel speed, pump group flow, etc. according to the construction progress, environmental changes and simulation data to ensure that the construction efficiency reaches the optimal state. The optimized construction parameters (such as pump group operating parameters, propeller power settings, hull attitude adjustment, etc.) will be uploaded to the dredging ship control and management platform, through which the construction parameters will be adjusted. The dredging ship control system will adjust the working status of the pump group, propeller and other systems in real time according to the optimized parameters to achieve the best construction effect and ensure the smooth progress of the dredging operation.

[0100] Step S6: Collect real-time environmental status data based on the lake environment sensor network to obtain real-time dredging environmental status data; analyze the hull module disassembly strategy based on the real-time dredging environmental status data and the modular hull structure design data to obtain the hull module disassembly strategy; integrate the hull module transportation strategy based on the lake transportation environment data and the hull module disassembly strategy to obtain the hull module transportation strategy.

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

[0104] In this embodiment, detailed hydrological data including the target lake and reservoir are obtained through a variety of sensors and data acquisition equipment. These data include hydrological and meteorological data such as water flow velocity, lake water level, temperature, salinity, sediment concentration, flow direction, precipitation, air pressure, temperature, etc., as well as environmental factors such as bottom type, lake topography, and transportation channels. In addition, long-term data related to the ecological environment around the lake and reservoir, such as water quality, vegetation coverage, lake biological community, etc., will also be collected to comprehensively evaluate the lake and reservoir environment. The collection of hydrological data is carried out through multiple channels such as online public information, deployed hydrological monitoring stations, remote sensing satellite data, drone aerial photography, and fixed and floating sensor networks. Data preprocessing includes steps such as data denoising, supplementing missing values, and time synchronization to ensure the consistency and accuracy of the data. Specifically, for the hydrological data collected by the sensor, the Kalman filter algorithm is used to remove noise and complete the intermittent data of the water level sensor to obtain high-quality data to be analyzed. The processed data will be stored in a standardized format for subsequent classification and analysis.

[0105] Step S12: classifying 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 bottom of lakes and reservoirs. 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, thereby obtaining a lake reservoir surrounding terrain depth map, and performing a lake reservoir transportation environment analysis based on the lake reservoir surrounding terrain depth map, thereby obtaining lake reservoir transportation environment data;

[0108] In this embodiment, the surrounding terrain analysis is performed based on the lake and reservoir terrain data, and the specific terrain features are extracted from the lake bottom depth data using the digital elevation model (DEM) technology. By analyzing the terrain changes at the bottom and around the lake and reservoir, a terrain depth map around the lake and reservoir is generated. The depth map can describe the terrain undulations and slope changes of the lake in detail, and provide an accurate analysis of the bottom of the lake. Next, based on the terrain depth map around the lake and reservoir, the lake and reservoir transportation environment analysis is performed, mainly analyzing the impact of factors such as water flow, sediment concentration, lake depth, and road terrain slope on the transportation channel. For example, if the water flow in some areas is rapid, or the lake bottom slope is large, it will cause difficulties for ships to travel, and special operation plans are required. By comprehensively considering the terrain features, transportation channels, flow rates, sediment concentrations and other environmental factors around the lake and reservoir, the lake and reservoir transportation environment data is obtained. The transportation environment data will include an assessment of key parameters such as transportation safety, channel smoothness, 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 performed in combination with the lake and reservoir hydrological data and 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 bottom of the lake. Through comprehensive analysis of 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 waters, 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 waters and their 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 the 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, so as to obtain a three-dimensional data model of the lake and reservoir environment.

[0112] In this embodiment, the lake environment is three-dimensionally modeled by three-dimensional modeling technology (such as OpenGL, Unity3D, etc.) in combination with the lake water environment data and the lake transportation environment data. The model includes factors such as the topography, water area, and transportation channel of the lake, which can fully reflect the environmental characteristics of the lake. In the modeling process, the water depth, bottom type, water flow changes, and transportation routes of the lake are considered to create a dynamic and interactive three-dimensional lake environment model. The three-dimensional model can not only display the static topography and water environment of the lake, but also dynamically simulate the influence of factors such as water flow, sediment deposition, and environmental changes on dredging operations. For example, the model simulates the running track and operating efficiency of the dredging ship under different water flow rates and temperature conditions. These data will provide a scientific basis for the optimization of the operation 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:

[0114] Step S131: performing multi-source terrain data classification on lake and reservoir terrain data, thereby obtaining 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, etc.) are used to classify the collected data according to the type of sensor and the acquisition method. Lake bottom sonar sensor data usually contains information on the depth and terrain features 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 terrain point cloud. Specifically, the intensity of the sonar reflection signal is inversely proportional to the distance. Therefore, this characteristic is used to generate accurate lake bottom terrain point cloud data through weighted processing. These point cloud data show the undulations and terrain features of the lake bottom. For the lake lidar data, point cloud conversion is performed by analyzing the time delay and intensity of the laser reflection signal to generate lake and reservoir land surface terrain point cloud data. These point cloud data reflect the details of the terrain around the lake area, such as changes in the shoreline, vegetation coverage, etc. After processing, the lake and reservoir land surface terrain 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 the lake surrounding terrain point cloud data, and performing rasterization depth map conversion on the lake surrounding terrain point cloud data to obtain the lake surrounding terrain depth map;

[0119] In this embodiment, spatial merging is performed based on the generated lake bottom terrain point cloud data and lake 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 ups and downs 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: partitioning the lake and reservoir land surface terrain on the lake and reservoir surrounding terrain depth map, thereby obtaining lake and reservoir land surface terrain partitioning data, and calculating the proportion of gentle terrain based on the lake and reservoir land surface terrain partitioning data, thereby obtaining lake and 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 regarded as steep terrain, and an area with a slope less than a certain standard is regarded 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 transportation roads or carrying heavy equipment.

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

[0123] In this embodiment, aerial photography of the lake and reservoir area is performed by a drone to obtain a high-resolution image data set. Then, the edge detection algorithm (such as Canny edge detection) in computer vision technology 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 boundaries and shorelines 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 identification to identify transportation facilities such as roads and access paths in the area. Different road types (such as mud roads, gravel roads, hardened roads, etc.) are identified through edge detection and morphological processing technologies to generate road data for the lake and reservoir area. 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 the bottleneck of lake and reservoir land transportation based on the data of the proportion of gentle landforms on the lake and reservoir land surface and the road data in the lake and reservoir area, thereby obtaining the bottleneck data of lake and reservoir land transportation;

[0125] In this embodiment, the lake and reservoir land transportation bottlenecks are identified based on the lake and reservoir land surface flat terrain percentage data and the lake and reservoir area road data. The terrain percentage data is used to determine which areas have a higher degree of 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 excessive slopes or too rugged roads, and these roads may become obstacles to transportation. By identifying these bottleneck points, lake and reservoir land transportation bottleneck data are 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 to obtain 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 area, which is helpful for planning efficient dredging ship transportation routes, reducing stagnation time in transportation, and improving 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:

[0129] The depth map of the terrain around the lake is used to divide the water depth of the lake into different zones, thereby obtaining the water depth division 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 zoned 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 through a digital elevation model (DEM). For each water depth section, spatial analysis tools are 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 embodiment, the hydrological data and the water depth partition data are aligned using spatial registration technology. For example, the data of the hydrological sensor is paired with the water depth partition data through a spatial interpolation method to ensure that each hydrological data point corresponds to the correct water depth segment. These aligned data will be aggregated into hydrological-topographic fusion data to support further water environment analysis.

[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 embodiment, the hydrological-topographic fusion data is used to statistically analyze the flow velocity in different water depth sections to obtain the water flow velocity distribution data. For example, the flow velocity data in different water depth sections are statistically analyzed for mean, standard deviation, maximum and minimum values. For areas with a water depth of 2-5 meters, assuming that the flow velocity range is between 0.2-0.8 meters per second, a spatial distribution map of the water flow velocity is drawn based on the data to further analyze which areas have higher flow velocities and which areas have slower flow velocities. This data can help predict the sediment transportation path and the best operating area for dredging ships.

[0135] The temperature change trend of the deep water area is predicted based on the hydrological-topographic fusion data, so as to obtain the temperature change trend data of the deep water area;

[0136] In this embodiment, the temperature data obtained from the hydrological-topographic fusion data is combined with the water depth partition data, and a prediction algorithm (such as ARIMA, regression analysis or neural network) is used to predict the temperature change trend of the water depth area. For example, the water temperature fluctuation trend of a shallow water area in the past few months is analyzed, and the temperature change in the future is predicted by combining factors such as seasonal changes, sunlight exposure and water flow speed. The prediction results can provide data support for temperature-sensitive operations (such as ecological restoration, dredging operations, etc.).

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

[0138] In this embodiment, the sediment concentration data in the hydrological-topographic fusion data is combined with the water depth partition data to count the sediment concentration in different water depth areas. For example, for an area with a water depth of 2 meters, the sediment concentration in the area is counted, and its changing trend at different time points is analyzed. Assuming that the sediment concentration in the area reaches 300 mg / L in a certain period of time, the mean, maximum and minimum values ​​of the data will be calculated, and a risk assessment of silt deposition will be provided based on the changing trend of the sediment concentration. This data helps to determine the key operating areas of dredging ships.

[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, based on the water velocity distribution data, temperature change trend data and sediment concentration data in the water depth area, the hotspot areas of silt deposition are identified through multivariate analysis (such as weighted average method, cluster analysis, etc.). For example, by combining the areas with low flow velocity, high sediment concentration and relatively constant temperature, hotspot areas where sediment is more likely to deposit under these conditions can be identified. By combining these data, the hotspot area data of silt deposition is generated to help dredging ships to prioritize operations in these hotspot areas to improve 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 embodiment, after obtaining the data of silt deposition hotspot areas, these data are spatially integrated with data such as water flow velocity distribution, temperature change trend, and sediment concentration to form comprehensive lake and reservoir water environment data. For example, a GIS platform is used to spatially match and overlay various data to generate a comprehensive environmental model. The model can display the flow velocity, temperature, sediment concentration, and distribution of silt deposition hotspot areas in different water depths, helping decision makers optimize dredging operation plans, improve operation efficiency, and reduce environmental impact.

[0143] Optionally, step S2 specifically includes:

[0144] Step S21: performing environmental integration of the silt deposition hotspot area based on the three-dimensional data model of the lake 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) 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: Acquire the sample structure data of the dredging ship, and perform modular box structure decomposition on the sample structure data of the dredging ship, so as to obtain the sample hull module structure data and the sample function module structure data;

[0147] In this embodiment, the hull structure and the functional modules are designed separately by modularizing the existing dredging ship model structure data (such as hull, functional modules, power system, etc.). For example, the hull structure may include a support structure and a cabin, while the functional modules include silt treatment, a pumping system, a sensor array, etc. After modularization, detailed hull module structure data and functional module structure data are generated, including the size, weight, connection method and functional description of each module, for subsequent design and optimization.

[0148] Step S23: Designing the size and weight of the dredging ship hull module 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 ship hull module design structure data;

[0149] In this embodiment, the size and weight of the hull module are designed by combining the transportation environment data of the lake (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 waters, the hull design will take into account higher stability and greater load-bearing capacity. Finally, the dredging ship hull module design data that meets both transportation and operation requirements is generated, including the specific size, weight and special structural requirements (such as reinforcement points or weight reduction design) of each module.

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

[0151] In this embodiment, the functional modules of the dredging ship are designed and optimized according to 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 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. Finally, the 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 function module design structure data, thereby obtaining the dredging ship module connection design data; performing modular structure connection on the dredging ship hull module design structure data and the dredging ship function module design structure data according to the dredging ship module connection design data, thereby obtaining the initial modular hull structure design data;

[0153] In this embodiment, the hull module and the functional module of the dredging ship are connected and designed to ensure that each module can be reliably connected and have good coordination performance. Using the modular design principle, the connection method between the design modules, such as bolt connection, snap connection or slide connection, etc. Specifically, the hull module and the functional module are connected through the interface design, and the interface will be accurately calculated and optimized to ensure that the modules can fit closely and are easy to disassemble and reassemble. For example, when designing the connection between the silt suction system module and the hull, it is ensured that the connection point can withstand the huge power generated by the suction pump, and the connection point position is convenient for transportation and assembly. Through this process, the module connection design data is generated, and the detailed design files required for the subsequent modular structure connection are prepared. The specific operation of the modular structure connection is performed according to the design data. The hull module and the functional module are reasonably allocated and combined. For example, the power system and the dredging pump module can be connected to the hull module through a sliding track, which is convenient for rapid replacement and maintenance in different operating environments. Secondly, the interface design of the connection point needs to have a sealing function to prevent moisture from penetrating into 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 will be generated, and specific installation instructions, connection drawings and connection requirements will be provided for each module. These design data include the specific dimensions of the connection between modules, the required materials, the bearing capacity analysis of the connection, etc. Finally, these preliminary design data are combined with the module connection data to form the initial modular hull structure design data of the dredging vessel.

[0154] Step S26: Perform lightweight topological 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 use can be improved by adjusting the cabin distribution. Finally, the optimized modular hull structure design data is generated and uploaded to the dredging ship production management platform. The platform starts the production process based on these data and starts producing hull modules that meet the optimization requirements.

[0156] Optionally, step S24 is specifically:

[0157] According to the dredging ship 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.). In 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 calculate the force and dynamic response to ensure that the strength and stability of the module meet the hull load-bearing requirements.

[0159] The function module dual drive mode is adapted to the function module structure data to be designed by a preset shore power drive system and a preset diesel drive system, thereby obtaining the drive adaptation function 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 the diesel drive mode, the diesel engine will automatically adjust the speed according to the load to ensure sufficient power output. Through sensors that 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] Design the onboard sensor deployment based on the structural data of the drive adaptation function module according to the environmental data of the silt deposition hotspot area, so as to obtain the modular sensor deployment design data;

[0162] In this embodiment, the layout design of the shipboard sensors is formulated using environmental data (such as water depth, flow rate, sediment concentration, etc.) in the hotspot area of ​​silt deposition. According to the needs 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 ship 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, the water depth, flow rate, sediment concentration, meteorology and other data 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 module 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 sludge suction pump group is designed based on the structure data of the drive adaptation function module, so as to obtain the design data of the modular sludge suction pump group;

[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, deposition thickness, etc.). Assuming that there is a large depth of silt deposition 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 of the pump group, fluid dynamics simulation technology is used to optimize the efficiency and durability of the pump to ensure its stable operation under 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, so as to obtain the modular sludge suction pump group module design data;

[0166] In this embodiment, the ACC automatic mud control system will calculate the optimal working parameters of the pump group when working together based on the design data of the modular mud suction pump group. The ACC system can dynamically adjust the working rate and mud discharge pressure of each pump to ensure that the pump group works together under different operating conditions. For example, during the dredging operation, when the silt in a certain area is relatively dense, the suction of the corresponding mud suction pump will be automatically increased, while the load of other pumps will be reduced to achieve the effect of energy saving and emission reduction. The control system will also monitor the operating status of the pump group in real time. If a pump is found to be working abnormally, the system will automatically adjust or shut down the pump to avoid damage to other pumps or cause system instability.

[0167] According to the sediment data of the silt deposition hotspot area, the modular structure design of the multi-stage filtration of sediment and the modular structure design of sediment storage are carried out on the structure data of the drive adaptation function module, so as to obtain the design data of the modular sediment separation module;

[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 of the silt deposition hotspot area and the flow velocity data of the silt deposition hotspot area, the power module design and the propulsion module design are performed on the drive adaptation function module structure data, so as to obtain the 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 rate data of the silt deposition hotspot area. According to the water depth data, the power module will be designed to be adjustable to adapt to different water depth environments to ensure that the hull can obtain sufficient thrust in deep water or shallow water. The propulsion module is designed with thrusters and propellers of different specifications according to the flow rate data to meet the operating requirements under different flow rates. The propulsion efficiency of the hull under different water conditions is simulated by a computational fluid dynamics model to optimize the operating parameters of the propulsion system. The power module and the propulsion module are then combined to obtain a modular power module.

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

[0172] In this embodiment, the line connection of each module (sensor, dredge suction pump, sediment separation, shore power module) is designed. First, according to the power demand and communication demand of each module, design appropriate power transmission lines and data communication lines to ensure that each module can be efficiently interconnected during operation. The design of the power line takes into account power loss and current safety, and adopts high-efficiency and low-loss cables, while the data communication line is connected through optical fiber or wireless communication system to ensure real-time data transmission. When designing all lines, the convenience of modular disassembly and maintenance will be taken into account to ensure stable operation of the system.

[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, the working status data and construction environment status data of the hull relay pump are collected in real time through the environmental sensor network deployed in the lake. The working status of the hull relay pump obtains key data such as the pump speed, mud suction volume, mud discharge pressure, flow rate, etc. through sensors to determine whether it is operating within the predetermined working range. At the same time, the real-time construction environment data of the lake includes environmental variables such as water depth, flow rate, and sediment concentration. The environmental sensor and hull sensor data are collected synchronously and transmitted to the data processing platform in real time through the wireless communication network.

[0176] Step S42: evaluating the hull pump group efficiency according to the real-time construction environment status data of the lake 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, the mud discharge capacity of the pump group may be reduced, 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 ship through the dredging ship control management platform, and estimating the actual construction efficiency target completion degree of the actual construction efficiency data of the dredging ship according to the construction target of the dredging ship, thereby obtaining the actual construction efficiency target completion degree data;

[0179] In this embodiment, the dredging ship control and management platform receives the construction goals set by the construction management team (such as cleaning 50 cubic meters of silt per hour) and compares these goals with real-time work efficiency data. For example, if it is calculated that the current working efficiency of the hull is to clean 40 cubic meters of silt per hour, the actual completion rate will be 80%. At this time, 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 given 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 optimized for collaborative control 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 coordination of multiple pump groups is more efficient. Through this optimization process, an optimal pump group control parameter data is finally generated, and these data will be uploaded to the control platform.

[0182] Step S45: Upload the pump group control optimization parameter data to the dredging vessel control management platform to execute the pump group control task; obtain 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;

[0183] In this embodiment, the optimized pump group control parameter data is uploaded to the dredging ship control management platform via a wireless network, and the platform immediately executes the pump group control task. The control platform will adjust the pump speed, mud discharge pressure, flow rate, etc. based on the optimized parameters. After that, continue to monitor the working status of the pump group and the construction environment status in real time. Through the real-time data obtained by the sensor (such as the pump flow rate, suction pressure, etc.), the platform will judge whether the pump is working as expected based on these data to ensure that the construction progress is not affected.

[0184] Step S46: according to the construction target of the dredging vessel, 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 are reviewed for construction efficiency, thereby obtaining a construction efficiency review report;

[0185] In this embodiment, the dredging ship control management platform evaluates the working status and construction environment data of the pump group after control according to the construction target of the dredging ship. 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, 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 give improvement measures. For example, if the increase in water flow velocity causes the efficiency of the pump group to decrease, the report will suggest 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 will iteratively optimize the pump group control optimization parameters. Assuming that the report shows that the pump group flow rate does not meet expectations, the pump control parameters will be iteratively adjusted according to real-time environmental changes (such as water flow, sediment concentration). For example, if the pump group works inefficiently in an area with high sediment concentration, the pump suction pressure will be increased and the speed will be adjusted. The new optimization parameters will be uploaded to the control management platform via wireless transmission, and new pump group control tasks will be executed to ensure that the pump group continues to work in an optimized state.

[0188] Optionally, step S5 specifically includes:

[0189] Step S51: real-time sensor data collection is performed through the lake environment sensor network to obtain real-time environmental status data and real-time hull status data; actual dredging ship construction environment simulation is performed according to the real-time environmental status data to obtain dredging ship construction environment simulation data;

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

[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 dredging construction simulation of the dredging ship on the modular hull 3D model according to the dredging ship construction environment simulation data, thereby obtaining dredging construction simulation data of the dredging ship;

[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, the working state of the hull under these environments is simulated by fluid dynamics simulation (CFD) in combination with environmental simulation data, such as the water flow velocity and sediment concentration of the lake. 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 load of the pump group and changes in the hull posture are predicted. Finally, 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 hull simulation state data;

[0194] In this embodiment, the construction simulation data of the dredging ship includes multiple variables, such as pump group flow, pressure, hull speed, sediment concentration in the construction area, etc. By integrating these simulation data, the simulated hull state data of the dredging ship 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 state of the pump group, the position of the hull, environmental changes, etc.) are integrated to generate the simulated state data of the hull for error evaluation 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, so as to obtain actual working state error data of the dredging vessel;

[0196] In this embodiment, the hull status data (such as pump group flow, mud discharge pressure, hull speed, etc.) collected in real time are compared with the hull status data obtained through simulation. For example, assuming that in a specific construction area, the actual working flow of the hull is lower than the expected simulation value, this difference is calculated through error comparison to generate working status error data. This error data can reflect the performance deviation of the system, such as uneven load of the pump group, the influence of environmental factors, etc. The error data will be uploaded to the management platform in real time and provide guidance for subsequent optimization of construction parameters.

[0197] Step S55: obtaining a dredging vessel construction parameter set through the dredging vessel control management platform, and dynamically optimizing 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, including the working pressure, flow rate, and ship speed of the pump group, based on the construction objectives and real-time status data. 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 the construction efficiency. If the error data indicates that the hull is moving too slowly, the power of the propeller is increased or the posture of the hull is adjusted to improve work efficiency. These dynamically adjusted construction parameters will be continuously optimized under real-time monitoring to ensure that the construction objectives are achieved.

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

[0200] In this embodiment, the optimized dynamic construction parameters (such as pump group working pressure, mud discharge flow, hull speed, etc.) will be uploaded to the control and management platform of the dredging ship. 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, propeller, and power system. For example, the pump group can be instructed to adjust its mud suction pressure, or adjust the speed of the propeller according to real-time data, so that the hull can complete the dredging task in the best working state. The adjustment of these parameters will be reflected in the working state of the hull in real time to ensure 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 according to 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 construction target of the dredging ship to obtain dredging construction progress data;

[0203] In this embodiment, a variety of environmental parameters such as water flow, sediment concentration, water depth, and temperature are monitored in real time through an environmental sensor network deployed in the lake area. The data collected by the sensor is transmitted to the control and management platform of the hull through a wireless network. The platform combines real-time environmental data and 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-by-50-meter area in the lake area, the construction efficiency will be calculated based on the actual collected sediment concentration and water flow velocity, 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 a decrease in 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 real-time environmental status collection 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 completed 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 completed 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: acquiring 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 disassembled;

[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 sensors and marked according to the time point when the dredging is completed. Records marked as "hull status data to be disassembled" ensure 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 pump group of the hull is subject 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 disassembly sequence analysis of the hull modules according to the state data of the hull to be disassembled and the modular hull structure design data, thereby obtaining the disassembly sequence data of the hull modules;

[0209] In this embodiment, analysis is performed based on the modular design data of the hull (such as the installation order of the modules, the difficulty of disassembly, the weight of the modules, 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 due to their heavy weight or large space occupation 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 module of the hull. 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: predicting the environmental state change of the dredging completion environmental state data, thereby obtaining environmental state change prediction data, and analyzing the hull module disassembly sequence data for a stable hull module disassembly strategy according to 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 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) 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 according to 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, assuming that 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 more obstacles to ensure the safety and efficiency of the module transportation process. By integrating all these data, the final hull module transportation strategy is generated to ensure that the disassembled modules can be safely and efficiently transported to the designated location to complete 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 three-dimensional modular design and construction method of a dredging vessel as described above, and 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, so as to obtain 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, so as to obtain 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, so as to obtain the hull module design data, and to perform hull structure topology optimization on the hull module design data, so as to obtain modular hull structure design data, and upload it to the dredging ship production management platform to execute the hull structure production task;

[0217] The sensor data fusion module is used to obtain the hull sensor data and perform sensor data fusion based on multi-source lake and reservoir data and hull sensor data, so as to obtain the lake and reservoir environment sensor network; real-time sensor data collection is performed through the lake and reservoir environment sensor network, so as to obtain the real-time state data of the environment and the real-time state data of the hull;

[0218] The construction parameter dynamic optimization module is used to simulate the dredging construction of the dredging ship according to the real-time status data of the environment and the modular hull structure design data, so as to obtain the dredging construction simulation data of the dredging ship; dynamically optimize the actual dredging construction parameters of the dredging ship according to the dredging construction simulation data of the dredging ship, so as to obtain dynamic construction parameter data, and upload it to the dredging ship control management platform to execute the construction parameter adjustment task;

[0219] The pump group parameter iteration optimization module is used to collect the working status of the hull relay pump through the lake environment sensor network, so as to obtain the working status data of the hull relay pump; the pump group collaborative control optimization is performed on the working status data of the hull relay pump, so as to obtain the optimal pump group control parameter data, and upload it to the dredging ship control 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 environment sensor network, so as to obtain real-time dredging environmental status data; to analyze the hull module disassembly strategy based on the real-time dredging environmental status data and the modular hull structure design data, so as to obtain the hull module disassembly strategy; to integrate the hull module transportation strategy according to the lake and reservoir transportation environment data and the hull module disassembly strategy, so as to obtain the 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, so as to obtain 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, so as to obtain a three-dimensional data model of the lake and reservoir environment; Step S2: Design a hull module according to the three-dimensional data model of the lake environment to obtain hull module design data, and perform hull structure topology optimization on the hull module design data to obtain modular hull structure design data, and upload it to the dredging ship 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: collecting 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; optimizing the pump group coordinated control of the hull relay pump working status data to obtain the optimal pump group control parameter data, and uploading it to the dredging ship control management platform to execute the pump group control task; Step S5: simulating the dredging construction of the dredging ship according to the lake environment sensor network and the modular hull structure design data, thereby obtaining the dredging construction simulation data of the dredging ship; dynamically optimizing the actual dredging construction parameters of the dredging ship according to the dredging construction simulation data of the dredging ship, thereby obtaining dynamic construction parameter data, and uploading it to the dredging ship control management platform to execute the construction parameter adjustment task; Step S6: Collect real-time environmental status data based on the lake environment sensor network to obtain real-time dredging environmental status data; analyze the hull module disassembly strategy based on the real-time dredging environmental status data and the modular hull structure design data to obtain the hull module disassembly strategy; integrate the hull module transportation strategy based on the lake transportation environment 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, 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 multi-source lake and reservoir data to be analyzed; Step S12: classifying 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, thereby obtaining a lake reservoir surrounding terrain depth map, and performing a lake reservoir transportation environment analysis based on the lake reservoir surrounding terrain depth map, thereby obtaining 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, so as to obtain 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 is characterized in that: Step S13 is specifically as follows: Step S131: performing multi-source terrain data classification on lake and reservoir terrain data, thereby obtaining 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 the lake surrounding terrain point cloud data, and performing rasterization depth map conversion on the lake surrounding terrain point cloud data to obtain the lake surrounding terrain depth map; Step S134: partitioning the lake and reservoir land surface terrain on the lake and reservoir surrounding terrain depth map, thereby obtaining lake and reservoir land surface terrain partitioning data, and calculating the proportion of gentle terrain based on the lake and reservoir land surface terrain partitioning data, thereby obtaining lake and 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 contour data of the lake and reservoir area; Identify the roads in the lake and reservoir area according to the lake and reservoir area contour data, so as to obtain the road data in the lake and reservoir area; Step S136: identifying the bottleneck of lake and reservoir land transportation based on the data of the proportion of gentle landforms on the lake and reservoir land surface and the road data in the lake and reservoir area, thereby obtaining the bottleneck data of lake and reservoir land transportation; 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 to obtain 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 water depth of the lake into different zones, thereby obtaining the water depth division 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, so as to obtain 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, so as 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, 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 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: Acquire the sample structure data of the dredging ship, and perform modular box structure decomposition on the sample structure data of the dredging ship, so as to obtain the sample hull module structure data and the sample function module structure data; Step S23: Designing the size and weight of the dredging ship hull module 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 ship hull module design structure data; Step S24: Designing a dredging ship functional module based on the sample functional module structure data according to the silt deposition hotspot area environmental data, thereby obtaining the dredging ship functional module design structure data; Step S25: performing module connection design on the hull module design structure data of the dredging ship and the functional module design structure data of the dredging ship, thereby obtaining the module connection design data of the dredging ship; According to the dredging ship module connection design data, modular structural connection is performed on the dredging ship hull module design structure data and the dredging ship function module design structure data, so as to obtain initial modular hull structure design data; Step S26: Perform lightweight topological 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 ship 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; The function module dual drive mode is adapted to the function module structure data to be designed by a preset shore power drive system and a preset diesel drive system, thereby obtaining the drive adaptation function module structure data; Design the onboard sensor deployment based on the structural data of the drive adaptation function module according to the environmental data of the silt deposition hotspot area, so as to obtain the modular sensor deployment design data; According to the environmental data of the silt deposition hotspot area, the modular sludge suction pump group is designed based on the structure data of the drive adaptation function module, so as to obtain the design data of the modular sludge suction pump group; 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, so as to obtain the modular sludge suction pump group module design data; According to the sediment data of the silt deposition hotspot area, the modular structure design of the multi-stage filtration of sediment and the modular structure design of sediment storage are carried out on the structure data of the drive adaptation function module, so as to obtain the design data of the modular sediment separation module; According to the water depth data of the silt deposition hotspot area and the flow velocity data of the silt deposition hotspot area, the power module design and the propulsion module design are performed on the drive adaptation function module structure data, so as to obtain the modular power module design data; The modular sensor deployment design data, modular dredge suction pump module design data, modular sediment separation module design data and modular shore power module design data are connected to the drive module lines 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 according to the real-time construction environment status data of the lake 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 ship through the dredging ship control management platform, and estimating the actual construction efficiency target completion degree of the actual construction efficiency data of the dredging ship according to the construction target of the dredging ship, 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: Upload the pump group control optimization parameter data to the dredging vessel control management platform to execute the pump group control task; obtain 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; Step S46: according to the construction target of the dredging vessel, 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 are reviewed for construction efficiency, 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: real-time sensor data collection is performed through the lake environment sensor network to obtain real-time environmental status data and real-time hull status data; actual dredging ship construction environment simulation is performed according to the real-time environmental status data to obtain dredging ship 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 dredging construction simulation of the dredging ship on the modular hull 3D model according to the dredging ship construction environment simulation data, thereby obtaining dredging construction simulation data of the dredging ship; 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 hull simulation 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, so as to obtain actual working state error data of the dredging vessel; Step S55: obtaining a dredging vessel construction parameter set through the dredging vessel control management platform, and dynamically optimizing 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 execute 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 according to 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 construction target of the dredging ship 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 real-time environmental status collection until the dredging construction progress reaches the preset construction progress threshold; Step S63: acquiring 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 disassembled; Step S64: performing a disassembly sequence analysis of the hull modules according to the state data of the hull to be disassembled and the modular hull structure design data, thereby obtaining the disassembly sequence data of the hull modules; Step S65: predicting the environmental state change of the dredging completion environmental state data, thereby obtaining environmental state change prediction data, and analyzing the hull module disassembly sequence data for a stable hull module disassembly strategy according to 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 for the three-dimensional modular design and construction of a dredging vessel 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, so as to obtain 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, so as to obtain 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, so as to obtain the hull module design data, and to perform hull structure topology optimization on the hull module design data, so as to obtain modular hull structure design data, and upload it to the dredging ship production management platform to execute the hull structure production task; The sensor data fusion module is used to obtain the hull sensor data and perform sensor data fusion based on multi-source lake and reservoir data and hull sensor data, so as to obtain the lake and reservoir environment sensor network; real-time sensor data collection is performed through the lake and reservoir environment sensor network, so as to obtain the real-time state data of the environment and the real-time state data of the hull; The construction parameter dynamic optimization module is used to simulate the dredging construction of the dredging ship according to the real-time status data of the environment and the modular hull structure design data, so as to obtain the dredging construction simulation data of the dredging ship; dynamically optimize the actual dredging construction parameters of the dredging ship according to the dredging construction simulation data of the dredging ship, so as to obtain dynamic construction parameter data, and upload it to the dredging ship control management platform to execute the construction parameter adjustment task; The pump group parameter iteration optimization module is used to collect the working status of the hull relay pump through the lake environment sensor network, so as to obtain the working status data of the hull relay pump; the pump group collaborative control optimization is performed on the working status data of the hull relay pump, so as to obtain the optimal pump group control parameter data, and upload it to the dredging ship control 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 environment sensor network, so as to obtain real-time dredging environmental status data; to analyze the hull module disassembly strategy based on the real-time dredging environmental status data and the modular hull structure design data, so as to obtain the hull module disassembly strategy; to integrate the hull module transportation strategy according to the lake and reservoir transportation environment data and the hull module disassembly strategy, so as to obtain the hull module transportation strategy.

Images