Pipeline inner wall spraying simulation control method and device based on digital twinning

Abstract

The embodiment of the invention provides a pipeline inner wall spraying simulation control method and device based on digital twinning, and the method comprises the steps: importing a guide rail model and a motion mechanism model into offline programming software, configuring a kinematic pair, and constructing a virtual scene corresponding to an actual pipeline inner wall spraying production line based on a digital twinning technology; importing a pipeline three-dimensional model, configuring workpiece parameters, and calibrating coordinate data; identifying the boundary of a spraying area on the inner wall of the pipeline, and determining the spraying range; an initial spraying track is generated, parameters are adjusted according to boundary conditions, and the track is optimized; configuring spray gun parameters, importing a flange plate and a spray gun model, and completing basic tool configuration; a tool model is imported, tool parameters are configured, spraying parameters are configured based on the sampling data, and a spraying tool is obtained; according to the embodiment of the invention, simulation is carried out based on the virtual scene, the optimized track and the spraying tool; and presenting the simulation effect in a three-dimensional visualization manner in real time. Spraying track optimization, parameter dynamic matching and real-time visualization of the spraying effect are achieved.

Description

Technical Field

[0001] The embodiments disclosed herein relate to the field of digital twin technology for pipeline spraying and related technical fields, specifically to a simulation control method and apparatus for pipeline inner wall spraying based on digital twins. Background Technology

[0002] In traditional pipeline inner wall spraying operations, manual operation faces problems such as limited space, high construction difficulty, and health hazards from paint volatilization; although industrial robots can effectively overcome the limitations of manual operation, there is still a key bottleneck that cannot verify the effectiveness of the operation in real time.

[0003] Therefore, a new solution is urgently needed. Summary of the Invention

[0004] The embodiments described herein provide a simulation control method and apparatus for spraying inner walls of pipes based on digital twins, which solves the problems existing in the prior art.

[0005] According to a first aspect of this disclosure, a simulation control method for spraying coating on the inner wall of a pipeline based on digital twins is provided, comprising:

[0006] Import the guide rail model and motion mechanism model into the offline programming software, configure the kinematic pair definition, and build a virtual scene corresponding to the actual pipeline inner wall spraying production line based on digital twin technology;

[0007] Import the 3D model of the pipeline into the virtual scene, configure the workpiece parameters, and calibrate the coordinate numerical data;

[0008] Boundary identification and definition are performed on the inner wall spraying area of ​​the pipeline 3D model to clarify the spraying operation scope;

[0009] An initial spraying trajectory for the inner wall of the pipe is generated in the offline programming software. The trajectory parameters are adjusted according to the boundary conditions of the area to be sprayed to generate an optimized spraying trajectory for the inner wall of the pipe.

[0010] Configure the spray gun parameters and import the flange model and spray gun model to complete the basic tool parameter configuration;

[0011] Import the tool model, configure the tool parameters, and configure the spray brush parameters based on the received on-site parameter sampling data to obtain the spraying tool;

[0012] Based on the configured virtual scene, the optimized pipe inner wall spraying trajectory, and the spraying tool, the optimized pipe inner wall spraying trajectory is simulated.

[0013] The spraying effect simulated in the virtual scene is presented in real time using a three-dimensional visualization method.

[0014] In some embodiments of this disclosure, the step of identifying and defining the boundary of the inner wall area of ​​the pipe model to be sprayed, and clarifying the spraying operation range, includes:

[0015] The regions are divided according to the shape of the pipe model, the curvature of the inner wall of the pipe model, the pipe diameter, or the location of special structures.

[0016] In some embodiments of this disclosure, prior to the step of configuring the spray brush parameters, the method further includes:

[0017] The on-site parameter sampling data were collected from the actual pipeline inner wall spraying production line.

[0018] In some embodiments of this disclosure, the spraying parameters include: spray brush name, geometric parameters, material parameters, motion parameters, and atomization parameters.

[0019] The geometric parameters include the optimal spraying distance, the range of cross-sectional diameters, and the maximum spraying height;

[0020] The material parameters include paint solids content, viscosity, and paint viscosity;

[0021] The motion parameters include spray gun speed and flow rate;

[0022] The atomization parameters include sector pressure and atomization pressure.

[0023] In some embodiments of this disclosure, after the step of simulating the optimized pipe inner wall spraying trajectory based on the configured virtual scene, the optimized pipe inner wall spraying trajectory, and the spraying tool, the method includes:

[0024] During the simulation, the collected real-time monitoring data is compared and analyzed in real time. When the real-time monitoring data exceeds the preset range, an alarm is triggered or the spraying is stopped. The real-time monitoring data includes: spraying flow rate, pressure, spray gun position, moving speed, usage frequency, and environmental data of the inner wall of the pipe. The environmental data includes temperature data and humidity data.

[0025] In some embodiments of this disclosure, the step of presenting the simulated spraying effect in the virtual scene in real time using a three-dimensional visualization method includes:

[0026] The real-time operating conditions of the actual pipeline inner wall spraying production line are virtually mapped to the virtual scene, and the visualization is performed using 3D fixed-view animation technology, with the animation rendered using Unite technology.

[0027] Data on the spraying process during simulation is collected and displayed in the form of a line graph or a pie chart. The spraying data includes: speed, flow rate, fan width, and film thickness.

[0028] The effect of spraying is presented by the motion mechanism model in the virtual scene when the inner wall of the pipe is being sprayed.

[0029] In some embodiments of this disclosure, the step of presenting the simulated spraying effect in the virtual scene in real time using a three-dimensional visualization method further includes:

[0030] The spraying effect data is converted into two-dimensional gridded quality analysis units;

[0031] The pass rate of each unit is calculated based on color consistency analysis, and unqualified areas are marked as three-dimensional leak points.

[0032] By analyzing the leak data, the spray brush parameters are adjusted, and the mapping relationship between the spray brush and the spraying effect is configured to obtain a new spraying tool. Based on the new spraying tool, the optimized spraying trajectory of the inner wall of the pipe is re-simulated. Through multiple simulations and adjustments, the optimal spray brush parameters and the mapping relationship between the spray brush and the spraying effect are obtained.

[0033] According to a second aspect of this disclosure, a simulation control device for spraying coating on the inner wall of a pipe based on digital twins is provided, comprising:

[0034] The import and build module is used to import guide rail models and motion mechanism models into offline programming software, configure motion pair definitions, and build a virtual scene corresponding to the actual pipeline inner wall spraying production line based on digital twin technology.

[0035] The import and configuration module is used to import the 3D model of the pipeline into the virtual scene, configure the workpiece parameters, and calibrate the coordinate numerical data.

[0036] The region division module is used to identify and define the boundaries of the spraying area on the inner wall of the pipeline 3D model, and to clarify the scope of the spraying operation.

[0037] The trajectory generation module is used to generate an initial pipeline inner wall spraying trajectory in the offline programming software, and adjust the trajectory parameters according to the boundary conditions of the area to be sprayed to generate an optimized pipeline inner wall spraying trajectory.

[0038] The configuration and import module is used to configure spray gun parameters and import flange and spray gun models to complete the basic tool parameter configuration.

[0039] The parameter configuration module is used to import the tool model, configure the tool parameters, and configure the spray brush parameters based on the received on-site parameter sampling data to obtain the spraying tool.

[0040] The simulation module is used to simulate the optimized spraying trajectory of the inner wall of the pipe based on the configured virtual scene, the optimized spraying trajectory of the inner wall of the pipe, and the spraying tool.

[0041] The display control module is used to present the simulated spraying effect generated in the virtual scene in real time in a three-dimensional visualization manner.

[0042] According to a third aspect of this disclosure, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method as described in any of the above embodiments.

[0043] According to a fourth aspect of this disclosure, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the method as described in any of the above embodiments.

[0044] The pipeline inner wall spraying simulation control method and device based on digital twin provided in this disclosure imports guide rail models and motion mechanism models into offline programming software, configures kinematic pairs, and constructs a virtual scene corresponding to the actual pipeline inner wall spraying production line based on digital twin technology; imports a 3D pipeline model, configures workpiece parameters, and calibrates coordinate data; identifies the boundary of the pipeline inner wall spraying area and clarifies the spraying range; generates an initial spraying trajectory, adjusts parameters according to boundary conditions, and optimizes the trajectory; configures spray gun parameters, imports flange and spray gun models, and completes basic tool configuration; imports tool models, configures tool parameters, and configures brush parameters based on sampled data to obtain the spraying tool; performs simulation based on the virtual scene, optimized trajectory, and spraying tool; and presents the simulation effect in real time as a 3D visualization. Based on this, the digital twin visualization platform can dynamically map the coverage state of the spraying robot on the pipeline inner wall, accurately locate abnormal areas such as missed spraying and incorrect spraying, and trace the cause of the problem. The platform simultaneously integrates key process parameters such as spraying speed, paint flow rate, spray angle, and film thickness distribution for visualization, constructing an efficient operation management closed loop of "real-time monitoring - anomaly early warning - data-driven". This technology establishes a digital virtual environment for the spraying operation, enabling precise replication of the actual process logic in virtual space. Virtual debugging significantly reduces the trial-and-error costs of physical production lines, and digital twin technology supports transparent control throughout the entire process. While ensuring spraying quality (film thickness uniformity, coverage integrity), it continuously optimizes process parameters through data-driven approaches, making it particularly suitable for scenarios such as the inner walls of pipelines and complex workpieces where "manual inspection is difficult and spraying accuracy requirements are high."

[0045] The above description is merely an overview of the technical solutions of the embodiments of this application. In order to better understand the technical means of the embodiments of this application and to implement them in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the embodiments of this application more obvious and understandable, specific implementation methods of this application are described below. Attached Figure Description

[0046] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings of the embodiments will be briefly described below. It should be understood that the drawings described below only relate to some embodiments of this disclosure and are not intended to limit this disclosure, wherein:

[0047] Figure 1 This is a schematic flowchart of a method for simulating and controlling the spraying of the inner wall of a pipe, provided in an embodiment of this disclosure.

[0048] Figure 2 This is a schematic diagram of the structure of a device for simulating and controlling the spraying of the inner wall of a pipe, provided in an embodiment of this disclosure;

[0049] Figure 3 This is a flowchart illustrating another example of simulation control for spraying on the inner wall of a pipe, provided in this embodiment of the present disclosure.

[0050] Figure 4 This is a schematic diagram of the structure of a computer device provided in an embodiment of this disclosure.

[0051] In the accompanying diagram, markers with the same last two digits correspond to the same elements. It should be noted that the elements in the diagram are schematic and not drawn to scale. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this disclosure. All other embodiments obtained by those skilled in the art based on the described embodiments of this disclosure without creative effort are also within the scope of protection of this disclosure.

[0053] The term "embodiment" as used herein means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of the phrase "embodiment" in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0054] In this article, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists, A and B exist simultaneously, or B exists. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0055] Furthermore, in all embodiments of this disclosure, terms such as “first” and “second” are used only to distinguish one component (or part of a component) from another component (or another part of a component).

[0056] In the description of this application, unless otherwise stated, "multiple" means two or more (including two), and similarly, "multiple groups" means two or more (including two groups).

[0057] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0058] Based on the problems existing in the prior art, the pipeline inner wall spraying simulation control method based on digital twin provided in this disclosure constructs a virtual mapping system for pipeline inner wall spraying in offline programming software through digital twin technology, so as to realize spraying trajectory optimization, dynamic parameter matching and real-time visualization of spraying effect. Figure 1 This is a schematic flowchart of a method for simulating and controlling the spraying of paint on the inner wall of a pipe, as provided in an embodiment of this disclosure. Figure 1 As shown, the specific process of the simulation control method for spraying on the inner wall of a pipeline includes:

[0059] S110. Import the guide rail model and robot model into the offline programming software, configure the motion pairs, and construct a virtual scene corresponding to the actual pipeline inner wall spraying production line based on digital twin technology.

[0060] Optional offline programming software includes PQ Art, RobotStudio, ProcessSimulate, etc.

[0061] In a specific implementation, the guide rail model and robot model are imported from a preset CAD database into the offline programming software. Then, the motion constraints and interaction relationships of the robot joints are configured by configuring the kinematic pairs. Furthermore, a high-fidelity virtual scene is constructed based on digital twin technology. This scene accurately maps the physical layout, dynamic behavior, and operation process of the actual pipeline inner wall spraying production line, encompassing spraying path planning, environmental parameter setting, and real-time monitoring functions, laying a solid foundation for subsequent offline program debugging and process optimization.

[0062] Configure the kinematic pairs, specifically how the robot moves on the guide rail and how the joints rotate, so that the motion logic of the virtual model is consistent with reality (for example, if the robot moves at a speed of 0.5m / s in reality, the virtual model will also move in sync).

[0063] Based on the spatial relationship of the spraying operation area, the external axis system connection relationship between the guide rail model and the robot model is constructed, and the actual mechanical coupling logic of the production line is synchronously mapped; at the same time, the robot model is adjusted in position, and its initial position is set at the origin of the spraying operation.

[0064] S120. Import the 3D model of the pipeline into the virtual scene, configure the workpiece parameters, and calibrate the coordinate numerical data.

[0065] In a specific implementation, the 3D model of the pipeline is imported from the CAD file into the virtual scene to ensure model format compatibility; then, the workpiece parameters are configured, including the size, material, surface condition and connection details of the pipeline, to optimize the model performance and make the physical properties of the virtual pipeline (such as paint adhesion and coefficient of thermal expansion) match reality; finally, the coordinate numerical data is calibrated, and the position of the model in the scene is calibrated by precise measurement and adjustment to ensure data accuracy.

[0066] Optional, also includes:

[0067] In the virtual scene, the specific position and orientation of the pipeline workpiece are determined. Position refers to its three-dimensional spatial coordinates, and orientation refers to its rotation angle and orientation.

[0068] S130. Identify and define the boundaries of the spraying area on the inner wall of the pipeline 3D model to clarify the scope of the spraying operation;

[0069] In a specific implementation, the shape, length, diameter, bending radius, curvature variation of the inner wall of the three-dimensional pipe model, pipe diameter variation or special structural location, curvature abrupt change point, and diameter transition zone are all considered.

[0070] In a specific implementation, for the inner wall spraying area of ​​the pipeline 3D model, the boundary is identified and defined by analyzing the model data and using computer-aided design software to clarify the spraying operation range; specifically, based on factors such as the shape, length, diameter, bending radius, inner wall curvature change, pipe diameter change, special structural positions, curvature change points, and diameter transition areas of the pipeline 3D model, the spraying path is calculated to ensure uniform spraying coverage and avoid missing key areas.

[0071] S140. Generate an initial spraying trajectory for the inner wall of the pipe in the offline programming software. Adjust the trajectory parameters according to the boundary conditions of the area to be sprayed to generate an optimized spraying trajectory for the inner wall of the pipe.

[0072] In a specific implementation, the offline programming software first generates an initial spraying trajectory for the inner wall of the pipe; then, based on the boundary conditions of the area to be sprayed, such as the pipe geometry, the characteristics of the spraying material, and environmental constraints, the trajectory parameters such as speed, overlap rate, and spraying angle are finely adjusted; finally, through simulation verification, an optimized spraying trajectory for the inner wall of the pipe is generated.

[0073] Different spraying path planning strategies are adopted for different types of pipe sections. For straight pipe sections, a path planning algorithm is used to generate a straight trajectory parallel to the pipe axis, and a reciprocating spraying path is used to ensure the efficiency and stability of the spraying process. For elbow sections, a spiral trajectory is generated. By accurately calculating the pitch and radius of the spiral, the spray gun can move along the curved surface of the elbow, ensuring that the coating evenly covers the curved surface and avoiding spraying blind spots or missed areas. Similarly, for pipes with reducers or tees, the system pre-plans the movement path and angle of the spray gun in these special parts to ensure full coverage spraying even in complex structural areas.

[0074] To eliminate blind spots in the spraying process, the system sets the trajectory spacing within the range of 80-120mm based on the spray gun's fan width. A smoothing algorithm is used to optimize the trajectory, reducing abrupt changes and achieving a smooth transition between adjacent trajectories. Simultaneously, the system adjusts the area boundaries, for example, expanding the processing range of elbow sections by 20mm to ensure that the coating thickness and uniformity at the boundaries meet process requirements. During optimization, the system simulates the spraying process in real time, displaying the spraying effect through a visual interface, allowing operators to intuitively observe and adjust trajectory parameters.

[0075] After optimizing the trajectory spacing and boundary transitions, the system integrates the spraying trajectories of each processing area to form a complete processing trajectory. The complete processing trajectory not only considers the spraying requirements of a single processing area, but also fully takes into account the connection and transition between adjacent areas, ensuring full coverage spraying on the inner walls of pipes with different diameters, curvatures, and complex structures, thus guaranteeing the uniformity and integrity of the coating.

[0076] In the specific implementation strategy, the offline programming software can optimize the movement trajectory of the spray gun through algorithms according to the specific requirements of the spraying process, ensuring full coverage of the inner wall of pipes with different diameters, curvatures, and complex structures, thereby avoiding blind spots or omissions in the spraying. For example, for pipes with special structures such as elbows, reducers, or tees, the system can pre-design the movement path and angle of the spray gun in these specific parts to ensure the uniformity and integrity of the coating.

[0077] S150, Configure the spray gun parameters, and import the flange model and spray gun model to complete the basic tool parameter configuration;

[0078] In a specific implementation, the spray gun parameters are configured, including setting the spray pressure, flow rate, and angle, such as setting the spray gun atomization cone angle (30-80°), and importing the flange model and spray gun model, binding the positional constraint relationship between the spray gun and the flange, and ensuring that the model format is compatible and loaded correctly; then, the basic tool parameters are configured, such as calibrating the tool position, movement range, and coordinate offset.

[0079] S160. Import the tool model, configure the tool parameters, configure the spray brush parameters based on the received field parameter sampling data, establish the spray brush, and thus obtain the spraying tool.

[0080] Optionally, import 3D models of the spray gun and accessories (such as quick-connect fittings and paint hoses).

[0081] Define the tool coordinate system (FL, TCP): calibrate the coordinate system of the spray gun's tool center point to ensure that the movement and spraying range of the spray gun in virtual space are completely aligned with the real world; define the transformation relationship between the tool coordinate system (TCP) and the flange coordinate system (FL) to ensure that it is accurately aligned with the robot coordinate system.

[0082] FL (Frame Link): Tool coordinate system, defines the tool's own spatial coordinate system;

[0083] TCP (Tool Center Point): The tool center point, which marks the "point of action" of the tool when performing a process (such as the center point of the nozzle of a spray gun).

[0084] Configure the transformation matrix between the spray gun tool coordinate system (TCP) and the robot flange coordinate system (FL) to ensure accurate mapping between virtual and physical space.

[0085] Optionally, the spray brush parameters include spray brush name, geometric parameters, material parameters, motion parameters, and atomization parameters:

[0086] Geometric parameters: Optimal spraying distance is 200±10mm, cross-sectional diameter range is 80-150mm, and maximum spraying height is [missing information].

[0087] Material parameters: Paint solids content 60%, viscosity 15s (Ford Cup 4), paint viscosity;

[0088] Motion parameters: spray gun speed is 100-200 mm / s, flow rate is 250 mL / min;

[0089] Atomization parameters: fan-shaped pressure is 0.4MPa, atomization pressure is 0.5MPa.

[0090] In a specific implementation, the spray brush parameters are configured based on the received on-site parameter sampling data.

[0091] The entity parameters obtained from "parameter field data sampling" are transformed into "digital parameter rules" that can be recognized by the virtual environment.

[0092] In a specific implementation, firstly, the system automatically collects sampling data of parameters such as temperature and humidity in the field environment; secondly, it analyzes and processes these data in real time; and finally, it dynamically adjusts parameters such as pressure, flow rate, and angle of the spraying equipment based on the processing results to ensure operational accuracy and efficiency.

[0093] In a specific implementation, the establishment of the spray brush involves converting the process parameters (pressure, flow rate, and gun speed) collected online into "digital instructions" for the virtual spray gun (e.g., a pressure of 0.4 MPa in reality corresponds to a "pressure parameter of 0.4" in the virtual spray gun), so that the process logic of virtual spraying is consistent with reality.

[0094] In a specific implementation, pairing "spray brush" with "spray brush effect" involves configuring the spray brush color control and spray brush transparency in offline programming software.

[0095] Precise spraying path planning and parameter control can effectively suppress over-spraying of paint. Digital twin models, by simulating the spatial distribution characteristics of paint, accurately predict the paint demand for different structural parts, optimizing the supply and demand of paint and minimizing raw material consumption costs. Furthermore, the reduced frequency of rework due to coating quality defects simultaneously lowers labor costs, equipment maintenance costs, and indirect economic losses caused by production stoppages.

[0096] The system deeply analyzes the geometric features and spatial location of each processing area. For pipe processing areas with smaller diameters, due to limited spraying space, the spraying speed is reduced and the range of spraying angle variation is minimized to avoid collision between the spray gun and the pipe wall. For elbow processing areas with large curvatures, the rotation speed and oscillation amplitude of the spray gun are increased to ensure uniform coating coverage of the curved surface. Simultaneously, based on the area size of the processing area and according to the preset paint consumption ratio, the required paint amount is initially calculated, providing basic data support for the spraying parameters.

[0097] Digital twin technology supports fully automated path planning and intelligent control mechanisms, significantly reducing the complexity and uncertainty of manual operations and improving the speed and efficiency of spraying operations. The system can generate efficient spraying plans in real time based on pipe geometry parameters (such as length and diameter) and drive automated equipment to execute them precisely. Multiple spray guns or robotic arms can work in parallel under the collaborative scheduling of the digital twin system, further shortening the construction cycle, making it particularly suitable for large-scale pipe spraying projects.

[0098] Optionally, before step S160, the method further includes:

[0099] S100. Collect the on-site parameter sampling data from the actual pipeline inner wall spraying production line;

[0100] The on-site parameter sampling data includes: optimal spraying distance, maximum cross-sectional diameter range, maximum spraying height, paint solids content, viscosity, paint viscosity, spray gun speed, flow rate, fan-shaped pressure, and atomization pressure.

[0101] Optionally, prior to S100, the following steps are also included:

[0102] 1. Online Data Acquisition: Pipe inner wall thickness data is acquired using a laser thickness gauge (accuracy ≤1μm), coating viscosity is obtained using a Brookfield rotary viscometer, and environmental temperature and humidity data are collected using temperature and humidity sensors. Film thickness directly correlates with quality, coating viscosity affects spray uniformity, and environmental temperature and humidity affect coating drying and adhesion. Simultaneously, online process parameters (such as spray pressure threshold 0.3-0.5MPa, coating flow rate constraint 200-300mL / min), offline quality standards (film thickness uniformity deviation ≤5%, spray coverage ≥98%), and equipment operation logic (such as triggering an alarm when pressure changes exceed 10%) are acquired. Online process parameters include real-time monitoring data of spray flow rate, pressure, spray gun position, and movement speed, as well as environmental data on the temperature and humidity of the pipe inner wall.

[0103] 2. Offline inspection: Use a metallographic microscope (to observe coating porosity) and a gloss meter (to measure surface reflectivity) to inspect the microscopic quality of the "coated workpiece". Offline inspection quality data includes quantitative indicators of film thickness uniformity, coating coverage and flow mark defects.

[0104] For example: high porosity → coating is easy to peel off; uneven gloss → poor surface smoothness.

[0105] 3. Synchronous Recording: Synchronize PLC logs (such as spraying pressure fluctuation curves) to align "equipment operation data" with "quality data" in time (e.g., how film thickness changes during sudden pressure changes); sample by shift (morning / noon / evening) and equipment time period (1 hour of operation / 4 consecutive hours) to capture time-varying effects such as "equipment heat loss, paint settling" (e.g., whether spraying accuracy decreases after 4 hours of continuous operation). Utilize the PLC interface to acquire robot motion parameters and spray gun operating status; synchronized data includes data obtained by aligning the timestamps of equipment logs and quality data.

[0106] Synchronizing and linking PLC logs of related equipment (such as spraying pressure fluctuations) deeply binds process data with equipment status, laying the foundation for subsequent analysis. Linking PLC logs (such as spraying pressure fluctuation curves and robot motion trajectories) deeply binds equipment operating status with process results, allowing for the tracing of causal relationships such as "pressure mutation → abnormal film thickness".

[0107] 4. Spatial dimension: The surface of the sprayed workpiece is marked with "grid-like points" (for example, "inspection points" are set up in an area of ​​200mm×200mm, with a focus on monitoring corners and edges (key locations prone to defects).

[0108] 5. Time dimension: Sampling is performed according to production shift (morning / noon / evening) and equipment running time (e.g., 1 hour after startup, 4 hours of continuous operation) to capture the time-varying effects of equipment heat loss, paint settling, etc.

[0109] 6. Preliminary causal hypothesis (data association logic):

[0110] Based on experience and preliminary data, establish correlation hypotheses between "parameters" and "quality" (e.g., increased spraying speed → decreased film thickness; increased spraying pressure → decreased atomized particles → decreased surface roughness). Through correlation verification such as "pressure-thickness" and "flow rate-uniformity," construct mapping rules between process parameters and quality results, allowing quality data to be accurately located to its "spatial position" (e.g., whether the film thickness is generally thicker at corners). The equipment operation logic includes pressure surge alarm thresholds, parameter correlation rules, and anomaly response strategies.

[0111] S170. Based on the configured virtual scene and the spraying tool, simulate the optimized spraying trajectory of the inner wall of the pipe.

[0112] Specifically, based on the configured virtual scene and the spraying tool, the offline programming software performs a detailed simulation process on the optimized spraying trajectory of the inner wall of the pipe to simulate the actual spraying operation and verify the accuracy and efficiency of the trajectory.

[0113] Optionally, the spraying path can be optimized through simulation to determine the optimal helix angle, spray gun movement speed, paint flow rate, predicted coverage, and film thickness uniformity deviation.

[0114] In a specific implementation method

[0115] 1. Data preparation stage

[0116] After configuring the virtual scene and spraying tools, comprehensive data preparation is necessary before conducting spraying trajectory simulation. First, precise trajectory data is extracted from the previous work optimizing the spraying trajectory on the inner wall of the pipe. This data includes key information such as the 3D coordinates of trajectory points, the connection order between points, and the direction of movement, ensuring that subsequent simulations can accurately reproduce the actual spraying path. Simultaneously, detailed pipe parameters are collected, such as pipe diameter, length, curvature, and material, which affect the coverage effect of the spraying and the motion characteristics of the spraying tools; and specific parameters of the spraying tools, including the spray gun model, nozzle size, spray flow rate, and atomization angle, which determine the actual spraying effect. The above data is then formatted and standardized to meet the data input requirements of the offline programming software, providing a reliable data foundation for subsequent simulations.

[0117] 2. Model building and scene integration

[0118] Based on the prepared data, high-precision models of the pipe inner wall and spraying tools are built in offline programming software. For the pipe inner wall model, the software's modeling function is used to accurately draw the inner wall shape according to the pipe's geometric parameters, ensuring that the model matches the actual pipe in terms of size, curvature, etc. The spraying tool model is created according to its actual structure and dimensions, and the position and orientation of the tool's working parts (such as the spray gun nozzle) are accurately set. Then, the constructed pipe inner wall model and spraying tool model are integrated into a pre-configured virtual scene, precisely placed according to the actual installation and working position relationships, while setting the physical interaction attributes between the models, such as collision detection parameters, to make the virtual scene simulate the actual spraying environment as realistically as possible.

[0119] 3. Simulation parameter settings

[0120] Before starting the simulation, all parameters need to be carefully set. In the offline programming software, set the simulation time parameters, including the total simulation duration and time step. The time step determines the precision of the simulation; a smaller time step can more accurately capture the movement of the spraying tool and changes in the spraying effect, but it increases the computational load. Simultaneously, set the speed parameters for the spraying process. Based on the actual spraying process requirements, determine the speed at which the spraying tool moves along the trajectory, as well as the opening and closing time of the spray gun, and the change curve of the spray flow rate, to simulate the actual conditions at different spraying stages. Furthermore, set the simulation display parameters, such as the model's display accuracy, rendering effect, and visualization method of the spraying effect, to facilitate subsequent observation and analysis of the simulation process and results.

[0121] 4. Simulation Execution Process

[0122] After completing the above preparations, activate the simulation function of the offline programming software. The software will drive the spraying tool model along the optimized spraying trajectory of the pipe's inner wall in the virtual scene, based on the set parameters and input data. During the movement, the spraying process is simulated according to the set spraying parameters, generating real-time visual images of the spraying effect, intuitively displaying the coating coverage and thickness distribution on the pipe's inner wall. Simultaneously, the software monitors the interaction between the spraying tool model and the pipe's inner wall model in real time. When a collision is detected or approaching a collision threshold, an alarm is issued and relevant information is recorded for subsequent analysis to determine the trajectory's rationality and identify any risk of interference between the spraying tool and the pipe's inner wall. The entire simulation process progresses step-by-step according to the set time steps until the total simulation time is reached.

[0123] 5. Results Evaluation and Feedback

[0124] After the simulation, the analysis tools provided by the offline programming software are used to comprehensively evaluate the simulation results. The uniformity of the coating and the presence of any missed or over-sprayed areas are visually examined from the images. The software's data statistics function is used to obtain data such as the average and standard deviation of the coating thickness, quantitatively assessing the quality of the spraying effect. Simultaneously, the rationality of the spraying tool's trajectory is analyzed, identifying any issues such as uneven movement or sharp turns that might affect spraying efficiency and quality, as well as the risk of interference with the pipe's inner wall. Based on the evaluation results, if problems are found, the spraying trajectory, simulation parameters, or model are promptly adjusted and optimized, such as modifying the position and connection method of trajectory points, adjusting the spraying speed and flow rate, etc. The simulation is then repeated until satisfactory results are obtained, providing a reliable reference for actual pipe inner wall spraying operations.

[0125] Digital twin technology can parametrically simulate the coating formation process under different spraying conditions. By optimizing key process parameters (such as spray gun distance, angle, moving speed, and paint flow rate) through virtual simulation, it can achieve optimized control of key performance indicators such as coating uniformity, adhesion, and porosity in actual spraying. At the same time, it can dynamically adjust the spraying strategy based on real-time monitoring data, effectively compensating for factors such as equipment status fluctuations, changes in material properties, and environmental interference, avoiding coating defects such as sagging, peeling, and bubbling, and significantly improving the overall coating quality and service reliability.

[0126] Step 180: Present the simulated spraying effect generated in the virtual scene in real time through 3D visualization;

[0127] In a specific implementation, the real-time operating conditions of the actual pipeline inner wall spraying production line are virtually mapped into a virtual scene. Three-dimensional fixed-view animation technology is used for visualization, the animation is rendered using Unity technology, and a visualization interface is developed in the Unity 3D engine to display the robot's motion trajectory, spray gun status, film thickness distribution cloud map, and key process parameter dashboard in real time.

[0128] Optionally, multiple windows can be displayed simultaneously:

[0129] Main view: The pipe is sectioned along the Z-axis, and the coating distribution on the inner wall is shown using a heat map;

[0130] Data panel: Statistical Process Control (SPC) chart for film thickness, defect thermal map.

[0131] Visualization Perspective and Animation Configuration: "Key perspectives" are preset in the digital twin screen, including a top-down view of the pipe inlet, a side view of the unfolded inner wall, and a close-up of the robot's spray gun. The perspective automatically switches according to the spraying process nodes. For example, when the robot enters the pipe, it automatically switches to the top-down inlet perspective; during spraying, it switches to the unfolded inner wall view, allowing operators to observe the spraying process from different angles. Simultaneously, the motion speed of the virtual animation is ensured to be synchronized with the process sequence and real-world conditions at millisecond levels, and the physical process of paint "spraying-adhesion-leveling" is simulated. This makes the animation not only visually realistic but also conforms to physical laws, enhancing the authenticity and reliability of the visualization.

[0132] Optionally, it also includes:

[0133] The spraying effect data is converted into two-dimensional gridded quality analysis units;

[0134] The pass rate of each unit is calculated based on color consistency analysis, and unqualified areas are marked as three-dimensional leak points.

[0135] By analyzing the leak data, the spraying parameters were adjusted, the mapping relationship between spraying and spraying effect was optimized, and a new spraying tool was generated. Based on this tool, the optimized spraying trajectory of the inner wall of the pipe was re-simulated. After multiple simulations and adjustments, the optimal spraying parameters and the mapping relationship between spraying and spraying effect were obtained.

[0136] In some embodiments, spraying process data (including speed, flow rate, fan width, and film thickness) during the simulation process are collected and the entire process data is visualized in a line graph (speed and flow rate fluctuations) or a ring graph (film thickness distribution ratio).

[0137] The spraying effect is achieved by presenting a motion mechanism model in a virtual scene when performing a spraying operation on the inner wall of a pipe.

[0138] In the digital twin visualization screen, the entire process of "robot entering the pipeline → spraying along the path → exiting the pipeline" is dynamically recreated using a 3D model, and the animation effect is synchronized with the real working conditions (robot movement speed, spray gun switch status) in real time.

[0139] For the inner wall, a "pipe unfolded view" is provided (the inner wall of the cylinder is unfolded into a plane), and a color heat map is used to show the film thickness distribution (red = extra thick, blue = extra thin, green = acceptable); local magnification is supported (such as at corners) to facilitate observation of detailed defects (missed spray, drips);

[0140] Preset "key perspectives" (such as top view of pipe inlet, side view of unfolded inner wall, close-up of robot spray gun) and automatically switch according to process nodes (such as switching to top view of inlet when robot enters pipe; switching to unfolded inner wall view when spraying).

[0141] Optionally, after step S170, the method further includes:

[0142] Real-time monitoring and feedback: During the spraying process, the digital twin system collects process parameters such as spray gun position, movement speed, spray flow rate, and pressure in real time, as well as environmental data such as pipe inner wall temperature and humidity. The system compares and analyzes the collected real-time monitoring data with simulation data generated by the digital twin model. When the real-time monitoring data exceeds a preset threshold range, the system immediately triggers an alarm mechanism or stops the spraying process. For example, by using an infrared sensor to provide real-time feedback on coating thickness data and combining this with an aerosol particle counter to detect the diameter of atomized particles, the system can dynamically adjust the spraying air pressure parameters, thereby controlling the coating thickness error within a preset threshold range. This allows for timely detection of abnormalities during the spraying process and the implementation of corresponding measures, ensuring the safety of the spraying process and the stability of the coating quality, achieving real-time intelligent monitoring and emergency handling of the spraying process.

[0143] For example, during continuous production, the system detected a -7% deviation in the average film thickness of a batch of pipes, triggering a real-time alert. The data analysis module automatically correlated the abnormal data and found a correlation with a 12% increase in coating viscosity, generating adjustment recommendations: increase the spray gun pressure from 6 MPa to 6.8 MPa, and simultaneously increase the diluent addition by 0.5%. After implementing the adjustments, the film thickness deviation returned to within ±2%, production efficiency increased by 15%, and coating waste decreased by 8%.

[0144] Quality Traceability and Management: The digital twin system comprehensively records all data from the entire spraying process, covering process parameters, equipment operating status, and environmental data, forming a traceable spraying process archive. This archive provides an objective basis for subsequent pipeline coating quality assessment and analysis. If coating quality problems occur, the specific stage and cause of the problem can be quickly located, providing strong support for maintenance decisions and process improvements. Simultaneously, the system's data recording function helps achieve refined management of the construction process, improving overall management levels.

[0145] Equipment maintenance optimization: Based on the analysis of equipment operation data, the digital twin system model can predict potential equipment failures and wear trends of key components, providing a scientific basis for developing equipment maintenance plans and achieving preventative maintenance goals. This strategy helps reduce unplanned downtime, extend equipment lifespan, and lower maintenance costs. For example, by continuously monitoring key parameters such as spray gun usage frequency and spraying pressure, the system can predict the replacement cycle of vulnerable spray gun parts, facilitating the preparation of spare parts and scheduling maintenance work in advance, effectively avoiding production interruptions caused by sudden equipment failures.

[0146] High-fidelity 3D visualization technology dynamically maps the inner wall condition, enabling precise defect location. Utilizing 3D virtual mapping and dynamic restoration technology, implicit processes are transformed into visualized and traceable digital scenes, effectively addressing key pain points such as insufficient visualization of the production process and lack of data support for problem traceability. Through 3D dynamic simulation and real-time feedback of the spraying effect using heat maps, the anomaly response time is reduced from 30 minutes of manual inspection to seconds, significantly improving the efficiency of missed spray repair.

[0147] Based on the above embodiments, this disclosure also provides a device for simulating and controlling the spraying of coatings on the inner wall of a pipe, such as... Figure 2 As shown, the device for simulating and controlling the spraying of the inner wall of the pipeline includes:

[0148] The import and build module 210 is used to import guide rail models and motion mechanism models into offline programming software, configure motion pair definitions, and build a virtual scene corresponding to the actual pipeline inner wall spraying production line based on digital twin technology.

[0149] Import and configuration module 220 is used to import the three-dimensional model of the pipeline into the virtual scene, configure workpiece parameters, and calibrate coordinate numerical data.

[0150] The region division module 230 is used to identify and define the boundary of the inner wall spraying area of ​​the pipeline 3D model, and to clarify the spraying operation range;

[0151] The trajectory generation module 240 is used to generate an initial pipeline inner wall spraying trajectory in the offline programming software, and adjust the trajectory parameters according to the boundary conditions of the area to be sprayed to generate an optimized pipeline inner wall spraying trajectory.

[0152] The configuration and import module 250 is used to configure the spray gun parameters and import the flange model and spray gun model to complete the basic tool parameter configuration.

[0153] The parameter configuration module 260 is used to import the tool model, configure the tool parameters, configure the spray brush parameters according to the received on-site parameter sampling data, and complete the mapping relationship between the spray brush and the spraying effect to obtain the spraying tool.

[0154] The simulation module 270 is used to simulate the optimized spraying trajectory of the inner wall of the pipe based on the configured virtual scene, the optimized spraying trajectory of the inner wall of the pipe, and the spraying tool.

[0155] The display control module 280 is used to present the simulated spraying effect generated in the virtual scene in real time in a three-dimensional visualization manner.

[0156] In a specific implementation, the region division module 230 is specifically used to divide regions based on the shape of the pipe model, the curvature change of the inner wall of the pipe model, the pipe diameter change, or the location of special structures.

[0157] In a specific implementation, a data acquisition module 200 is also included, used to acquire the field parameter sampling data from the actual pipeline inner wall spraying production line. The field parameter sampling data includes online process parameters, offline detection quality data, synchronous recording data, spatial dimension information, temporal dimension information, and equipment operation logic. The online process parameters include real-time monitoring data of spraying flow rate, pressure, spray gun position, and moving speed, as well as environmental data such as temperature and humidity of the pipeline inner wall. The offline detection quality data includes quantitative indicators of film thickness uniformity, spraying coverage, and flow mark defects. The synchronous recording data includes data obtained by aligning the equipment log with the quality data timestamp. The equipment operation logic includes pressure change alarm thresholds, parameter association rules, and abnormal response strategies.

[0158] In a specific implementation, the display control module 280 is used to virtually map the real-time working conditions of the actual pipeline inner wall spraying production line to the virtual scene, and to visualize the display using 3D fixed-view animation technology and render the animation using Unite technology.

[0159] Data on the spraying process during simulation is collected and displayed in the form of a line graph or a pie chart. The spraying data includes: speed, flow rate, fan width, and film thickness.

[0160] The effect of spraying is presented by the motion mechanism model in the virtual scene when the inner wall of the pipe is being sprayed.

[0161] In a specific implementation, the display control module 280 is also used to convert the spraying effect data into a two-dimensional gridded quality analysis unit;

[0162] The pass rate of each unit is calculated based on color consistency analysis, and unqualified areas are marked as three-dimensional leak points.

[0163] By analyzing the leak data, the spray brush parameters are adjusted, and the mapping relationship between the spray brush and the spraying effect is configured to obtain a new spraying tool. Based on the new spraying tool, the optimized spraying trajectory of the inner wall of the pipe is re-simulated. Through multiple simulations and adjustments, the optimal spray brush parameters and the mapping relationship between the spray brush and the spraying effect are obtained.

[0164] In a specific implementation, a monitoring module 290 is also included, which is used to compare and analyze the real-time monitoring data collected during the simulation process. When the real-time monitoring data exceeds the preset range, an alarm is triggered or the spraying is stopped. The real-time monitoring data includes: spraying flow rate, pressure, spray gun position, moving speed, usage frequency, and environmental data of the inner wall of the pipe. The environmental data includes temperature data and humidity data.

[0165] This disclosure provides a device for simulating and controlling the spraying of the inner wall of a pipeline. It enables optimization of the spraying trajectory, dynamic matching of parameters, and real-time visualization of the spraying effect.

[0166] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of the present invention according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0167] The following is a specific example to illustrate this:

[0168] Example 1: Figure 3 As shown, it includes the following steps:

[0169] 1: Data Collection

[0170] Multi-dimensional data collection:

[0171] Online data acquisition: Using laser thickness gauges, viscometers, temperature sensors, etc., to capture parameters such as film thickness, viscosity, and temperature in real time;

[0172] Offline inspection: Microscopic quality is measured using a metallographic microscope and gloss meter;

[0173] Online data acquisition: Parameters are recorded in real time using a laser thickness gauge (accuracy ±1µm), a viscometer (such as a Brookfield rotational viscometer), and a temperature and humidity sensor;

[0174] Offline inspection: Microscopic quality data are obtained using a metallographic microscope (to observe coating porosity) and a gloss meter (to measure surface reflectance);

[0175] Synchronous recording: Align the equipment PLC logs (such as the spraying pressure fluctuation curve) with the quality data timestamps to facilitate subsequent correlation analysis;

[0176] Spatial dimension: Detection points are arranged on the surface of the workpiece in a grid pattern (e.g., one detection point is set every 200mm x 200mm), with a focus on corners, edges, and other areas prone to coating defects.

[0177] Time dimension: Sampling is performed according to production shift (morning / noon / evening) and equipment running time (1 hour after startup, 4 hours of continuous operation) to capture the time-varying effects of equipment heat loss, paint settling and other factors.

[0178] Establish preliminary causal hypotheses, such as "Gun velocity 1 → film thickness!" or "Spraying pressure ↑ → atomized particles ↓ → surface roughness ↓".

[0179] 2: Model and Parameter Construction

[0180] Model import and definition: It is necessary to import robot model and guide rail model, configure kinematic pair definition, establish virtual environment, import flange model and quick tool model, configure basic tools, import pipe model to be processed by spraying process, configure workpiece, import tool model, configure tools, configure brush parameters, establish brush, match brush with spraying effect, configure spraying tools, which is equivalent to "building a pipe inner wall spraying production line" in virtual environment, so that the virtual spraying logic is close to the real process.

[0181] Virtual debugging layout: Based on the real environment, a virtual scene is replicated 1:1. After completion, the virtual and real signals are matched and the robot logic is verified to ensure the linkage between the virtual and the real.

[0182] 3: Matching and acquiring virtual and real signals;

[0183] 4: Robot logic verification;

[0184] 5: Twin-driven:

[0185] Digital twin: Collects data such as "speed, flow rate, and film thickness" during the spraying process and displays it in multiple dimensions using "line graphs and pie charts". It can also virtually map the real spraying effect (3D fixed view / animated view), which is equivalent to "watching the spraying process in real time" on a computer.

[0186] Motion twin: Virtually maps real-world working conditions and presents 3D fixed-view animation effects. The animation is rendered using Unity, making the spraying action and workpiece status more intuitive and helping to understand the production line operation.

[0187] 6: Visualization and Decision Optimization:

[0188] Ultimately, the results are presented through a "digital visualization screen" (digital twin, motion twin), solving the pain points of industrial spraying:

[0189] In the scenario of spraying the inner wall of a pipe, robots are used instead of digital twins for visualization, allowing people to "see" the spraying effect clearly.

[0190] If issues such as missed spraying or uneven film thickness occur, the system can quickly locate the problem (data traceability) and monitor "speed, flow rate, and film thickness" in real time, thereby improving spraying efficiency and quality.

[0191] This application also provides a computer device. Please refer to the following for details. Figure 4 , Figure 4 This is a basic structural block diagram of the computer device in this embodiment.

[0192] The computer device includes a memory 410 and a processor 420 that are interconnected via a system bus. It should be noted that only a computer device with components 410-420 is shown in the figure; however, it should be understood that it is not required to implement all the shown components, and more or fewer components may be implemented alternatively. Those skilled in the art will understand that the computer device described herein is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0193] Computer devices can include desktop computers, laptops, handheld computers, and cloud servers. These devices allow for human-computer interaction with users through keyboards, mice, remote controls, touchpads, or voice-activated devices.

[0194] The memory 410 includes at least one type of readable storage medium, including non-volatile memory or volatile memory, such as flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. RAM may include static RAM or dynamic RAM. In some embodiments, the memory 410 may be an internal storage unit of a computer device, such as the hard disk or RAM of the computer device. In other embodiments, the memory 410 may also be an external storage device of the computer device, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or FlashCard. Of course, the memory 410 may include both internal and external storage units of the computer device. In this embodiment, the memory 410 is typically used to store the operating system and various application software installed on the computer device, such as the program code of the methods described above. Furthermore, the memory 410 may also be used to temporarily store various types of data that have been output or will be output.

[0195] Processor 420 is typically used to perform overall operations of a computer device. In this embodiment, memory 410 is used to store program code or instructions, including computer operation instructions, and processor 420 is used to execute the program code or instructions stored in memory 410 or process data, such as program code that runs the methods described above.

[0196] In this article, the bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. This bus system can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0197] Another embodiment of this application also provides a computer-readable medium, which may be a computer-readable signal medium or a computer-readable medium. A processor in a computer reads computer-readable program code stored in the computer-readable medium, enabling the processor to execute the functional actions specified in each step or combination of steps in the above method; and to generate means for implementing the functional actions specified in each block or combination of blocks in the block diagram.

[0198] Computer-readable media include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared memory or semiconductor systems, devices or apparatuses, or any suitable combination thereof, wherein the memory is used to store program code or instructions, the program code including computer operation instructions, and the processor is used to execute the program code or instructions of the above-described methods stored in the memory.

[0199] The definitions of memory and processor can be found in the description of the foregoing computer device embodiments, and will not be repeated here.

[0200] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0201] In the various embodiments of this application, the functional units or modules can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0202] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0203] Unless otherwise expressly indicated by the context, the singular form of words used herein and in the appended claims includes the plural form, and vice versa. Thus, when referring to the singular, the plural form of the corresponding term is generally included. Similarly, the terms “comprising” and “including” shall be interpreted as including rather than exclusively. Likewise, the terms “including” and “or” shall be interpreted as including unless such interpretation is expressly prohibited herein. Where the term “example” is used herein, particularly when it follows a set of terms, the “example” is merely exemplary and illustrative and should not be considered exclusive or extensive.

[0204] Further aspects and scope of adaptation become apparent from the description provided herein. It should be understood that various aspects of this application may be implemented individually or in combination with one or more other aspects. It should also be understood that the descriptions and specific embodiments herein are for illustrative purposes only and are not intended to limit the scope of this application.

[0205] Several embodiments of this disclosure have been described in detail above. However, it is obvious that those skilled in the art can make various modifications and variations to the embodiments of this disclosure without departing from the spirit and scope of this disclosure. The scope of protection of this disclosure is defined by the appended claims.

Claims

1. A simulation control method for spraying coating on the inner wall of a pipe based on digital twin, characterized in that, include: Import the guide rail model and motion mechanism model into the offline programming software, configure the kinematic pair definition, and build a virtual scene corresponding to the actual pipeline inner wall spraying production line based on digital twin technology; Import the 3D model of the pipeline into the virtual scene, configure the workpiece parameters, and calibrate the coordinate numerical data; Boundary identification and definition are performed on the inner wall spraying area of ​​the pipeline 3D model to clarify the spraying operation scope; An initial spraying trajectory for the inner wall of the pipe is generated in the offline programming software. The trajectory parameters are adjusted according to the boundary conditions of the area to be sprayed to generate an optimized spraying trajectory for the inner wall of the pipe. Configure the spray gun parameters and import the flange model and spray gun model to complete the basic tool parameter configuration; Import the tool model, configure the tool parameters, and configure the spray brush parameters based on the received on-site parameter sampling data to obtain the spraying tool; Based on the configured virtual scene, the optimized pipe inner wall spraying trajectory, and the spraying tool, the optimized pipe inner wall spraying trajectory is simulated. The spraying effect simulated in the virtual scene is presented in real time using a three-dimensional visualization method.

2. The method according to claim 1, characterized in that, The steps of identifying and defining the boundary of the inner wall area of ​​the pipeline model to be sprayed, and clarifying the scope of the spraying operation, include: The regions are divided according to the shape of the pipe model, the curvature of the inner wall of the pipe model, the pipe diameter, or the location of special structures.

3. The method according to claim 1, characterized in that, Prior to the step of configuring the spray brush parameters, the following is also included: The on-site parameter sampling data were collected from the actual pipeline inner wall spraying production line.

4. The method according to claim 1, characterized in that, The spraying parameters include: spray brush name, geometric parameters, material parameters, motion parameters, and atomization parameters. The geometric parameters include the optimal spraying distance, the range of cross-sectional diameters, and the maximum spraying height; The material parameters include paint solids content, viscosity, and paint viscosity; The motion parameters include spray gun speed and flow rate; The atomization parameters include sector pressure and atomization pressure.

5. The method according to claim 1, characterized in that, Following the step of simulating the optimized pipe inner wall spraying trajectory based on the configured virtual scene, the optimized pipe inner wall spraying trajectory, and the spraying tool, the process includes: During the simulation, the collected real-time monitoring data is compared and analyzed in real time. When the real-time monitoring data exceeds the preset range, an alarm is triggered or the spraying is stopped. The real-time monitoring data includes: spraying flow rate, pressure, spray gun position, moving speed, usage frequency, and environmental data of the inner wall of the pipe. The environmental data includes temperature data and humidity data.

6. The method according to claim 1, characterized in that, The step of presenting the simulated spraying effect in the virtual scene in real time using a three-dimensional visualization method includes: The real-time operating conditions of the actual pipeline inner wall spraying production line are virtually mapped to the virtual scene, and the visualization is performed using 3D fixed-view animation technology, with the animation rendered using Unite technology. Data on the spraying process during simulation is collected and displayed in the form of a line graph or a pie chart. The spraying data includes: speed, flow rate, fan width, and film thickness. The effect of spraying is presented by the motion mechanism model in the virtual scene when the inner wall of the pipe is being sprayed.

7. The method according to claim 6, characterized in that, The step of presenting the simulated spraying effect in the virtual scene in real time using a three-dimensional visualization method further includes: The spraying effect data is converted into two-dimensional gridded quality analysis units; The pass rate of each unit is calculated based on color consistency analysis, and unqualified areas are marked as three-dimensional leak points. By analyzing the leak data, the spray brush parameters are adjusted, and the mapping relationship between the spray brush and the spraying effect is configured to obtain a new spraying tool. Based on the new spraying tool, the optimized spraying trajectory of the inner wall of the pipe is re-simulated. Through multiple simulations and adjustments, the optimal spray brush parameters and the mapping relationship between the spray brush and the spraying effect are obtained.

8. A simulation control device for spraying inner wall coating of pipes based on digital twins, characterized in that, include: The import and build module is used to import guide rail models and motion mechanism models into offline programming software, configure motion pair definitions, and build a virtual scene corresponding to the actual pipeline inner wall spraying production line based on digital twin technology. The import and configuration module is used to import the 3D model of the pipeline into the virtual scene, configure the workpiece parameters, and calibrate the coordinate numerical data. The region division module is used to identify and define the boundaries of the spraying area on the inner wall of the pipeline 3D model, and to clarify the scope of the spraying operation. The trajectory generation module is used to generate an initial pipeline inner wall spraying trajectory in the offline programming software, and adjust the trajectory parameters according to the boundary conditions of the area to be sprayed to generate an optimized pipeline inner wall spraying trajectory. The configuration and import module is used to configure spray gun parameters and import flange and spray gun models to complete the basic tool parameter configuration. The parameter configuration module is used to import the tool model, configure the tool parameters, and configure the spray brush parameters based on the received on-site parameter sampling data to obtain the spraying tool. The simulation module is used to simulate the optimized spraying trajectory of the inner wall of the pipe based on the configured virtual scene, the optimized spraying trajectory of the inner wall of the pipe, and the spraying tool. The display control module is used to present the simulated spraying effect generated in the virtual scene in real time in a three-dimensional visualization manner.

9. A computer device, characterized in that, include: One or more processors; Storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 7.

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