Digital twinning system and method for displaying ship spraying robot flow field through universe

Abstract

The invention discloses a digital twinning system and method for displaying a ship spraying robot flow field through element universe, and belongs to the technical field of intelligent equipment. The digital twinning system comprises a physical entity layer, a data and tool layer, a digital twinning layer and a virtual application layer. The working method comprises the steps that actual parameters of the ship spraying robot are collected, and entity mapping is carried out; building of a virtual physical model of the ship spraying robot is completed; establishing a digital model of the spraying flow field of the ship spraying robot; building a virtual flow field simulation model based on a digital model; checking of ship spraying simulation results is completed through the simulation model, and simulation parameters are optimized; and completing entity improvement and reconstruction of the ship spraying robot. The visual display method for the spraying flow field of the ship spraying robot is constructed for the spraying operation scene in the dock by adopting a virtual simulation method based on the universe and the digital twinning technology. And the working efficiency is improved while the labor cost is reduced.

Description

Technical Field

[0001] The present invention relates to a digital twin display method for ship spraying, and belongs to the field of artificial intelligence and computer simulation, and in particular to a digital twin system and method for displaying the flow field of a ship spraying robot in a metaverse. Background Art

[0002] Painting the outer plate of a ship is an indispensable step in the process of ship construction and maintenance. If the quality of the spraying is not high, it will seriously restrict the safety of the ship's navigation and reduce the service life of the ship. This work requires spraying operations 40 meters in the air within the dock, and the thickness of the paint must be kept uniform after spraying to avoid leaks. The existing shipyard spraying is mainly carried out by manual spraying on an elevated vehicle. In the actual spraying process, the spraying effect is easily affected by external factors such as wind direction, wind speed, ambient humidity, and air dust. In addition, it is also affected by factors such as the viscosity of the sprayed paint itself, and due to the complex surface of the ship, the spraying flow field is often unevenly distributed.

[0003] For the above reasons, ship robots are used to replace manual spraying, which solves the safety problem of high-altitude spraying. However, problems such as spray leakage, paint overflow, and gun blockage still exist, resulting in unsatisfactory spraying results. After the robot sprays, manual inspection is required. After the spraying quality problem is found, a lot of energy needs to be spent on screening the fault point. This process seriously restricts the efficiency of spraying. At the same time, since the ship stays in the dock for too long, it will lead to inefficient use of dock resources, affecting the turnover rate and profit of the shipyard. Summary of the invention

[0004] In response to the problems existing in the operation process of the above-mentioned ship spraying robot, the present invention aims to solve: to provide a digital twin system and method for displaying the flow field of the ship spraying robot through the metaverse, aiming to build a virtual simulation model of the spraying flow field of the ship spraying robot, and to realize the visual display and monitoring of the changing state of the flow field during the operation of the spraying robot, so as to help achieve performance optimization.

[0005] The technical solution of the present invention is: a digital twin system of the flow field of a ship spraying robot is displayed in the metaverse, and the system includes a physical entity layer, a data and tool layer, a digital twin layer, and a virtual application layer; The physical entity layer includes a ship spraying robot unit, a dock equipment operating environment unit, and an operating target ship unit; the physical entity layer is the basic layer of the system architecture, which is used to describe the hardware facilities and equipment in the actual spraying work scene; The ship spraying robot unit is used to replace manual work to perform the actual execution of the ship surface spraying operation; the dock equipment operating environment unit provides the physical parameters and restriction conditions of the ship spraying robot operating environment; the operation target ship unit is the target ship for the designated spraying operation, including its main scale, position and shape information; The data and tool layer stores the equipment and facility data of the physical entity layer through various sensors and measurement tools, and further uploads them for processing; the data and tool layer includes: robot parameter unit, spray parameter unit, environmental factor unit, ship parameter unit, 3D modeling software, flow field simulation software, data display engine, digital twin engine; The robot parameter unit is used to store the parameters and characteristics of the spraying robot; the spraying parameter unit contains specific parameters of the spraying operation: spraying material type, spraying speed; the environmental factor unit is used to record the impact of environmental factors on the spraying operation; the ship parameter unit records various parameters of the target ship as basic data for the construction operation of the spraying robot; the three-dimensional modeling software is used to establish a physical model of the spraying robot and support mechanical structure simulation and optimization; the flow field simulation software is used to simulate the working conditions of the flow field, optimize the robot nozzle mesh quality and adjust the nozzle exit angle according to the spraying particle rate, pressure distribution and flow field distribution; the data display engine uses sensors deployed on the equipment entity to collect and monitor real-time data, convert and process the data, compare and analyze the data with the actual parameters, and display the processed data in a visual form; the digital twin engine is used to reflect the working status of the on-site spraying robot and the changes in the spraying flow field in real time, and display the physical model of the spraying robot and the simulation results of the simulated flow field in a visual form.

[0006] The digital twin layer completes the model architecture through the data and tool layer, and performs operation mapping and data transmission on the required parameters, and then uploads and feedbacks them after combined iteration and instruction interaction; the digital twin layer includes: a virtual physical model of a ship spraying robot, a spraying digital model, and a virtual flow field simulation model; The virtual application layer includes: a ship spraying application display module and a ship spraying robot intelligent decision-making module; the ship spraying application display module includes: a human-machine interface and a model display; the ship spraying robot intelligent decision-making module includes: spraying quality control, spraying fault diagnosis, spraying path optimization, and robot optimization and improvement; The virtual application layer visualizes the spraying flow field of the ship spraying robot through the application display module, and uses the intelligent decision-making module to monitor and diagnose, helping the robot to optimize and improve its operations, predict possible faults that may occur during the spraying process, and provide a treatment plan; The human-machine interface is used for on-site staff to operate the robot and monitor data; the model display is used to visualize the working status of the ship spraying robot and the spraying flow field; The spraying quality control is used to automatically detect and adjust the coating thickness and quality to ensure that the spraying results meet the standard requirements; the spraying fault diagnosis is used to analyze abnormal conditions and diagnose the cause of the fault in real time; the spraying path optimization includes setting the initial spraying point A1 and the subsequent path distribution points A n The number of distribution points is set according to the coating width of the spraying flow field on the hull wall, and the system determines the optimal spraying path and parameters based on the calculation and analysis results; the robot optimization and improvement includes adjusting the nozzle direction and angle of the ship spraying robot according to the spraying flow field and the coating quality, controlling the operation of the telescopic mechanism of the ship spraying robot and the ground movement rate to ensure uniform coating without sagging, adjusting the caliber of the ship spraying robot's spray gun, and completing the optimization of the performance and structure of the ship spraying robot.

[0007] Furthermore, the construction of the virtual physical model of the ship spraying robot includes a multi-level virtual physical model construction module of the ship spraying robot, a spraying scene extraction module in the dock, and a multi-objective model combination iteration module, which specifically includes the following steps: The multi-level virtual physical model building module of the ship spraying robot described in S2.1 covers multiple influencing factors and modeling processes in the operation process of the spraying robot, reflecting the dynamic process of the entire ship spraying robot operation, and specifically includes a multi-production factor model and a dynamic model of the spraying operation process; Establishing a multi-factor model for spraying flow field M α , the ship spraying robot and the target ship are subjected to three-dimensional scanning reconstruction, finite element analysis and three-dimensional model rendering, which is expressed as: (1) In the formula, α represents 3D scanning reconstruction, β represents finite element analysis, δ Represents 3D model rendering; S2.2 Establish a dynamic model of the spraying process M β , spraying space analysis, spraying flow field analysis, time domain analysis, operation mapping, frequency domain analysis and spraying operation optimization are performed, which can be expressed as: (2) In the formula, X n1 Represents spray space analysis, X n2 Represents spray flow field analysis, X n3 represents time domain analysis, X n4 Represents the job mapping, X n5 represents frequency domain analysis,X n6 Indicates optimization of spraying operation; The spraying space analysis includes the spatial layout of the robot spraying operation area, spraying distance control, and safety distance limit to meet the need to cover the spraying area of ​​the ship's outer plate; the spraying flow field analysis includes the distribution of paint particles and environmental impact; the time domain analysis is the change of the paint state over time during the spraying process, as well as the time planning of the spraying path and the influence of the paint drying time on continuous spraying; the operation mapping is to map the actual spraying operation task on the spraying robot model; the frequency domain analysis is to analyze the vibration influence of the up and down displacement of the spraying robot during the spraying process and the influence of the nozzle vibration during the spraying process; the spraying operation optimization is to adjust the spraying parameter robot path and operation process based on the above analysis; S2.3 Establishing a multi-objective combined model M γ , extract dock environment data, ship information, robot arm structure parameters, working posture, auxiliary equipment layout and staff position information, integrate and verify the spray robot operation requirements, spray operation process, model interaction interface design and data, systematically organize and analyze model static data, spray flow field data and spray operation process, compare model accuracy, implement feedback for iterative optimization, and complete the establishment of a multi-objective combination model: (3) In the formula, Z n1 Indicates the results of the spraying operation demand analysis. Z n2 Indicates the analysis of the spraying operation process. Z n3 Represents the design elements of the model interaction interface, Z n4 Indicates data fusion integration, Z n5 represents the results of model performance verification, Z n6 Indicates the static data of spraying. Z n7 represents flow field data, Z n8 Represents process planning data, Z n9 Indicates iterative upgrade of the model; S2.4 Integrated spraying flow field multi-production factor model M α , Dynamic model of spraying process M β , Multi-objective combination model M γ, the construction of the virtual physical model of the ship spraying robot is completed, which is expressed as: (4).

[0008] Furthermore, the establishment of the digital model of the spraying flow field is to mesh and encrypt the robot spraying flow field based on the constructed virtual physical model. The system selects the precision mesh according to the actual spraying equipment and model parameters, and sets the mesh size, density and quality control parameters to capture the detailed flow characteristics; for the single flow field around the robot, an unstructured mesh is selected; the flow field flow model is called and numerical simulation is performed; The spraying flow field volume of the ship spraying robot is expressed as:

[0009] Among them, H 喷枪 Indicates the distance between the spray gun and the ship's outer plate, R1 indicates the maximum radius of the effective spraying area, R2 indicates the shortest radius of the effective spraying area, S 喷涂 Indicates the effective spraying area of ​​the ship's outer plate.

[0010] Furthermore, the construction of the virtual flow field simulation model is based on the spraying flow field digital model, and the process includes: S4.1 Import the digital model of the spraying flow field of the ship spraying robot into the simulation software; S4.2 Initialize the data parameters in the flow field of the ship spraying robot, select the initial condition area, complete the speed setting according to the paint mist emission speed obtained by the speed sensor and the robot spraying movement speed data, complete the temperature parameter setting according to the ambient temperature and paint film temperature detected by the temperature sensor installed on the ship spraying robot, set the pressure according to the pressure parameters obtained by the pipeline pressure sensor of the ship spraying robot, set the inlet conditions and pressure outlet conditions in the system to complete the boundary conditions; initialize the flow field state at the beginning of spraying, input the viscosity and density parameters of the specific spray paint according to the paint model used in the actual operation to complete the initialization variable condition setting, check the flow field state of the ship spraying robot, and complete the initialization setting; S4.3 Set the number of system iterations and complete the formulation of convergence criteria, including whether the momentum and mass equations are less than 1×10 -6 , whether the average flow rate change exceeds 0.02% and whether the film thickness meets 180μm, monitor the real-time speed and pressure changes during the iteration process and issue early warning; S4.4 determines whether the parameter solution converges according to the set convergence standard; if the solution converges, proceed to the next step; if the solution does not converge, re-initialize the flow field parameters of the ship spraying robot, adjust the numerical parameters such as pressure and temperature, and optimize the spraying flow field digital model for recalculation; S4.5 Calculation result visualization. The visualization process includes establishing isosurfaces in the system to display the internal conditions of the spray robot flow field. The obtained spray flow field calculation results are exported. The data includes the spray velocity vector, the pressure change distribution and the temperature change gradient during the spraying process, forming the spray robot spray flow field data set. S4.6 Verify the calculation results of the ship spraying robot to verify whether the simulation result parameters are valid, compare the obtained ship spraying robot flow field simulation initialization parameters with the actual spraying coverage area and paint utilization index on site to complete the error correction; if the error range is met, proceed to the next step, if the error range is not met, re-initialize and correct the spraying flow field data, and re-import it for calculation and simulation; until the construction of the virtual flow field simulation model of the ship spraying robot is completed.

[0011] Metaverse demonstrated the working method of the digital twin system of the ship spraying robot flow field, including the following steps: S5.1 collects actual parameters of the ship spraying robot; the actual parameters include the ship spraying robot's arm up and down extension speed distribution, the robot's ground translation speed distribution, spray paint viscosity, spray speed distribution, spray gun spray angle, spray robot shape and size parameters, ship outer plate size data, and performs entity data mapping; data mapping is data transmission and command interaction, and the process includes: data initialization and connection stage, data interaction stage, and model display end stage; S5.2 Build the virtual physical model of the ship spraying robot, the digital model of the spraying flow field, and the virtual flow field simulation model; S5.3 Improvement and reconstruction of the ship spraying robot entity. Based on the virtual simulation results of the spraying flow field, analyze the coating thickness, airflow distribution, and pressure and temperature characteristics of the spraying flow field. Optimize the shape, quantity and layout of the nozzle of the physical entity of the ship spraying robot. Replan the operation path trajectory of the ship spraying robot, reset the robot's lifting speed, and adjust the lateral displacement rate.

[0012] Furthermore, the step data mapping process is specifically as follows: S6.1 Data initialization and connection stage: the server establishes a socket and performs port binding, waiting for the spraying robot to connect; the spraying robot establishes a socket and completes the connection to the corresponding server; S6.2 Data interaction stage: the spray robot sends the initial data of the spray robot and completes the read response; the server first reads the initial data and completes the data processing and confirmation before sending; At the end of S6.3 model display, the server completes the transmission of the spray robot data model, reads the close request and closes the Socket; the spray robot reads the data and updates the local model, sends a read close request and closes the connection.

[0013] The beneficial effects of the present invention are: During the spraying operation of the ship's outer plate, affected by the external environment and paint performance, paint blockage, spray leakage, excessive spray impurities, etc. often occur, resulting in poor spraying effect of the ship's outer plate, seriously affecting the ship's life and navigation safety. The present invention provides a digital twin system and method for displaying the flow field of a ship spraying robot in the metaverse, replacing manual spraying fault screening and reducing worker participation. By simulating and modeling the spraying flow field, the flow field performance parameters during the operation of the ship spraying robot can be visualized, and fault diagnosis and spraying optimization suggestions can be given after a fault occurs, which will further reduce the investment of manpower and material resources and promote the development of the ship spraying field. Through this method, the spraying flow field data obtained in the embodiment are as follows: (1) The actual utilization rate of the two-nozzle paint is 73.27%, and the average film thickness surface is about 102.1μm; (2) The utilization rate of the four-nozzle paint is 75.08%, and the average film thickness area is 116.28μm.

[0014] Metaverse technology can connect the real world with the virtual world, build a virtual entity space, and help simulate the working state of ship spraying robots. Digital twin technology is a technology that combines the physical entity of the equipment in reality with its virtual digital model, which can visualize the working process. Through this technology, the working state of the physical entity in reality can be accurately simulated in a virtual environment, and it can be monitored, optimized and predicted, while meeting the performance and safety requirements in the actual operation process, while minimizing the investment in labor costs. The use of metaverse and digital twin technology can not only provide enterprises with more efficient production, operation and management methods, but also further accelerate the transformation and upgrading of enterprises. Combining metaverse technology, digital twin technology with ship spraying robot technology to empower the traditional ship spraying industry can continuously improve the efficiency and quality of ship spraying operations, thereby improving the productivity level of the shipbuilding industry and driving the development of this field. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 The digital twin system architecture diagram of the flow field of a ship spraying robot displayed in the Metaverse provided in an embodiment of the present invention.

[0016] Figure 2 A flow chart of the working method of the digital twin system of the flow field of a ship spraying robot displayed in the monistic universe provided in an embodiment of the present invention.

[0017] Figure 3A virtual physical model architecture flow chart of the working method of the digital twin system of the flow field of a ship spraying robot displayed in the metaverse provided in an embodiment of the present invention.

[0018] Figure 4 The metaverse provided in an embodiment of the present invention displays a data transmission and command interaction process diagram of the working method of the digital twin system of the flow field of a ship spraying robot.

[0019] Figure 5 The Metaverse provided in an embodiment of the present invention displays the working method of the digital twin system of the ship spraying robot flow field and the flow chart of the spraying flow field virtual simulation model.

[0020] Figure 6 The metaverse provided in the embodiment of the present invention displays the working method of the digital twin system of the flow field of a ship spraying robot and a spatial distribution model of the paint mist flow field.

[0021] Figure 7 The metaverse provided in the embodiment of the present invention displays the working method of the digital twin system of the flow field of a ship spraying robot and the spatial distribution diagram of the diameter of splashing particles during double nozzle spraying.

[0022] Figure 8 Another four-nozzle spray flow field display diagram provided for the patent of this invention.

[0023] Fig. 9 This is a display diagram of the recycling spray flow field provided for the patent of this invention.

[0024] In the figure: 1. A virtual physical model of a ship spraying robot; 2. A virtual simulation model of a ship outer plate. DETAILED DESCRIPTION

[0025] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific implementation modes.

[0026] Figure 1 The digital twin system architecture diagram of the ship spraying robot flow field displayed by the metaverse provided in the embodiment of the present invention includes: physical entity layer, data and tool layer, digital twin layer, virtual application layer. The physical entity layer includes: ship spraying robot unit, dock equipment operating environment unit, and operating target ship unit.

[0027] The physical entity layer is the basic layer of the system architecture, which is used to describe the hardware facilities and equipment in the actual spraying work scene. The ship spraying robot unit is responsible for replacing the actual execution of the manual ship surface spraying operation, including operations such as movement and spraying, and is an existing robot entity; the dock equipment operating environment unit provides the physical parameters and restrictions of the ship spraying robot operating environment; the construction operation target ship unit contains the target ship for the specified spraying operation, including its main scale, position, shape and other information.

[0028] The data and tool layer includes: robot parameter unit, spraying parameter unit, environmental factor unit, ship parameter unit, 3D modeling software, flow field simulation software, data display engine, digital twin engine. The data and tool layer stores the equipment and facility data of the physical entity layer through various sensors and measurement tools, and further uploads them for processing. The robot parameter unit stores the parameters and characteristics of the spraying robot for subsequent optimization and upgrading; the spraying operation parameter unit contains specific parameters of the spraying operation, such as spraying materials, spraying speed, etc.; the environmental factor unit records the impact of environmental factors such as wind speed on the spraying operation, which is used to adjust the behavior of the spraying robot; the ship parameter unit records various parameters of the target ship as basic data for the construction operation of the spraying robot; the three-dimensional modeling software is used to establish a physical model of the spraying robot and support mechanical structure simulation and optimization; the flow field simulation software is used to simulate the working conditions of the flow field and optimize and improve according to the flow field distribution; the data display engine and digital twin engine use digital twin technology to import the constructed spray robot and its scene physical model into the engine, collect and monitor real-time data, convert and process the data, compare and analyze it with the actual parameters, and finally display the processed data in a visual form to assist in system optimization.

[0029] The digital twin layer includes: a virtual physical model of a ship spraying robot, a spraying digital model, and a virtual flow field simulation model. The digital twin layer completes the model architecture through the data and tool layer, and performs operation mapping and data transmission on the required parameters, and then uploads and feedbacks them after combined iteration and instruction interaction.

[0030] The virtual application layer includes: a ship spraying application display module and a ship spraying robot intelligent decision-making module. The ship spraying application display module includes: a human-machine interface and a model display; the ship spraying robot intelligent decision-making module includes: spraying quality control, spraying fault diagnosis, spraying path optimization, and robot optimization and improvement.

[0031] The virtual application layer visualizes the status of the spraying flow field of the ship spraying robot through the application display module, and uses the intelligent decision-making module for monitoring and diagnosis, helping the robot to complete the optimization and improvement of the operation, predicting the possible faults in the spraying process, and providing a treatment plan. The human-machine interface is used for on-site staff to operate the robot and monitor various data; the model display is used to visualize the working status of the ship spraying robot, spraying flow field, etc. The spraying quality control is used to automatically detect and adjust the coating thickness and quality to ensure that the spraying results meet the standard requirements; the spraying fault diagnosis is used to analyze abnormal conditions and diagnose the cause of the fault in real time, helping to reduce the operation downtime of the ship spraying robot and further reduce the equipment maintenance cost; the spraying path optimization is used to calculate and analyze to determine the optimal spraying path and parameters; the robot optimization and improvement is to optimize its performance and structure based on the robot spraying process behavior data.

[0032] Metaverse demonstrated the working method of the digital twin system of the ship spraying robot flow field, including the following steps: S1. Collect the actual parameters of the ship spraying robot, including the upper and lower extension speed distribution of the ship spraying robot's big arm, the ground translation speed distribution of the robot, the spraying paint viscosity, the spraying speed distribution, the spray gun spray angle, the spraying robot shape and size parameters, and the ship outer plate size data parameters, and perform entity data mapping; The data mapping in step S1 includes: data transmission and instruction interaction, and the process includes: data initialization and connection stage, data interaction stage, and model display end stage.

[0033] S1.1 Data initialization and connection stage: the server establishes a Socket and binds the port, waiting for the spray robot to connect; the spray robot establishes a Socket and completes the connection to the corresponding server.

[0034] S1.2 Data interaction stage: The spray robot sends the initial data of the spray robot and completes the read response; the server first reads the initial data and completes the data processing and confirmation before sending; At the end of S1.3 model display, the server completes the transmission of the spray robot data model, reads the close request and closes the Socket; the spray robot reads the data and updates the local model, sends a read close request and closes the connection.

[0035] S2. Complete the construction of the virtual physical model of the ship spraying robot, including: S2.1 Establishing a multi-factor model for spraying flow field M α , the three-dimensional scanning reconstruction, finite element analysis and three-dimensional model rendering of the ship spraying robot and the target ship can be expressed as: (1) In the formula, α represents 3D scanning reconstruction, β represents finite element analysis, δ Represents 3D model rendering; S2.2 Establish a dynamic model of the spraying process M β , spraying space analysis, spraying flow field analysis, time domain analysis, operation mapping, frequency domain analysis and spraying operation optimization can be expressed as: (2) In the formula, X n1 Represents spray space analysis, X n2 Represents spray flow field analysis, X n3 represents time domain analysis, X n4 Represents the job mapping, X n5 represents frequency domain analysis, X n6 Indicates optimization of spraying operation; The spraying space analysis includes the spatial layout of the robot spraying operation area, spraying distance control, and safety distance limit to meet the need to cover the spraying area of ​​the ship's outer plate; the spraying flow field analysis includes the distribution of paint particles and environmental impact; the time domain analysis is the change of the paint state over time during the spraying process, as well as the time planning of the spraying path and the influence of the paint drying time on continuous spraying; the operation mapping is to map the actual spraying operation task on the spraying robot model; the frequency domain analysis is to analyze the vibration influence of the up and down displacement of the spraying robot during the spraying process and the influence of the nozzle vibration during the spraying process; the spraying operation optimization is to adjust the spraying parameter robot path and operation rhythm based on the above analysis; S2.3 Establishing a multi-objective combined model M γ , extract dock environment data, ship information, robot arm structure parameters, working posture, auxiliary equipment layout and staff position information, integrate and verify the spray robot operation requirements, spray operation process, model interaction interface design and data, sort out the model static data, spray flow field data and spray operation process, compare the model accuracy, implement feedback for iterative optimization, and complete the establishment of a multi-objective combination model, which is specifically expressed as follows: (3) In the formula, Z n1Indicates the results of the spraying operation demand analysis. Z n2 Indicates the analysis of the spraying operation process. Z n3 Represents the design elements of the model interaction interface, Z n4 Indicates data fusion integration, Z n5 represents the results of model performance verification, Z n6 Indicates the static data of spraying. Z n7 represents flow field data, Z n8 Represents process planning data, Z n9 Indicates iterative upgrade of the model; S2.4 Integrated spraying flow field multi-production factor model M α , Dynamic model of spraying process M β , Multi-objective combination model M γ , the construction of the virtual physical model of the ship spraying robot is completed, which is expressed as: (4); S3. Establish a digital model of the spraying flow field of the ship spraying robot, and mesh and encrypt the robot spraying flow field based on the constructed virtual physical model. The system selects a grid with appropriate accuracy according to the actual spraying equipment and model parameters, and sets the grid size, density and quality control parameters to capture detailed flow characteristics; for a single flow field around the robot, select an unstructured grid to meet the complex structural characteristics of the spraying robot; call the flow field flow model and perform numerical simulation; S4. Build a virtual flow field simulation model based on the digital model; the spraying flow field virtual simulation model process includes: S4.1 Import the physical model of the ship spraying robot flow field into the simulation software; S4.2 Initialize the data parameters in the flow field of the ship spraying robot, select the initial condition area, complete the speed setting according to the paint mist emission speed obtained by the speed sensor and the robot spraying movement speed data, complete the temperature parameter setting according to the ambient temperature and paint film temperature detected by the temperature sensor installed on the ship spraying robot, set the pressure according to the pressure parameters obtained by the pipeline pressure sensor of the ship spraying robot, set the inlet conditions and pressure outlet conditions in the system to complete the boundary conditions; initialize the flow field state at the beginning of spraying, input the viscosity and density parameters of the specific spray paint according to the paint model used in the actual operation to complete the initialization variable condition setting, check the flow field state of the ship spraying robot, and complete the initialization setting; S4.3 Set the number of system iterations and complete the formulation of convergence criteria, including whether the momentum and mass equations are less than 1×10 -6 , whether the average flow rate change exceeds 0.02% and whether the film thickness meets 180μm, monitor the real-time speed and pressure changes during the iteration process and issue early warning; S4.4 determines whether the parameter solution converges according to the set convergence standard; if the solution converges, proceed to the next step; if the solution does not converge, re-initialize the flow parameters of the ship spraying robot, adjust the numerical parameters such as pressure and temperature, and optimize the digital model of the spraying flow field for recalculation; S4.5 Calculation result visualization. The visualization process includes establishing isosurfaces in the system to display the internal conditions of the spray robot flow field. The obtained spray flow field calculation results are exported. The data includes the spray velocity vector, the pressure change distribution and the temperature change gradient during the spraying process, forming the spray robot spray flow field data set. S4.6 Verify the calculation results of the ship spraying robot to verify whether the simulation result parameters are valid, compare the obtained ship spraying robot flow field simulation initialization parameters with the actual spraying coverage area and paint utilization index on site to complete the error verification; if the error range is met, proceed to the next step, if the error range is not met, re-initialize and correct the spraying flow field data, and re-import it for calculation and simulation; complete the construction of the virtual simulation model of the ship spraying robot's spraying flow field.

[0036] S5. Complete the verification of ship spraying simulation results through simulation, optimize the simulation parameters, and complete the physical improvement and reconstruction of the ship spraying robot.

[0037] Figure 2 A flow chart showing the working method of the digital twin system of the ship spraying robot flow field is provided to Metaverse, including the use of various sensors to complete the parameter collection and entry of the ship spraying robot before the spraying work begins, and to achieve overall data mapping of the ship spraying robot.

[0038] The three-dimensional scanning and data collection of the ship's outer plate and the overall shape of the ship's spraying robot are completed through total stations and other equipment to generate fine point cloud data; the flow rate change data of the ship's spraying robot nozzle is collected through flow meters and other sensors; the spraying distance parameters are collected through laser rangefinders; and the spraying pressure data is measured through pressure sensors. In addition, it also includes parameters such as the up and down extension speed distribution of the ship's spraying robot's big arm, the robot's ground translation speed distribution, the spray paint viscosity, the spray speed distribution, and the spray gun's spray angle.

[0039] By importing modeling software such as SolidWorks, CATIA, Rhino software, etc. according to the obtained spraying flow field parameters, paint parameters, robot parameters, spraying operation environment parameters, etc., the construction of the virtual physical model of the ship spraying robot is completed.

[0040] A digital model of ship painting robot spraying was built to simulate the turbulence numerical value of the flow in the flow field.

[0041] The spraying flow field is simulated by simulation software such as ANSYS software, and a virtual flow field simulation model of the ship spraying robot is built. The incident source coordinates of the ship spraying robot are set through the incident source definition window of the discrete phase model (DPM). The specific properties are shown in Table 1.

[0042]

[0043] The obtained simulation model results are checked and some data parameters are optimized.

[0044] Based on the final optimization results, the spraying flow field working performance evaluation of the ship spraying robot entity is completed, and the technical staff is guided to complete the improvement and reconstruction of the robot.

[0045] Figure 3 This is a flow chart of the virtual physical model architecture. The architecture includes: a multi-level virtual physical model building module for ship spraying robots, a spraying scene extraction module in the dock, and a multi-objective model combination iteration module; first, the multi-level virtual physical model building of the ship spraying robot is completed, and then the spraying scene extraction in the dock is completed, and then the multi-objective model combination iteration is completed; The multi-level virtual physical model building module of the ship spraying robot consists of multi-production factor modeling and dynamic modeling of the spraying operation process; multi-production factor modeling requires three-dimensional scanning and reconstruction, finite element analysis and three-dimensional model rendering of the ship spraying robot and the target ship; dynamic modeling of the spraying operation process requires the completion of spraying space analysis, spraying flow field analysis, time domain analysis, operation mapping, frequency domain analysis and spraying operation optimization; The spraying space analysis includes the spatial layout of the robot spraying operation area, spraying distance control, safety distance limit, etc., to meet the need to cover the spraying area of ​​the ship's outer plate; the spraying flow field analysis includes the distribution of paint particles, environmental impact, etc., to meet the spraying process requirements; the time domain analysis considers the changes in the paint state over time during the spraying process, as well as the time planning of the spraying path and the influence of the paint drying time on continuous spraying; the operation mapping is to map the actual spraying operation task on the spraying robot model; the frequency domain analysis mainly analyzes the vibration influence of the up and down displacement of the spraying robot during the spraying process and the influence of the nozzle vibration during the spraying process; the spraying operation optimization is to adjust the spraying parameters (such as spray gun speed, nozzle type, paint flow), robot path, operation rhythm, etc. on the basis of the above analysis to minimize material consumption, improve operation efficiency and coating quality, and achieve overall optimization of the spraying operation.

[0046] The spraying scene extraction module in the dock includes the extraction of dock environment data, ship information, robot arm structure parameters, working posture, auxiliary equipment layout and staff position; The multi-objective model combination iteration module includes spray robot operation demand analysis, spray operation process analysis, model interaction interface design and data fusion integration, multi-objective model combination, and complete performance verification of each model; model static data, spray flow field data and spray operation process planning; model iteration upgrade.

[0047] Figure 4 This is a diagram of the data transmission and command interaction process. In order to ensure the accuracy and real-time performance of the data model transmission process, it is mainly divided into the spray robot side and the robot control server side.

[0048] The spray robot initializes the Socket for communication with the server; further, by calling the connect() function, it connects to the specified port of the server; further, it sends the initialized digital twin data and spraying task information to the server; further, it receives the data sent by the server and updates the model parameters; further, it sends a connection closing request to the server and completes the preparation task for closing the communication channel.

[0049] The spray robot control server initializes the Socket for network data communication; further, binds to the specified port for connection with the spray robot; further, calls the listen() function to prepare to accept the connection request from the spray robot; further, receives the connection request from the spray robot and establishes a communication channel; further, receives the data sent by the spray robot and processes it; further, maps the received digital twin data to the ship model in the metaverse; further, generates and sends the ship spray robot data model; further, closes the Socket connection on the server.

[0050] Figure 5 To provide a flow chart of the virtual simulation model of the spraying flow field, firstly, the physical model of the ship spraying robot flow field is imported into the simulation software; Furthermore, the data parameters in the flow field of the ship spraying robot are initialized and set in the flow field simulation software; since the critical Weber number is an empirical constant in the random secondary breakup model, when other control parameters remain unchanged, increasing the critical Weber number will increase the critical breakup diameter of the droplets, and the particle diameter, velocity and impact kinetic energy are higher than the results of the simulation with a low critical Weber number, that is, the atomization level of the paint is relatively lower. Therefore, the critical Weber number is increased to 10 for spraying simulation settings; Perform meshing and node calculation on the spraying flow field, import the model into the simulation software, and redefine the appropriate mesh size and the number of boundary layers and other parameters; The discrete equation of the spraying flow field of the ship spraying robot is established. The coordinate information of the spraying endpoints of different sources is set in the incident source definition window of the discrete phase model (DPM). The specific data is shown in Table 1.

[0051] Establish the motion formula for spray paint particles: (5) In the formula, u q Indicates the movement speed of paint mist particles. t Indicates time, Indicates the resistance encountered by paint mist particles during movement. is the acceleration due to gravity, is the dispersed phase particle density, is the density of the continuous phase, represents additional force; The discrete equation is solved for parameters, and the discrete equation is solved using numerical methods to calculate the physical quantities of the flow field in each grid unit; Determine whether the parameter solution converges. If the solution converges, proceed to the next step. If the solution does not converge, re-initialize the smooth parameters of the ship spraying robot and recalculate until the parameter solution converges. Based on the obtained parameter solutions, the flow field simulation data model of the ship spraying robot is established, and the spatial distribution and variation law of various flow field parameters are also established; Verify whether the simulation result parameters are valid, and perform error check on the obtained ship spray robot flow field simulation data model parameters and the actual spray data parameters on site. If the error range is met, proceed to the next flow field. If the error range is not met, the spray flow field data is corrected and re-imported for calculation and simulation; Finally, continuous iteration and optimization were carried out to determine a set of spray robot flow field data parameters and complete the construction of a virtual simulation model of the ship spray robot's spraying flow field.

[0052] Figure 6 The figure is a spatial distribution diagram of the paint mist flow field, which includes a virtual physical model 1 of a ship spraying robot and a virtual simulation model 2 of a ship outer plate. In order to better display the spraying flow field of the ship spraying robot, a spatial distribution model of the flow field is established. A Point is the spraying point of the ship spraying robot. O Point is the center point of the effective spraying area of ​​the ship's outer plate. H The spray gun indicates the distance between the spray gun and the ship's outer plate. R 1 Indicates the maximum radius of the effective spraying area. R 2 Indicates the shortest radius of the effective spraying area. S 喷涂 Indicates the effective spraying area of ​​the ship's outer plate. Under ideal conditions, without considering external factors such as wind direction, D Represents the flow field volume, then the spray flow field volume distribution model of the ship spraying robot can be expressed as: (6) Through the volume distribution model of the spraying flow field of the ship spraying robot, it can be obtained that the spraying flow field volume can be expressed as .

[0053] Figure 7 A two-nozzle spray flow field display embodiment is provided for an embodiment of the present invention.

[0054] Set the spraying distance to 0.3m, the spraying diffusion angle to 60°, and arrange the nozzles symmetrically along the y-axis direction about the xz plane. Let the theoretical overlap ratio of the two nozzle spray liquid films be 1 / 3. By simple calculation, the y-direction point coordinates of each nozzle can be obtained as shown in Table 1. This figure shows the diameter distribution of spraying and splashing free particles in the spraying flow domain at the same time from different perspectives. Here, only the particles with a residence time of 0.02s in the flow domain are displayed. The number of splashing particles is very large, with diameters ranging from a few microns to tens of microns, and the splashing of paint is more significant in the direction perpendicular to the spray fan. The average film thickness surface in the deposition stable area is about 102.1μm. By calculating the output results of the statistical information of the two incident sources in the calculation domain at the current moment, the actual utilization rate of the paint is 73.27%.

[0055] Figure 8 Another four-nozzle spray flow field demonstration example provided for the patent of this invention.

[0056] The spraying distance is set to 0.3m, the spraying diffusion angle is set to 60°, and the nozzles are arranged symmetrically along the y-axis direction about the xz plane. The theoretical overlap ratio of the four nozzle spraying liquid films is set to 1 / 3. By simple calculation, the y-direction point coordinates of each nozzle can be obtained as shown in Table 2.

[0057]

[0058] Complete the setting of the y coordinates of each incident source. The simulation results are as follows: Figure 8 As shown. The splashing paint mist is clearly divided into two categories, namely the first category of large splashing and large particle size paint mist and the second category of small splashing angle and small particle size paint mist. The distribution uniformity of the paint mist is good, and the overlap ratio of the spray width of adjacent incident sources is slightly less than 1 / 3. It is also because of the mutual inhibition of particle diffusion in the overlapping area that the width of the corresponding liquid film overlap area becomes narrower. The average film thickness area is 116.28μm, and the utilization rate of the paint is 75.08%.

[0059] Fig. 9 This is an example of the recycling spray flow field provided by the patent of this invention.

[0060] The distribution of paint mist particle velocity within the recovery flow field retention time of 0~0.03s is displayed. In order to display the particle distribution in the recovery cover, the particles with a retention time of 0~0.03s in the recovery cover are filtered and displayed. The range of particle diameter is set to 0~100μm. It can be seen that the diameter of the particles ejected by the virtual nozzle continues to decrease with the atomization process, and the diameter of most particles before hitting the wall is above 60μm. The splashing particles after hitting the wall are divided into two parts: the first type is large-diameter paint mist particles with a large splash angle, and the second type is small-diameter particles with a small splash angle, which basically spread outward along the hull plate, and the number of this part is larger. Similarly, the particle velocity range is set to 0~100m / s and 0~10m / s respectively, and the diameter distribution of paint mist diameter within the retention time of 0~0.03s is displayed to intuitively compare the speed of the paint mist before and after hitting the wall and the speed of the two types of splashing paint mist. The results are shown in the figure. The speed of the paint particles before hitting the wall is basically above 40m / s, and the speed of the splashing particles after hitting the wall is basically within 15m / s. The speeds of the two types of paint spray are roughly within 9 m / s, but the speed of the first type of large paint spray varies widely, and the speed of a small amount of particles can reach more than 10 m / s.

[0061] All equivalent substitutions and obvious modifications made by any technician familiar with the art under the guidance of this specification fall within the substantive scope of this specification and should be protected by the present invention.

Claims

1. Metaverse demonstrates the digital twin system of the ship spraying robot flow field, which is characterized by: The system includes a physical entity layer, a data and tool layer, a digital twin layer, and a virtual application layer; The physical entity layer includes a ship spraying robot unit, a dock equipment operating environment unit, and an operating target ship unit; the physical entity layer is the basic layer of the system, representing the hardware facilities and equipment in the actual spraying work scene; The ship spraying robot unit is a ship spraying robot used for performing ship surface spraying operations; the dock equipment operating environment unit provides physical parameters and restriction conditions of the ship spraying robot operating environment; the operation target ship unit is a target ship for the designated spraying operation, and the target ship information includes main scale, position and shape information; The data and tool layer stores the hardware facility equipment data of the physical entity layer through various sensors and measurement tools, and uploads it for processing; The data and tool layer includes: robot parameter unit, spraying parameter unit, environmental factor unit, ship parameter unit, 3D modeling software, flow field simulation software, data display engine, and digital twin engine; The robot parameter unit is used to store the parameters and characteristics of the spraying robot; the spraying parameter unit contains specific parameters of the spraying operation: spraying material type, spraying speed; the environmental factor unit is used to record the impact of environmental factors on the spraying operation; the ship parameter unit records the parameters of the target ship as the basic data for the construction operation of the spraying robot; the three-dimensional modeling software is used to establish a physical model of the spraying robot and support mechanical structure simulation and optimization; the flow field simulation software is used to simulate the working conditions of the flow field, optimize the mesh quality of the robot nozzle and adjust the nozzle exit angle according to the spraying particle rate, pressure distribution and flow field distribution; the data display engine uses sensors deployed on hardware facilities and equipment to collect and monitor real-time data, convert and process the data, compare and analyze the collected parameters with the actual parameters, and display the processed data in a visual form; the digital twin engine is used to reflect the working status of the on-site spraying robot and the changes in the spraying flow field in real time, and display the physical model of the spraying robot and the simulation results of the simulated flow field in a visual form; The digital twin layer completes the model architecture through the data and tool layer, and performs operation mapping and data transmission on the required parameters, and then uploads and feedbacks them after combined iteration and instruction interaction; the digital twin layer includes: a virtual physical model of a ship spraying robot, a spraying digital model, and a virtual flow field simulation model; The virtual application layer includes a ship spraying application display module and a ship spraying robot intelligent decision-making module; the ship spraying application display module includes: human-machine interface, model display; the ship spraying robot intelligent decision-making module includes: spraying quality control, spraying fault diagnosis, spraying path optimization, robot optimization and improvement; The virtual application layer visualizes the spraying flow field of the ship spraying robot through the ship spraying application display module, and uses the ship spraying robot intelligent decision-making module to monitor and diagnose, helping the robot to optimize and improve the operation, predict possible faults in the spraying process, and provide a treatment plan; The human-machine interface is used for on-site staff to operate the robot and monitor data; the model display is used to visualize the working status of the ship spraying robot and the spraying flow field; The spraying quality control is used to automatically detect and adjust the coating thickness and quality to ensure that the spraying results meet the standard requirements; the spraying fault diagnosis is used to analyze abnormal conditions and diagnose the cause of the fault in real time; the spraying path optimization includes setting the initial spraying point A1 and the path distribution point A n , the number of distribution points is set according to the coating width of the spraying flow field on the hull wall, and the spraying path and parameters are determined; the robot optimization and improvement includes adjusting the nozzle direction and angle of the ship spraying robot according to the spraying flow field and the coating quality, controlling the operation of the telescopic mechanism of the ship spraying robot and the ground movement rate, and adjusting the caliber of the ship spraying robot's spray gun.

2. According to claim 1, the digital twin system of the Metaverse displaying the flow field of a ship spraying robot is characterized by: The construction of the virtual physical model of the ship spraying robot includes a multi-level virtual physical model construction module of the ship spraying robot, a spraying scene extraction module in the dock, and a multi-objective model combination iteration module, and specifically includes the following steps: The multi-level virtual physical model building module of the ship spraying robot described in S2.1 covers multiple influencing factors and modeling processes in the operation process of the spraying robot, reflecting the dynamic process of the entire ship spraying robot operation. The specific contents include a multi-production factor model and a dynamic model of the spraying operation process; Establishing the multi-factor model of spraying flow field α , the ship spraying robot and the target ship are subjected to three-dimensional scanning reconstruction, finite element analysis and three-dimensional model rendering, which is expressed as: （1） In the formula, α represents 3D scanning reconstruction, β represents finite element analysis, and δ represents 3D model rendering; S2.2 Establishing the dynamic model M of the spraying process β , spraying space analysis, spraying flow field analysis, time domain analysis, operation mapping, frequency domain analysis and spraying operation optimization are performed, which can be expressed as: （2） Where, X n1 Indicates spraying space analysis, X n2 represents the spraying flow field analysis, X n3 represents time domain analysis, X n4 represents job mapping, X n5 represents frequency domain analysis, X n6 Indicates optimization of spraying operation; The spraying space analysis includes the spatial layout of the robot spraying operation area, spraying distance control, and safety distance limit to meet the need to cover the spraying area of ​​the ship's outer plate; the spraying flow field analysis includes the distribution of paint particles and environmental impact; the time domain analysis is the change of the paint state over time during the spraying process, as well as the time planning of the spraying path and the influence of the paint drying time on continuous spraying; the operation mapping is to map the actual spraying operation task on the spraying robot model; the frequency domain analysis is to analyze the vibration influence of the up and down displacement of the spraying robot during the spraying process and the influence of the nozzle vibration during the spraying process; the spraying operation optimization is to adjust the spraying parameter robot path and operation process according to the spraying space analysis, spraying flow field analysis, time domain analysis, operation mapping and frequency domain analysis; S2.3 Establishing multi-objective combination model M γ , extract dock environment data, ship information, robot arm structure parameters, working posture, auxiliary equipment layout and staff position information, integrate and verify the spray robot operation requirements, spray operation process, model interaction interface design and data, systematically organize and analyze model static data, spray flow field data and spray operation process, compare model accuracy, implement feedback for iterative optimization, and complete the establishment of a multi-objective combination model: （3） In the formula, Z n1 represents the analysis result of spraying operation demand, Z n2 Indicates the analysis of spraying operation process, Z n3 Represents the design elements of the model interaction interface, Z n4 represents data fusion integration, Z n5 represents the result of model performance verification, Z n6 Indicates the static data of spraying, Z n7 represents flow field data, Z n8 Represents process planning data, Z n9 Indicates iterative upgrade of the model; S2.4 Integrated spraying flow field multi-production factor model M α , Dynamic model of spraying process M β , multi-objective combination model M γ , the construction of the virtual physical model of the ship spraying robot is completed, which is expressed as: （4）。 3. The digital twin system of the Metaverse displaying the flow field of a ship spraying robot according to claim 2 is characterized by: The digital model of the spraying flow field is established by meshing and encrypting the robot spraying flow field based on the constructed virtual physical model. The system selects the precision grid according to the actual spraying equipment and model parameters, and sets the grid size, density and quality control parameters to capture the detailed flow characteristics; for the single flow field around the robot, an unstructured grid is selected; the flow field flow model is called and numerical simulation is performed; The spraying flow field volume of the ship spraying robot is expressed as: ； Among them, H 喷枪 Indicates the distance between the spray gun and the ship's outer plate, R1 indicates the maximum radius of the effective spraying area, R2 indicates the shortest radius of the effective spraying area, S 喷涂 Indicates the effective spraying area of ​​the ship's outer plate.

4. The digital twin system of the Metaverse displaying the flow field of a ship spraying robot according to claim 3 is characterized by: The construction of the virtual flow field simulation model is based on the spraying flow field digital model, and the process includes: S4.1 Import the digital model of the spraying flow field of the ship spraying robot into the simulation software; S4.2 Initialize the data parameters in the flow field of the ship spraying robot, select the initial condition area, complete the speed setting according to the paint mist emission speed obtained by the speed sensor and the robot spraying movement speed data, complete the temperature parameter setting according to the ambient temperature and paint film temperature detected by the temperature sensor installed on the ship spraying robot, set the pressure according to the pressure parameters obtained by the pipeline pressure sensor of the ship spraying robot, set the inlet conditions and pressure outlet conditions in the system to complete the boundary conditions; initialize the flow field state at the beginning of spraying, input the viscosity and density parameters of the specific spray paint according to the paint model used in the actual operation to complete the initialization variable condition setting, check the flow field state of the ship spraying robot, and complete the initialization setting; S4.3 Set the number of system iterations and complete the formulation of convergence criteria, including whether the momentum and mass equations are less than 1×10 -6 , whether the average flow rate change exceeds 0.02% and whether the film thickness meets 180μm, monitor the real-time speed and pressure changes during the iteration process and issue early warning; S4.4 determines whether the parameter solution converges according to the set convergence standard; if the solution converges, proceed to the next step; if the solution does not converge, re-initialize the flow field parameters of the ship spraying robot, adjust the numerical parameters such as pressure and temperature, and optimize the spraying flow field digital model for recalculation; S4.5 Calculation result visualization. The visualization process includes establishing isosurfaces in the system to display the internal conditions of the spray robot flow field. The obtained spray flow field calculation results are exported. The data includes the spray velocity vector, the pressure change distribution and the temperature change gradient during the spraying process, forming the spray robot spray flow field data set. S4.6 Verify the calculation results of the ship spraying robot to verify whether the simulation result parameters are valid, compare the obtained ship spraying robot flow field simulation initialization parameters with the actual spraying coverage area and paint utilization index on site to complete the error verification; if the error range is met, proceed to the next step, if the error range is not met, re-initialize and correct the spraying flow field data, and re-import it for calculation and simulation; until the construction of the virtual flow field simulation model of the ship spraying robot is completed.

5. The digital twin system of the Metaverse displaying the flow field of a ship spraying robot according to claim 4 is characterized in that: The working method of the system includes the following steps: S5.1 collects actual parameters of the ship spraying robot; the actual parameters include the ship spraying robot's arm up and down extension speed distribution, the robot's ground translation speed distribution, spray paint viscosity, spray speed distribution, spray gun spray angle, spray robot shape and size parameters, ship outer plate size data, and performs entity data mapping; data mapping is data transmission and command interaction, and the process includes: data initialization and connection stage, data interaction stage, and model display end stage; S5.2 Build the virtual physical model of the ship spraying robot, the digital model of the spraying flow field, and the virtual flow field simulation model; S5.3 Improvement and reconstruction of the ship spraying robot entity. Based on the virtual simulation results of the spraying flow field, analyze the coating thickness, airflow distribution, and pressure and temperature characteristics of the spraying flow field. Optimize the shape, quantity and layout of the nozzle of the physical entity of the ship spraying robot. Replan the operation path trajectory of the ship spraying robot, reset the robot's lifting speed, and adjust the lateral displacement rate.

6. The digital twin system of the Metaverse displaying the flow field of a ship spraying robot according to claim 5 is characterized in that: The data mapping process is specifically as follows: S6.1 Data initialization and connection stage: the server establishes a socket and performs port binding, waiting for the spraying robot to connect; the spraying robot establishes a socket and completes the connection with the corresponding server; S6.2 Data interaction stage: the spray robot sends the initial data of the spray robot and completes the read response; the server first reads the initial data and completes the data processing and confirmation before sending; At the end of S6.3 model display, the server completes the transmission of the spray robot data model, reads the close request and closes the Socket; the spray robot reads the data and updates the local model, sends a read close request and closes the connection.

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