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

Abstract

The invention relates to the technical field of three-dimensional model construction, in particular to a three-dimensional model construction method of green low-carbon ecological dredging equipment. The method comprises the following steps that a water body sludge sample is collected and subjected to particle screening, particle grading data is generated, then sludge characteristics are analyzed based on the data to obtain sludge characteristic data, historical dredging data are obtained, the dredging state is analyzed, a preferable dredging method is matched, boundary constraint analysis is carried out, and component boundary conditions are determined; the method comprises the following steps: designing the configuration of a desilting component, carrying out three-dimensional modeling, generating a desilting unit component, analyzing the flocculation requirement of sludge characteristic data, generating a solidification unit component, carrying out transportation simulation, obtaining simulated transportation data, laying components according to the data, generating a transmission unit component, integrating a desilting unit, a solidification unit and the transmission unit component, and carrying out transportation simulation. And a complete three-dimensional dredging equipment model is constructed. The three-dimensional model construction method of the dredging equipment is more efficient.

Description

Technical Field

[0001] The present invention relates to the technical field of three-dimensional model construction, and in particular to a three-dimensional model construction method for green and low-carbon ecological dredging equipment. Background Art

[0002] With the acceleration of urbanization, water pollution and siltation problems are becoming increasingly serious, causing significant impacts on the ecological environment. Traditional dredging methods are often inefficient and cause damage to the ecosystem. It is urgent to explore more green and low-carbon dredging equipment and technologies to achieve sustainable management of water bodies. However, most of the existing dredging equipment lacks systematic design and is difficult to meet the needs of different water areas and silt characteristics, resulting in unsatisfactory dredging results, serious waste of resources, and increased environmental impact. Many studies focus on a single dredging technology or equipment, lacking an overall solution that comprehensively considers the silt characteristics of water bodies, historical dredging data, and the working environment. In particular, the connection between silt characteristic analysis and dredging means matching has not been fully explored, resulting in the inability to effectively cope with various complex environmental conditions in practical applications, affecting the scientificity and effectiveness of dredging work. In addition, the lack of systematic modeling of dredging, solidification, and transportation links results in low integration of dredging equipment and difficulty in achieving efficient operation processes. Summary of the invention

[0003] Based on this, 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 technical problems.

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

[0005] Step S1: collecting a water body sludge sample; performing particle screening on the water body sludge sample to generate particle grading data; performing sludge characteristic analysis on the water body sludge sample based on the particle grading data to obtain sludge characteristic data;

[0006] Step S2: Acquire historical dredging data; match dredging means with the siltation state according to the historical data to obtain an optimal dredging method; perform boundary constraint analysis on the optimal dredging method to obtain component boundary conditions;

[0007] Step S3: Performing dredging component configuration based on the component boundary conditions to obtain dredging configuration data; performing three-dimensional modeling on the dredging configuration data to generate dredging unit components;

[0008] Step S4: flocculation demand analysis is performed on the sludge characteristic data to obtain flocculation demand data; solidification unit modeling is performed on the flocculation demand data according to the component boundary conditions and the dredging unit components to generate solidification unit components;

[0009] Step S5: performing transport simulation on the sludge characteristic data based on the component boundary conditions to obtain simulated transport data; performing component layout processing based on the simulated transport data to generate a transmission unit component;

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

[0011] The present invention generates particle grading data through particle screening of water body sludge samples, which provides a basis for subsequent sludge characteristic analysis. The formation of sludge characteristic data can describe the physical and chemical properties of sludge in detail. The acquisition of historical dredging data provides an important reference for siltation state analysis. The matching of optimal dredging methods ensures the scientificity and effectiveness of dredging strategies. The implementation of boundary constraint analysis provides necessary constraints for component design. The generation of dredging component configuration forms dredging configuration data, which lays a foundation for subsequent modeling. The process of three-dimensional modeling generates dredging unit components, making the design of dredging equipment more intuitive and practical. The coagulation demand analysis ensures the optimization of the usage and effect of flocculants. The implementation of solidification unit modeling provides structural support for ensuring the sludge treatment effect. The transport simulation generates simulated transport data, which provides a dynamic analysis basis for the effective transmission of sludge. The implementation of component layout processing forms transmission unit components, making the overall design more reasonable. The integration of dredging unit components, solidification unit components and transmission unit components generates a three-dimensional dredging equipment model, which improves the efficiency and environmental protection of dredging operations as a whole, provides effective technical support and visualization tools for green and low-carbon ecological dredging, and promotes the sustainable development of ecological environment governance.

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

[0013] Step S11: sampling the water body at multiple points to obtain water body sludge samples; identifying the particle distribution of the water body sludge samples to obtain sludge particle distribution data;

[0014] Step S12: Screening the sludge particle distribution data by particle size to generate particle grading data;

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

[0016] Step S14: Fusing the particle grading data, sample water content and sample pollutant data to generate sludge characteristic data.

[0017] The present invention realizes the comprehensive collection of water body sludge samples through multi-point sampling through the three-dimensional model construction method of green and low-carbon ecological dredging equipment. The particle distribution identification provides basic data for subsequent characteristic analysis. The result of particle size screening makes the particle grading characteristics of the sludge clearer. The combination of water content determination and heavy metal detection ensures that the pollutant status of the sample is accurately evaluated. The data fusion technology effectively integrates the particle grading, water content and pollutant data. The generated sludge characteristic data provides a scientific basis for equipment design, ensuring that the dredging equipment can be optimized for different water conditions, improving the efficiency and environmental protection of dredging operations as a whole, promoting the restoration and protection of water ecology, and promoting the implementation of the concept of sustainable development.

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

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

[0020] Step S22: Correlation is performed on the historical dredging and silt characteristic data to generate similarity indicators; hierarchical clustering is performed on the similarity indicators to obtain a spectrum of engineering types;

[0021] Step S23: matching equipment capabilities with the engineering type spectrum based on the preset existing equipment functions to obtain an equipment screening scheme; screening dredging means based on the equipment screening scheme to generate an optimal dredging method;

[0022] Step S24: Decomposing the working condition of the preferred dredging method to obtain operating condition data;

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

[0024] The present invention provides a rich foundation for subsequent data analysis by acquiring historical engineering cases. The 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 equipment capacity matching 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 means. 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, which provides necessary constraints for subsequent design. The determination of component boundary conditions ensures the scientificity and rationality of model design, which improves the design accuracy and efficiency of green and low-carbon ecological dredging equipment as a whole, and provides 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 graded to obtain project scale data; the project scale data is decomposed into functional requirements to generate functional requirement data;

[0027] Based on the preset existing equipment functions, the adaptability of the functional requirement data is evaluated to obtain the equipment adaptation data; based on the equipment adaptation data, the equipment scheme is screened to generate the equipment screening scheme;

[0028] Conduct cost-benefit calculation on the equipment screening scheme to obtain cost-benefit data; conduct construction cycle forecast on the cost-benefit data to obtain cycle forecast data;

[0029] The combination of means is optimized based on cost-effectiveness data and cycle prediction data to generate the preferred dredging method.

[0030] The present invention provides a systematic classification of different projects through the scale classification of the project type spectrum. The generation of project scale data lays a foundation for subsequent functional demand analysis. The formation of functional demand data ensures a comprehensive understanding of project requirements. The implementation of adaptability evaluation can effectively judge the degree of matching between existing equipment and functional requirements. The generation of equipment adaptation data provides a scientific basis for the selection of equipment solutions. The formulation of equipment screening solutions ensures the rationality and feasibility of the selected equipment. The process of cost-benefit accounting provides an economic analysis for subsequent decision-making. The generation of cost-benefit data can clearly show the economic value of different solutions. 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 means combination optimization enables the effective combination of different dredging methods to be realized. The optimal dredging method finally generated can achieve a balance between efficiency, economy and environmental protection, which improves the design scientificity and practicality of green and low-carbon ecological dredging equipment as a whole, and provides effective technical support and decision-making basis for sustainable water environment management.

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

[0032] Step S31: Perform engineering parameter identification on 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: parametric modeling is performed on the component layout parameters to obtain dredging configuration data; the dredging configuration data is finely divided according to the component boundary conditions to generate component key parameters;

[0035] Step S34: Reconstruct the key parameters of the component into three-dimensional entities to generate a dredging unit component.

[0036] The present 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 provides support for component layout optimization. The process of component interference verification 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 real and reliable model for the generation of dredging unit components, which improves the design efficiency and implementation effect of dredging equipment as a whole, promotes the practical application of green and low-carbon technologies in the dredging field, and promotes the implementation of eco-friendly design concepts.

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

[0038] Step S41: matching the sludge characteristic data with the flocculant ratio to obtain flocculation ratio data;

[0039] Step S42: performing flocculation demand analysis on the sludge characteristic data based on the flocculation ratio data to generate flocculation demand data;

[0040] Step S43: performing flocculation dynamics deduction based on the flocculation ratio data to generate flocculation dynamics data; performing flocculation cavity design on the flocculation dynamics data and component boundary data to obtain cavity layout data;

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

[0042] The present invention provides an accurate basis for the subsequent flocculation process by matching the flocculant ratio with the 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 the design and operation of the equipment. The generated flocculation demand data can clarify the amount and method of flocculant usage. The development of flocculation kinetics deduction provides a dynamic analysis basis for the flocculation process. The generated flocculation dynamics data can reflect the physical changes in the flocculation process. The implementation of cavity design ensures the optimization of flocculation effect. The generation of cavity layout data provides support for the effective integration of solidification units. The successful implementation of flocculation unit integration makes the combination of dredging unit components and solidification unit components closer, which improves the functionality and efficiency of green and low-carbon ecological dredging equipment as a whole, and provides 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 series to obtain the flocculation reaction sequence; the flocculation reaction sequence is calculated by tassel gradient to generate flow velocity distribution data;

[0045] Conduct reaction intensity evaluation on tassel distribution data to obtain intensity evaluation data; deduce flocculation efficiency based on intensity evaluation data to generate flocculation dynamics data;

[0046] The flocculation dynamic data is mapped to spatial demand to obtain the flocculation spatial demand; the flocculation spatial demand is projected to structural morphology to generate structural morphology data;

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

[0048] The present invention provides a basis for the time management of the flocculation process through the reaction time sequence division of the flocculation ratio data, the generation of the flocculation reaction sequence can effectively identify the reaction characteristics of different stages, the implementation of the tassel gradient calculation ensures the rationality and uniformity of the flow velocity distribution, the formation of the flow velocity distribution data provides a basis for the subsequent reaction intensity evaluation, the acquisition of the intensity evaluation data provides a quantitative analysis for the optimization of the flocculation effect, the development of the flocculation efficiency deduction can predict the flocculation effect under different conditions, the generated flocculation dynamic data reflects the dynamic characteristics of the flocculation process, the implementation of the space demand mapping provides a spatial layout basis for the cavity design, the accurate identification of the flocculation space demand ensures the rationality of the design, the generation of the structural morphology projection provides a morphological basis for the subsequent flow field distribution simulation, the acquisition of the simulated flow field distribution data can intuitively display the movement law of the fluid in the cavity, the implementation of the cavity optimization design ensures the maximization of the flocculation effect, and the overall improvement of the efficiency and scientificity of the green and low-carbon ecological dredging equipment in the flocculation process provides effective technical support and innovative solutions for environmental governance.

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

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

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

[0052] Step S53: identifying optimizable points according to the simulated transport data to obtain optimizable points;

[0053] Step S54: Component layout is performed according to the optimizable points to generate transmission unit components.

[0054] The present invention provides necessary physical parameters for subsequent flow analysis through rheological property characterization processing of sludge characteristic data, the generation of rheological property data ensures accurate understanding of sludge flow behavior, the implementation of pipeline flow simulation provides a basis for the creation of virtual flow field, the generation of virtual flow field can intuitively display 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 of different areas, the implementation of high energy consumption area positioning provides a basis for the formulation of optimization plans, the generation of simulated transportation data can reflect the energy efficiency in the transportation process, the successful implementation of optimization point identification provides a clear improvement direction for subsequent design, the determination of optimizable points ensures the rationality and effectiveness of component layout, the implementation of component layout processing provides a specific plan for the generation of transmission unit components, and overall improves the energy efficiency and practicality of green and low-carbon 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] Extract the flow resistance of the virtual flow field to obtain the flow resistance data; perform pressure drop calculation on the flow resistance data to generate pressure drop distribution data;

[0057] Performing power conversion processing on the voltage reduction distribution data to generate power distribution data; performing energy consumption evaluation based on the power distribution data to obtain energy consumption distribution data;

[0058] Based on the preset energy consumption stratification threshold, the energy consumption distribution data is subjected to threshold stratification processing to obtain energy consumption level data; energy consumption regions are divided according to the energy consumption level data to generate regional distribution data;

[0059] The critical point calibration is performed on the regional distribution data to generate the critical point data; the transport parameters are integrated based on the critical point data to generate the simulation operation data.

[0060] The present invention provides key data for subsequent flow analysis through the flow resistance extraction of the virtual flow field. The generation of flow resistance data ensures a comprehensive understanding of the flow resistance. The implementation of pressure reduction measurement can reflect the pressure changes in the flow process. The acquisition of pressure reduction distribution data provides a basis for subsequent power conversion. The development of power conversion processing ensures the effectiveness of energy utilization. The generation of power distribution data provides a scientific basis for energy consumption evaluation. The implementation of energy consumption evaluation can clearly display the energy consumption in different regions. The setting of energy consumption stratification thresholds ensures the systematic nature of energy efficiency analysis. The generation of energy consumption hierarchy data provides a clear standard for regional division. The acquisition of regional distribution data can reflect 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 reasonable integration of transport parameters. The formation of simulated operation data can effectively predict and optimize the flow and transport process, which improves the scientificity and effectiveness of green and low-carbon ecological dredging equipment in resource utilization and energy efficiency management as a whole, and provides a practical technical path for environmental governance.

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

[0062] Step S61: locating the connection points of the desilting unit components to obtain connection point data;

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

[0064] Step S63: performing assembly sequence planning on the adapter interface data and the 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: integrating the dredging unit components, the solidification unit components and the transmission unit components based on the whole machine integration data to generate a three-dimensional dredging equipment model.

[0067] The present invention provides basic data for subsequent component integration by locating the connection points of the dredging unit components. The generation of connection point data ensures the effective connection between the components. The implementation of interface adaptation and matching provides a scientific basis for the combination of the solidification unit components and the dredging units. The formation of adaptation interface data enhances the compatibility and adaptability of the components. The development of assembly sequence planning provides an optimization solution for the assembly efficiency of the components. 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 coordination of the components in the overall system. The acquisition of whole-machine integrated 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 overall improves the scientificity and efficiency of green and low-carbon ecological dredging equipment in the design and manufacturing process, and provides reliable technical support and guidance for practical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 A schematic diagram of the steps of a three-dimensional model construction method for green, low-carbon and ecological dredging equipment;

[0069] Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart;

[0070] Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG.

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

[0072] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0073] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

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

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

[0076] Step S1: collecting a water body sludge sample; performing particle screening on the water body sludge sample to generate particle grading data; performing sludge characteristic analysis on the water body sludge sample based on the particle grading data to obtain sludge characteristic data;

[0077] Step S2: Acquire historical dredging data; match dredging means with the siltation state according to the historical data to obtain an optimal dredging method; perform boundary constraint analysis on the optimal dredging method to obtain component boundary conditions.

[0078] Step S3: Performing dredging component configuration based on the component boundary conditions to obtain dredging configuration data; performing three-dimensional modeling on the dredging configuration data to generate dredging unit components;

[0079] Step S4: flocculation demand analysis is performed on the sludge characteristic data to obtain flocculation demand data; solidification unit modeling is performed on the flocculation demand data according to the component boundary conditions and the dredging unit components to generate solidification unit components;

[0080] Step S5: Performing transport simulation on the sludge characteristic data based on the component boundary conditions to obtain simulated transport data; performing component layout processing based on the simulated transport data to generate a transmission unit component.

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

[0082] The present invention generates particle grading data through particle screening of water body sludge samples, which provides a basis for subsequent sludge characteristic analysis. The formation of sludge characteristic data can describe the physical and chemical properties of sludge in detail. The acquisition of historical dredging data provides an important reference for siltation state analysis. The matching of optimal dredging methods ensures the scientificity and effectiveness of dredging strategies. The implementation of boundary constraint analysis provides necessary constraints for component design. The generation of dredging component configuration forms dredging configuration data, which lays a foundation for subsequent modeling. The process of three-dimensional modeling generates dredging unit components, making the design of dredging equipment more intuitive and practical. The coagulation demand analysis ensures the optimization of the usage and effect of flocculants. The implementation of solidification unit modeling provides structural support for ensuring the sludge treatment effect. The transport simulation generates simulated transport data, which provides a dynamic analysis basis for the effective transmission of sludge. The implementation of component layout processing forms transmission unit components, making the overall design more reasonable. The integration of dredging unit components, solidification unit components and transmission unit components generates a three-dimensional dredging equipment model, which improves the efficiency and environmental protection of dredging operations as a whole, provides effective technical support and visualization tools for green and low-carbon ecological dredging, and promotes the sustainable development of ecological environment governance.

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

[0084] Step S1: collecting a water body sludge sample; performing particle screening on the water body sludge sample to generate particle grading data; performing sludge characteristic analysis on the water body sludge sample based on the particle grading data to obtain sludge characteristic data;

[0085] In this embodiment, representative sludge sample collection points in typical waters are selected, and the sludge is collected using a sediment sampler. The collection depth is set within a range of 0.5 meters to 1 meter, and the sampling interval is set according to the actual sedimentation environment. After each sampling, the sludge is placed in a sealed container to avoid contamination or volatilization. The samples are subjected to particle screening by a graded screening device in the laboratory. The screening particle sizes are 0.075 mm, 0.25 mm and 1 mm sieves for step-by-step screening. The masses of particles with different particle sizes are recorded and the grading data is calculated. Subsequently, a laser particle size analyzer is used to perform particle distribution analysis on the samples to generate particle grading data. According to the particle grading data, a rheometer is used to test the rheological properties of the sludge samples, including yield stress and viscosity tests. At the same time, the sludge is tested for heavy metal pollutants by ICP-MS (inductively coupled plasma mass spectrometer), and sludge characteristic data is generated by combining the rheological properties with the pollutant content.

[0086] Step S2: Acquire historical dredging data; match dredging means with the siltation state according to the historical data to obtain an optimal dredging method; perform boundary constraint analysis on the optimal dredging method to obtain component boundary conditions;

[0087] In this embodiment, after obtaining permission, historical dredging data are obtained from relevant water management departments, including parameters such as siltation volume, dredging depth, dredging technology and equipment type. The data format is Excel table and GIS data file. The data is formatted and input into the database management system. The hierarchical clustering algorithm is used to analyze and classify the dredging conditions. The classification results are matched with appropriate dredging technical means. The weight assignment method is used to select the optimal dredging method, which is used as the preferred dredging method. Subsequently, the preferred dredging method is subjected to boundary constraint analysis based on the fluid mechanics simulation software CFD (computational fluid dynamics). The analysis content includes water flow rate, particle diameter and operating range of dredging equipment. Finally, the component boundary conditions are formed and output in numerical form.

[0088] Step S3: Performing dredging component configuration based on the component boundary conditions to obtain dredging configuration data; performing three-dimensional modeling on the dredging configuration data to generate dredging unit components;

[0089] In this embodiment, in combination with the boundary conditions of the components, CAD software (computer-aided design software) is used to design the basic configuration of the dredging head, including the diameter of the suction port, the length and inclination of the dredging pipe, to generate preliminary dredging configuration data, and CFD is used to perform flow field simulation analysis on the dredging configuration data to optimize the flow distribution of the suction port and reduce the silt backflow phenomenon. According to the optimized configuration data, geometric reconstruction is performed in a three-dimensional modeling tool to construct a three-dimensional model of the dredging unit component. The model needs to include key components such as the dredging head, the dredging pipe and the delivery pump to ensure the size matching of each component and the stability of the mechanical connection.

[0090] Step S4: flocculation demand analysis is performed on the sludge characteristic data to obtain flocculation demand data; solidification unit modeling is performed on the flocculation demand data according to the component boundary conditions and the dredging unit components to generate solidification unit components;

[0091] In this embodiment, based on the sludge characteristic data, a flocculant screening device is used to conduct a flocculant compatibility test, and cationic polyacrylamide is determined to be the main flocculant component. Sedimentation experiments are carried out according to different flocculant concentrations to evaluate the flocculation demand data. Subsequently, combined with the component boundary conditions and the dredging unit components, CAE (computer-aided engineering) software is used to model and optimize the fluid flow characteristics in the flocculation chamber, and the stirring speed of the flocculation chamber stirring device is set to 30 rpm to generate a three-dimensional model of the solidification unit component, including the flocculation chamber, sedimentation tank, stirring device and other parts.

[0092] Step S5: performing transport simulation on the sludge characteristic data based on the component boundary conditions to obtain simulated transport data; performing component layout processing based on the simulated transport data to generate a transmission unit component;

[0093] In this embodiment, the sludge characteristic data is input into the discrete element method (DEM) and fluid dynamics joint simulation platform to simulate the particle sedimentation behavior and flow resistance characteristics in the conveying pipeline to obtain the transport simulation data. The length and bending radius of the conveying pipeline are optimized according to the simulation results, and the delivery pump pressure is set to 3 MPa. At the same time, the position of the pumping station is adjusted to reduce the energy loss of the pipeline. Finally, the optimized transport parameters are imported into the three-dimensional modeling tool to carry out pipeline layout and power system model generation to form an overall model of the transmission unit components.

[0094] Step S6: Integrate the dredging unit components, the solidification unit components and the 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 three-dimensional modeling software, and the dimensions and interfaces of each model are respectively docked and 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 three-dimensional model of green and low-carbon ecological dredging equipment.

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

[0097] Step S11: sampling the water body at multiple points to obtain water body sludge samples; identifying the particle distribution of the water body sludge samples to obtain sludge particle distribution data;

[0098] Step S12: Screening the sludge particle distribution data by particle size to generate particle grading data;

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

[0100] Step S14: Fusing the particle grading data, sample water content and sample pollutant data to generate sludge characteristic data.

[0101] In this embodiment, in the selected water area, sampling points are planned according to the water area and silt distribution characteristics. The sampling points are distributed in a grid division manner, and the grid interval is set to 50 meters. The center point of each grid is used as a sampling point. A gravity column sampler is used to collect water body silt samples. The collection depth is controlled between 0.5 meters and 1.5 meters. During the sampling process, the sampler is ensured to be vertically inserted into the bottom of the water body to avoid disturbing the silt layered structure. The samples of each sampling point are numbered and stored in a chemically resistant polypropylene sampling container. An appropriate amount of deionized water is added to the container to maintain the original humidity and chemical properties of the sample. Subsequently, a laser particle size analyzer is used in the laboratory to identify the particle distribution of the sample. The scanning range of the laser particle size analyzer is set to 0.02 microns to 2000 microns. After the scanning is completed, the light scattering signal detected by the instrument is analyzed to generate the silt particle distribution data of each sampling point. The data includes the volume distribution and particle size distribution percentage of the particles. The silt particle distribution data of each sampling point is imported into the particle grading analysis system. The screening standard set in the analysis system is based on the "Standard for Geotechnical Test Methods" (GB / T 50123-2019), the sludge samples were screened step by step from 0.Six screening particle size segments are set from 0.75 mm to 2 mm. After each screening, an electronic balance is used to accurately weigh the mass of the screened material and calculate its relative proportion. After the screening is completed, the particle grading data is generated. The data records the proportion of particles in each particle size segment in each sampling point in a tabular form, and is accompanied by a cumulative particle size distribution curve. The curve is used to intuitively represent the grading characteristics of the sample. The particle grading data is then associated with the sampling point coordinate information to form a comprehensive database that can be used for further analysis. 200 grams of wet sludge is taken from the sludge sample at each sampling point, and the moisture content is determined using the oven method. First, the sample is placed in a constant temperature oven at 105 degrees Celsius and dried for 24 hours. After drying, the dry weight of the sample is weighed and recorded, and the moisture content percentage of the sample is calculated. At the same time, 50 grams of sludge sample is taken for heavy metal detection. The sample is pre-treated with a microwave digester. The digestion solution is prepared by mixing nitric acid (HNO3) and hydrogen peroxide (H2O2). The dissolved liquid is tested for the content of heavy metal elements such as lead (Pb), cadmium (Cd), mercury (Hg), and chromium (Cr) in the sample using ICP-MS (inductively coupled plasma mass spectrometer), and the sample pollutant data of each sampling point is generated. The data is output in the form of an element content table and marked in milligrams per kilogram (mg / kg). The particle grading data, sample water content, and sample pollutant data of each sampling point are imported into the data fusion processing system. The system first spatially matches the three types of data according to the sampling point coordinates, and integrates the data belonging to the same sampling point into a data set. The data set includes particle distribution ratio, water content percentage, and heavy metal content parameters. Then, the principal component analysis (PCA) is used to reduce the dimensionality of the three types of data, extract the principal component indicators that can comprehensively reflect the characteristics of sludge, generate a feature data table containing the weights of sludge characteristics, and finally integrate the feature data of all sampling points into a feature matrix. .

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

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

[0104] Step S22: Correlation is performed on the historical dredging and silt characteristic data to generate similarity indicators; hierarchical clustering is performed on the similarity indicators to obtain a spectrum of engineering types;

[0105] Step S23: matching equipment capabilities with the engineering type spectrum based on the preset existing equipment functions to obtain an equipment screening scheme; screening dredging means based on the equipment screening scheme to generate an optimal dredging method;

[0106] Step S24: Decomposing the working condition of the preferred dredging method to obtain operating condition data;

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

[0108] In this embodiment, historical engineering case data is obtained from public industry databases and internal enterprise databases. The data sources include completed river dredging, port desilting, lake regulation and other engineering reports. The cases are screened using a keyword search tool, and the keywords are set to "dredging depth", "silt type", "construction environment" and other contents. After the search, the relevant information including construction parameters, equipment list, operation efficiency and construction environment is exported. The exported case data is imported into a text analysis system for further decomposition and structured processing, and key data such as dredging depth range, construction equipment model, silt particle distribution, operation time and unit energy consumption in each case are extracted to generate a historical dredging data table. The historical dredging data and silt characteristic data are imported into the data analysis system, and the Pearson correlation coefficient is used to perform correlation analysis on the two types of data. The correlation between dredging depth and particle grading distribution, equipment model and water content, and construction efficiency and heavy metal content is calculated respectively. The calculation results are normalized to generate a similarity index table. The similarity index includes a multidimensional vector with a value range of 0 to 1. The similarity index is used as input data to import into a hierarchical clustering model. The shortest distance method (Single Linkage Method) calculates the clustering distance between each data sample, sets the clustering level to three levels to ensure that the output spectrum has a clearly classified structure, matches the engineering type spectrum with the existing equipment database, which contains the specification parameters, applicable environment and energy consumption data of the existing dredging equipment, uses the dynamic programming algorithm to match the equipment capacity of each type node in the spectrum, sets the matching conditions as the equipment rated power, operating depth, applicable silt particle distribution range and corrosion resistance, and includes the equipment that meets the matching conditions in the equipment screening plan. The dredging means are screened according to the equipment screening plan. The screening basis includes multiple indicators such as equipment operating efficiency, energy consumption and environmental impact. The multi-objective decision model is used to prioritize the candidate dredging means, and a list of preferred dredging methods is generated. The list includes recommended equipment, operation methods and construction steps. The single import working condition analysis system decomposes the construction steps according to the dredging operation scenario, arranges the decomposed working steps in chronological order and spatial range, and generates operating condition data. The operating condition data includes the construction area, equipment used, energy consumption and operating environment data of each step. During the decomposition process, the key parameters of the equipment operation such as rotation speed, operating depth, mud flow rate, etc. are calibrated, and the operating condition data is imported into the numerical inversion system. The optimal process conditions for each 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 transportation 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, and the mechanical properties, material durability and dimensional design of the components are constrained according to the process conditions to generate a boundary condition table of components that meet the conditions.

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

[0110] The project type spectrum is graded to obtain project scale data; the project scale data is decomposed into functional requirements to generate functional requirement data;

[0111] Based on the preset existing equipment functions, the adaptability of the functional requirement data is evaluated to obtain the equipment adaptation data; based on the equipment adaptation data, the equipment scheme is screened to generate the equipment screening scheme;

[0112] Conduct cost-benefit calculation on the equipment screening scheme to obtain cost-benefit data; conduct construction cycle forecast on the cost-benefit data to obtain cycle forecast data;

[0113] The combination of means is optimized based on cost-effectiveness data and cycle prediction data to generate the preferred dredging method.

[0114] In this embodiment, the analytic hierarchy process is used to classify the scale of the project type spectrum, and the project type data of different levels in the spectrum are imported into the classification model. According to the project scale characteristics in the spectrum, such as dredging depth, operating area, silt thickness and particle grading range, the classification standard is delineated. The main standards for setting scale classification include small projects (dredging depth less than 10 meters, operating area less than 1 square kilometer), medium-sized projects (dredging depth of 10 to 20 meters, operating area of ​​1 to 5 square kilometers), and large projects (dredging depth greater than 20 meters, operating area of ​​more than 5 square kilometers). The nodes in the project spectrum are regrouped by a recursive decomposition method, and the project scale data are input into the functional requirement decomposition system. The functional requirements are decomposed according to the dredging objectives, construction environment and environmental protection requirements corresponding to the project scale. The modular analysis method is used to decompose each project requirement into equipment requirements, energy consumption requirements and operation efficiency requirements. The rate requirements are combined with specific environmental constraints during the decomposition process, such as river flow rate, water depth in the construction area, and distribution of surrounding ecological sensitive areas. Each functional requirement is stored in the form of a data set. The functional requirement data fields include target parameters, environmental parameters, and operating constraints. The functional requirement data is matched and evaluated with the existing equipment database. The equipment database contains equipment specification parameters, applicable environment, and operating constraints. The fuzzy evaluation method is used to calculate the compatibility between the equipment and the functional requirements. The calculation is scored based on the degree of match between the target parameters in the requirements and the equipment performance. The fitness score range is set to 0 to 1. After the scoring is completed, the results are normalized and the evaluation results are output as equipment adaptation data. The adaptation data includes equipment number, fitness score, and reasons for incompatibility. The equipment adaptation data is imported into the equipment screening model. The screening model sorts the equipment based on the fitness score and selects the equipment with a fitness score greater than 0.8 equipment is selected as candidate equipment, and equipment that does not meet the adaptation requirements is eliminated from the plan. The generated equipment screening plan includes the number of candidate equipment, operating parameters and corresponding functional requirements matching. The equipment screening plan is integrated and analyzed with the construction budget parameters, and the overall benefit is calculated based on the unit energy consumption, labor cost and maintenance cost of equipment operation. In the cost accounting process, the project budget ceiling and expected construction efficiency are combined, and the interval analysis method is used to quantitatively evaluate the benefits of each equipment combination. The calculated benefit results are stored as a cost-effectiveness data table. The data fields include equipment number, total cost, expected income and benefit score. The cost-effectiveness data is input into the cycle prediction system. The construction period is calculated based on the construction rate and the area of ​​the construction area. The dynamic time planning algorithm is used to refine the construction steps to reduce the construction time. The time consumption of each construction stage is modeled in combination with equipment performance and project scale data. The final generated cycle prediction data includes the length of the construction period of each stage, the equipment operation time and the environmental factor analysis affecting the period. The cost-effectiveness data and the cycle prediction data are imported into the combined optimization model. The genetic algorithm is used to combine and optimize the dredging means. With the maximization of benefits and the shortest construction period as the objective function, the equipment configuration, construction method and operation parameters are iteratively optimized. The generated optimal dredging method includes equipment combination mode, construction steps and resource allocation strategy.

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

[0116] Step S31: Perform engineering parameter identification on 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: parametric modeling is performed on the component layout parameters to obtain dredging configuration data; the dredging configuration data is finely divided according to the component boundary conditions to generate component key parameters;

[0119] Step S34: Reconstruct the key parameters of the component into three-dimensional entities to generate a dredging unit component.

[0120] In this embodiment, the component geometric model of the dredging equipment is imported into the finite element analysis system, and the engineering parameter identification is performed through the preset boundary conditions, including the fixed end points, active constraint positions and external load parameters of the components. During the identification process, a laser scanner is used to obtain the three-dimensional point cloud data of the component surface, and the point cloud data is subjected to curvature analysis in combination with digital image processing technology to extract the geometric information of the key constraint points. Then, 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, and the fields include component number, boundary type, constraint force value and geometric size. The engineering parameter data is imported into the topological modeling tool, and the structural topology mapping is performed through the component connection relationship and geometric size. During the mapping process, the three-dimensional point set mapping technology is used to analyze the connection mode between the components, and the node matrix is ​​used to describe the connection position and direction between the components. The generated structural topology data is stored in the graph database format, and the fields include node number, connection type and node spacing. Then, the components are interfered based on the topological data, and the dynamic interference detection method is used in the verification. The motion trajectory of adjacent components is simulated by the constraint-based parametric modeling method (Constraint-Based Parametric Modeling) to determine whether there is motion interference or spatial overlap. After the verification is completed, the component layout parameters are output, and the fields include component number, spatial position, motion interference status and adjustment suggestions. The component layout parameters are imported into the parametric modeling tool, and the constraint-based parametric modeling method (Constraint-Based Parametric Modeling) is used to set the constraint conditions for the size, position and motion trajectory of the component. The generated parametric model contains the size variables and constraint relationships of all components. The parametric model is then finely divided according to the boundary conditions. The voxel segmentation technology (VoxelSegmentation Technology) is used in the fine division to divide the component into multiple independent units. The boundary conditions and geometric features of each unit are accurately marked and extracted. The key parameters of the component generated include unit number, boundary force value, stress distribution and deformation. The key parameters of the component are imported into the 3D reconstruction system, and the 3D entity of the component is reconstructed through the reverse modeling technology (Reverse Modeling Technology). The reconstruction process is combined with multi-view stereo matching (Multi-View StereoMatching) technology is used to restore the shape and surface of the components in detail, and a 3D printer is used to produce the solid dredging unit components. Polylactic acid (PLA) is used as the printing material during the printing process to ensure the dimensional accuracy and surface finish of the printed components. After completion, the size of the solid components is detected by 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 comprises the following steps:

[0122] Step S41: matching the sludge characteristic data with the flocculant ratio to obtain flocculation ratio data;

[0123] Step S42: performing flocculation demand analysis on the sludge characteristic data based on the flocculation ratio data to generate flocculation demand data;

[0124] Step S43: performing flocculation dynamics deduction based on the flocculation ratio data to generate flocculation dynamics data; performing flocculation cavity design on the flocculation dynamics data and component boundary data to obtain cavity layout data;

[0125] Step S44: integrating the cavity layout data and the 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 is measured by a particle size analyzer to obtain the particle characteristic data of the sludge, and then a high-performance liquid chromatograph (HPLC) is used to detect the organic matter content in the sludge, and the pH value and conductivity of the sludge are determined in combination with an ion selective electrode method. The obtained sludge characteristic data are input into a flocculant ratio matching model. The model matches the optimal flocculant combination and its ratio, including the addition amount of polyacrylamide (PAM) and aluminum sulfate, through a method based on multivariate regression analysis. The generated flocculation ratio data is output in a tabular form, and the fields include flocculant type, addition ratio and adaptation target. The generated flocculation ratio data and sludge characteristic data are imported into a flocculation demand analysis system. The system analyzes the influence of different flocculant ratios on the aggregation behavior of particles in the sludge by simulating the reaction kinetic model of the flocculation process. The multiscale numerical simulation technology (Multiscale Numerical Simulation The flocculation technology was used to evaluate the rate and stability of particle aggregation. Combined with the sedimentation rate data measured by the laboratory stirring sedimentation test, the flocculation demand data was generated. The output content included flocculant consumption, sedimentation time and particle removal rate. The flocculation dynamics model was constructed through the flocculation ratio data. The model was deduced based on the viscosity of the sludge, the particle size distribution and the characteristic parameters of the flocculant. During the deduction process, the Lagrangian particle tracking algorithm was used to simulate the movement trajectory and collision probability of the particles during the flocculation process. The generated flocculation dynamics data included flocculation reaction time, collision frequency and particle agglomeration strength. The flocculation cavity was designed using 3D modeling software in combination with the component boundary data. The cavity design simulated the fluid distribution and particle aggregation effect in the cavity through fluid mechanics analysis software. The generated cavity layout data included cavity geometric parameters, fluid inlet position and flocculation efficiency index. The cavity layout data and the dredging unit components were imported into the modular assembly platform. The integration of the flocculation unit was completed through automated assembly technology. The precision laser alignment technology was used during the assembly process. Technology) is used to accurately locate the connection interface between the cavity and the dredging component, and a bolt pre-tightening tool is used to achieve high-strength fixation. After completion, an X-ray detection device is used to scan the internal structure of the integrated flocculation unit to ensure that the connection between the cavity and the component is gap-free and well-sealed. The final solidification 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 series to obtain the flocculation reaction sequence; the flocculation reaction sequence is calculated by tassel gradient to generate flow velocity distribution data;

[0129] Conduct reaction intensity evaluation on tassel distribution data to obtain intensity evaluation data; deduce flocculation efficiency based on intensity evaluation data to generate flocculation dynamics data;

[0130] The flocculation dynamic data is mapped to spatial demand to obtain the flocculation spatial demand; the flocculation spatial demand is projected to structural morphology to generate structural morphology data;

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

[0132] In this embodiment, the whole flocculation reaction process is captured in real time by a high-frequency optical monitoring system, and the interaction time series data between particles and flocculants are recorded. The discrete time series analysis method is used to divide the reaction steps in the flocculation ratio data into an initial mixing stage, a particle agglomeration stage, and a settling stabilization stage according to the time nodes. Each stage corresponds to a specific time interval and reaction intensity parameter. 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 the fluid analysis platform, and the flow velocity distribution characteristics of each time series stage are calculated by using the Shear Gradient Model. The particle distribution and the gradient change of the fluid shear force in the flocculation process are obtained by analyzing the flow velocity vector field. The laser Doppler flowmeter is used to analyze the flow velocity distribution characteristics of each time series stage. Doppler Velocimeter (LDV) was used to verify the simulation results and generate velocity distribution data, including velocity range, shear force distribution and particle movement path. Multi-point sampling analysis was performed on the velocity distribution data, and the reaction intensity factor model was used to quantitatively evaluate the relationship between shear force and flocculation efficiency. High-precision particle counting equipment was used to record the trend of particle concentration during the reaction. The reaction intensity data was calculated based on the particle removal rate. The data fields included shear force peak value, particle rupture rate and flocculation efficiency change rate. The intensity evaluation data was imported into the kinetic deduction model and the Lagrangian method was used. The collision frequency between particles and the distribution efficiency of flocculants were deduced and analyzed. The boundary conditions set in the deduction process included the particle size range, flocculant concentration and fluid shear force changes. The generated flocculation dynamics data covered the time required for reaction, particle collision probability and flocculant strength. By analyzing the particle collision frequency and reaction time parameters in the flocculation dynamics data, the volume and geometric shape required for the flocculation reaction were mapped and analyzed in combination with the reaction space characteristics. The fluid path in the reaction space was simulated using a three-dimensional finite element analysis tool. The output of the flocculation space requirements included the minimum effective volume, reactor height and fluid inlet and outlet diameters. The geometric shapes in the flocculation space requirements were projected in three dimensions using a parametric modeling tool. A preliminary structural model was established based on the projection results. A computer-aided design (CAD) method was used to simulate the fluid path in the reaction space. The curved surfaces and key nodes in the model are adjusted by using CAD (Computer Design) software. The generated structural morphology data include the projection curve orientation, node connection parameters and surface roughness index. The structural morphology data are imported into the fluid mechanics simulation software. The fluid distribution and particle motion trajectory inside the flocculation cavity are simulated by using the numerical simulation method. The input parameters set during the simulation process include fluid flow rate, inlet pressure and particle concentration distribution. The simulated flow field distribution data include the distribution diagrams of flow 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,The geometry of the cavity, inlet design and fluid distributor layout are adjusted using a structural optimization algorithm. After the optimization is completed, the results are re-input into the fluid mechanics simulation tool to verify its effectiveness. The final output cavity layout data includes cavity geometry parameters, fluid distributor position and structural stability data.

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

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

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

[0136] Step S53: identifying optimizable points according to the simulated transport data to obtain optimizable points;

[0137] Step S54: Component layout is performed according to the optimizable points to generate transmission unit components.

[0138] In this embodiment, a sampling device is used to collect sludge samples distributed at multiple points from the target dredging area, and a laser particle size analyzer is used to analyze the particle size distribution of the sample particles, and the particle diameter range and average particle size are determined. The sample is placed in a rheometer, and the shear rate range is set to 1 to 500s-1. The relationship between shear stress and shear rate is recorded, and the yield stress, viscosity and viscoelastic modulus of the sludge are calculated in combination with the rheological curve characteristics. The output rheological characteristic data include 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 parameter of the inner wall of a circular pipe with a diameter of 0.5 meters, an inlet flow rate of 2m / s, and an outlet pressure of 101.3kPa, the finite element method (FEM) is used to numerically simulate the fluid in the pipeline. The fluid properties set in the simulation model correspond to the rheological characteristics data. The simulated virtual flow field data includes velocity distribution field, pressure distribution field and particle concentration distribution field. The laser particle image velocimetry (PIV) is used to experimentally verify the simulation data. On the basis of the virtual flow field model, the energy consumption per unit volume of the fluid in the pipeline is distributed and calculated through the built-in energy equation, the flow resistance is associated with the flow velocity gradient, and the energy consumption distribution map is generated in combination with the pressure field data. In the mapping process, the high energy consumption area and the low energy consumption area are marked with a color gradient, and the energy consumption data are layered. The generated energy consumption distribution data includes the energy consumption value per unit fluid volume, the total energy consumption on the fluid transportation path and the energy distribution characteristics. The high energy consumption area in the energy consumption distribution data is located, and the position of the high energy consumption area is associated with the fluid velocity and pressure changes. The high energy consumption points are marked through a three-dimensional space coordinate system, and the energy consumption distribution is evaluated using an optimization algorithm. The coordinate information of the marked position is The information includes the axial distance from the inlet, the radial coordinates and the energy consumption value corresponding to the area. The generated simulated transportation data includes the number, location and energy consumption value distribution of high energy consumption areas. The simulated transportation data is input into the optimization decision module. The high energy consumption areas marked in the energy consumption distribution are identified and analyzed by the multi-objective optimization algorithm. The input parameters in the optimization model include the location, pressure gradient and fluid resistance distribution of the high energy consumption area. The geometric shape, wall smoothness and flow rate adjustment possibility of the point are identified. The identification results include the coordinate information of the optimized point, the range of optimizable parameters and adjustment suggestions. The data of the optimizable point is output, and the 3D modeling analysis is performed on the optimizable point. The transmission unit components are designed according to the geometric characteristics of the point and the fluid transportation requirements. The pipe cross-sectional shape is optimized by computer-aided design (CAD) software. Fluid guide components and pressure regulating devices are added to the high energy consumption points. Model samples are generated by 3D printing technology. The samples are assembled experimentally to verify the performance. The transmission unit component data finally generated includes the geometric parameters of the components, material selection and fluid guide design scheme. .

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

[0140] Extract the flow resistance of the virtual flow field to obtain the flow resistance data; perform pressure drop calculation on the flow resistance data to generate pressure drop distribution data;

[0141] Performing power conversion processing on the voltage reduction distribution data to generate power distribution data; performing energy consumption evaluation based on the power distribution data to obtain energy consumption distribution data;

[0142] Based on the preset energy consumption stratification threshold, the energy consumption distribution data is subjected to threshold stratification processing to obtain energy consumption level data; energy consumption regions are divided according to the energy consumption level data to generate regional distribution data;

[0143] The critical point calibration is performed on the regional distribution data to generate the critical point data; the transport parameters are integrated based on the critical point data to generate the simulation operation data.

[0144] In this embodiment, computational fluid dynamics software (CFD) is used to analyze the virtual flow field, and the pressure distribution and velocity distribution data of the pipeline fluid are input into the flow resistance extraction module. The local flow resistance is calculated by the fluid mechanics model, wherein the input parameters include the inner diameter of the pipeline 0.5 meters, the fluid density 1000 kilograms per cubic meter, and the flow velocity 2 meters per second. The output flow resistance data includes the local pressure loss value along the pipeline axis, the friction resistance coefficient, and the total resistance value. The flow resistance data is imported into the pressure reduction calculation module, and the pressure reduction distribution along the pipeline is measured point by point in combination with the inlet pressure and outlet pressure conditions of the pipeline. The pressure drop estimation model is used in the measurement process, and the input parameters include the dynamic viscosity of the fluid, the roughness of the pipeline wall, and the flow velocity distribution. The pressure drop distribution is obtained. The data includes the local pressure drop value and the total pressure drop value along the pipeline. The pressure drop distribution of each measuring point is displayed using a color-coded two-dimensional graph. The pressure drop distribution data is input into the power conversion module. The pressure drop data is converted into the corresponding power value based on the power calculation method required per unit fluid volume. The input parameters of the conversion process include the fluid flow rate of 0.3 cubic meters per second and the flow rate distribution data. The output power distribution data is the power consumption value per unit volume along the pipeline axis. The power distribution is visualized through a three-dimensional bar graph. The power distribution data is input into the energy consumption evaluation module. The total energy consumption per unit time is integrated and calculated in combination with the time period parameters of pipeline fluid transportation. The input parameters include the transportation time of 3600 seconds and the unit power. The output energy consumption distribution data includes the location of high energy consumption areas, high energy consumption values ​​and their correlation with pipeline sections. The energy consumption data is spatially annotated through a two-dimensional heat map, and the energy consumption distribution data is classified using a preset energy consumption stratification threshold. The threshold is set to be less than 100 watts per cubic meter in low energy consumption areas, 100 to 300 watts per cubic meter in medium energy consumption areas, and greater than 300 watts per cubic meter in high energy consumption areas. The stratified energy consumption data is marked and the energy consumption level data is output. The distribution of different energy consumption levels is displayed using stratified color coding. The energy consumption level data is input into the regional division module, and the energy consumption distribution area is divided according to the spatial position clustering algorithm. The division standard is the energy consumption level of adjacent areas. The area with the same energy consumption level and continuous distribution is regarded as an energy consumption area. The output regional distribution data includes the number of energy consumption areas, the spatial range of each area and the average energy consumption in the area. The vector diagram is used to accurately calibrate the regional boundaries. The critical points are calibrated using the boundaries of high energy consumption areas and low energy consumption areas in the regional distribution data. The position of the critical point is calculated in combination with the actual geometric shape 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 of the point. The critical point data is input into the transport parameter integration module, and the transport speed, pressure and flow parameters are adjusted according to the characteristics of the high energy consumption area. The integrated simulation operation data includes the optimized flow velocity distribution, pressure distribution and flow value.

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

[0146] Step S61: locating the connection points of the desilting unit components to obtain connection point data;

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

[0148] Step S63: performing assembly sequence planning on the adapter interface data and the 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: integrating the dredging unit components, the solidification unit components and the transmission unit components based on the whole machine integration data to generate a three-dimensional dredging equipment model.

[0151] In this embodiment, a 3D modeling software (such as SolidWorks) is used to model the dredging unit component in detail. The geometric parameters of the input component include a length of 5 meters, a width of 1.2 meters, and a height of 1 meter. The virtual assembly technology is used to determine the position of the key connection points through the connection parts of the component. The connection points include the interface position, the connection method, and the surrounding support area. The coordinate data of all connection points are output in the form of X, Y, and Z coordinates. The obtained connection point data includes the spatial relationship between the dredging unit and other components. The connection point data of the dredging unit component is matched with the joint interface of the solidification unit component, and the virtual assembly is performed using CAD software. During the assembly process, the interface parameters of the curing unit are input, including the size, shape, bearing capacity and other information of the interface. The connection points of the two components are docked using the automatic matching algorithm. The system calculates the matching degree according to the geometric shape of the interface and the matching tolerance to ensure that the matching accuracy is within 0.1 mm. The obtained adapter interface data includes the size, position and relative angle of each interface. The assembly sequence of the adapter interface data and the transmission unit components is planned. The assembly sequence of each component is optimized based on the assembly path planning algorithm. The input parameters include the weight of the component, the difficulty of assembly and the time required. Combined with the position restrictions and workpieces in the workspace, the system can optimize the assembly sequence of each component. The system automatically calculates and generates assembly sequence data according to the industrial environment conditions. The output results include assembly sequence, positioning method of each component and tools and equipment required for assembly. The assembly sequence data is imported into the whole machine collaborative verification module. According to the collaborative working relationship between the components of each unit in the assembly process, the interference and collision in the assembly process are simulated by numerical calculation. The input data includes the spatial position, posture and assembly sequence of each component. During the verification process, it will check whether the relative position during assembly meets the space restriction requirements. If interference or assembly conflict is found, the system will give correction suggestions and re-plan the assembly plan. 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 input into the 3D modeling tool. According to the verified data, the dredging unit components, curing unit components and transmission unit components 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 position and angle between the components to ensure that all components are perfectly connected. Finally, a 3D dredging equipment model is generated. The output result is a 3D file in standard STL format, which contains the assembly information, size and spatial layout of all components, which is convenient for subsequent actual manufacturing and testing.

[0152] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0153] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be 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 present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for constructing a three-dimensional model of green, low-carbon and ecological dredging equipment, characterized in that: The following steps are involved: Step S1: Collecting water sludge samples; Perform particle screening on water body sludge samples to generate particle grading data; perform sludge characteristic analysis on water body sludge samples based on the particle grading data to obtain sludge characteristic data; Step S2: Acquire historical dredging data; match dredging means with the siltation state according to the historical data to obtain an optimal dredging method; perform boundary constraint analysis on the optimal dredging method to obtain component boundary conditions; Step S3: Performing dredging component configuration based on the component boundary conditions to obtain dredging configuration data; performing three-dimensional modeling on the dredging configuration data to generate dredging unit components; Step S4: Flocculation demand analysis is performed on the sludge characteristic data to obtain flocculation demand data; Solidification unit modeling is performed on flocculation demand data according to component boundary conditions and dredging unit components to generate solidification unit components; Step S5: performing transport simulation on the sludge characteristic data based on the component boundary conditions to obtain simulated transport data; performing component layout processing based on the simulated transport data to generate a transmission unit component; Step S6: Integrate the dredging unit components, the solidification unit components and the transmission unit components to generate a three-dimensional dredging equipment model.

2. The three-dimensional model construction method of green, low-carbon and ecological dredging equipment according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: sampling the water body at multiple points to obtain water body sludge samples; identifying the particle distribution of the water body sludge samples to obtain sludge particle distribution data; Step S12: Screening the sludge particle distribution data by particle size to generate particle grading data; Step S13: measuring the water content of the water sludge sample to obtain the sample water content; performing heavy metal detection on the water sludge sample to generate sample pollutant data; Step S14: Fusing the particle grading data, sample water content and sample pollutant data to generate sludge characteristic data.

3. The three-dimensional model construction method of green, low-carbon and ecological dredging equipment according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: Acquire historical engineering cases; perform dredging case search on historical engineering cases to obtain historical dredging data; Step S22: Correlation is performed on the historical dredging and silt characteristic data to generate similarity indicators; hierarchical clustering is performed on the similarity indicators to obtain a spectrum of engineering types; Step S23: matching equipment capabilities with the engineering type spectrum based on the preset existing equipment functions to obtain an equipment screening scheme; screening dredging means based on the equipment screening scheme to generate an optimal dredging method; Step S24: Decomposing the working condition of the preferred dredging method to obtain operating condition data; Step S25: Perform process condition inversion on the operating condition parameters to generate process condition data; perform constraint limitation based on the process condition data to obtain component boundary conditions.

4. The method for constructing a three-dimensional model of 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 graded to obtain project scale data; the project scale data is decomposed into functional requirements to generate functional requirement data; Based on the preset existing equipment functions, the adaptability of the functional requirement data is evaluated to obtain the equipment adaptation data; based on the equipment adaptation data, the equipment scheme is screened to generate the equipment screening scheme; Conduct cost-benefit calculation on the equipment screening scheme to obtain cost-benefit data; conduct construction cycle forecast on the cost-benefit data to obtain cycle forecast data; The combination of means is optimized based on cost-effectiveness data and cycle prediction data to generate the preferred dredging method.

5. The method for constructing a three-dimensional model of green, low-carbon and ecological dredging equipment according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: Perform engineering parameter identification on 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: parametric modeling is performed on the component layout parameters to obtain dredging configuration data; the dredging configuration data is finely divided according to the component boundary conditions to generate component key parameters; Step S34: Reconstruct the key parameters of the component into three-dimensional entities to generate a dredging unit component.

6. The three-dimensional model construction method of green, low-carbon and ecological dredging equipment according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: matching the sludge characteristic data with the flocculant ratio to obtain flocculation ratio data; Step S42: performing flocculation demand analysis on the sludge characteristic data based on the flocculation ratio data to generate flocculation demand data; Step S43: performing flocculation dynamics deduction based on the flocculation ratio data to generate flocculation dynamics data; performing flocculation cavity design on the flocculation dynamics data and component boundary data to obtain cavity layout data; Step S44: integrating the cavity layout data and the dredging unit components into a flocculation unit to generate a solidification unit component.

7. The method for constructing a three-dimensional model of 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 series to obtain the flocculation reaction sequence; the flocculation reaction sequence is calculated by tassel gradient to generate flow velocity distribution data; Conduct reaction intensity evaluation on tassel distribution data to obtain intensity evaluation data; deduce flocculation efficiency based on intensity evaluation data to generate flocculation dynamics data; The flocculation dynamic data is mapped to spatial demand to obtain the flocculation spatial demand; the flocculation spatial demand is projected to structural morphology to generate structural morphology data; The flow field distribution is simulated on the structural morphology data to generate simulated flow field distribution data; the cavity is optimized and designed according to the simulated flow field distribution data to obtain the cavity layout data.

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

9. The method for constructing a three-dimensional model of green, low-carbon and ecological dredging equipment according to claim 7, characterized in that: Step S52 includes the following steps: Extract the flow resistance of the virtual flow field to obtain the flow resistance data; perform pressure drop calculation on the flow resistance data to generate pressure drop distribution data; Performing power conversion processing on the voltage reduction distribution data to generate power distribution data; performing energy consumption evaluation based on the power distribution data to obtain energy consumption distribution data; Based on the preset energy consumption stratification threshold, the energy consumption distribution data is subjected to threshold stratification processing to obtain energy consumption level data; energy consumption regions are divided according to the energy consumption level data to generate regional distribution data; The critical point calibration is performed on the regional distribution data to generate the critical point data; the transport parameters are integrated based on the critical point data to generate the simulation operation data.

10. The method for constructing a three-dimensional model of green, low-carbon and ecological dredging equipment according to claim 1, characterized in that: Step S6 includes the following steps: Step S61: locating the connection points of the desilting unit components to obtain connection point data; Step S62: performing interface adaptation and matching on the connection point data and the solidification unit components to generate adaptation interface data; Step S63: performing assembly sequence planning on the adapter interface data and the 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: integrating the dredging unit components, the solidification unit components and the transmission unit components based on the whole machine integration data to generate a three-dimensional dredging equipment model.

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