A three-dimensional model construction method of green low-carbon ecological dredging equipment

Abstract

The present application relates to the technical field of three-dimensional model construction, and particularly relates to a three-dimensional model construction method of green low-carbon ecological dredging equipment. The method comprises the following steps: collecting water silt samples and performing particle screening to generate particle grading data, then analyzing silt characteristics based on the data to obtain silt characteristic data, obtaining historical dredging data and analyzing siltation state to match an optimal dredging method and perform boundary constraint analysis, determining component boundary conditions, designing the configuration of a dredging component and performing three-dimensional modeling, generating a dredging unit component, analyzing the flocculation requirement of silt characteristic data, generating a solidification unit component, performing transport simulation to obtain simulated transport data, and arranging components according to the data to generate a transmission unit component, integrating the dredging unit component, the solidification unit component and the transmission unit component to construct a complete three-dimensional dredging equipment model. The present application realizes a more efficient three-dimensional model construction method of dredging equipment.

Description

Technical Field

[0001] This invention relates to the field of three-dimensional model construction technology, and in particular to a method for constructing a three-dimensional model of green, low-carbon, and ecological dredging equipment. Background Technology

[0002] With the acceleration of urbanization, water pollution and siltation have become increasingly serious problems, significantly impacting the ecological environment. Traditional dredging methods are often inefficient and damage ecosystems, necessitating the exploration of greener, low-carbon dredging equipment and technologies to achieve sustainable water management. However, existing dredging equipment often lacks systematic design, making it difficult to meet the needs of different water bodies and silt characteristics, resulting in unsatisfactory dredging effects, serious resource waste, and exacerbated environmental impact. Many studies focus on single dredging technologies or equipment, lacking comprehensive solutions that consider water and silt characteristics, historical dredging data, and the working environment. In particular, the connection between silt characteristic analysis and dredging method matching has not been fully explored, making it difficult to effectively cope with various complex environmental conditions in practical applications, affecting the scientific nature and effectiveness of dredging work. Furthermore, the lack of systematic modeling for dredging, solidification, and transportation processes results in low integration of dredging equipment, making it difficult to achieve efficient operation processes. Summary of the Invention

[0003] Therefore, it is necessary to provide a three-dimensional model construction method for green, low-carbon, and ecological dredging equipment to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, a method for constructing a three-dimensional model of green, low-carbon, and ecological dredging equipment includes the following steps:

[0005] Step S1: Collect water body silt samples; perform particle sieving on the water body silt samples to generate particle size distribution data; analyze the silt characteristics of the water body silt samples based on the particle size distribution data to obtain silt characteristic data.

[0006] Step S2: Obtain historical dredging data; match dredging methods to the siltation state based on historical data to obtain the preferred dredging method; perform boundary constraint analysis on the preferred dredging method to obtain the component boundary conditions;

[0007] Step S3: Based on the component boundary conditions, construct the dredging component configuration to obtain dredging configuration data; perform 3D modeling on the dredging configuration data to generate dredging unit components;

[0008] Step S4: Perform flocculation requirement analysis on sludge characteristic data to obtain flocculation requirement data; perform solidification unit modeling on flocculation requirement data based on component boundary conditions and dredging unit components to generate solidification unit components;

[0009] Step S5: Perform transport simulation on silt characteristic data based on component boundary conditions to obtain simulated transport data; perform component layout processing based on simulated transport data to generate transport unit components;

[0010] Step S6: Integrate the dredging unit components, solidification unit components, and transmission unit components to generate a three-dimensional dredging equipment model.

[0011] This invention generates particle size distribution data through particle sieving of water sludge samples, providing a foundation for subsequent sludge characteristic analysis. The formation of sludge characteristic data allows for a detailed description of the sludge's physical and chemical properties. The acquisition of historical dredging data provides important reference for siltation state analysis. The matching of optimal dredging methods ensures the scientific validity and effectiveness of the dredging strategy. The implementation of boundary constraint analysis provides necessary constraints for component design. The generation of dredging component configurations forms dredging configuration data, laying the foundation for subsequent modeling. The 3D modeling process generates dredging unit components, making the design of dredging equipment more intuitive and practical. The coagulation demand analysis ensured the optimization of flocculant usage and effectiveness. The implementation of solidification unit modeling provided structural support to ensure sludge treatment effectiveness. The transportation simulation generated simulated transportation data, providing dynamic analysis basis for effective sludge transport. The implementation of component layout and treatment formed the transport unit components, making the overall design more reasonable. The integration of dredging unit components, solidification unit components, and transport unit components generated a three-dimensional dredging equipment model, which improved the efficiency and environmental friendliness of dredging operations. It provided effective technical support and visualization tools for green, low-carbon, and ecological dredging, and promoted the sustainable development of ecological environment governance.

[0012] Preferably, step S1 includes the following steps:

[0013] Step S11: Collect water samples from multiple points to obtain water silt samples; identify the particle distribution of the water silt samples to obtain silt particle distribution data.

[0014] Step S12: Perform particle size sieving on the sludge particle distribution data to generate particle size distribution data;

[0015] Step S13: Determine the water content of the water body sludge sample to obtain the sample water content; perform heavy metal detection on the water body sludge sample to generate sample pollutant data;

[0016] Step S14: Perform data fusion on particle size distribution data, sample moisture content and sample pollutant data to generate sludge characteristic data.

[0017] This invention utilizes a three-dimensional model construction method for green, low-carbon, and ecological dredging equipment to achieve comprehensive collection of silt samples from water bodies through multi-point sampling. Particle distribution identification provides fundamental data for subsequent characteristic analysis, and particle size sieving results clarify the particle size distribution characteristics of the silt. The combination of water content measurement and heavy metal detection ensures accurate assessment of the pollutant status of the samples. Data fusion technology effectively integrates particle size distribution, water content, and pollutant data, and the generated silt characteristic data provides a scientific basis for equipment design. This ensures that the dredging equipment can be optimized for different water conditions, comprehensively improving the efficiency and environmental friendliness of dredging operations, promoting the restoration and protection of aquatic ecosystems, and advancing the implementation of sustainable development concepts.

[0018] Preferably, step S2 includes the following steps:

[0019] Step S21: Obtain historical engineering cases; perform dredging case retrieval on historical engineering cases to obtain historical dredging data;

[0020] Step S22: Correlate historical dredging and silt characteristic data to generate similarity indicators; perform hierarchical clustering on the similarity indicators to obtain the engineering type spectrum;

[0021] Step S23: Based on the preset existing equipment functions, perform equipment capability matching on the engineering type spectrum to obtain an equipment screening scheme; based on the equipment screening scheme, screen dredging methods to generate the preferred dredging method;

[0022] Step S24: Decompose the preferred dredging method into operating conditions to obtain operating condition data;

[0023] Step S25: Perform process condition inversion on the operating condition parameters to generate process condition data; apply constraints based on the process condition data to obtain the component boundary conditions.

[0024] This invention provides a rich foundation for subsequent data analysis by acquiring historical engineering cases. Dredging case retrieval ensures the comprehensiveness and accuracy of historical dredging data. The generation of similarity indicators provides a quantitative basis for the relationship between different cases. The implementation of hierarchical clustering forms a spectrum of engineering types, making the classification and comparison of related projects more systematic. The process of matching equipment capabilities ensures the reasonable connection between existing equipment and engineering types. The formation of equipment screening schemes provides a scientific basis for the selection of dredging methods. The generation of optimal dredging methods can effectively improve the efficiency and effect of the project. The implementation of working condition decomposition provides detailed information for the acquisition of operating condition data. The development of process condition inversion generates reliable process condition data, providing necessary constraints for subsequent design. The determination of component boundary conditions ensures the scientificity and rationality of model design. Overall, it improves the design accuracy and efficiency of green, low-carbon, and ecological dredging equipment, providing technical support and innovative ideas for the sustainable development of the dredging field.

[0025] Preferably, step S23 includes the following steps:

[0026] The project type spectrum is classified by scale to obtain project scale data; the project scale data is then decomposed into functional requirements to generate functional requirements data.

[0027] Based on the pre-defined existing equipment functions, the functional requirement data is evaluated for compatibility to obtain equipment compatibility data; based on the equipment compatibility data, equipment schemes are screened to generate equipment screening schemes.

[0028] Cost-benefit analysis was performed on the equipment selection scheme to obtain cost-benefit data; the construction period was then predicted based on the cost-benefit data to obtain period prediction data.

[0029] The optimal dredging method is generated by combining and optimizing cost-benefit data and cycle prediction data.

[0030] This invention provides a systematic classification of different projects through the scale grading of engineering type spectrum. The generation of project scale data lays the foundation for subsequent functional requirement analysis. The formation of functional requirement data ensures a comprehensive understanding of project requirements. The implementation of adaptability assessment can effectively determine the degree of matching between existing equipment and functional requirements. The generation of equipment adaptability data provides a scientific basis for the selection of equipment schemes. The formulation of equipment screening schemes ensures the rationality and feasibility of the selected equipment. The cost-benefit accounting process provides economic analysis for subsequent decision-making. The generation of cost-benefit data can clearly demonstrate the economic value of different schemes. The implementation of construction cycle prediction provides a time reference for project implementation. The generation of cycle prediction data ensures the reasonable planning of project progress. The implementation of method combination optimization enables the effective combination of different dredging methods. The final generated optimal dredging method can achieve a balance between efficiency, economy and environmental protection. Overall, it improves the design scientificity and practicality of green, low-carbon ecological dredging equipment and provides effective technical support and decision-making basis for sustainable water environment governance.

[0031] Preferably, step S3 includes the following steps:

[0032] Step S31: Identify the engineering parameters of the component boundary conditions to obtain engineering parameter data;

[0033] Step S32: Perform structural topology mapping on the engineering parameter data to generate structural topology data; perform component interference check on the structural topology data to obtain component layout parameters;

[0034] Step S33: Perform parametric modeling of component layout parameters to obtain dredging configuration data; refine the dredging configuration data according to component boundary conditions to generate key component parameters;

[0035] Step S34: Perform three-dimensional solid reconstruction of the key parameters of the component to generate the dredging unit component.

[0036] This invention provides a detailed understanding of component boundary conditions through engineering parameter identification. The acquisition of engineering parameter data lays a solid foundation for subsequent design. The implementation of structural topology mapping ensures the efficiency and rationality of the design. The generated structural topology data supports the optimization of component layout. The component interference verification process ensures the mutual adaptation between different components. The establishment of component layout parameters provides a clear basis for subsequent modeling. The application of parametric modeling improves the flexibility and adaptability of dredging configuration data. The fine division of key component parameters ensures the accuracy of the design. The completion of three-dimensional solid reconstruction provides a realistic and reliable model for the generation of dredging unit components. Overall, it improves the design efficiency and implementation effect of dredging equipment, promotes the practical application of green and low-carbon technologies in the dredging field, and facilitates the implementation of eco-friendly design concepts.

[0037] Preferably, step S4 includes the following steps:

[0038] Step S41: Match the flocculant ratio based on the sludge characteristic data to obtain the flocculation ratio data;

[0039] Step S42: Based on the flocculation ratio data, perform flocculation demand analysis on the sludge characteristic data to generate flocculation demand data;

[0040] Step S43: Based on the flocculation ratio data, perform flocculation kinetics deduction to generate flocculation dynamic data; design the flocculation cavity based on the flocculation dynamic data and component boundary data to obtain cavity layout data;

[0041] Step S44: Integrate the cavity layout data and dredging unit components into a flocculation unit to generate a solidification unit component.

[0042] This invention provides a precise basis for the subsequent flocculation process by matching flocculant ratios based on sludge characteristic data. The generation of flocculation ratio data ensures the scientific selection of flocculants required for different sludge characteristics. The implementation of flocculation demand analysis provides detailed guidance for equipment design and operation. The generated flocculation demand data clarifies the amount and method of flocculant usage. The development of flocculation kinetics provides a dynamic analysis basis for the flocculation process. The generated flocculation dynamic data reflects the physical changes during the flocculation process. The implementation of cavity design ensures the optimization of flocculation effect. The generation of cavity layout data supports the effective integration of solidification units. The successful implementation of flocculation unit integration makes the combination of dredging unit components and solidification unit components more compact, thus improving the functionality and efficiency of green, low-carbon, and ecological dredging equipment as a whole, and providing sustainable technical solutions and innovative ideas for environmental governance.

[0043] Preferably, step S43 includes the following steps:

[0044] The flocculation ratio data is divided into reaction time sequences to obtain the flocculation reaction sequence; the flow gradient is calculated from the flocculation reaction sequence to generate flow velocity distribution data.

[0045] The reaction intensity of the tassel distribution data is evaluated to obtain intensity evaluation data; based on the intensity evaluation data, the flocculation efficiency is extrapolated to generate flocculation dynamics data.

[0046] Spatial demand mapping is performed on the flocculation dynamic data to obtain the flocculation spatial demand; structural morphology projection is performed on the flocculation spatial demand to generate structural morphology data.

[0047] The flow field distribution is simulated based on the structural morphology data to generate simulated flow field distribution data; the cavity is optimized based on the simulated flow field distribution data to obtain cavity layout data.

[0048] This invention provides a basis for time management of the flocculation process by dividing the reaction time sequence of flocculation ratio data. The generation of flocculation reaction sequences can effectively identify the reaction characteristics of different stages. The implementation of tassel gradient calculation ensures the rationality and uniformity of velocity distribution. The formation of velocity distribution data provides a basis for subsequent reaction intensity assessment. The acquisition of intensity assessment data provides quantitative analysis for optimizing flocculation effect. The development of flocculation efficiency extrapolation can predict the flocculation effect under different conditions. The generated flocculation dynamic data reflects the dynamic characteristics of the flocculation process. The implementation of spatial requirement mapping provides a spatial layout basis for cavity design. The accurate identification of flocculation space requirements ensures the rationality of the design. The generation of structural morphology projection provides a morphological basis for subsequent flow field distribution simulation. The acquisition of simulated flow field distribution data can intuitively show the movement law of fluid in the cavity. The implementation of cavity optimization design ensures the maximization of flocculation effect. Overall, it improves the efficiency and scientific nature of green, low-carbon, and ecological dredging equipment in the flocculation process, and provides effective technical support and innovative solutions for environmental governance.

[0049] Preferably, step S5 includes the following steps:

[0050] Step S51: Perform rheological characteristic processing on the sludge feature data to obtain rheological characteristic data; perform pipeline flow simulation on the rheological characteristic data and component boundary conditions to generate a virtual flow field;

[0051] Step S52: Map the energy consumption distribution of the virtual flow field to obtain energy consumption distribution data; locate high-energy-consuming areas based on the energy consumption distribution data to generate simulated transportation data;

[0052] Step S53: Identify optimizable locations based on simulated transport data to obtain optimizable locations;

[0053] Step S54: Component layout processing is performed based on optimizable points to generate transmission unit components.

[0054] This invention provides necessary physical parameters for subsequent flow analysis by characterizing the rheological properties of sludge feature data. The generation of rheological property data ensures an accurate understanding of sludge flow behavior. The implementation of pipeline flow simulation provides a foundation for the creation of a virtual flow field, which can intuitively demonstrate the flow of sludge in the pipeline. The development of energy consumption distribution mapping provides data support for energy use optimization. The acquisition of energy consumption distribution data can clearly identify the energy consumption in different areas. The implementation of high-energy-consuming area location provides a basis for the formulation of optimization schemes. The generation of simulated transportation data can reflect the energy efficiency during transportation. The successful implementation of optimized point identification provides a clear direction for improvement in subsequent design. The determination of optimizable points ensures the rationality and effectiveness of component layout. The implementation of component layout processing provides a specific scheme for the generation of transmission unit components. Overall, it improves the energy efficiency and practicality of green, low-carbon, and ecological dredging equipment in the sludge transportation process, and provides effective technical means and support for environmental protection and resource utilization.

[0055] Preferably, step S52 includes the following steps:

[0056] Flow resistance is extracted from the virtual flow field to obtain flow resistance data; pressure drop is calculated from the flow resistance data to generate pressure drop distribution data.

[0057] The step-down distribution data is processed by power conversion to generate power distribution data; energy consumption is assessed based on the power distribution data to obtain energy consumption distribution data.

[0058] The energy consumption distribution data is processed by threshold stratification based on a preset energy consumption stratification threshold to obtain energy consumption stratification data; energy consumption regions are divided according to the energy consumption stratification data to generate regional distribution data.

[0059] Critical points are calibrated on the regional distribution data to generate critical point data; transport parameters are integrated based on the critical point data to generate simulated operation data.

[0060] This invention provides crucial data for subsequent flow analysis by extracting flow resistance from a virtual flow field. The generation of flow resistance data ensures a comprehensive understanding of flow resistance. The implementation of pressure reduction calculation reflects pressure changes during the flow process. The acquisition of pressure reduction distribution data provides a foundation for subsequent power conversion. The implementation of power conversion processing ensures the effectiveness of energy utilization. The generation of power distribution data provides a scientific basis for energy consumption assessment. The implementation of energy consumption assessment clearly shows the energy consumption situation in different regions. The setting of energy consumption stratification thresholds ensures the systematic nature of energy efficiency analysis. The generation of energy consumption stratification data provides a clear standard for regional division. The acquisition of regional distribution data reflects the energy consumption characteristics of different regions. The successful implementation of critical point calibration provides an important reference for subsequent optimization. The generation of critical point data provides a basis for the rational integration of transport parameters. The formation of simulated operation data enables effective prediction and optimization of flow and transport processes. Overall, it improves the scientificity and effectiveness of green, low-carbon, and ecological dredging equipment in resource utilization and energy efficiency management, and provides a practical and feasible technical path for environmental governance.

[0061] Preferably, step S6 includes the following steps:

[0062] Step S61: Locate the connection points of the dredging unit components and obtain the connection point data;

[0063] Step S62: Perform interface adaptation and matching on the connection point data and solidified unit components to generate adaptation interface data;

[0064] Step S63: Plan the assembly sequence for the adapter interface data and transmission unit components to obtain assembly sequence data;

[0065] Step S64: Perform whole-machine collaborative verification on the assembly sequence data to generate whole-machine integration data;

[0066] Step S65: Based on the integrated data of the whole machine, integrate the dredging unit components, solidification unit components and transmission unit components to generate a three-dimensional dredging equipment model.

[0067] This invention provides foundational data for subsequent component integration by locating the connection points of dredging unit components. The generation of connection point data ensures effective connections between components. The implementation of interface adaptation and matching provides a scientific basis for the integration of solidified unit components and dredging units. The formation of adaptation interface data enhances the compatibility and adaptability of components. The development of assembly sequence planning provides an optimization scheme for component assembly efficiency. The generation of assembly sequence data ensures the orderliness and rationality of the assembly process. The implementation of whole-machine collaborative verification ensures the effective cooperation of each component in the overall system. The acquisition of whole-machine integration data provides a comprehensive basis for the construction of the final model. The successful implementation of component integration forms a three-dimensional dredging equipment model, which improves the scientificity and efficiency of green, low-carbon, and ecological dredging equipment in the design and manufacturing process, and provides reliable technical support and guidance for practical applications. Attached Figure Description

[0068] Figure 1 A flowchart illustrating the steps involved in constructing a three-dimensional model of a green, low-carbon, and ecological dredging equipment.

[0069] Figure 2 for Figure 1 A detailed flowchart illustrating the implementation steps of step S2.

[0070] Figure 3 for Figure 1 A detailed flowchart illustrating the implementation steps of step S3.

[0071] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0072] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0073] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0074] 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 merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0075] To achieve the above objectives, please refer to Figures 1 to 3 A method for constructing a three-dimensional model of green, low-carbon, and ecological dredging equipment includes the following steps:

[0076] Step S1: Collect water body silt samples; perform particle sieving on the water body silt samples to generate particle size distribution data; analyze the silt characteristics of the water body silt samples based on the particle size distribution data to obtain silt characteristic data.

[0077] Step S2: Obtain historical dredging data; match dredging methods to the siltation state based on historical data to obtain the preferred dredging method; perform boundary constraint analysis on the preferred dredging method to obtain the component boundary conditions.

[0078] Step S3: Based on the component boundary conditions, construct the dredging component configuration to obtain dredging configuration data; perform 3D modeling on the dredging configuration data to generate dredging unit components;

[0079] Step S4: Perform flocculation requirement analysis on sludge characteristic data to obtain flocculation requirement data; perform solidification unit modeling on flocculation requirement data based on component boundary conditions and dredging unit components to generate solidification unit components;

[0080] Step S5: Perform transport simulation on silt characteristic data based on component boundary conditions to obtain simulated transport data; perform component layout processing based on simulated transport data to generate transport unit components.

[0081] Step S6: Integrate the dredging unit components, solidification unit components, and transmission unit components to generate a three-dimensional dredging equipment model.

[0082] This invention generates particle size distribution data through particle sieving of water sludge samples, providing a foundation for subsequent sludge characteristic analysis. The formation of sludge characteristic data allows for a detailed description of the sludge's physical and chemical properties. The acquisition of historical dredging data provides important reference for siltation state analysis. The matching of optimal dredging methods ensures the scientific validity and effectiveness of the dredging strategy. The implementation of boundary constraint analysis provides necessary constraints for component design. The generation of dredging component configurations forms dredging configuration data, laying the foundation for subsequent modeling. The 3D modeling process generates dredging unit components, making the design of dredging equipment more intuitive and practical. The coagulation demand analysis ensured the optimization of flocculant usage and effectiveness. The implementation of solidification unit modeling provided structural support to ensure sludge treatment effectiveness. The transportation simulation generated simulated transportation data, providing dynamic analysis basis for effective sludge transport. The implementation of component layout and treatment formed the transport unit components, making the overall design more reasonable. The integration of dredging unit components, solidification unit components, and transport unit components generated a three-dimensional dredging equipment model, which improved the efficiency and environmental friendliness of dredging operations. It provided effective technical support and visualization tools for green, low-carbon, and ecological dredging, and promoted the sustainable development of ecological environment governance.

[0083] In this embodiment of the invention, the method for constructing a three-dimensional model of the green, low-carbon, and ecological dredging equipment includes the following steps:

[0084] Step S1: Collect water body silt samples; perform particle sieving on the water body silt samples to generate particle size distribution data; analyze the silt characteristics of the water body silt samples based on the particle size distribution data to obtain silt characteristic data.

[0085] In this embodiment, representative silt sample collection points from typical water areas were selected, and silt was collected using a sediment sampler at a depth of 0.5 to 1 meter. The sampling interval was set according to the actual sedimentation environment. After each sampling, the silt was placed in a sealed container to prevent contamination or volatilization. In the laboratory, the samples were sieved using a grading and screening device. The sieve sizes were selected as 0.075 mm, 0.25 mm, and 1 mm screens for progressive sieving. The mass of particles of different sizes was recorded and the gradation data was calculated. Subsequently, a laser particle size analyzer was used to analyze the particle distribution of the samples and generate particle gradation data. Based on the particle gradation data, a rheometer was used to test the rheological properties of the silt samples, including yield stress and viscosity. At the same time, heavy metal pollutants in the silt were detected by ICP-MS (inductively coupled plasma mass spectrometry). The silt characteristic data were generated by combining the rheological properties and pollutant content.

[0086] Step S2: Obtain historical dredging data; match dredging methods to the siltation state based on historical data to obtain the preferred dredging method; perform boundary constraint analysis on the preferred dredging method to obtain the component boundary conditions;

[0087] In this embodiment, after obtaining permission, historical dredging data is obtained from the relevant water management department, including parameters such as siltation volume, dredging depth, dredging technology and equipment type. The data is in Excel spreadsheet and GIS data file format. After formatting the data, it is input into the database management system. A hierarchical clustering algorithm is used to analyze and classify the dredging conditions. For the classification results, a suitable dredging technology is matched, and the optimal dredging method is selected using a weighted assignment method. This method is then used as the preferred dredging method. Subsequently, the boundary constraint analysis of the preferred dredging method is performed based on the fluid dynamics simulation software CFD (Computational Fluid Dynamics). The analysis includes water flow velocity, particle diameter, and the operating range of the dredging equipment. Finally, the component boundary conditions are formed and output in numerical form.

[0088] Step S3: Based on the component boundary conditions, construct the dredging component configuration to obtain dredging configuration data; perform 3D modeling on the dredging configuration data to generate dredging unit components;

[0089] In this embodiment, the basic configuration of the suction head is designed using CAD software (computer-aided design software) based on the component boundary conditions, including the suction inlet diameter, suction pipe length and inclination angle, generating preliminary dredging configuration data. Flow field simulation analysis is performed on the dredging configuration data using CFD to optimize the inlet flow distribution and reduce sediment backflow. Based on the optimized configuration data, geometric reconstruction is performed in a 3D modeling tool to construct a 3D model of the dredging unit component. The model must include key components such as the suction head, suction pipe and delivery pump to ensure dimensional matching and mechanical connection stability of each component.

[0090] Step S4: Perform flocculation requirement analysis on sludge characteristic data to obtain flocculation requirement data; perform solidification unit modeling on flocculation requirement data based on component boundary conditions and dredging unit components to generate solidification unit components;

[0091] In this embodiment, based on sludge characteristic data, a flocculant compatibility test was conducted using a flocculant screening device to determine cationic polyacrylamide as the main flocculant component. Sedimentation experiments were conducted based on different flocculant concentrations to evaluate flocculation requirements. Subsequently, combined with the component boundary conditions and sludge dredging unit components, CAE (Computer-Aided Engineering) software was used to model and optimize the fluid flow characteristics in the flocculation chamber. The stirring speed of the flocculation chamber stirring device was set to 30 rpm, and a three-dimensional model of the solidification unit components was generated, including the flocculation chamber, sedimentation tank, and stirring device.

[0092] Step S5: Perform transport simulation on silt characteristic data based on component boundary conditions to obtain simulated transport data; perform component layout processing based on simulated transport data to generate transport unit components;

[0093] In this embodiment, sludge characteristic data is input into a discrete element method (DEM) and fluid dynamics joint simulation platform to simulate particle settling behavior and flow resistance characteristics in the transport pipeline, thereby obtaining transport simulation data. Based on the simulation results, the length and bending radius of the transport pipeline are optimized, the transport pump pressure is set to 3 MPa, and the pump station location is adjusted to reduce pipeline energy loss. Finally, the optimized transport parameters are imported into a 3D modeling tool to perform pipeline layout and power system model generation, forming an overall model of the transport unit components.

[0094] Step S6: Integrate the dredging unit components, solidification unit components, and transmission unit components to generate a three-dimensional dredging equipment model.

[0095] In this embodiment, the models of dredging unit components, solidification unit components, and transmission unit components are imported into the 3D modeling software. The dimensions of each model are aligned and the interfaces are corrected. The integrated model is subjected to stress and fluid simulation analysis through finite element analysis (FEA) to verify the mechanical stability and flow balance of each component under working conditions. Finally, the model is saved as a standard STEP format file to form a complete 3D model of the green, low-carbon, and ecological dredging equipment.

[0096] Preferably, step S1 includes the following steps:

[0097] Step S11: Collect water samples from multiple points to obtain water silt samples; identify the particle distribution of the water silt samples to obtain silt particle distribution data.

[0098] Step S12: Perform particle size sieving on the sludge particle distribution data to generate particle size distribution data;

[0099] Step S13: Determine the water content of the water body sludge sample to obtain the sample water content; perform heavy metal detection on the water body sludge sample to generate sample pollutant data;

[0100] Step S14: Perform data fusion on particle size distribution data, sample moisture content and sample pollutant data to generate sludge characteristic data.

[0101] In this embodiment, sampling points are planned within the selected water area based on the water area and silt distribution characteristics. The sampling points are distributed using a grid system with a grid interval of 50 meters. The center point of each grid serves as the sampling point. A gravity column sampler is used to collect silt samples from the water body. The sampling depth is controlled between 0.5 and 1.5 meters. During sampling, the sampler is ensured to be inserted vertically into the bottom of the water body to avoid disturbing the silt layer structure. Samples from each sampling point are numbered and stored in chemically resistant polypropylene sampling containers. An appropriate amount of deionized water is added to the containers to maintain the original humidity and chemical properties of the samples. Subsequently, a laser particle size analyzer is used in the laboratory to identify the particle distribution of the samples. The laser particle size analyzer scanning range is set to 0.02 micrometers to 2000 micrometers. After scanning, the light scattering signal detected by the instrument is analyzed to generate silt particle distribution data for each sampling point. The data includes the volume distribution and particle size distribution percentage of the particles. The silt particle distribution data for each sampling point is imported into a particle size distribution analysis system. The sieving standards set in the analysis system are based on the "Standard for Geotechnical Testing Methods" (GB / T). The particle size classification defined in 50123-2019 was used to sieve the sludge samples step by step, starting from 0.Six particle size ranges from 0.75 mm to 2 mm were set. After each sieving, the mass of the material remaining on the sieve was accurately weighed using an electronic balance, and its relative proportion was calculated. Particle size distribution data was generated after sieving. The data was recorded in tabular form, showing the proportion of particles in each size range at each sampling point, along with a cumulative particle size distribution curve. This curve was used to visually represent the gradation characteristics of the sample. Subsequently, the particle size distribution data was correlated with the coordinate information of the sampling points to form a comprehensive database that can be used for further analysis. 200 grams of wet sludge were taken from the sludge sample at each sampling point, and the moisture content was determined using the oven drying method. First, the sample was placed in a constant temperature oven at 105 degrees Celsius for 24 hours to dry. After drying, the dry weight of the sample was weighed and recorded, and the percentage of moisture content was calculated. At the same time, 50 grams of sludge sample was taken for heavy metal detection. The sample was pretreated using a microwave digester. The digestion solution was prepared by mixing nitric acid (HNO3) and hydrogen peroxide (H2O2). After dissolution, the liquid samples were analyzed using ICP-MS (Inductively Coupled Plasma Mass Spectrometry) to detect the content of heavy metals such as lead (Pb), cadmium (Cd), mercury (Hg), and chromium (Cr). Pollutant data for each sampling point was generated and output as a table of elemental contents, labeled with units of milligrams per kilogram (mg / kg). The particle size distribution data, sample moisture content, and pollutant data for each sampling point were imported into a data fusion processing system. The system first performed spatial matching of the three types of data according to the sampling point coordinates, integrating data belonging to the same sampling point into a single dataset. This dataset included particle distribution ratio, moisture content percentage, and heavy metal content parameters. Subsequently, Principal Component Analysis (PCA) was used to reduce the dimensionality of the three types of data, extracting principal component indicators that comprehensively reflect the characteristics of the sludge, generating a feature data table containing weights for sludge characteristics. Finally, the feature data from all sampling points were integrated into a feature matrix.

[0102] Preferably, step S2 includes the following steps:

[0103] Step S21: Obtain historical engineering cases; perform dredging case retrieval on historical engineering cases to obtain historical dredging data;

[0104] Step S22: Correlate historical dredging and silt characteristic data to generate similarity indices; perform hierarchical clustering on the similarity indices to obtain an engineering type spectrum;

[0105] Step S23: Based on the preset existing equipment functions, perform equipment capability matching on the engineering type spectrum to obtain an equipment screening scheme; based on the equipment screening scheme, screen dredging methods to generate the preferred dredging method;

[0106] Step S24: Decompose the preferred dredging method into operating conditions to obtain operating condition data;

[0107] Step S25: Perform process condition inversion on the operating condition parameters to generate process condition data; apply constraints based on the process condition data to obtain the component boundary conditions.

[0108] In this embodiment, historical engineering case data is obtained from publicly available industry databases and internal enterprise databases. Data sources include reports from completed river dredging, port dredging, and lake remediation projects. A keyword search tool is used to filter the cases, with keywords such as "dredging depth," "silt type," and "construction environment." After retrieval, relevant information including construction parameters, equipment lists, operational efficiency, and construction environment is exported. The exported case data is imported into a text analysis system for further decomposition and structuring. Key data such as dredging depth range, construction equipment model, silt particle distribution, operating time, and unit energy consumption are extracted from each case to generate a historical dredging data table. The historical dredging data and silt characteristic data are imported into a data analysis system. Pearson correlation coefficients are used to perform correlation analysis between the two types of data, calculating the correlations between dredging depth and particle size distribution, equipment model and water content, and construction efficiency and heavy metal content. The calculation results are normalized to generate a similarity index table. The similarity index includes a multi-dimensional vector with a value range of 0 to 1. The similarity index is used as input data to a hierarchical clustering model, employing the shortest distance method (Single Linkage). The method calculates the cluster distance between each data sample, setting the clustering level to three levels to ensure the output spectrum has a clear classification structure. The engineering type spectrum is matched with an existing equipment database containing specifications, applicable environments, and energy consumption data for existing dredging equipment. A dynamic programming algorithm is used to match equipment capabilities for each type node in the spectrum, setting matching conditions as rated power, operating depth, applicable silt particle distribution range, and corrosion resistance. Equipment meeting the matching conditions is included in the equipment screening scheme. Dredging methods are then screened based on the equipment screening scheme, with screening criteria including equipment operating efficiency, energy consumption, and environmental impact. A multi-objective decision model is used to prioritize candidate dredging methods, generating a list of preferred dredging methods. The list includes recommended equipment, operating methods, and construction steps. The single-import working condition analysis system decomposes the construction steps according to the dredging operation scenario. The decomposed work steps are arranged according to time sequence and spatial scope to generate operating condition data. The operating condition data includes the construction area, equipment used, energy consumption, and operating environment data for each step. During the decomposition process, key equipment operating parameters such as rotational speed, working depth, and mud flow rate are calibrated. The operating condition data is then imported into the numerical inversion system, and the optimal process conditions for each working condition are derived through the inversion algorithm. The process conditions include information such as the optimal operating parameters of the equipment, energy utilization rate, and mud transport efficiency. The constraints used in the inversion process include equipment performance parameters and environmental adaptability range. The process condition data generated by the inversion is imported into the boundary condition generation module. Based on the process conditions, the mechanical properties, material durability, and dimensional design of the components are constrained, and a component boundary condition table that meets the conditions is generated.

[0109] Preferably, step S23 includes the following steps:

[0110] The project type spectrum is classified by scale to obtain project scale data; the project scale data is then decomposed into functional requirements to generate functional requirements data.

[0111] Based on the pre-defined existing equipment functions, the functional requirement data is evaluated for compatibility to obtain equipment compatibility data; based on the equipment compatibility data, equipment schemes are screened to generate equipment screening schemes.

[0112] Cost-benefit analysis was performed on the equipment selection scheme to obtain cost-benefit data; the construction period was then predicted based on the cost-benefit data to obtain period prediction data.

[0113] The optimal dredging method is generated by combining and optimizing cost-benefit data and cycle prediction data.

[0114] In this embodiment, the Analytic Hierarchy Process (AHP) is used to classify the engineering type spectrum by scale. Engineering type data from different levels within the spectrum are imported into the classification model. Based on the engineering scale characteristics within the spectrum, such as dredging depth, operating area, silt thickness, and particle size distribution, classification standards are established. The main standards for scale classification include small-scale projects (dredging depth less than 10 meters, operating area less than 1 square kilometer), medium-scale projects (dredging depth 10 to 20 meters, operating area 1 to 5 square kilometers), and large-scale projects (dredging depth greater than 20 meters, operating area greater than 5 square kilometers). The nodes in the engineering spectrum are regrouped using a recursive decomposition method. Engineering scale data is input into the functional requirements decomposition system. Functional requirements are decomposed based on the dredging objectives, construction environment, and environmental protection requirements corresponding to the engineering scale. A modular analysis method is used to break down each engineering requirement into equipment requirements, energy consumption requirements, and operational efficiency requirements. The efficiency requirements are decomposed by considering specific environmental constraints, such as river flow velocity, water depth in the construction area, and the distribution of surrounding ecologically sensitive areas. Each functional requirement is stored as a dataset, with fields including target parameters, environmental parameters, and operational constraints. The functional requirement data is then matched and evaluated against an existing equipment database containing equipment specifications, applicable environments, and operational constraints. A fuzzy evaluation method is used to calculate the fit between the equipment and the functional requirements. The calculation scores the degree of matching between the target parameters in the requirements and the equipment performance, with a fit score range of 0 to 1. After scoring, the results are normalized, and the evaluation results are output as equipment fit data. This fit data includes the equipment number, fit score, and explanation of any mismatch. The equipment fit data is then imported into an equipment screening model, which sorts the equipment based on the fit score and selects those with a fit score greater than 0.Eight pieces of equipment were selected as candidate equipment, while equipment that did not meet the compatibility requirements was removed from the plan. The generated equipment screening plan included the candidate equipment numbers, operating parameters, and corresponding functional requirement matching. The equipment screening plan was integrated and analyzed with the construction budget parameters. The overall benefits were calculated based on the unit energy consumption, labor cost, and maintenance cost of the equipment operation. In the cost accounting process, the project budget ceiling and expected construction efficiency were combined, and interval analysis was used to quantitatively evaluate the benefits of each equipment combination. The calculated benefit results were stored as a cost-benefit data table, with data fields including equipment number, total cost, expected revenue, and benefit score. The cost-benefit data was input into the periodic prediction system, based on the equipment operating efficiency... The construction cycle is calculated based on the efficiency and area of ​​the construction zone. A dynamic time programming algorithm is used to refine the allocation of construction steps to reduce construction time. The time consumption of each construction stage is modeled by combining equipment performance and project scale data. The final cycle prediction data includes the construction cycle length of each stage, equipment operating time, and environmental factor analysis affecting the cycle. Cost-benefit data and cycle prediction data are imported into a combined optimization model. A genetic algorithm is used to optimize the combination of dredging methods, with the objective functions of maximizing benefits and minimizing the construction cycle. Iterative optimization of equipment configuration, construction methods, and operating parameters is performed, generating a preferred dredging method that includes equipment combination, construction steps, and resource allocation strategies.

[0115] Preferably, step S3 includes the following steps:

[0116] Step S31: Identify the engineering parameters of the component boundary conditions to obtain engineering parameter data;

[0117] Step S32: Perform structural topology mapping on the engineering parameter data to generate structural topology data; perform component interference check on the structural topology data to obtain component layout parameters;

[0118] Step S33: Perform parametric modeling of component layout parameters to obtain dredging configuration data; refine the dredging configuration data according to component boundary conditions to generate key component parameters;

[0119] Step S34: Perform three-dimensional solid reconstruction of the key parameters of the component to generate the dredging unit component.

[0120] In this embodiment, the geometric model of the dredging equipment components is imported into the finite element analysis system. Engineering parameters are identified using preset boundary conditions, including fixed endpoints, active constraint positions, and external load parameters. During identification, a laser scanner is used to acquire 3D point cloud data of the component surface. Digital image processing technology is then used to perform curvature analysis on the point cloud data, extracting geometric information of key constraint points. Subsequently, a boundary identification algorithm is used to match the boundary conditions with the structural mechanical properties. The generated engineering parameter data is output in the form of a structured data table, with fields including component number, boundary type, constraint force value, and geometric dimensions. The engineering parameter data is then imported into a topology modeling tool, where structural topology mapping is performed based on component connection relationships and geometric dimensions. During the mapping process, 3D point set mapping technology is used to analyze the connection methods between components. A node matrix is ​​used to describe the connection positions and directions between components. The generated structural topology data is stored in a graph database format, with fields including node number, connection type, and node spacing. Finally, interference verification is performed on the components based on the topology data, using a dynamic interference detection method. The detection process simulates the motion trajectories of adjacent components to determine if there is motion interference or spatial overlap. After verification, it outputs component layout parameters, including component number, spatial position, motion interference status, and adjustment suggestions. These parameters are then imported into a parametric modeling tool. Constraint-Based Parametric Modeling is used to set constraints on the component's dimensions, position, and motion trajectory. The generated parametric model contains the dimensional variables and constraint relationships of all components. Subsequently, the parametric model is finely divided based on boundary conditions. Voxel segmentation technology is used to divide the component into multiple independent elements, and precise boundary condition annotation and geometric feature extraction are performed on each element. The generated key component parameters include element number, boundary force value, stress distribution, and deformation. These key parameters are then imported into a 3D reconstruction system. Reverse Modeling technology is used to reconstruct the component into a 3D solid model, incorporating multi-view stereo matching during the reconstruction process. StereoMatching technology is used to reproduce the shape and surface details of the components. A 3D printer is then used to produce solid dredging unit components. Polylactic acid (PLA) is selected as the printing material during the printing process to ensure dimensional accuracy and surface finish of the printed components. After completion, the dimensions of the solid components are checked using a digital measuring instrument.Record the measurement results and compare them with the design parameters to ensure that the accuracy of the physical components meets the requirements.

[0121] Preferably, step S4 includes the following steps:

[0122] Step S41: Match the flocculant ratio based on the sludge characteristic data to obtain the flocculation ratio data;

[0123] Step S42: Based on the flocculation ratio data, perform flocculation demand analysis on the sludge characteristic data to generate flocculation demand data;

[0124] Step S43: Based on the flocculation ratio data, perform flocculation kinetics deduction to generate flocculation dynamic data; design the flocculation cavity based on the flocculation dynamic data and component boundary data to obtain cavity layout data;

[0125] Step S44: Integrate the cavity layout data and dredging unit components into a flocculation unit to generate a solidification unit component.

[0126] In this embodiment, the particle size distribution of the collected sludge samples was measured using a particle size analyzer to obtain particle characteristic data. Subsequently, high-performance liquid chromatography (HPLC) was used to detect the organic matter content in the sludge, and the pH value and conductivity of the sludge were determined using an ion-selective electrode method. The obtained sludge characteristic data was input into a flocculant ratio matching model. This model, based on multivariate regression analysis, matched the optimal flocculant combination and its ratio, including the addition amounts of polyacrylamide (PAM) and aluminum sulfate. The generated flocculation ratio data was output in tabular form, with fields including flocculant type, addition ratio, and suitability target. The generated flocculation ratio data and sludge characteristic data were imported into a flocculation demand analysis system. This system analyzed the influence of different flocculant ratios on particle aggregation behavior in sludge using a reaction kinetic model simulating the flocculation process. Multiscale numerical simulation (MLS) was employed in the simulation process. Technology evaluates the rate and stability of particle aggregation. Combining sedimentation rate data measured in laboratory stirring and settling tests, flocculation requirement data is generated, including flocculant consumption, settling time, and particle removal rate. A flocculation kinetic model is constructed using flocculation ratio data. This model is extrapolated based on sludge viscosity, particle size distribution, and flocculant characteristic parameters. The Lagrangian Particle Tracking Algorithm is used to simulate particle trajectory and collision probability during flocculation. The generated flocculation dynamic data includes flocculation reaction time, collision frequency, and particle agglomeration strength. A 3D modeling software is used to design the flocculation chamber using component boundary data. The chamber design simulates fluid distribution and particle aggregation effects within the chamber using fluid dynamics analysis software. The generated chamber layout data includes chamber geometry parameters, fluid inlet location, and flocculation efficiency indicators. The chamber layout data and sludge removal unit components are imported into a modular assembly platform. The flocculation unit is integrated using automated assembly technology, employing Precision Laser Alignment technology during assembly. The technology precisely positions the connection interface between the cavity and the dredging component, and uses a bolt pre-tightening tool to achieve high-strength fixation. After completion, X-ray inspection equipment is used to scan the internal structure of the integrated flocculation unit to ensure that the connection between the cavity and the component is seamless and well-sealed. Finally, the solidified unit component is tested for its overall stiffness and flocculation performance parameters by a dynamic mechanical tester, and all data are recorded in the equipment quality inspection report.

[0127] Preferably, step S43 includes the following steps:

[0128] The flocculation ratio data is divided into reaction time sequences to obtain the flocculation reaction sequence; the flow gradient is calculated from the flocculation reaction sequence to generate flow velocity distribution data.

[0129] The reaction intensity of the tassel distribution data is evaluated to obtain intensity evaluation data; based on the intensity evaluation data, the flocculation efficiency is extrapolated to generate flocculation dynamics data.

[0130] Spatial demand mapping is performed on the flocculation dynamic data to obtain the flocculation spatial demand; structural morphology projection is performed on the flocculation spatial demand to generate structural morphology data.

[0131] The flow field distribution is simulated based on the structural morphology data to generate simulated flow field distribution data; the cavity is optimized based on the simulated flow field distribution data to obtain cavity layout data.

[0132] In this embodiment, a high-frequency optical monitoring system is used to capture the entire flocculation reaction process in real time, recording the time-series data of the interaction between particles and flocculant. Discrete-time series analysis is used to divide the reaction steps in the flocculation ratio data into initial mixing, particle agglomeration, and settling stabilization stages, each corresponding to a specific time interval and reaction intensity parameters. The output flocculation reaction sequence includes the time nodes of each stage, the rate of change of reactant concentration, and the initial flocculation efficiency. The flocculation reaction sequence is input into a fluid analysis platform, and the flow velocity distribution characteristics of each time stage are calculated using a shear gradient model. The gradient changes in particle distribution and fluid shear force during flocculation are obtained through the analysis of the flow velocity vector field. A laser Doppler velocimeter is used. The simulation results were validated using a Doppler Velocity Meter (LDV), generating velocity distribution data including velocity range, shear force distribution, and particle motion paths. Multi-point sampling analysis of the velocity distribution data was performed, and a reaction intensity factor model was used to quantitatively evaluate the relationship between shear force and flocculation efficiency. A high-precision particle counting device was used to record the changing trend of particle concentration during the reaction. Reaction intensity data was calculated based on the particle removal rate, including peak shear force, particle breakage rate, and flocculation efficiency change rate. The intensity assessment data was then imported into a kinetic model, and the Lagrangian method was used. The method involves extrapolating and analyzing the collision frequency between particles and the flocculant distribution efficiency. Boundary conditions set during the extrapolation include particle size range, flocculant concentration, and fluid shear force variations. The generated flocculation dynamics data covers reaction time, particle collision probability, and floc strength. By analyzing the particle collision frequency and reaction time parameters in the flocculation dynamics data, and combining them with reaction space characteristics, the volume and geometry required for the flocculation reaction are mapped and analyzed. A three-dimensional finite element analysis tool is used to simulate the fluid path in the reaction space, outputting the flocculation space requirements, including minimum effective volume, reactor height, and fluid inlet / outlet diameter. A parametric modeling tool is used to perform a three-dimensional projection of the geometry in the flocculation space requirements. Based on the projection results, a preliminary structural model is established, and computer-aided design (CAD) is used. Design (CAD) software is used to adjust the surfaces and key nodes in the model, generating structural morphology data including the orientation of the projected curves, node connection parameters, and surface roughness indices. This structural morphology data is then imported into fluid dynamics simulation software, where numerical simulation methods are used to simulate the fluid distribution and particle motion trajectory inside the flocculation chamber. The input parameters set during the simulation include fluid flow rate, inlet pressure, and particle concentration distribution. The simulated flow field distribution data includes distribution maps of the velocity field, pressure field, and particle concentration field. Based on the fluid velocity uniformity and particle flocculation efficiency in the simulated flow field distribution data...Structural optimization algorithms were used to adjust the cavity geometry, inlet design, and fluid distributor layout. After optimization, the results were re-input into a fluid dynamics simulation tool to verify their effectiveness. The final output cavity layout data includes cavity geometric parameters, fluid distributor location, and structural stability data.

[0133] Preferably, step S5 includes the following steps:

[0134] Step S51: Perform rheological characteristic processing on the sludge feature data to obtain rheological characteristic data; perform pipeline flow simulation on the rheological characteristic data and component boundary conditions to generate a virtual flow field;

[0135] Step S52: Map the energy consumption distribution of the virtual flow field to obtain energy consumption distribution data; locate high-energy-consuming areas based on the energy consumption distribution data to generate simulated transportation data;

[0136] Step S53: Identify optimizable locations based on simulated transport data to obtain optimizable locations;

[0137] Step S54: Component layout processing is performed based on optimizable points to generate transmission unit components.

[0138] In this embodiment, a sampling device is used to collect silt samples distributed at multiple points from the target dredging area. A laser particle size analyzer is used to analyze the particle size distribution of the samples, determining the particle diameter range and average particle size. The samples are placed in a rheometer, and the shear rate is set to a range of 1 to 500 s⁻¹. The relationship between shear stress and shear rate is recorded. The yield stress, viscosity, and viscoelastic modulus of the silt are calculated based on the rheological curve characteristics. The output rheological characteristic data includes parameters such as shear stress distribution, yield stress, and apparent viscosity. The rheological characteristic data is input into a fluid dynamics simulation tool, and the boundary conditions are set as the smoothness parameters of the inner wall of a circular pipe with a diameter of 0.5 meters, the inlet velocity of 2 m / s, and the outlet pressure of 101.A numerical simulation of the fluid in a pipeline at 3 kPa was conducted using the Finite Element Method (FEM). The fluid properties and rheological characteristics in the simulation model corresponded to the simulated fluid properties. The simulated virtual flow field data included velocity distribution, pressure distribution, and particle concentration distribution. The simulation data was experimentally verified using a Particle Image Velocimetry (PIV) instrument. Based on the virtual flow field model, the energy consumption per unit volume of fluid in the pipeline was calculated using a built-in energy equation. Flow resistance was correlated with the velocity gradient, and an energy consumption distribution map was generated by combining the pressure field data. During the mapping process, color gradients were used to mark high-energy-consuming and low-energy-consuming regions. The energy consumption data was layered, and the generated energy consumption distribution data included the energy consumption per unit fluid volume, the total energy consumption along the fluid transport path, and energy distribution characteristics. High-energy-consuming regions were located using the energy consumption distribution data, and their positions were correlated with fluid velocity and pressure changes. High-energy-consuming points were marked using a three-dimensional spatial coordinate system, and an optimization algorithm was used to evaluate the energy consumption distribution. The coordinate information of the marked locations was then analyzed. The information includes the axial distance from the inlet, radial coordinates, and the corresponding energy consumption value of the area. The generated simulated transportation data includes the number, location, and energy consumption distribution of high-energy-consuming areas. This simulated transportation data is input into the optimization decision module. A multi-objective optimization algorithm is used to identify and analyze the high-energy-consuming areas marked in the energy consumption distribution. The input parameters in the optimization model include the location of the high-energy-consuming areas, pressure gradient, and fluid resistance distribution. The geometry, wall smoothness, and flow velocity adjustment possibilities at each point are identified. The identification results include the coordinate information of the optimized points, the range of optimizable parameters, and adjustment suggestions. Optimizable point data is output. Three-dimensional modeling analysis is performed on the optimizable points. Based on the geometric characteristics of the points and fluid transport requirements, transmission unit components are designed. The pipe cross-sectional shape is optimized using Computer-Aided Design (CAD) software. Fluid guiding components and pressure regulating devices are added at high-energy-consuming points. 3D printing technology is used to generate model prototypes, which are then experimentally assembled to verify performance. The final generated transmission unit component data includes the component's geometric parameters, material selection, and fluid guiding design scheme.

[0139] Preferably, step S52 includes the following steps:

[0140] Flow resistance is extracted from the virtual flow field to obtain flow resistance data; pressure drop is calculated from the flow resistance data to generate pressure drop distribution data.

[0141] The step-down distribution data is processed by power conversion to generate power distribution data; energy consumption is assessed based on the power distribution data to obtain energy consumption distribution data.

[0142] The energy consumption distribution data is processed by threshold stratification based on a preset energy consumption stratification threshold to obtain energy consumption stratification data; energy consumption regions are divided according to the energy consumption stratification data to generate regional distribution data.

[0143] Critical points are calibrated on the regional distribution data to generate critical point data; transport parameters are integrated based on the critical point data to generate simulated operation data.

[0144] In this embodiment, computational fluid dynamics (CFD) software is used to analyze the virtual flow field. Pressure and velocity distribution data of the fluid in the pipe are input into the flow resistance extraction module. Local flow resistance is calculated using a fluid dynamics model. Input parameters include a pipe inner diameter of 0.5 meters, a fluid density of 1000 kg / m³, and a flow velocity of 2 m / s. Output flow resistance data includes local pressure loss along the pipe axis, friction coefficient, and total resistance. This flow resistance data is then imported into the pressure drop calculation module. The pressure drop distribution along the pipe is calculated point-by-point based on the inlet and outlet pressure conditions. The calculation process uses a pressure drop estimation model, with input parameters including the fluid's dynamic viscosity, pipe wall roughness, and velocity distribution. The resulting pressure drop distribution... The data includes local and total pressure drops along the pipeline. A color-coded 2D graph displays the pressure drop distribution at each measuring point. This pressure drop data is input into a power conversion module, which converts the data into corresponding power values ​​based on the power required per unit fluid volume calculation method. Input parameters for this conversion process include the fluid flow rate (0.3 cubic meters per second) and velocity distribution data. The output power distribution data is the power consumption per unit volume along the pipeline axis. A 3D bar chart visualizes the power distribution. This power distribution data is then input into an energy consumption assessment module, which integrates the total energy consumption per unit time based on the pipeline fluid transport time period. Input parameters include the transport time (3600 seconds) and the power consumption per unit volume. The energy consumption data output includes the location of high-energy-consumption areas, their high energy consumption values, and their correlation with pipeline sections. The energy consumption data is spatially labeled using a two-dimensional heatmap. Preset energy consumption stratification thresholds are used to classify the data: low-energy-consumption areas (less than 100 watts per cubic meter), medium-energy-consumption areas (100 to 300 watts per cubic meter), and high-energy-consumption areas (greater than 300 watts per cubic meter). The stratified energy consumption data is then labeled and output as energy consumption stratification data. Different energy consumption stratification levels are displayed using stratified color coding. The energy consumption stratification data is input into the region division module, which divides the energy consumption distribution region based on a spatial location clustering algorithm. The division criterion is the energy consumption stratification of adjacent regions. Regions of the same energy consumption level and continuous distribution are considered as one energy consumption zone. The output regional distribution data includes the number of energy consumption zones, the spatial range of each zone, and the average energy consumption within the zone. Vector maps are used to accurately mark the boundaries of the zones. The critical points are marked using the boundaries between high-energy-consumption zones and low-energy-consumption zones in the regional distribution data. The location of the critical points is calculated by combining the actual geometry of the pipeline and the flow velocity distribution characteristics. The output critical point data includes spatial coordinates, the energy consumption gradient of the surrounding area, and the flow velocity value at that point. The critical point data is input into the transport parameter integration module. The transport speed, pressure, and flow parameters are adjusted according to the characteristics of the high-energy-consumption zone. The integrated simulation operation data includes the optimized flow velocity distribution, pressure distribution, and flow value.

[0145] Preferably, step S6 includes the following steps:

[0146] Step S61: Locate the connection points of the dredging unit components and obtain the connection point data;

[0147] Step S62: Perform interface adaptation and matching on the connection point data and solidified unit components to generate adaptation interface data;

[0148] Step S63: Plan the assembly sequence for the adapter interface data and transmission unit components to obtain assembly sequence data;

[0149] Step S64: Perform whole-machine collaborative verification on the assembly sequence data to generate whole-machine integration data;

[0150] Step S65: Based on the integrated data of the whole machine, integrate the dredging unit components, solidification unit components and transmission unit components to generate a three-dimensional dredging equipment model.

[0151] In this embodiment, 3D modeling software (such as SolidWorks) is used to create detailed models of the dredging unit components. The input geometric parameters of the components include a length of 5 meters, a width of 1.2 meters, and a height of 1 meter. Virtual assembly technology is used to determine the locations of key connection points through the connection parts of the components. These connection points include interface locations, connection methods, and surrounding support areas. The coordinate data of all connection points is output in the form of X, Y, and Z coordinates. The obtained connection point data contains the spatial relationships between the dredging unit and other components. The connection point data of the dredging unit components is matched with the joint interfaces of the solidified unit components, and virtual assembly is performed using CAD software. In the assembly process, the interface parameters of the curing unit are input, including the size, shape, and load-bearing capacity of the interface. An automatic matching algorithm is used to align the connection points of the two components. The system calculates the matching degree based on the interface geometry and tolerances, ensuring a fit accuracy within 0.1 mm. The obtained adapter interface data includes the size, position, and relative angle of each interface. This adapter interface data is then used to plan the assembly sequence of the transmission unit components. An assembly path planning algorithm optimizes the assembly sequence of each component, with input parameters including component weight, assembly difficulty, and required time, combined with positional constraints in the workspace. Under the given environmental conditions, the system automatically calculates and generates assembly sequence data. The output includes the assembly sequence, the positioning method of each component, and the tools and equipment required for assembly. The assembly sequence data is imported into the whole machine collaborative verification module. Based on the collaborative working relationship between each unit component during the assembly process, interference and collisions during the assembly process are simulated through numerical calculations. The input data includes the spatial position, posture, and assembly sequence of each component. During the verification process, the system checks whether the relative positions during assembly meet the spatial constraints. If interference or assembly conflicts are found, the system will provide correction suggestions and replan the assembly scheme. The final generated whole machine integration data includes the verified assembly sequence, the corrected spatial layout, and assembly parameters. The whole machine integration data is then input into the 3D modeling tool. Based on the verified data, the dredging unit component, the solidification unit component, and the transmission unit component are integrated one by one. The integration process is completed step by step according to the assembly sequence. The material properties, structural strength, and installation position of each component are input. The system will automatically adjust the relative positions and angles between the components to ensure perfect docking of all components. Finally, a 3D dredging equipment model is generated, and the output is a standard STL format 3D file. This file contains the assembly information, dimensions, and spatial layout of all components, which is convenient for subsequent actual manufacturing and testing.

[0152] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0153] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for constructing a three-dimensional model of green, low-carbon, and ecological dredging equipment, characterized in that, Includes the following steps: Step S1: Collect water silt samples; The silt samples from the water body were subjected to particle sieving to generate particle size distribution data; based on the particle size distribution data, the silt characteristics of the water body silt samples were analyzed to obtain silt feature data. Step S2: Obtain historical dredging data; match dredging methods to the siltation state based on historical data to obtain the preferred dredging method; perform boundary constraint analysis on the preferred dredging method to obtain the component boundary conditions; Step S3: Based on the component boundary conditions, construct the dredging component configuration to obtain dredging configuration data; perform 3D modeling on the dredging configuration data to generate dredging unit components; Step S4: Perform flocculation demand analysis on the sludge characteristic data to obtain flocculation demand data; Based on the component boundary conditions and dredging unit components, solidification unit modeling is performed on the flocculation requirement data to generate solidification unit components; Step S5: Perform transport simulation on silt characteristic data based on component boundary conditions to obtain simulated transport data; perform component layout processing based on simulated transport data to generate transport unit components; Step S6: Integrate the dredging unit components, solidification unit components, and transport unit components to generate a three-dimensional dredging equipment model; Step S6 includes the following steps: Step S61: Locate the connection points of the dredging unit components and obtain the connection point data; Step S62: Perform interface adaptation and matching on the connection point data and solidified unit components to generate adaptation interface data; Step S63: Plan the assembly sequence for the adapter interface data and transmission unit components to obtain assembly sequence data; Step S64: Perform whole-machine collaborative verification on the assembly sequence data to generate whole-machine integration data; Step S65: Based on the integrated data of the whole machine, integrate the dredging unit components, solidification unit components and transmission unit components to generate a three-dimensional dredging equipment model.

2. The method for constructing a three-dimensional model of the green, low-carbon, and ecological dredging equipment according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Collect water samples from multiple points to obtain water silt samples; identify the particle distribution of the water silt samples to obtain silt particle distribution data. Step S12: Perform particle size sieving on the sludge particle distribution data to generate particle size distribution data; Step S13: Determine the water content of the water body sludge sample to obtain the sample water content; perform heavy metal detection on the water body sludge sample to generate sample pollutant data; Step S14: Perform data fusion on particle size distribution data, sample moisture content and sample pollutant data to generate sludge characteristic data.

3. The method for constructing a three-dimensional model of the green, low-carbon, and ecological dredging equipment according to claim 1, characterized in that, Step S2 includes the following steps: Step S21: Obtain historical engineering cases; perform dredging case retrieval on historical engineering cases to obtain historical dredging data; Step S22: Correlate historical dredging and silt characteristic data to generate similarity indicators; perform hierarchical clustering on the similarity indicators to obtain the engineering type spectrum; Step S23: Based on the preset existing equipment functions, perform equipment capability matching on the engineering type spectrum to obtain an equipment screening scheme; based on the equipment screening scheme, screen dredging methods to generate the preferred dredging method; Step S24: Decompose the preferred dredging method into operating conditions to obtain operating condition data; Step S25: Perform process condition inversion on the operating condition parameters to generate process condition data; apply constraints based on the process condition data to obtain the component boundary conditions.

4. The method for constructing a three-dimensional model of the green, low-carbon, and ecological dredging equipment according to claim 3, characterized in that, Step S23 includes the following steps: The project type spectrum is classified by scale to obtain project scale data; the project scale data is then decomposed into functional requirements to generate functional requirements data. Based on the pre-defined existing equipment functions, the functional requirement data is evaluated for compatibility to obtain equipment compatibility data; based on the equipment compatibility data, equipment schemes are screened to generate equipment screening schemes. Cost-benefit analysis was performed on the equipment selection scheme to obtain cost-benefit data; the construction period was then predicted based on the cost-benefit data to obtain period prediction data. The optimal dredging method is generated by combining and optimizing cost-benefit data and cycle prediction data.

5. The method for constructing a three-dimensional model of the green, low-carbon, and ecological dredging equipment according to claim 1, characterized in that, Step S3 includes the following steps: Step S31: Identify the engineering parameters of the component boundary conditions to obtain engineering parameter data; Step S32: Perform structural topology mapping on the engineering parameter data to generate structural topology data; perform component interference check on the structural topology data to obtain component layout parameters; Step S33: Perform parametric modeling of component layout parameters to obtain dredging configuration data; refine the dredging configuration data according to component boundary conditions to generate key component parameters; Step S34: Perform three-dimensional solid reconstruction of the key parameters of the component to generate the dredging unit component.

6. The method for constructing a three-dimensional model of the green, low-carbon, and ecological dredging equipment according to claim 1, characterized in that, Step S4 includes the following steps: Step S41: Match the flocculant ratio based on the sludge characteristic data to obtain the flocculation ratio data; Step S42: Based on the flocculation ratio data, perform flocculation demand analysis on the sludge characteristic data to generate flocculation demand data; Step S43: Based on the flocculation ratio data, perform flocculation kinetics deduction to generate flocculation dynamic data; design the flocculation cavity based on the flocculation dynamic data and component boundary data to obtain cavity layout data; Step S44: Integrate the cavity layout data and dredging unit components into a flocculation unit to generate a solidification unit component.

7. The method for constructing a three-dimensional model of the green, low-carbon, and ecological dredging equipment according to claim 6, characterized in that, Step S43 includes the following steps: The flocculation ratio data is divided into reaction time sequences to obtain the flocculation reaction sequence; the flow gradient is calculated from the flocculation reaction sequence to generate flow velocity distribution data. The reaction intensity of the tassel distribution data is evaluated to obtain intensity evaluation data; based on the intensity evaluation data, the flocculation efficiency is extrapolated to generate flocculation dynamics data. Spatial demand mapping is performed on the flocculation dynamic data to obtain the flocculation spatial demand; structural morphology projection is performed on the flocculation spatial demand to generate structural morphology data. The flow field distribution is simulated based on the structural morphology data to generate simulated flow field distribution data; the cavity is optimized based on the simulated flow field distribution data to obtain cavity layout data.

8. The method for constructing a three-dimensional model of the green, low-carbon, and ecological dredging equipment according to claim 1, characterized in that, Step S5 includes the following steps: Step S51: Perform rheological characteristic processing on the sludge feature data to obtain rheological characteristic data; perform pipeline flow simulation on the rheological characteristic data and component boundary conditions to generate a virtual flow field; Step S52: Map the energy consumption distribution of the virtual flow field to obtain energy consumption distribution data; locate high-energy-consuming areas based on the energy consumption distribution data to generate simulated transportation data; Step S53: Identify optimizable locations based on simulated transport data to obtain optimizable locations; Step S54: Component layout processing is performed based on optimizable points to generate transmission unit components.

9. The method for constructing a three-dimensional model of the green, low-carbon, and ecological dredging equipment according to claim 7, characterized in that, Step S52 includes the following steps: Flow resistance is extracted from the virtual flow field to obtain flow resistance data; pressure drop is calculated from the flow resistance data to generate pressure drop distribution data. The step-down distribution data is processed by power conversion to generate power distribution data; energy consumption is assessed based on the power distribution data to obtain energy consumption distribution data. The energy consumption distribution data is processed by threshold stratification based on a preset energy consumption stratification threshold to obtain energy consumption stratification data; energy consumption regions are divided according to the energy consumption stratification data to generate regional distribution data. Critical points are calibrated on the regional distribution data to generate critical point data; transport parameters are integrated based on the critical point data to generate simulated operation data.

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