Metacosmic digital twinborn ship robot spraying equipment management platform and method

Abstract

The invention discloses a meta-universe digital twinning ship robot spraying equipment management platform and method, and belongs to the technical field of intelligent equipment. The platform comprises a dock working scene layer, a sensing tool layer, a digital twinborn layer and an intelligent equipment management layer. The sensing tool layer is used for performing data acquisition sensing mapping on the dock working scene layer; the digital twinborn layer is used for establishing a twinborn spraying intelligent equipment virtual model, constructing a twinborn dock operation environment and parameters and completing equipment application; and the intelligent equipment management layer is used for carrying out optimal design on ship robot spraying equipment, carrying out reasonable distribution on intelligent equipment, monitoring the state of the spraying intelligent equipment and assisting equipment maintenance. According to the platform, monitoring and management of the intelligent spraying equipment of the ship robot are completed, distribution of working stations of the ship robot can be reasonably planned, the spraying working efficiency of the ship robot is improved, and the operation and maintenance management process of the equipment is optimized.

Description

Technical Field

[0001] The present invention relates to an intelligent equipment management platform for ship robots, and belongs to the field of artificial intelligence and computer simulation, and in particular to a working method of a ship robot spraying intelligent equipment management platform enabled by a metaverse digital twin. Background Art

[0002] The spraying of ship outer plates is usually carried out in the dock. The spraying of ship outer plates plays a significant role. High-quality spraying can effectively prevent seawater, atmospheric corrosion and marine biological attachment, extend the life of the ship, and protect the structural strength of the hull itself. At present, there is still a large gap between my country's ship coating technology and that of foreign countries, which is prominently reflected in the long coating cycle, low efficiency, and high cost. According to relevant information, the current production efficiency of ship coating in my country is only equivalent to 1 / 4 to 1 / 3 of that of foreign advanced shipyards, which seriously affects the international competitiveness of my country's ship products. Among them, the low degree of intelligence of spraying equipment, low efficiency of spraying equipment operation management, and high spraying rework rate are important factors restricting the efficiency of ship spraying. Secondly, considering that ship coating is a technical job integrating chemical, mechanical, electrical, fluid power and other technologies, it has high requirements for the professional technology of operators. Therefore, in the future, the ship spraying industry urgently needs a way to change the existing situation and improve the efficiency of spraying operations.

[0003] Digital twin technology is a virtual entity based on a digital model. It can accurately simulate and predict the behavior of real-world entities through virtual reflection of reality, helping to optimize monitoring. Metaverse technology combines virtual reality and augmented reality technologies, emphasizes virtual interaction, and can well simulate real-world situations.

[0004] The role of metaverse and digital twin technology in the ship robot spraying intelligent equipment management platform includes improving efficiency, reducing maintenance costs, realizing remote monitoring and operation, etc., providing new solutions and technical support for ship spraying equipment management. Summary of the invention

[0005] In response to the problems existing in the operation process of the above-mentioned ship spraying robots, the present invention provides a metaverse digital twin ship robot spraying equipment management platform and method, which not only solves the current operation efficiency problems in the ship painting process, but also can achieve comprehensive management, remote monitoring and operation, fault diagnosis and optimization, and improve safety, etc., and solve many problems in the management of ship spraying equipment.

[0006] The technical solution of the present invention is:

[0007] A ship robot spraying intelligent equipment management platform enabled by Metaverse digital twin, which includes a dock work scene layer, a perception tool layer, a digital twin layer, and an intelligent equipment management layer;

[0008] The dock work scene layer includes: staff, ship spraying robots, ship spraying equipment, and dock operation areas; the ship spraying robot is manually remotely operated to perform high-altitude spraying, and is the basic equipment in the spraying intelligent equipment management platform; the ship spraying equipment includes ship paint feeding system, ship paint mixing system, paint can opening equipment and other auxiliary equipment supporting the ship robot; the dock operation area includes the environmental layout and target construction ship entity in the construction dock area;

[0009] The perception tool layer includes: sensor equipment, cameras, 3D laser scanners, communication equipment, interface equipment, and data processing equipment, which are used to perform data collection, perception and mapping of entities in the dock work scene layer, and further complete spraying operation planning, coating material control, data transmission, and interface protocol conversion;

[0010] The sensor equipment, camera, and 3D laser scanner are used for entity parameter acquisition and parameter mapping, including the ship robot swing angle, equipment displacement distance, operating environment temperature, and ship shape and size; the communication equipment, interface equipment, and data processing equipment are used for data storage, data transmission, and interface protocol conversion, and for spraying equipment process supervision, equipment data transmission, and remote control; tracking the operating status and parameters of ship spraying equipment;

[0011] The digital twin layer includes: a twin working parameter unit, a twin spraying intelligent equipment virtual model unit, and a twin dock operating environment unit;

[0012] The twin working parameter unit performs data analysis and management within the system; the twin spraying intelligent equipment virtual model unit simulates and models the structure, performance and operation of the spraying equipment, and the system completes the fusion of multiple models and completes the model correction; the twin dock operation environment unit simulates the dock spraying operation environment, including the structure of the ship, the layout of the spraying equipment, and the space restrictions, and completes the environmental simulation configuration;

[0013] The intelligent equipment management layer applies model parameters according to the digital twin results, including a spraying intelligent equipment optimization unit, a spraying intelligent equipment station allocation unit, an intelligent equipment operation detection unit, and an intelligent equipment quality maintenance unit;

[0014] The spraying intelligent equipment optimization unit uses digital twin results and model parameters to optimize and improve the spraying angle, spray robot running speed, spray path selection, and paint configuration ratio during the spraying process; the spraying intelligent equipment workstation allocation unit allocates the ship spraying robot group, formulates detailed workstation layout strategies, and optimizes the operation process; the intelligent equipment operation detection unit is responsible for monitoring key technical indicators, including robot positioning errors, material viscosity fluctuations, and paint supply pressure fluctuations; the intelligent equipment quality maintenance unit is used to perform operation and maintenance management of the spraying equipment, including checking the spray lubrication system, gas control system, and paint supply system of the spray gun.

[0015] A working method of a ship robot spraying intelligent equipment management platform enabled by a metaverse digital twin, comprising the following steps:

[0016] S1. Dynamic and static target recognition in the ship spraying area: In the dock working area, different categories are divided according to the state of the target entity, namely static targets and dynamic targets; according to the entity state, the operating target hull, spray feeding equipment, paint mixing equipment, and carrying equipment are static targets, and on-site staff and ship spraying robots in mobile operations are dynamic targets;

[0017] S2. Reconstruction of the target virtual physical model and the real-time sensing model, wherein the target virtual physical model is represented as a three-dimensional model of a static target in the spraying area of ​​the dock, and the real-time sensing model reflects the real-time data changes of dynamic targets in the spraying area of ​​the dock, covering the parameter feedback and response of the spraying intelligent equipment;

[0018] S3. Data fusion under operation status. The fusion process includes extracting data features, removing outliers and noise to complete data cleaning, converting different data and analyzing correlations; fusion data involves monitoring and analysis of specific data under operation status, including equipment spraying area range A i , spraying robot equipment accessibility A 可达 , spraying robot operation safety factor S i , Real-time monitoring capability 控制 , average spraying speed of spraying robot operation R 平均 , Maximum spraying speed R 最大 , Accuracy of equipment maintenance assessment prediction P 准度 , the actual predicted remote control efficiency of the spraying robot R 控制 , Theoretical maintenance time required for spraying equipment failure T 维修 2. Paint supply status of spraying operation in dock I j , Equipment's own operation response time parameter T 响应, static target data and dynamic target data are integrated, and data rows and columns are converted and stored; at the same time, a database containing mixed variables and basic parameter information is established, and the monitoring data and basic parameter information are horizontally fitted to form a comprehensive data set, in which each row contains mixed variable parameters and corresponding basic parameter data;

[0019] S4. Application of virtual spraying intelligent equipment management platform. The platform receives data from the data fusion system, maps the physical state and performance parameters of the actual spraying intelligent equipment to the platform's large screen, dynamically displays the robot's operation process, and realizes real-time monitoring and simulation of the equipment status. In addition, the platform automatically analyzes and generates deployment decisions for the spraying intelligent equipment based on the received data.

[0020] The step S2 specifically includes the following sub-steps:

[0021] S2.1 Control and drive the ship spraying robot, spraying feeding equipment, and paint mixing equipment into the dock;

[0022] S2.2 Information collection and processing of intelligent spraying equipment: In terms of obtaining spatial position and posture data in the robot equipment dock, the robot end-effector moving acceleration and spatial XYZ axis posture position are confirmed by installing an inertial measurement unit, and the robot equipment moving distance and ground moving position are confirmed by distance sensors. Based on the robot's operating distance and speed per unit time, the average spraying speed R of the theoretical spraying robot is automatically calculated. 平均 , Maximum spraying speed R 最大 Calculation; The robot telescopic arm angle and robot travel offset angle data are collected through the angle sensor, and the hull spraying image is collected through the visual sensor to detect the smoothness and color difference of the hull surface coating, and determine whether there is sagging and orange grain phenomenon. The color difference deviation range is 0-5°. According to the actual completion rate from the start of the operation to the end of the operation, the equipment operation efficiency ratio E is automatically calculated. fj ; Through the laser radar built into the equipment, the laser beam is continuously emitted and the reflected signal is received to complete the three-dimensional point cloud data collection of the surrounding environment in the dock;

[0023] In terms of environmental and spraying data collection, the temperature of the paint surface after spraying is measured by a temperature sensor to obtain the paint curing temperature data. At the same time, the external environmental temperature data is collected. The humidity sensor is used to detect the working environmental humidity conditions, and the environmental parameters are converted. The system compares and judges to obtain the equipment working environment adaptability E 环境 When the robot is spraying the outer surface of the hull, the three-dimensional spatial position of the small area to be sprayed is calibrated through the binocular camera, the distance between the spray robot and the hull surface is detected, the height and spray angle of the nozzle are adjusted, and the accessibility of the spray robot equipment is determined according to the size of the space in the dock and the actual volume parameters of the spray equipment.可达 , used to define the robot's working range; use total stations and three-dimensional laser scanning equipment to complete the collection of spray equipment and hull curvature parameters, including setting multiple calibration points for the equipment to be measured, measuring the coordinates, distance and angle data of each calibration point, and transmitting the measured spray equipment and hull parameters to the dock information storage device in real time. Based on the standard safety distance of spraying operations in the dock, the safety factor of the spray robot operation is verified;

[0024] S2.3 Build a multi-physics model and complete the solid-liquid multi-physics simulation: obtain point cloud data and perform noise reduction and smoothing, use reconstruction algorithm to convert the processed ship spray robot and ship point cloud into a computable geometric model, reconstruct it into a mesh, import the geometric model into the simulation software, call the k-ε turbulence model, heat conduction model and multiphase flow model to obtain a three-dimensional physical model; define the properties of paint, air, and spray hull wall, set the air speed and temperature of the robot spray gun, and complete the spray boundary condition setting; set the spray robot paint spray flow rate and solid properties, set the solution to steady state, set the number of multi-physics model iterations, start the model solution, monitor the residuals and key parameters, and ensure convergence;

[0025] S2.4 Determine whether the simulation results are reasonable: First, compare the simulation results with the calculated results by comparing the theoretical values, and compare the coating deposition thickness distribution data of the spray robot, the size of the flow field change parameters on the hull surface and the sensitivity index; further change the paint viscosity and flow rate to verify the validity of the simulation results, and calculate the confidence interval of the spray film thickness output results to determine whether the model's prediction of the thickness is reasonable; if the result exceeds the confidence interval, perform data correction and re-perform the coupled simulation; if it falls within the confidence interval, complete the simulation;

[0026] S2.5 completes the twinning of the virtual model of the intelligent spraying equipment, and optimizes and improves the model through the operating parameters of the multi-physical field combined with the on-site spraying quality parameters. The specific optimization methods include: optimizing the nozzle's spraying speed, paint input and output pressures, and paint temperature data during spraying based on the on-site data in the dock and the spraying simulation results of different areas of the hull, adjusting the paint and coating ratio parameters, and the spray robot nozzle exit angle data.

[0027] The step S4 specifically includes the following sub-steps:

[0028] S4.1 Design the coating scheme for the target construction ship in the dock, clarify the requirements for coating materials and spraying processes in different areas and parts of the ship, clarify the value of the area to be sprayed in the rib area of ​​the hull, control the thickness of the dry film and wet film, and set the detailed robot spraying trajectory change range; conduct a survey on the dock spraying environment, master the parameter information of the working space size, and set the equipment deployment target according to the data parameters collected by the perception tool layer;

[0029] S4.2 Evaluate the performance of the ship robot, including the work efficiency coefficient and spraying efficiency, and use the preset standards for preliminary calculation and screening; establish the spraying robot equipment efficiency evaluation formula:

[0030] (1)

[0031] Among them, E j represents the working efficiency coefficient of the j-th type of spraying robot, R 平均 represents the theoretical average spraying speed of the spraying robot, R 最大 Indicates the theoretical maximum spraying speed of the spraying robot, E fj Indicates the actual operating efficiency ratio during the operation, including equipment failure rate and downtime;

[0032] Select the type of spraying robot according to the division of spraying tasks and the deployment plan of space equipment in the dock, and collect and incorporate the key parameters of the spraying equipment into the system, including the temperature resistance of the coating, viscosity, corrosion resistance of the equipment, and environmental suitability, and use the efficiency evaluation formula for analysis;

[0033] S4.3 Evaluate the spraying operation environment of the dock and establish the environmental adaptability evaluation formula of the spraying robot equipment:

[0034] (2)

[0035] Among them, S ij It represents the environmental adaptability score, reflecting the ability of the j-type equipment to withstand specific temperature and humidity at the operating position i. 耐腐蚀 Indicates the corrosion resistance of spray equipment, T 耐温 Indicates the temperature resistance of the spraying equipment, E 环境 Indicates the adaptability to the working environment;

[0036] Environmental assessment analyzes the suitability of the pre-selected spraying stations to ensure that there is enough space around the spraying robot and spraying feeding equipment for spraying operations, and the impact of air flow on the spraying effect needs to be considered; establish a physical space assessment formula:

[0037] (3)

[0038] Among them, F P (i) represents the physical environment factors of the spraying location i, including the space size in the dock and the safety distance requirements, A i Indicates the spraying area range of the spraying robot equipment, A 参考 Represents the spraying reference area, which is used to standardize the area impact of different areas. 可达 represents the accessibility of the spraying robot equipment, S iIndicates the safety factor of the spray robot operation, based on the compliance level of the standard safety distance;

[0039] The mapping value of the virtual spraying equipment model during the operation process is analyzed, including the difficulty of operation, maintenance, control, and monitoring of the spraying operation station, and a mapping efficiency evaluation formula is established:

[0040] (4)

[0041] Among them, V ij It represents the mapping value of data and measures the gain brought by the digital twin of the device in the virtual space in terms of monitoring, predictive maintenance, and remote control. 控制 represents the monitoring capability of real-time operations, P 准度 Indicates the accuracy of predicting equipment maintenance needs, R 控制 It indicates the actual predicted remote control efficiency of the painting robot;

[0042] S4.4 analyzes the operation and maintenance efficiency of spraying equipment, considers equipment maintenance supply and technical staff response, simulates the optimal work path and process under the premise of different spraying equipment allocation layout, and establishes the operation and maintenance evaluation formula of spraying robot equipment:

[0043] (5)

[0044] Among them, M ij represents the maintenance convenience index of spraying equipment, which measures the convenience of maintaining the j-th type of spraying robot at dock location i, T 维修 Indicates the theoretical maintenance time required for spray equipment failure. j Indicates the paint supply status, T 响应 Indicates the robot's own operation response time;

[0045] S4.5 completes the evaluation of the deployment efficiency of the spraying intelligent equipment according to step S4.4, and helps the decision maker optimize the deployment strategy of the spraying robot through quantitative analysis;

[0046] The evaluation calculation process includes physical space layout, equipment function, operation efficiency, environmental factors and mapping relationship with virtual space; under ideal working conditions, the operation allocation efficiency evaluation formula of the ship robot spraying intelligent equipment working platform is established:

[0047] (6)

[0048] Right now: (7)

[0049] Among them, D(i,j) represents the efficiency score of deploying the jth type of spraying robot at position i; if the score is less than the set threshold, it needs to be revised and improved, and re-evaluated and calculated; if the score is greater than or equal to the set threshold, the deployment allocation requirements are met, and the on-site operators deploy the operating equipment in the dock according to the evaluation results given by the intelligent management platform and the location zoning indicated by the platform, and input the given operating parameters to perform the operation;

[0050] S4.6 The staff remotely controls the operation of the spraying robot through the interactive remote control device, and displays the spraying operation process through the VR visual display device. The spraying speed, paint thickness, equipment operating temperature, spray flow rate, and the body temperature of the workers are displayed in real time on the display device. The spraying results are predicted based on the obtained spraying speed, paint thickness, equipment operating temperature, and spray flow rate information, and the operation status is monitored; if the data result fluctuation range exceeds 0.1% and is an abnormal state, the equipment data is corrected and the spraying tasks and plans are re-formulated; if the detection fluctuation range is less than 0.1% and is normal, background data analysis is performed to provide equipment allocation and process optimization suggestions for subsequent spraying operations.

[0051] Beneficial effects: The present invention empowers the ship spraying industry through the Metaverse digital twin, builds a ship robot spraying intelligent equipment management platform, and realizes efficient management, intelligent monitoring, predictive maintenance and safety compliance verification of spraying operations. The present invention reduces manual participation, replaces manual analysis and decision-making, greatly improves operation efficiency and quality, reduces operating costs, and promotes the intelligent upgrade of the shipbuilding industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 An architecture diagram of a ship robot spraying intelligent equipment management platform enabled by a metaverse digital twin provided for an example of the present invention.

[0053] Figure 2 An operation flow chart of a ship robot spraying intelligent equipment management platform enabled by a metaverse digital twin is provided as an example of the present invention.

[0054] Figure 3 A flow chart of a modeling method for a ship robot spraying intelligent equipment management platform enabled by a metaverse digital twin is provided as an example of the present invention.

[0055] Figure 4 A metaverse digital twin-enabled ship robot spraying intelligent equipment management platform spraying intelligent equipment deployment decision flow chart is provided for an example of the present invention.

[0056] Figure 5 A visualization interface of a ship robot spraying intelligent equipment management platform enabled by a metaverse digital twin is provided for an example of the present invention. DETAILED DESCRIPTION

[0057] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific implementation modes.

[0058] Figure 1 A system architecture of a ship robot spraying intelligent equipment management platform enabled by a metaverse digital twin provided in an embodiment of the present invention, the architecture comprising: a dock work scene layer, a perception tool layer, a digital twin layer, and an intelligent equipment management layer;

[0059] The dock work scene layer includes: staff, ship spraying robots, ship spraying equipment, and dock operation areas. Staff are located in the dock and are mainly responsible for the operation and maintenance of spraying equipment, and the completion of spraying tasks; ship spraying robots use mechanization and intelligence to replace manual high-altitude spraying, which can realize manual remote operation and unmanned operation. It is an important basic equipment in the spraying intelligent equipment management platform; ship spraying equipment includes ship paint feeding system, ship paint mixing system, paint can opening equipment and other auxiliary equipment supporting ship robots; the dock operation area includes key elements such as the environmental layout of the construction dock area and the target construction ship entity.

[0060] The perception tool layer includes: sensor equipment, cameras, 3D laser scanners, communication equipment, interface equipment, data processing equipment, etc. Through various sensor equipment, the relative moving position and moving speed of dynamic targets such as on-site staff and spray robots are obtained, and the obtained parameters are further transmitted to the data processing equipment in real time to further complete the spraying operation planning, coating material control, data transmission and interface protocol conversion. Sensor equipment, cameras, and 3D laser scanners are mainly used for entity parameter collection and parameter mapping functions, including ship robot swing angle, equipment displacement distance, operating environment temperature, ship shape and size and other parameters; communication equipment, interface equipment, and data processing equipment are mainly used to complete the spraying operation equipment process supervision, equipment data transmission and remote control, and track the operating status and parameters of ship spraying equipment.

[0061] The digital twin layer includes: twin working parameter unit, twin spraying intelligent equipment virtual model unit, and twin dock operating environment unit. The twin working parameter unit further completes data analysis and management within the system; the twin spraying intelligent equipment virtual model unit mainly simulates and models the structure, performance, and operation of the spraying equipment. The system completes the fusion of multiple models and completes the model correction; the twin dock operating environment unit needs to simulate the dock spraying operating environment, including the structure of the ship, the layout of the spraying equipment, space restrictions and other factors, and complete the environmental simulation configuration. The models and data established by each unit can share and interact with other units.

[0062] The intelligent equipment management layer applies model parameters according to the digital twin results, including: spray intelligent equipment optimization unit, spray intelligent equipment station allocation unit, intelligent equipment operation detection unit, and intelligent equipment quality maintenance unit. The spray intelligent equipment optimization unit uses the digital twin results and model parameters to optimize and improve the spray angle, spray robot operation speed, spray path selection, paint configuration ratio, etc. during the spraying process; the spray intelligent equipment station allocation unit is responsible for allocating the ship spray robot group, formulating detailed station layout strategies, and optimizing the operation process; the intelligent equipment operation detection unit is responsible for monitoring key technical indicators, such as robot positioning error, material viscosity fluctuation, paint supply pressure fluctuation, etc., to ensure the safe and efficient operation of the equipment; the intelligent equipment quality maintenance unit mainly performs operation and maintenance management of the spray equipment, including checking the spray lubrication system, gas control system, paint supply system, etc. of the spray gun to ensure the normal working state of the spray gun.

[0063] Figure 2 The workflow diagram of a ship robot spraying intelligent equipment management platform enabled by a metaverse digital twin provided by the present invention includes: dynamic and static target recognition in the ship spraying area, reconstruction of the target virtual physical model and real-time sensing model, data integration under the operating state, and application of the virtual spraying intelligent equipment management platform. The working method specifically includes the following steps:

[0064] S1. Dynamic and static target recognition in the ship spraying area is to classify different categories according to the state of the target entity in the dock working area, namely static targets and dynamic targets. According to the entity state, the operating target hull, spray feeding equipment, paint mixing equipment, carrying equipment, etc. are static targets, and on-site staff, ship spraying robots in mobile operations, etc. are dynamic targets.

[0065] S2. The reconstruction of the target virtual physical model and real-time sensing model is to collect data and map the system of different targets through sensor equipment, cameras, 3D laser scanners and other equipment, quickly scan the target operating ship and ship spraying equipment through the 3D laser scanner, obtain the point cloud data of the geometry, shape and structure of the static target, upload it to the database through the communication equipment, and import it into the 3D software after preprocessing the data. Through a variety of sensor equipment, the information state perception and capture of the dynamic target posture, position, etc. is completed, and the data is uploaded. After completing the model comparison of the target transformation part, determine the part that needs to be adjusted and reconstructed, and apply the model reconstruction algorithm to complete the update and optimization of the model.

[0066] S3. Data integration under the operating state, in order to present the overall operating scene of the ship spraying intelligent equipment, realize the global data analysis of the spraying process, optimize the relative accuracy of decision-making and prediction, and further integrate the data-driven whole under the spraying operation state. The fusion data involves the monitoring and analysis of specific data under the operating state, including the equipment spraying area range A i , spraying robot equipment accessibility A 可达 , spraying robot operation safety factor S i , Real-time monitoring capability 控制 , average spraying speed of spraying robot operation R 平均 , Maximum spraying speed R 最大 , Accuracy of equipment maintenance assessment prediction P 准度 , the actual predicted remote control efficiency of the spraying robot R 控制 , Theoretical maintenance time required for spraying equipment failure T 维修 2. Paint supply status of spraying operation in dock I j , Equipment's own operation response time parameter T 响应 , static target data and dynamic target data are integrated, and the data are stored after row-column conversion; at the same time, a database containing mixed variables and basic parameter information is established, and the monitoring data and basic parameter information are horizontally fitted to form a comprehensive data set, in which each row contains mixed variable parameters and corresponding basic parameter data.

[0067] S4. The application of the virtual spraying intelligent equipment management platform sends the fused data to the virtual spraying intelligent equipment management platform for data model display and analysis and evaluation. Remote collaborative monitoring of the above-mentioned spraying equipment can also be achieved through VR interactive equipment, and on-site staff wear VR visual equipment to control the on-site spraying operation status. The command transmission to the spraying equipment is completed through the interactive remote control device, and the equipment operation is controlled. The data and interface of the virtual spraying intelligent equipment management platform provide real-time feedback on the operation process and equipment status, including model accuracy, spraying error rate, environmental adaptability and spraying efficiency, which are visualized through VR visual equipment.

[0068] Figure 3 The flowchart for reconstructing the target virtual physical model and the real-time sensing model in the above step S2 includes:

[0069] S2.1 Control and drive the ship spraying robot, spraying feeding equipment, paint mixing equipment, etc. into the dock;

[0070] S2.2 Information collection and processing of intelligent spraying equipment: In terms of obtaining spatial position and posture data in the robot equipment dock, the robot end-effector moving acceleration and spatial XYZ axis posture position are confirmed by installing an inertial measurement unit, and the robot equipment moving distance and ground moving position are confirmed by distance sensors. Based on the robot's operating distance and speed per unit time, the average spraying speed R of the theoretical spraying robot is automatically calculated. 平均 , Maximum spraying speed R 最大 Calculation; The robot telescopic arm angle and robot travel offset angle data are collected through the angle sensor, and the hull spraying image is collected through the visual sensor to detect the smoothness and color difference of the hull surface coating, and determine whether there is sagging and orange grain phenomenon. The color difference deviation range is 0-5°. According to the actual completion rate from the start of the operation to the end of the operation, the equipment operation efficiency ratio E is automatically calculated. fj ; Through the laser radar built into the equipment, the laser beam is continuously emitted and the reflected signal is received to complete the three-dimensional point cloud data collection of the surrounding environment in the dock;

[0071] In terms of environmental and spraying data collection, the temperature of the paint surface after spraying is measured by a temperature sensor to obtain the paint curing temperature data. At the same time, the external environmental temperature data is collected. The humidity sensor is used to detect the working environmental humidity conditions. The above environmental parameters are converted and the system compares and judges to obtain the equipment working environment adaptability E. 环境 When the robot is spraying the outer surface of the hull, the three-dimensional spatial position of the small area to be sprayed is calibrated through the binocular camera, the distance between the spray robot and the hull surface is detected, the height and spray angle of the nozzle are adjusted, and the accessibility of the spray robot equipment is determined according to the size of the space in the dock and the actual volume parameters of the spray equipment. 可达 , used to define the robot's working range; use total stations and three-dimensional laser scanning equipment to complete the collection of spray equipment and hull curvature parameters, including setting multiple calibration points for the equipment to be measured, measuring the coordinates, distance and angle data of each calibration point, and transmitting the measured spray equipment and hull parameters to the dock information storage device in real time. Based on the standard safety distance of spraying operations in the dock, the safety factor of the spray robot operation is verified;

[0072] S2.3 builds a physical field model to complete the solid-liquid multi-physics field simulation; obtains point cloud data and performs noise reduction and smoothing, reconstructs the processed ship spray robot and ship data into mesh, and further obtains a three-dimensional model; imports the geometric model into the simulation software, calls the k-ε turbulence model, heat conduction model and multiphase flow model to obtain a three-dimensional physical model; defines the properties of paint, air, and spray hull wall, sets the air speed and temperature of the robot spray gun, and completes the spray boundary condition setting; sets the spray robot paint spray velocity and solid properties, sets the solution to steady state, sets the number of multi-physics field model iterations, starts the model solution, monitors the residuals and key parameters, and ensures convergence; S2.4 Determine whether the simulation results are reasonable; first, compare the simulation results with the calculated results by comparing the theoretical values, compare the deposition thickness distribution data of the spray robot hull coating, the size of the hull surface flow field change parameters and the sensitivity index, and confirm the rationality; further change the paint viscosity and flow rate, verify the validity of the simulation results, and determine the robustness of the model; for the output results of the spray film thickness, calculate its confidence interval to determine whether the model's prediction of the thickness is reasonable; if the result exceeds the confidence interval, perform data correction and re-perform the coupled simulation; if it falls within the confidence interval, complete the simulation;

[0073] S2.5 completes the twinning of the virtual model of the spraying intelligent equipment, and optimizes and improves the model through the operation parameters of the multi-physical field combined with the on-site spraying quality parameters. The specific optimization methods include: optimizing the spraying speed of the nozzle, the input and output pressure of the paint, and the paint temperature data during spraying according to the on-site data in the dock and the spraying simulation results of different areas of the hull, adjusting the paint ratio parameters, and the spray robot nozzle exit angle data.

[0074] Figure 4 The above step S4 is a decision flow chart for the deployment of the spraying intelligent equipment, which includes:

[0075] S4.1 Design a coating scheme for the target construction ship in the dock, clarify the requirements for coating materials and spraying processes in different areas and parts of the ship, clarify the numerical value of the area to be sprayed in the rib area of ​​the hull, control the thickness of the dry and wet film, and set the detailed robot spraying trajectory change range; conduct a survey on the spraying environment of the dock, master the parameter information of the working space size, and set the equipment deployment target according to the data parameters collected by the perception tool layer. According to the different spraying parts and different usage requirements of the target construction ship, select reasonable coating varieties to formulate supporting plans to ensure that all coating varieties, colors, specifications and models meet the coating technical requirements before painting, and that the spraying robots and other equipment have arrived at the designated area and are constructed in accordance with the requirements of the instructions;

[0076] S4.2 Evaluate the performance of intelligent spraying equipment such as ship robots. The evaluation scope includes data such as work efficiency coefficient and spraying efficiency. Use preset standards for preliminary calculation and screening; establish the efficiency evaluation formula of spraying robot equipment:

[0077] (1)

[0078] Among them, E j R represents the working efficiency coefficient of the jth type of spraying robot. Considering the differences in the operating capabilities of robots of different models or technical specifications under the same conditions, 平均 represents the theoretical average spraying speed of the spraying robot, R 最大 Indicates the theoretical maximum spraying speed of the spraying robot, E fj Indicates the actual operating efficiency ratio during the operation process, including factors such as equipment failure rate and downtime; selects a suitable spraying robot based on the detailed spraying task division and equipment deployment plan, collects the working parameters of the spraying equipment, and uses the efficiency evaluation formula for analysis;

[0079] S4.3 Evaluate the spraying operation environment of the dock and establish the environmental adaptability evaluation formula of the spraying robot equipment:

[0080] (2)

[0081] Among them, S ij It represents the environmental adaptability score, reflecting the ability of the j-type equipment to withstand specific temperature and humidity at the operating position i. 耐腐蚀 Indicates the corrosion resistance of spray equipment, T 耐温 Indicates the temperature resistance of the spraying equipment, E 环境 Indicates the adaptability of the working environment. During the environmental assessment process, the focus is on analyzing the suitability of the pre-selected spraying stations. It is necessary to ensure that there is enough space around the spraying robot and the spraying feeding equipment for spraying operations to avoid collisions with the outer surface of the ship and other dock equipment. At the same time, it is also necessary to consider the impact of air flow on the spraying effect; establish a physical space assessment formula:

[0082] (3)

[0083] Among them, F P (i) represents the physical environment factors of the spraying location i, including the space size in the dock, safety distance requirements, etc., A i Indicates the spraying area range of the spraying robot equipment, A 参考 Represents the spraying reference area, which is used to standardize the area impact of different areas. 可达 represents the accessibility of the spraying robot equipment, S i Indicates the safety factor of the spraying robot, based on the degree of compliance with the standard safety distance;

[0084] The mapping value of the virtual model in the operation process is analyzed, including the difficulty of operation, maintenance, control, and monitoring of the spraying operation station, and a mapping efficiency evaluation formula is established:

[0085] (4)

[0086] Among them, V ij It represents the mapping value of data and measures the gain brought by the digital twin of the device in virtual space in terms of monitoring, predictive maintenance, remote control, etc. 控制 represents the monitoring capability of real-time operations, P 准度 Indicates the accuracy of predicting equipment maintenance needs, R 控制 It indicates the actual predicted remote control efficiency of the painting robot;

[0087] S4.4 analyzes the operation and maintenance efficiency of spraying equipment, considers equipment maintenance supply, technical staff response, etc., simulates the optimal work path and process under the premise of different equipment allocation layout, and establishes the operation and maintenance evaluation formula of spraying robot equipment:

[0088] (5)

[0089] Among them, M ij represents the maintenance convenience index of spraying equipment, which measures the convenience of maintaining the j-th type of spraying robot at dock location i, taking into account factors such as spare parts acquisition and professional accessibility. 维修 Indicates the theoretical maintenance time required for spray equipment failure. j Indicates the paint supply status, T 响应 Indicates the robot's own operation response time;

[0090] S4.5 According to step S4.4, the evaluation of the deployment efficiency of the spraying intelligent equipment is completed, and the decision makers are helped to optimize the deployment strategy of the spraying robot through quantitative analysis; the evaluation calculation process includes the physical space layout, equipment function, operation efficiency, environmental factors and the mapping relationship with the virtual space; under the ideal working state, the evaluation formula of the operation allocation efficiency of the ship robot spraying intelligent equipment working platform is established:

[0091] (6)

[0092] In summary, the calculation formula for evaluating the operation allocation efficiency of ship robot spraying intelligent equipment is:

[0093] (7)

[0094] Where D(i,j) represents the efficiency score of arranging the j-th type of spraying robot at position i, and the evaluation level is referred to Table 1;

[0095] Table 1 Equipment operation allocation efficiency level

[0096]

[0097] If the result value is ≤300, it is poor; if the final evaluation result value is between 301 and 1000, it is medium; if the evaluation value is between 1001 and 2000, it is good; if the value is ≥2001, it is excellent; if the evaluation result is poor or medium, the deployment allocation plan does not meet the requirements and needs to be revised and improved, and re-evaluated and calculated; if the evaluation result is good or excellent, the deployment allocation requirements are met, and the decision-making deployment of intelligent spraying intelligent equipment is carried out;

[0098] S4.6 monitors actual spray flow, spray thickness, spray efficiency and other parameters. If any abnormality occurs, the data of the spray equipment will be corrected, and the spraying tasks will be divided and the targets will be set again. If the data is normal, the operation and maintenance of the intelligent spray equipment will be optimized based on the data obtained, and further process improvement plans will be given to optimize parameter indicators and improve operating efficiency.

[0099] In one embodiment of this patent, a spraying robot and other equipment are used to perform ship touch-up spraying operations in a dock. The operation area is a certain area of ​​the freeboard of a certain type of ship, and the robot model is selected as JS1008. The specific parameter values ​​of the operation evaluation are: A i =40㎡, A 可达 =9, A 参考 =50㎡, S i =8, R 平均 =420㎡ / h, R 最大 =500㎡ / h, E fj =0.8, T 维修 =1h, I j =0.9, T 响应 =0.2h, C 耐腐蚀 =0.8, T 耐温 =0.9, E 环境 =0.8,M 控制 =9,P 准度 =0.8, R 控制 =0.9;

[0100] The numerical calculation process of robot operation efficiency evaluation is as follows:

[0101] According to the equipment operation allocation efficiency evaluation index, the calculated result value belongs to the range of 1001-2000, and the operation allocation efficiency of this embodiment is good. After comparing the detailed analysis results, the construction personnel can adjust and optimize the operation process to improve the operation efficiency.

[0102] Figure 5 A visualization interface of a ship robot spraying intelligent equipment management platform enabled by a metaverse digital twin is provided in an embodiment of the present invention, and the interface includes: a spraying intelligent equipment information statistics module, a virtual model status winter display module, and an efficiency level display module.

[0103] The information statistics module of intelligent spraying equipment includes the distribution of the operating status of the spraying equipment, which can display the three states of operation, operation and maintenance, and failure; the paint consumption status display mainly displays the statistics of the consumption of the spraying main agent, diluent, and curing agent, which helps to grasp the material information; the distribution of the number and location of the spraying equipment can display the model of the ship spraying robot, the number and area of ​​the feeding system, and the paint supply system. The virtual model status winter display module includes the operating target ship status, the working status of the spraying robot, and the status of the operator, and uses a three-dimensional model to display the specific parameter information in detail. The efficiency level display module displays the different energy efficiency levels of the spraying equipment and gives specific evaluation parameters.

[0104] 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. A ship robot spraying intelligent equipment management platform enabled by Metaverse digital twin, characterized by: The platform includes dock work scene layer, perception tool layer, digital twin layer and intelligent equipment management layer; The dock work scene layer includes: staff, ship spraying robots, ship spraying equipment, and dock operation areas; the ship spraying robot is manually remotely operated to perform high-altitude spraying, and is the basic equipment in the spraying intelligent equipment management platform; the ship spraying equipment includes a ship paint feeding system, a ship paint mixing system, a paint can opening device, and auxiliary equipment supporting the ship robot; the dock operation area includes the environmental layout and target construction ship entity in the construction dock area; The perception tool layer includes: sensor equipment, cameras, 3D laser scanners, communication equipment, interface equipment, and data processing equipment, which are used to perform data collection, perception and mapping of entities in the dock work scene layer, and further complete spraying operation planning, coating material control, data transmission, and interface protocol conversion; The sensor equipment, camera, and 3D laser scanner are used for entity parameter acquisition and parameter mapping, including the ship robot swing angle, equipment displacement distance, operating environment temperature, and ship shape and size; the communication equipment, interface equipment, and data processing equipment are used for data storage, data transmission, and interface protocol conversion, and for spraying equipment process supervision, equipment data transmission, and remote control; tracking the operating status and parameters of ship spraying equipment; The digital twin layer includes: a twin working parameter unit, a twin spraying intelligent equipment virtual model unit, and a twin dock operating environment unit; The twin working parameter unit performs data analysis and management within the system; the twin spraying intelligent equipment virtual model unit simulates and models the structure, performance and operation of the spraying equipment, and the system completes the fusion of multiple models and completes the model correction; the twin dock operation environment unit simulates the dock spraying operation environment, including the structure of the ship, the layout of the spraying equipment, and the space restrictions, and completes the environmental simulation configuration; The intelligent equipment management layer applies model parameters according to the digital twin results, including a spraying intelligent equipment optimization unit, a spraying intelligent equipment station allocation unit, an intelligent equipment operation detection unit, and an intelligent equipment quality maintenance unit; The spraying intelligent equipment optimization unit uses digital twin results and model parameters to optimize and improve the spraying angle, spray robot running speed, spray path selection, and paint configuration ratio during the spraying process; the spraying intelligent equipment workstation allocation unit allocates the ship spraying robot group, formulates the workstation layout strategy, and optimizes the operation process; the intelligent equipment operation detection unit is responsible for monitoring key technical indicators, including robot positioning error, material viscosity fluctuation, and paint supply pressure fluctuation; the intelligent equipment quality maintenance unit is used to perform operation and maintenance management of the spraying equipment, including checking the spray lubrication system, gas control system, and paint supply system of the spray gun.

2. According to claim 1, the working method of a ship robot spraying intelligent equipment management platform enabled by a metaverse digital twin is characterized in that: The following steps are involved: S1. Dynamic and static target recognition in the ship spraying area: In the dock working area, different categories are divided according to the state of the target entity, namely static targets and dynamic targets; according to the entity state, the operating target hull, spray feeding equipment, paint mixing equipment, and carrying equipment are static targets, and on-site staff and ship spraying robots in mobile operations are dynamic targets; S2. Reconstruction of the target virtual physical model and the real-time sensing model, wherein the target virtual physical model is represented as a three-dimensional model of a static target in the spraying area of ​​the dock, and the real-time sensing model reflects the real-time data changes of dynamic targets in the spraying area of ​​the dock, covering the parameter feedback and response of the spraying intelligent equipment; S3. Data fusion under operation status. The fusion process includes extracting data features, removing outliers and noise to complete data cleaning, converting different data and analyzing correlations; fusion data involves monitoring and analysis of specific data under operation status, including equipment spraying area range A i , spraying robot equipment accessibility A 可达 , spraying robot operation safety factor S i , Real-time monitoring capability 控制 , average spraying speed of spraying robot operation R 平均 , Maximum spraying speed R 最大 , Accuracy of equipment maintenance assessment prediction P 准度 , the actual predicted remote control efficiency of the spraying robot R 控制 , Theoretical maintenance time required for spraying equipment failure T 维修 2. Paint supply status of spraying operation in dock I j , Equipment's own operation response time parameter T 响应 , static target data and dynamic target data are integrated, and data rows and columns are converted and stored; at the same time, a database containing mixed variables and basic parameter information is established, and the monitoring data and basic parameter information are horizontally fitted to form a comprehensive data set, in which each row contains mixed variable parameters and corresponding basic parameter data; S4. Application of virtual spraying intelligent equipment management platform. The platform receives data from the data fusion system, maps the physical state and performance parameters of the actual spraying intelligent equipment to the platform's large screen, dynamically displays the robot's operation process, and realizes real-time monitoring and simulation of the equipment status. In addition, the platform automatically analyzes and generates deployment decisions for the spraying intelligent equipment based on the received data.

3. The working method of a ship robot spraying intelligent equipment management platform enabled by a metaverse digital twin according to claim 2 is characterized in that: The step S2 specifically includes the following sub-steps: S2.1 Control and drive the ship spraying robot, spraying feeding equipment, and paint mixing equipment into the dock; S2.2 Information collection and processing of intelligent spraying equipment: In acquiring the spatial position and posture data in the robot equipment dock, the inertial measurement unit is used to complete the movement acceleration of the robot end effector and the spatial XYZ axis posture position confirmation, and the distance sensor is used to complete the movement distance and ground movement position confirmation of the robot equipment. According to the robot operation distance and speed per unit time, the average spraying speed R of the theoretical spraying robot is automatically calculated. 平均 , Maximum spraying speed R 最大 Calculation; complete the robot telescopic arm angle and robot travel offset angle data collection through the angle sensor, collect the hull spraying image through the visual sensor, detect the smoothness and color difference of the hull surface coating, and judge whether there is sagging and orange grain phenomenon. The color difference deviation range is 0-5°; according to the actual completion rate from the start of the operation to the end of the operation, calculate the actual operation efficiency ratio E of the equipment during the operation fj ; Through the laser radar built into the equipment, the laser beam is continuously emitted and the reflected signal is received to complete the three-dimensional point cloud data collection of the surrounding environment in the dock; In terms of environmental and spraying data collection, the temperature of the paint surface after spraying is measured by a temperature sensor to obtain the paint curing temperature data. At the same time, the external environmental temperature data is collected. The humidity sensor is used to detect the working environmental humidity conditions, and the environmental parameters are converted. The system compares and judges to obtain the equipment working environment adaptability E 环境 When the robot is spraying the outer surface of the hull, the three-dimensional spatial position of the small area to be sprayed is calibrated through the binocular camera, the distance between the spray robot and the hull surface is detected, the height and spray angle of the nozzle are adjusted, and the accessibility of the spray robot equipment is determined according to the size of the space in the dock and the actual volume parameters of the spray equipment. 可达 , used to define the robot's working range; use total stations and three-dimensional laser scanning equipment to complete the collection of spray equipment and hull curvature parameters, including setting multiple calibration points for the equipment to be measured, measuring the coordinates, distance and angle data of each calibration point, and transmitting the measured spray equipment and hull parameters to the dock information storage device in real time. Based on the standard safety distance of spraying operations in the dock, the safety factor of the spray robot operation is verified; S2.3 Build a multi-physics model and complete the solid-liquid multi-physics simulation: obtain point cloud data and perform noise reduction and smoothing, use reconstruction algorithm to convert the processed ship spray robot and ship point cloud into a computable geometric model, reconstruct it into a mesh, import the geometric model into the simulation software, call the k-ε turbulence model, heat conduction model and multiphase flow model to obtain a three-dimensional physical model; define the properties of paint, air, and spray hull wall, set the air speed and temperature of the robot spray gun, and complete the spray boundary condition setting; set the spray robot paint spray flow rate and solid properties, set the solution to steady state, set the number of multi-physics model iterations, start the model solution, monitor the residuals and key parameters, and ensure convergence; S2.4 Determine whether the simulation results are reasonable: First, compare the simulation results with the calculated results by comparing the theoretical values, and compare the coating deposition thickness distribution data of the spray robot, the size of the flow field change parameters on the hull surface and the sensitivity index; further change the paint viscosity and flow rate to verify the validity of the simulation results, and calculate the confidence interval of the spray film thickness output results to determine whether the model's prediction of the thickness is reasonable; if the result exceeds the confidence interval, perform data correction and re-perform the coupled simulation; if the result falls within the confidence interval, complete the simulation; S2.5 completes the twinning of the virtual model of the intelligent spraying equipment, and optimizes and improves the model through the operating parameters of the multi-physical field combined with the on-site spraying quality parameters. The specific optimization methods include: optimizing the nozzle's spraying speed, paint input and output pressures, and paint temperature data during spraying based on the on-site data in the dock and the spraying simulation results of different areas of the hull, adjusting the paint and coating ratio parameters, and the spray robot nozzle exit angle data.

4. The working method of a ship robot spraying intelligent equipment management platform enabled by a metaverse digital twin according to claim 2 is characterized in that: The step S4 specifically includes the following sub-steps: S4.1 Design the coating scheme for the target construction ship in the dock, clarify the requirements for coating materials and spraying processes in different areas and parts of the ship, clarify the value of the area to be sprayed in the hull rib area, control the thickness of the dry film and wet film, and set the detailed robot spraying trajectory change range; conduct a survey on the dock spraying environment, determine the parameter information of the working space size, and set the equipment deployment target according to the data parameters collected by the perception tool layer; S4.2 Evaluate the performance of the ship robot, including the work efficiency coefficient and spraying efficiency, and use the preset standards for calculation and screening; establish the spraying robot equipment efficiency evaluation formula: （1） Among them, E j represents the working efficiency coefficient of the j-th type of spraying robot, R 平均 represents the theoretical average spraying speed of the spraying robot, R 最大 Indicates the theoretical maximum spraying speed of the spraying robot, E fj Indicates the actual operating efficiency ratio during the operation; Select the type of spraying robot according to the division of spraying tasks and the deployment plan of space equipment in the dock, and collect and incorporate the key parameters of the spraying equipment into the system. The key parameters include the temperature resistance of the coating, viscosity, corrosion resistance of the equipment, and environmental suitability, and analyze them using the efficiency evaluation formula; S4.3 Evaluate the spraying operation environment of the dock and establish the environmental adaptability evaluation formula of the spraying robot equipment: （2） Among them, S ij It represents the environmental adaptability score, reflecting the ability of the j-type equipment to withstand specific temperature and humidity at the operating position i. 耐腐蚀 Indicates the corrosion resistance of spray equipment, T 耐温 Indicates the temperature resistance of the spraying equipment, E 环境 Indicates the adaptability to the working environment; Environmental assessment analyzes the suitability of the pre-selected spraying stations to ensure that there is enough space around the spraying robot and spraying feeding equipment for spraying operations, and the impact of air flow on the spraying effect needs to be considered; establish a physical space assessment formula: （3） Among them, F P (i) represents the physical environment factors of the spraying location i, including the space size in the dock and the safety distance requirements, A i Indicates the spraying area range of the spraying robot equipment, A 参考 Represents the spraying reference area, which is used to standardize the area impact of different areas. 可达 represents the accessibility of the spraying robot equipment, S i Indicates the safety factor of the spray robot operation, based on the compliance level of the standard safety distance; The mapping value of the virtual spraying equipment model during the operation process is analyzed, including the difficulty of operation, maintenance, control, and monitoring of the spraying operation station, and a mapping efficiency evaluation formula is established: （4） Among them, V ij It represents the mapping value of data and measures the gain brought by the digital twin of the device in the virtual space in terms of monitoring, predictive maintenance, and remote control. 控制 represents the monitoring capability of real-time operations, P 准度 Indicates the accuracy of predicting equipment maintenance needs, R 控制 It indicates the actual predicted remote control efficiency of the painting robot; S4.4 analyzes the operation and maintenance efficiency of spraying equipment, considers equipment maintenance supply and technical staff response, simulates the optimal work path and process under the premise of different spraying equipment allocation layout, and establishes the operation and maintenance evaluation formula of spraying robot equipment: （5） Among them, M ij represents the maintenance convenience index of spraying equipment, which measures the convenience of maintaining the j-th type of spraying robot at dock location i, T 维修 Indicates the theoretical maintenance time required for spray equipment failure. j Indicates the paint supply status, T 响应 Indicates the robot's own operation response time; S4.5 completes the evaluation of the deployment efficiency of the spraying intelligent equipment according to step S4.4, and helps the decision maker optimize the deployment strategy of the spraying robot through quantitative analysis; The evaluation calculation process includes physical space layout, equipment function, operation efficiency, environmental factors and mapping relationship with virtual space; under ideal working conditions, the operation allocation efficiency evaluation formula of the ship robot spraying intelligent equipment working platform is established: （6） Right now: (7) Among them, D(i,j) represents the efficiency score of deploying the j-th type of spraying robot at position i; if the score is less than the set threshold, it needs to be revised and improved, and re-evaluated and calculated; if the score is greater than or equal to the set threshold, the deployment allocation requirements are met, and the on-site operators deploy the operating equipment in the dock according to the evaluation results given by the intelligent management platform and the location zoning indicated by the platform, and input the given operating parameters to perform the operation; S4.6 The staff remotely controls the operation of the spraying robot through the interactive remote control device, and displays the spraying operation process through the VR visual display device. The spraying speed, paint thickness, equipment operating temperature, spray flow rate, and the body temperature of the workers are displayed in real time on the display device. The spraying results are predicted based on the obtained spraying speed, paint thickness, equipment operating temperature, and spray flow rate information, and the operating status is monitored; if the data result fluctuation range exceeds 0.1% and is an abnormal state, the equipment data is corrected and the spraying task and plan are re-formulated; if the detection fluctuation range is less than 0.1% and is normal, background data analysis is performed.

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