Virtual platform simulation monitoring method and device

Abstract

The embodiment of the invention provides a virtual platform simulation monitoring method and device, and the method comprises the steps: carrying out the signal analysis of a received programmable logic controller signal of an actual production line through a set code analysis model, obtaining a corresponding virtual signal table, building a motion track of a simulation model in simulation software according to the virtual signal table, and carrying out the simulation monitoring of a virtual platform. A simulation event is added to the key control node; outputting a virtual production line model in a 3D model file format, and issuing the virtual production line model and the message queue connection parameters to a webpage end; after a ready state signal of the webpage end is monitored, starting to send position information and state change information of a virtual production line model to a message queue service, so that the webpage end updates a corresponding model position and a model state after receiving the position information and the state change information; according to the invention, the automation and real-time interactivity of simulation monitoring of the virtual platform can be effectively improved.

Description

Technical Field

[0001] The present application relates to the field of simulation experiments, and in particular to a virtual platform simulation monitoring method and device. Background Art

[0002] With the rapid development of industry and intelligent manufacturing, the digitization and intelligence of production lines have become an important direction for the upgrading of manufacturing industry. Traditional production line monitoring systems mainly rely on physical equipment and on-site operations, and have problems such as high monitoring costs, poor flexibility, and difficulty in remote management. In order to solve these problems, virtual platform simulation monitoring technology came into being and became one of the current research hotspots.

[0003] Currently, production line monitoring mainly relies on signals sent by programmable logic controllers (PLCs). These signals are usually low-level hardware signals that are directly used to control the movement and status of equipment. However, how to convert these low-level signals into visual and easy-to-understand virtual models remains a challenge. Some existing methods usually require a lot of manual intervention and programming work, making it difficult to achieve automation and real-time performance.

[0004] Simulation technology has been widely used in the industrial field, but traditional simulation software is often a closed system that is difficult to interact with the data of the actual production line in real time. How to organically combine simulation technology with the monitoring of the actual production line to realize a monitoring system that combines virtual and real is an important direction of current research.

[0005] The development of 3D modeling and visualization technology provides technical support for the construction of virtual platforms. However, how to achieve lightweight and real-time rendering while ensuring model accuracy, especially how to achieve smooth 3D model display on the web, still faces challenges.

[0006] Physics computing engines have been widely used in the fields of games and animation, but their application in the simulation of industrial production lines is still in the exploratory stage. How to accurately simulate the motion characteristics and dynamic behavior of complex industrial equipment while ensuring computing efficiency is a problem that needs to be solved.

[0007] The development of web technology has provided new possibilities for remote monitoring. Technologies such as WebGL make it possible to implement complex 3D rendering in browsers. However, how to achieve real-time updates and interactions of large-scale 3D models on the web while ensuring performance and compatibility remains a technical challenge.

[0008] Message queue technology is widely used in distributed systems, but its application in real-time data interaction between virtual platforms and actual production lines is relatively new. How to design an efficient and reliable message delivery mechanism to support real-time status updates of a large number of devices is an important issue to consider.

[0009] In addition, existing virtual platform simulation monitoring systems often lack sufficient flexibility and scalability. How to design a general framework that can adapt to different types of production lines and equipment and easily integrate new functions and technologies is an important direction of current research.

[0010] In summary, although virtual platform simulation monitoring technology has made certain progress, there are still many challenges in signal analysis, real-time simulation, 3D visualization, physical simulation, web rendering, and real-time data transmission, which require further research and innovation. Summary of the invention

[0011] In view of the problems in the prior art, the present application provides a virtual platform simulation monitoring method and device, which can effectively improve the automation and real-time interactivity of virtual platform simulation monitoring.

[0012] In order to solve at least one of the above problems, the present application provides the following technical solutions:

[0013] In a first aspect, the present application provides a virtual platform simulation monitoring method, comprising:

[0014] The received programmable logic controller signal of the actual production line is analyzed by setting a code parsing model to obtain a corresponding virtual signal table, and the motion trajectory of the simulation model is established in the simulation software according to the virtual signal table, and simulation events are added at key control nodes;

[0015] According to the motion trajectory and the simulation event, the actual motion characteristics and dynamic behaviors of the production line equipment are simulated by using a modeling method of a physical computing engine, a virtual production line model in a 3D model file format is output, and a web page service is started, and the virtual production line model and message queue connection parameters are published to the web page, so that the web page receives the virtual production line model and the message queue connection parameters, and connects to the corresponding message queue service, and sends a ready status signal to the message queue service;

[0016] After monitoring the ready status signal of the web page, the location information and status change information of the virtual production line model are sent to the message queue service, so that the web page updates the corresponding model position and model status after receiving the location information and the status change information.

[0017] Furthermore, before performing signal analysis on the received programmable logic controller signal of the actual production line by setting a code analysis model to obtain a corresponding virtual signal table, the method includes:

[0018] Obtain historical PLC programming code samples from a PLC signal library corresponding to a preset industrial communication protocol as training data for the initial model;

[0019] The training data is input into an initial model using a sequence-to-sequence architecture for model training, and parameters are tuned according to signal definitions and variable declarations output by the model to obtain a code parsing model.

[0020] Furthermore, the received programmable logic controller signal of the actual production line is subjected to signal analysis by setting a code analysis model to obtain a corresponding virtual signal table, including:

[0021] Performing signal analysis on the programmable logic controller signal of the actual production line by setting a code parsing model, and extracting key signal features according to the signal analysis results, wherein the key signal features include at least one of naming, data type, usage frequency, and relevance;

[0022] The key signal features are structurally integrated to obtain a corresponding virtual signal table.

[0023] Furthermore, the motion trajectory of the simulation model is established in the simulation software according to the virtual signal table, and simulation events are added at key control nodes, including:

[0024] Convert various input / output signals defined in the virtual signal table into analog signal sources and load devices in the simulation software, and construct the motion trajectory and dynamic behavior of the production line equipment in the simulation environment according to the logical relationship between the signals;

[0025] After establishing the basic simulation model, add corresponding simulation events to the key control nodes to simulate the execution process of the PLC control logic in the actual production line.

[0026] Furthermore, the actual motion characteristics and dynamic behaviors of the production line equipment are simulated by using a modeling method of a physical computing engine according to the motion trajectory and the simulation event, and a virtual production line model in a 3D model file format is output, including:

[0027] The preset physical engine is used to set the physical parameters of mass, inertia and friction of various production line equipment in the simulation software, and the actual motion process of the equipment under the action of motion trajectory and simulation events is simulated according to the calculation of the physical parameters;

[0028] The virtual production line scene constructed in the simulation software is exported into a visual virtual production line model using a 3D model file format.

[0029] Further, the publishing of the virtual production line model and the message queue connection parameters to the web page, so that the web page receives the virtual production line model and the message queue connection parameters, connects to the corresponding message queue service, and sends a ready status signal to the message queue service, including:

[0030] Publishing the virtual production line model and the corresponding message queue connection parameters to the web service in JSON format via HTTP protocol;

[0031] After receiving the virtual production line model and the message queue connection parameters, the web page establishes a connection channel with the message queue service according to the message queue connection parameters, and sends a ready status signal to the message queue service to indicate that the web page has successfully received and connected to the relevant resources.

[0032] Further, after monitoring the ready state signal of the web page, starting to send the location information and state change information of the virtual production line model to the message queue service, so that the web page updates the corresponding model position and model state after receiving the location information and the state change information, including:

[0033] The ready state signal of the web page is monitored by periodically polling the message queue service, and when the ready state signal of the web page is detected, the location information and state change information of the virtual production line model is pushed to the web page through the publish-subscribe mechanism of the message queue service;

[0034] After receiving the position information and status change information, the web page can update the display of the virtual production line model accordingly to reflect the operating status of the production line in real time.

[0035] In a second aspect, the present application provides a virtual platform simulation monitoring device, comprising:

[0036] The signal analysis module is used to perform signal analysis on the programmable logic controller signal received from the actual production line by setting a code parsing model to obtain a corresponding virtual signal table, establish a motion trajectory of the simulation model in the simulation software according to the virtual signal table, and add simulation events at key control nodes;

[0037] A model determination module is used to simulate the actual motion characteristics and dynamic behaviors of the production line equipment by adopting a modeling method of a physical computing engine according to the motion trajectory and the simulation event, output a virtual production line model in a 3D model file format, and start a web page service, publish the virtual production line model and message queue connection parameters to the web page, so that the web page receives the virtual production line model and the message queue connection parameters and connects to the corresponding message queue service, and sends a ready status signal to the message queue service;

[0038] The web page interaction module is used to start sending the location information and status change information of the virtual production line model to the message queue service after monitoring the ready status signal of the web page, so that the web page updates the corresponding model position and model status after receiving the location information and the status change information.

[0039] In a third aspect, the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the virtual platform simulation monitoring method when executing the program.

[0040] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the virtual platform simulation monitoring method when executed by a processor.

[0041] In a fifth aspect, the present application provides a computer program product, including a computer program / instruction, which implements the steps of the virtual platform simulation monitoring method when executed by a processor.

[0042] It can be seen from the above technical solution that the present application provides a virtual platform simulation monitoring method and device, which performs signal analysis on the received programmable logic controller signal of the actual production line by setting a code parsing model to obtain a corresponding virtual signal table, establishes the motion trajectory of the simulation model in the simulation software according to the virtual signal table, and adds simulation events at key control nodes; outputs the virtual production line model in the 3D model file format, and publishes the virtual production line model and message queue connection parameters to the web page; after monitoring the ready status signal of the web page, starts to send the location information and status change information of the virtual production line model to the message queue service, so that the web page receives the location information and status change information and updates the corresponding model position and model status, thereby effectively improving the automation and real-time interactivity of the virtual platform simulation monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0044] Figure 1 This is one of the flow charts of the virtual platform simulation monitoring method in the embodiment of the present application;

[0045] Figure 2 This is a second flow chart of the virtual platform simulation monitoring method in an embodiment of the present application;

[0046] Figure 3 The third flowchart of the virtual platform simulation monitoring method in the embodiment of the present application;

[0047] Figure 4 This is a fourth flow chart of the virtual platform simulation monitoring method in an embodiment of the present application;

[0048] Figure 5 This is a fifth flow chart of the virtual platform simulation monitoring method in the embodiment of the present application;

[0049] Figure 6 This is a sixth flow chart of the virtual platform simulation monitoring method in the embodiment of the present application;

[0050] Figure 7 FIG7 is a flow chart of the virtual platform simulation monitoring method in the embodiment of the present application;

[0051] Figure 8 It is a structural diagram of a virtual platform simulation monitoring device in an embodiment of the present application;

[0052] Fig. 9 It is a schematic diagram of the structure of an electronic device in an embodiment of the present application.

[0053] Reference numerals:

[0054] Electronic device 9600, central processing unit 9100, memory 9140, communication module 9110, input unit 9120, audio processor 9130, display 9160, power supply 9170, buffer memory 9141, application / function storage unit 9142, data storage unit 9143, driver program storage unit 9144, antenna 9111, speaker 9131, microphone 9132. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0056] The acquisition, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of national laws and regulations.

[0057] In view of the problems existing in the prior art, the present application provides a virtual platform simulation monitoring method and device, which performs signal analysis on the received programmable logic controller signal of the actual production line by setting a code parsing model to obtain a corresponding virtual signal table, establishes the motion trajectory of the simulation model in the simulation software according to the virtual signal table, and adds simulation events at key control nodes; outputs the virtual production line model in the 3D model file format, and publishes the virtual production line model and message queue connection parameters to the web page; after monitoring the ready status signal of the web page, starts to send the location information and status change information of the virtual production line model to the message queue service, so that the web page updates the corresponding model position and model status after receiving the location information and status change information, thereby effectively improving the automation and real-time interactivity of the virtual platform simulation monitoring.

[0058] In order to effectively improve the automation and real-time interactivity of virtual platform simulation monitoring, the present application provides an embodiment of a virtual platform simulation monitoring method, see Figure 1 The virtual platform simulation monitoring method specifically includes the following contents:

[0059] Step S101: Perform signal analysis on the received programmable logic controller signal of the actual production line by setting a code parsing model to obtain a corresponding virtual signal table, establish a motion trajectory of the simulation model in the simulation software according to the virtual signal table, and add simulation events at key control nodes;

[0060] Optionally, in this embodiment, by analyzing the programmable logic controller (PLC) signal of the actual production line, a virtual signal table is established, and the motion trajectory of the simulation model is constructed in the simulation software, and simulation events are added to key control nodes. This method can not only accurately simulate the operating conditions of the actual production line, but also provide strong support for production line optimization and fault diagnosis.

[0061] First, the system receives PLC signals from the actual production line. These signals usually contain status information of each component of the production line, control instructions, and sensor data. For example, for an automobile assembly line, these signals may include the position information of the robot arm, the speed of the conveyor belt, the readings of various sensors (such as temperature, pressure, distance, etc.), and various control instructions (such as start, stop, speed adjustment, etc.).

[0062] Next, the system uses a pre-set code parsing model to analyze these PLC signals. A code parsing model is an algorithm or set of rules specifically designed to interpret and understand PLC signals. The core function of this model is to convert the original PLC signal into a format that is easier to understand and process. For example, it may convert a binary control signal into a specific instruction description, or convert an analog signal into an actual physical quantity.

[0063] The design of the code parsing model is usually based on a deep understanding of the PLC programming language and communication protocol of a specific production line. It may use various techniques, such as regular expression matching, syntax analysis, semantic parsing, etc., to accurately interpret the meaning of PLC signals. This process may also involve pre-processing steps such as signal denoising and data verification to ensure the accuracy of the parsing results.

[0064] Through the processing of the code parsing model, the system obtains a virtual signal table. This virtual signal table is a high-level abstract representation of the actual PLC signal, which describes the various states and control information of the production line in a structured and standardized format. The virtual signal table may contain information in multiple dimensions, such as timestamp, device ID, signal type, signal value, etc. For example, for the action of a robot arm, the virtual signal table may contain information like: timestamp, robot ID, joint angle, end effector status, etc.

[0065] With the virtual signal table, the system begins to build the motion trajectory of the simulation model in the simulation software. This process involves converting the information in the virtual signal table into instructions that the simulation software can understand and execute. Simulation software is usually a tool specifically designed to simulate and visualize the behavior of complex systems, such as Siemens Tecnomatix PlantSimulation, AnyLogic, etc.

[0066] In the simulation software, the system creates various virtual objects (such as robots, conveyors, workstations, etc.) based on the information in the virtual signal table and defines the properties and behaviors of these objects. Then, the system generates the motion trajectories of these virtual objects based on the timing information and control instructions in the virtual signal table. For example, for a robot arm, the system accurately reproduces its motion process in the simulation environment based on the joint angle information in the virtual signal table.

[0067] In addition to the basic motion trajectory, the system also adds simulation events at key control nodes. These events may include state changes (such as machine start or stop), triggering of specific conditions (such as reaching a certain temperature threshold), simulation of abnormal situations (such as equipment failure), etc. The purpose of adding these events is to make the simulation more realistic and complete, and to better reflect the various situations and possible problems of the actual production line.

[0068] This method solves several key problems faced by traditional production line simulation. First, it improves the accuracy and authenticity of the simulation. Traditional simulation methods are often based on idealized or simplified models, which are difficult to fully reflect the complexity and variability of actual production lines. This simulation method based on actual PLC signals can very accurately simulate the behavior of real production lines, including various details and non-ideal situations.

[0069] Secondly, this method improves the real-time and dynamic nature of the simulation. Since the simulation is based on real-time PLC signals, it can promptly reflect the state changes and abnormal conditions of the production line. This makes the simulation no longer a static, preset process, but can dynamically track and simulate the operation of the actual production line.

[0070] Furthermore, this approach provides a powerful tool for production line optimization and fault diagnosis. By adding various events and monitoring points in the simulation, engineers can more easily identify bottlenecks, potential problems and optimization opportunities in the production line. For example, by analyzing the utilization and waiting time of each component in the simulation, the problem of unbalanced production capacity can be discovered; by simulating various abnormal situations, emergency response strategies can be tested and improved.

[0071] In terms of technical effects, this approach has brought significant improvements. First, it greatly improves the accuracy and reliability of production line simulation. By directly using actual PLC signals, the simulation results can be very close to the performance of the actual production line, which provides a more reliable basis for production line optimization and decision-making.

[0072] Secondly, this approach improves the efficiency of production line analysis and optimization. Since the simulation model can accurately reflect the behavior of the actual production line, engineers can quickly test various optimization schemes and improvement measures in the simulation environment without having to conduct time-consuming and labor-intensive tests on the actual production line. This not only saves time and cost, but also reduces risks in actual operations.

[0073] Step S102: According to the motion trajectory and the simulation event, the actual motion characteristics and dynamic behaviors of the production line equipment are simulated by using a modeling method of a physical computing engine, a virtual production line model in a 3D model file format is output, and a web page service is started, and the virtual production line model and message queue connection parameters are published to the web page, so that the web page receives the virtual production line model and the message queue connection parameters and connects to the corresponding message queue service, and sends a ready status signal to the message queue service;

[0074] Optionally, in this embodiment, the system uses a physical computing engine modeling method to simulate the actual motion characteristics and dynamic behaviors of production line equipment based on previously generated motion trajectories and simulation events. This process not only involves complex physical simulation, but also includes the generation of 3D models and the startup of web services, ultimately achieving visualization and real-time interaction of the virtual production line model.

[0075] First, the system uses a physical computing engine to simulate the actual motion characteristics and dynamic behavior of production line equipment. A physical computing engine is a software tool specifically used to simulate various phenomena in the physical world, such as Bullet, PhysX, or Havok. These engines can accurately simulate the physical characteristics of objects such as movement, collision, and deformation, so that objects in the virtual environment exhibit behaviors similar to those in the real world.

[0076] In this step, the system takes the previously generated motion trajectories and simulation events as input and processes them through the physical calculation engine. For example, for the movement of a robot arm, the physical engine will consider its mass, inertia, joint limitations and other factors to calculate the accurate motion path and speed changes. At the same time, it will also simulate various physical interactions, such as the assembly process of parts, the movement of items on a conveyor belt, the flow of liquids, etc. This physics-based simulation can capture many subtle dynamic behaviors, such as vibration, friction, elastic deformation, etc., which are often ignored in simple geometric simulations.

[0077] The use of the physical computing engine greatly improves the authenticity and accuracy of the virtual production line model. It can not only simulate the production line behavior under normal operating conditions, but also simulate various abnormal conditions and extreme conditions, such as equipment failure, overload operation, etc. This provides a valuable virtual testing platform for production line optimization and fault prevention.

[0078] After completing the physical simulation, the system outputs the simulation results as a virtual production line model in a 3D model file format. This process involves complex 3D modeling and rendering technology. The system needs to convert the results of the physical simulation into a visual 3D model, including geometric shapes, materials, textures, lighting and other elements. Commonly used 3D model file formats include OBJ, FBX, GLTF, etc. These formats can save detailed model information and are compatible with a variety of 3D rendering engines and visualization tools.

[0079] The generated 3D virtual production line model contains not only static geometric information, but also animation data, which can show the movement and state changes of the equipment. For example, a 3D model of an assembly line may include dynamic elements such as moving robot arms, rotating conveyor belts, and switch control panels. This dynamic 3D model can intuitively show the operation process of the production line, helping users better understand and analyze the behavior of the production line.

[0080] Next, the system starts the web service. The purpose of this step is to publish the virtual production line model to the Internet so that users can access and interact with it through a web browser. The web service is usually built using modern web technologies, such as Node.js as the backend server and WebGL or Three.js as the front-end 3D rendering engine.

[0081] The system publishes the generated virtual production line model and message queue connection parameters to the web page. A message queue is a middleware used to transmit messages in a distributed system, such as RabbitMQ, Apache Kafka, etc. Message queue connection parameters usually include server address, port number, authentication information, etc. These parameters enable the web page to establish real-time communication with the backend system.

[0082] When the web page receives the virtual production line model and message queue connection parameters, it automatically connects to the corresponding message queue service. This establishes a real-time two-way communication channel, allowing the web page to receive real-time updates from the backend system and send user operations and query requests to the backend.

[0083] After the connection is successful, the web page will send a ready status signal to the message queue service. This signal tells the backend system that the web page has completed initialization and connection and can start receiving real-time data and processing user interactions.

[0084] This web-based approach to virtual production line model visualization and interaction solves several key issues faced by traditional production line simulation systems. First, it improves the accessibility of the system. Through a web browser, users can access the virtual production line model anywhere and on any device without installing specialized software. This greatly reduces the threshold for using the system, allowing more people (such as managers, customers, remote experts, etc.) to participate in the analysis and optimization of the production line.

[0085] Secondly, this method improves the real-time and interactivity of the system. Through the real-time communication mechanism of the message queue, the system can promptly reflect the status changes of the production line, and users can also interact with the virtual model in real time. For example, users can adjust a parameter on the web page and immediately see the impact of this change on the operation of the entire production line.

[0086] Furthermore, this web-based approach facilitates multi-person collaboration. Multiple users can access the same virtual production line model at the same time to conduct analysis and discussion together. This is particularly helpful for cross-departmental and cross-regional team collaboration.

[0087] In terms of technical effects, this approach has brought significant improvements. First, it greatly improves the authenticity and accuracy of production line simulation. Through the precise simulation of the physical computing engine, the virtual production line model can be very close to the behavior of the actual production line, including various complex physical interactions and dynamic characteristics. This provides a more reliable basis for production line optimization and decision-making.

[0088] Secondly, this method improves the efficiency of production line analysis and optimization. Through real-time interaction on the web page, engineers can quickly try various optimization solutions and see the results immediately. This greatly shortens the cycle of production line optimization and reduces costs.

[0089] Finally, this approach opens up new possibilities for remote monitoring and management of production lines. The web-based virtual production line model can be synchronized with the actual production line data in real time to form a "digital twin" system. This makes remote monitoring, diagnosis and control possible, greatly improving management efficiency and response speed.

[0090] In general, this virtual production line model method based on physical computing engine and web technology represents an important progress in the digitalization and intelligence of manufacturing industry. It not only improves the accuracy and practicality of production line simulation, but also provides a powerful tool for remote management and collaborative optimization of production lines.

[0091] Step S103: After monitoring the ready status signal of the web page, start sending the location information and status change information of the virtual production line model to the message queue service, so that the web page updates the corresponding model position and model status after receiving the location information and the status change information.

[0092] Optionally, in this embodiment, the system implements a real-time update and synchronization mechanism for the virtual production line model. This step is a crucial part of the entire virtual production line simulation system, which ensures that the virtual production line model displayed on the web page can accurately reflect the state of the production line simulated in the background system. This real-time synchronization mechanism not only improves the accuracy and practicality of the simulation, but also provides users with a dynamic and interactive production line monitoring and analysis platform.

[0093] First, the system monitors the ready status signal from the web page. This signal is usually a simple message that may contain some basic information about the web page, such as client ID, user permissions, etc. After the system receives this signal, it knows that the web page has completed initialization and established a connection with the message queue service. This step ensures that the system only starts sending data when the web page is ready, avoiding data loss or inability to process correctly.

[0094] After receiving the ready signal, the system starts to send the location information and state change information of the virtual production line model to the message queue service. This information is generated in real time and reflects the latest status of the virtual production line model in the physical simulation. The location information usually includes the spatial coordinates and rotation angles of each component in the production line (such as robots, conveyor belts, workpieces, etc.). These data enable the web page to accurately update the position and posture of the 3D model. State change information may include the operating status of the equipment (such as on, off, standby), production parameters (such as speed, temperature, pressure), abnormal conditions (such as faults, warnings), etc.

[0095] The frequency with which the system sends this information depends on the accuracy requirements of the simulation and the limitations of the network bandwidth. For critical components that require high-precision real-time display, the system may send updates at a higher frequency (such as multiple times per second); while for components that change slowly or are less important, a lower update frequency may be used. This differentiated update strategy can optimize the use of network resources while ensuring display effects.

[0096] The message queue service plays a key middleman role in this process. It receives location and status information from the backend system and delivers this information quickly and reliably to all connected web clients. The message queue service usually adopts a publish-subscribe model, which means that multiple web clients can receive the same update information at the same time, realizing real-time synchronous observation by multiple users.

[0097] When the web page receives this position information and state change information, it will immediately update the corresponding model position and model state. This process usually involves complex 3D rendering technology. For example, for position information, the web page's 3D rendering engine (such as Three.js or Babylon.js) will update the transformation matrix of the corresponding 3D object to achieve translation, rotation or scaling of the model. For state change information, the web page may update the model's color, transparency, texture and other visual attributes, or trigger certain animation effects to intuitively reflect the change in state.

[0098] This real-time update mechanism solves several key problems faced by traditional production line monitoring systems. First, it greatly improves the synchronization between the virtual production line model and the actual production line status. In traditional systems, the update of production line status often has a certain delay, or can only be refreshed at a low frequency. Through this real-time update mechanism based on message queues, the virtual model can reflect the status changes of the production line in almost real time, allowing users to promptly discover and respond to various situations in the production line.

[0099] Secondly, this mechanism improves the scalability and flexibility of the system. Since the message queue is used as the middle layer, the system can easily support multiple clients to connect at the same time without causing too much pressure on the background system. In addition, this architecture also makes it easier to expand the system's functions, such as adding new data sources, adding more complex data processing logic, etc.

[0100] Furthermore, this real-time update mechanism provides strong support for remote monitoring and management of production lines. Through real-time updates on the web page, managers can monitor the operation of the production line anywhere and at any time, and quickly respond to various abnormal situations. For example, if the temperature of a certain device rises abnormally, the system can immediately reflect this change in the virtual model and remind the user through color changes or warning icons.

[0101] In terms of technical effects, this real-time update mechanism has brought significant improvements. First, it greatly improves the accuracy and reliability of the virtual production line model. Since the model status can reflect the changes in the actual production line in a timely manner, users can trust the information provided by the virtual model more and make more accurate decisions.

[0102] Secondly, this mechanism improves the efficiency of production line monitoring and analysis. Users no longer need to refresh manually or wait for regular updates, but can observe various changes in the production line in real time. This makes problem diagnosis and performance optimization faster and more accurate. For example, engineers can observe the flow of products on the production line in real time and immediately identify potential bottlenecks or inefficient links.

[0103] Finally, this real-time update mechanism lays the foundation for advanced analytical and predictive capabilities. By continuously receiving and processing real-time data, the system can perform a variety of complex analytical tasks, such as trend analysis, anomaly detection, performance prediction, etc. For example, the system can predict the maintenance needs of equipment based on real-time data, or optimize production plans to improve overall efficiency.

[0104] In general, this real-time update mechanism based on message queues represents an important application of industrial IoT and digital twin technology. It not only improves the accuracy and practicality of virtual production line models, but also provides a powerful tool for intelligent management and optimization of production lines. With this approach, manufacturing companies can better understand and control their production processes, improve production efficiency, reduce operating costs, and enhance market competitiveness.

[0105] With the further development and application of this technology, we can expect to see more innovative solutions for the manufacturing industry. For example, combined with artificial intelligence technology, the system can automatically analyze real-time data, make optimization suggestions or predict potential problems. Combined with augmented reality (AR) technology, maintenance personnel can see superimposed virtual information directly on the actual equipment through smart glasses, greatly improving maintenance efficiency. Combined with machine learning algorithms, the system can continuously learn and optimize its own prediction model, making production line management more and more intelligent and automated.

[0106] This technology also opens up new possibilities for cross-regional and cross-departmental collaboration. For example, experts in different locations can view the same virtual production line model at the same time, conduct remote diagnosis and collaborative optimization. The product design team can directly observe the operation of the production line, better understand production constraints, and thus optimize product design.

[0107] From the above description, it can be seen that the virtual platform simulation monitoring method provided in the embodiment of the present application can perform signal analysis on the received programmable logic controller signal of the actual production line by setting a code parsing model to obtain a corresponding virtual signal table, establish the motion trajectory of the simulation model in the simulation software according to the virtual signal table, and add simulation events at key control nodes; output the virtual production line model in the 3D model file format, and publish the virtual production line model and message queue connection parameters to the web page; after monitoring the ready status signal of the web page, start sending the location information and status change information of the virtual production line model to the message queue service, so that the web page receives the position information and status change information and updates the corresponding model position and model status, thereby effectively improving the automation and real-time interactivity of the virtual platform simulation monitoring.

[0108] In one embodiment of the virtual platform simulation monitoring method of the present application, see Figure 2 , and can also include the following:

[0109] Step S201: Acquire historical PLC programming code samples from a PLC signal library corresponding to a preset industrial communication protocol as training data for an initial model;

[0110] Step S202: input the training data into an initial model using a sequence-to-sequence architecture for model training, and perform parameter tuning according to the signal definition and variable declaration output by the model to obtain a code parsing model.

[0111] Optionally, in this embodiment, steps S201 and S202 constitute a complete machine learning process, aiming to develop an intelligent model that can parse and understand PLC (Programmable Logic Controller) programming code. This process involves key links such as data preparation, model training and parameter tuning, and the ultimate goal is to create a code parsing model that can accurately parse various PLC programming codes. This model has broad application prospects in the fields of industrial automation and intelligent manufacturing, and can greatly improve the efficiency and accuracy of PLC programming.

[0112] In step S201, the system first obtains historical PLC programming code samples from the PLC signal library corresponding to the preset industrial communication protocol. These samples will be used as training data for the initial model. The PLC signal library is usually a database containing a large amount of real-world PLC programming codes, which cover various industrial application scenarios such as production line control, process control, safety systems, etc. It is very important to use real historical code samples as training data because this ensures that the model can learn the programming patterns and best practices used in actual industrial environments.

[0113] PLC programming codes usually follow specific industrial communication protocols, such as Modbus, Profibus, EtherNet / IP, etc. Each protocol has its own specific signal definitions and communication rules. Therefore, obtaining samples from the signal library of a specific protocol ensures that the model can learn the characteristics and specifications of the protocol. This step solves an important problem faced in traditional manual programming: compatibility and consistency between different protocols. By using protocol-specific samples, the model can learn how to correctly interpret and generate code that meets the requirements of a specific protocol.

[0114] When preparing training data, the system may also need to perform some preprocessing steps. For example, standardize the code, remove comments and irrelevant whitespace characters, convert the code to a unified format, etc. These preprocessing steps can improve the quality of training data and make it easier for the model to learn the essential characteristics of the code.

[0115] Step S202 is the core link of model training and optimization. In this step, the system inputs the prepared training data into the initial model using the sequence-to-sequence (Seq2Seq) architecture for training. Seq2Seq is a powerful deep learning architecture that is particularly suitable for processing problems where both input and output are sequences, such as machine translation, text summarization, etc. In the scenario of PLC code parsing, the input sequence is the PLC code, and the output sequence is the parsed signal definition and variable declaration.

[0116] The Seq2Seq model usually consists of an encoder and a decoder. The encoder is responsible for encoding the input sequence (PLC code) into a fixed-length vector representation that captures the semantic information of the input sequence. The decoder generates an output sequence (signal definition and variable declaration) based on this vector representation. During the training process, the model learns how to best encode the input information and how to generate the correct output based on this information.

[0117] To improve the performance of the model, some advanced techniques such as attention mechanisms may be used. The attention mechanism allows the decoder to dynamically focus on different parts of the input sequence when generating each output element. This is particularly useful for parsing complex PLC codes, because different parts of the code may have different degrees of importance for different output elements.

[0118] The training process usually uses the back propagation algorithm and gradient descent method to optimize the model parameters. The system uses a loss function to measure the gap between the model output and the true label, and adjusts the model parameters by minimizing this loss function. Commonly used loss functions include cross entropy loss, mean square error, etc.

[0119] While training, the system will perform parameter tuning based on the signal definition and variable declaration of the model output. This process usually involves the use of a validation set. The system will periodically evaluate the performance of the model on the validation set and adjust the model's hyperparameters, such as learning rate, batch size, number of network layers, etc., based on the evaluation results. This dynamic tuning process helps find the optimal model configuration and improve the model's generalization ability.

[0120] The goal of parameter tuning is to enable the model to accurately identify and parse signal definitions and variable declarations in PLC code. This includes correctly identifying variable types (such as Boolean, integer, floating point, etc.), variable scopes (such as global variables, local variables), signal types (such as input signals, output signals, internal signals), etc. The model also needs to learn to handle different programming styles and code structures, because different programmers may have different coding habits.

[0121] Through this training and tuning process, the system finally obtains a code parsing model that can accurately parse PLC code. This model can automatically extract key information from PLC code, greatly improving the efficiency of code analysis and understanding.

[0122] This machine learning-based PLC code parsing method solves several key problems faced by traditional manual analysis methods. First, it greatly improves the efficiency of code analysis. Traditional methods require professionals to manually read and parse the code, which is a time-consuming and error-prone process. The machine learning model can complete the parsing of large amounts of code in seconds, significantly improving work efficiency.

[0123] Secondly, this approach improves the consistency and accuracy of code parsing. Manual parsing is easily affected by personal experience and subjective judgment, while machine learning models can maintain a high degree of consistency and will not make mistakes due to fatigue or lack of concentration.

[0124] Furthermore, this approach is highly scalable and adaptable. As new training data is continuously added, the model can continue to learn and improve, adapting to new programming patterns and industrial protocols. This enables the system to keep up with the pace of technological development and maintain long-term effectiveness.

[0125] In terms of technical effects, this machine learning-based PLC code parsing method has brought significant improvements. First, it greatly improves the efficiency of code understanding and maintenance. In large industrial projects, PLC codes can be very complex and large. Automated code parsing tools can quickly generate an overview of code structure and variable usage, helping engineers understand code logic faster, locate problems, and optimize code.

[0126] Secondly, this approach provides a powerful tool for code quality control and standardization. By analyzing a large number of code samples, the model can learn the best programming practices and common error patterns. This information can be used to develop code checking tools to automatically identify potential problems and irregular coding habits, and improve the quality and maintainability of the code.

[0127] Finally, this approach paves the way for intelligent and automated PLC programming. As the model continues to improve, it will not only be able to parse existing codes, but also have the potential to generate new codes. This may lead to the emergence of intelligent PLC programming assistants in the future, which can automatically generate PLC code based on natural language descriptions or high-level instructions, greatly lowering the threshold of programming and improving the development efficiency of industrial automation systems.

[0128] In one embodiment of the virtual platform simulation monitoring method of the present application, see Figure 3 , and can also include the following:

[0129] Step S301: performing signal analysis on the programmable logic controller signal of the actual production line by setting a code parsing model, and extracting key signal features according to the signal analysis results, wherein the key signal features include at least one of naming, data type, usage frequency and relevance;

[0130] Step S302: structurally integrate the key signal features to obtain a corresponding virtual signal table.

[0131] Optionally, in this embodiment, steps S301 and S302 constitute a complex and sophisticated process, which aims to convert the programmable logic controller (PLC) signal of the actual production line into a structured virtual signal table. This process not only involves advanced signal analysis technology, but also requires a deep understanding of the principles of PLC programming and industrial automation. Through these two steps, we can convert complex PLC signals into structured data that is easy to understand and use, providing an important foundation for subsequent production line optimization and intelligence.

[0132] In step S301, the system first analyzes the PLC signal of the actual production line. This process uses the code parsing model trained in the previous step. This model can understand and parse various PLC programming languages, such as ladder diagram, function block diagram, instruction list, etc. The input of the model is the original PLC code or signal data, and the output is the signal features after parsing and classification.

[0133] The code parsing model plays a key role in this step. It not only identifies the basic properties of each signal, but also understands the logical relationship between the signals and their role in the entire control system. This deep understanding enables the model to extract truly valuable key signal features, rather than just superficial data.

[0134] The extraction of key signal features is the core of this step. These features include but are not limited to signal naming, data type, frequency of use, and relevance. The naming of a signal usually reflects its function or purpose in the system. Correct parsing of the naming can help us understand the role of the signal. The data type (such as Boolean, integer, floating point, etc.) determines the type and range of information that the signal can carry. The frequency of use reflects the importance of the signal in the entire control system. Frequently used signals may be key components of the system. Relevance describes the logical relationship between signals, which is crucial to understanding the control logic.

[0135] For example, in a production line control system, there may be a signal named "conveyor_speed". Through analysis, the system may find that this is a floating-point data used to control the conveyor speed. It is used very frequently and is read or modified in almost every control cycle. At the same time, it has a strong correlation with other signals such as "product_weight" and "motor_current", which indicates that there may be some control relationship between conveyor speed, product weight and motor current.

[0136] In the process of extracting these features, the code parsing model may use a variety of techniques. For example, it may use natural language processing (NLP) technology to analyze the naming of signals, use statistical analysis to calculate the frequency of use, and use graph theory algorithms to analyze the correlation between signals. This multi-dimensional analysis enables the system to fully understand the characteristics and functions of each signal.

[0137] Step S302 is to structure and integrate the key signal features extracted in S301, and finally generate a virtual signal table. This process involves data organization and structuring technology. The system needs to design a suitable data structure to represent these signal features so that they can retain the original information and facilitate subsequent processing and analysis.

[0138] Virtual signal tables usually adopt a hierarchical structure. For example, signals can be organized according to functional modules or control objects, and each signal contains information such as its name, data type, usage frequency, and a list of associated signals. This structured representation allows engineers to quickly understand the structure of the entire control system and the role of each signal.

[0139] In the process of generating a virtual signal table, the system also needs to handle some complex situations. For example, some signals may have different meanings or functions in different contexts, and the system needs to be able to identify this situation and represent it appropriately. In addition, for those signal groups with strong correlations, the system may need to create special data structures to represent the relationship between them, such as using graphs or matrices to describe signal networks.

[0140] These two steps solve several key problems faced in traditional PLC programming and maintenance. First, it greatly improves the efficiency of understanding complex control systems. In traditional methods, engineers need to manually read and analyze a large amount of PLC code to understand the structure and logic of the system, which is a time-consuming and error-prone process. Through automated signal analysis and structuring, engineers can quickly obtain a global view of the system, greatly shortening the time for learning and understanding.

[0141] Secondly, this approach improves the maintainability of the control system. The virtual signal table provides a clear overview of the system structure, making it easier for engineers to locate problems, perform fault diagnosis, and optimize the system. For example, if a module is abnormal, engineers can quickly view all the signals and their characteristics related to the module, speeding up the location and resolution of the problem.

[0142] Furthermore, this method provides an important basis for the optimization and upgrading of control systems. By analyzing the frequency and correlation of signal usage, engineers can identify key parts and potential bottlenecks in the system. This information can guide system optimization, such as adjusting control strategies and redesigning certain modules.

[0143] In terms of technical effects, this machine learning-based signal analysis and structuring method has brought significant improvements. First, it greatly improves the quality and consistency of control system documentation. Under traditional methods, system documentation often relies on manual writing, which is prone to inconsistencies or omissions. The automatically generated virtual signal table can ensure the integrity and accuracy of the documentation, providing a reliable reference for the long-term maintenance of the system.

[0144] Secondly, this approach lays the foundation for the digitization and intelligence of control systems. The virtual signal table actually creates a digital twin of the control system, which provides important support for subsequent simulation, optimization and predictive maintenance. For example, based on the virtual signal table, a high-fidelity system simulation model can be developed for training, testing and optimization.

[0145] Finally, this approach promotes the accumulation and inheritance of knowledge. In traditional industrial environments, system knowledge often exists in the minds of experienced engineers in an implicit form. By extracting this knowledge in a structured form, it can not only reduce the dependence on specific individuals, but also better spread and apply this knowledge.

[0146] In one embodiment of the virtual platform simulation monitoring method of the present application, see Figure 4 , and can also include the following:

[0147] Step S401: converting various input / output signals defined in the virtual signal table into analog signal sources and load devices in the simulation software, and constructing the motion trajectory and dynamic behavior of the production line equipment in the simulation environment according to the logical relationship between the signals;

[0148] Step S402: After the basic simulation model is established, corresponding simulation events are added to key control nodes to simulate the execution process of the PLC control logic in the actual production line.

[0149] Optionally, in this embodiment, steps S401 and S402 constitute a complex and sophisticated process that aims to transform the abstract information in the virtual signal table into a concrete, visual simulation model. This process not only involves advanced simulation technology, but also requires a deep understanding of the working principle and control logic of the industrial automation system. Through these two steps, we can create a highly realistic digital twin model, providing a powerful tool for subsequent system optimization, fault diagnosis and operator training.

[0150] In step S401, the system first converts the various input / output signals defined in the virtual signal table into analog signal sources and load devices in the simulation software. This process involves the technology of signal mapping and model building. Each signal defined in the virtual signal table needs to find a corresponding representation in the simulation environment. For example, an analog input signal may be mapped to an adjustable voltage source in the simulation environment, while a digital output signal may be mapped to a switch or indicator light.

[0151] During this conversion process, the system needs to take into account the characteristics of different types of signals. For example, for analog signals, its range, accuracy, and sampling frequency need to be considered; for digital signals, its logic level and switching time need to be considered. This precise mapping ensures that the simulation model can truly reflect the behavior of the actual system.

[0152] Next, the system constructs the motion trajectory and dynamic behavior of the production line equipment in the simulation environment based on the logical relationship between the signals. This step involves the construction of complex physical models and kinematic models. For example, for a robotic arm, the system needs to calculate the position and posture of its end effector based on its joint angle signals and kinematic equations. For a conveyor belt system, the system needs to simulate the movement of materials based on speed signals and load information.

[0153] When building these dynamic behaviors, the system may use a variety of advanced simulation techniques. For example, finite element analysis (FEA) may be used to simulate the deformation and stress distribution of complex structures, computational fluid dynamics (CFD) may be used to simulate the behavior of fluid systems, or multi-body dynamics may be used to simulate the motion of complex mechanical systems. These advanced technologies enable the simulation model to accurately reflect the physical characteristics and dynamic behavior of the actual system.

[0154] In some cases, the system may also need to consider the impact of environmental factors. For example, when simulating outdoor equipment, it may be necessary to consider the impact of environmental parameters such as temperature, humidity, and wind speed on equipment performance. This comprehensive consideration ensures that the simulation model can accurately predict system behavior under various conditions.

[0155] Step S402 is to add corresponding simulation events to key control nodes after building the basic simulation model to simulate the execution process of PLC control logic in the actual production line. This step involves the technology of control system simulation and event-driven simulation.

[0156] In this process, the system needs to convert the PLC control logic into events and actions in the simulation environment. For example, a simple IF-THEN statement may be converted into a condition-triggered event, and when the condition is met, the corresponding action will be executed. More complex control logic, such as PID control, sequence control, etc., requires a more complex set of events and actions to simulate.

[0157] In order to accurately simulate the behavior of the PLC, the system also needs to consider the PLC's scan cycle, task priority and other characteristics. For example, discrete event simulation technology can be used to simulate the PLC's periodic scan behavior to ensure that the simulation model can accurately reflect the timing characteristics of the actual system.

[0158] When adding these simulation events, the system also needs to take into account various abnormal situations that may occur in the actual production line. For example, it may be necessary to simulate sensor failures, actuator jams, communication interruptions, etc. to test the robustness and fault tolerance of the control system. This comprehensive consideration enables the simulation model to not only simulate normal operating conditions, but also be used for abnormal situation analysis and the formulation of treatment plans.

[0159] These two steps solve several key problems faced in the development and optimization of traditional industrial automation systems. First, it greatly reduces the cost and risk of system development and testing. In traditional methods, many testing and optimization work needs to be performed on actual hardware systems, which is not only costly but also may pose security risks. With accurate simulation models, most of the testing and optimization work can be done in a virtual environment, saving costs and improving safety.

[0160] Secondly, this method significantly shortens the system development and optimization cycle. In the simulation environment, various design solutions and control strategies can be quickly tested without frequently modifying the actual hardware. This rapid iteration capability allows engineers to explore more possibilities in a shorter period of time and ultimately obtain a better solution.

[0161] Furthermore, this approach improves the quality of system design and control. With accurate simulation models, engineers can gain a deeper understanding of the system's dynamic behavior and the interactions between various factors. This in-depth understanding helps to identify potential problems and optimization opportunities, thereby designing more efficient and reliable systems.

[0162] In terms of technical effects, this simulation-based approach has brought significant improvements. First, it greatly improves the accuracy and reliability of system design. By conducting a large number of tests and verifications in a virtual environment, potential problems can be discovered and resolved before the system is actually deployed, greatly reducing the risk of serious failures after the system is put into use.

[0163] Second, this approach provides a powerful tool for ongoing optimization of the system. Even after the system is put into operation, the simulation model can still be used to analyze system performance, diagnose problems, and evaluate improvement plans. For example, a simulation model can be used to predict system performance under different load conditions or to evaluate the effect of a new control algorithm.

[0164] Finally, this approach provides an ideal platform for operator training. Through simulation models, various operating scenarios can be created, including normal operations and abnormal situation handling, allowing operators to gain valuable practical experience in a safe environment. This not only improves the operator's skill level, but also helps reduce human errors in actual operations.

[0165] In one embodiment of the virtual platform simulation monitoring method of the present application, see Figure 5 , and can also include the following:

[0166] Step S501: using a preset physical engine to set the physical parameters of mass, inertia and friction of various production line equipment in the simulation software, and simulating the actual motion process of the equipment under the motion trajectory and simulation events according to the calculation of the physical parameters;

[0167] Step S502: export the virtual production line scene constructed in the simulation software into a visual virtual production line model using a 3D model file format.

[0168] Optionally, in this embodiment, steps S501 and S502 constitute a complex and sophisticated process, which aims to further improve and transform the virtual production line model in the simulation software into a visual 3D model. This process not only involves advanced physical simulation technology, but also requires a deep understanding of the principles of 3D modeling and visualization. Through these two steps, we can create a highly realistic and interactive virtual production line model, providing a powerful tool for subsequent system analysis, optimization and presentation.

[0169] In step S501, the system first uses the preset physics engine to set the physical parameters of various production line equipment in the simulation software, including mass, inertia, and friction. This process involves complex physical modeling and parameter setting techniques. The physics engine is one of the core components of the simulation software, which is responsible for calculating and simulating various physical phenomena, such as gravity, collision, friction, etc. Common physics engines include PhysX, Bullet, Havok, etc. These engines provide rich APIs and tools, allowing engineers to accurately simulate various complex physical behaviors.

[0170] When setting physical parameters, engineers need to take into account the characteristics of each device. For example, for a robotic arm, it is necessary to set the mass, moment of inertia, friction coefficient, etc. of each joint. For a conveyor belt, it is necessary to set its mass, friction coefficient, elastic coefficient, etc. The precise setting of these parameters is crucial to the accuracy of the simulation. In actual operation, engineers may need to refer to the technical specifications of the equipment or obtain these parameters through actual measurements.

[0171] After setting the physical parameters, the system will calculate and simulate the actual movement of the device under the action of the motion trajectory and simulation events based on these parameters. This process involves complex physical calculations and numerical integration. The physics engine solves the equation of motion in each time step, taking into account the effects of various forces such as gravity, friction, collision, etc., and then updates the position and velocity of the object. This precise physical simulation allows the behavior of the virtual production line to be very close to the actual production line.

[0172] For example, when simulating a robot arm grasping an object, the physics engine will take into account factors such as the mass distribution of the robot arm, the friction of the joints, and the weight of the object, and calculate the acceleration, velocity, and position change of the robot arm during movement. At the same time, it will also simulate the contact and grasping process between the robot arm and the object, taking into account factors such as grasping force and friction. This sophisticated simulation allows engineers to predict and analyze possible problems, such as unstable grasping and object slipping.

[0173] In step S502, the system exports the virtual production line scene constructed in the simulation software as a visualized virtual production line model in a 3D model file format. This step involves 3D modeling, data conversion, and visualization technology. Commonly used 3D model file formats include OBJ, FBX, COLLADA, etc. These formats can save the model's geometric information, material information, animation information, etc.

[0174] During the export process, the system needs to take multiple factors into consideration. The first is the accuracy of the geometric information, ensuring that the exported model accurately reflects the size and shape of the original design. The second is the material and texture information, which is crucial to the visual effect of the model. The third is the animation information. For those devices with moving parts, their motion trajectory and keyframe information need to be correctly exported. Finally, the complexity and file size of the model also need to be considered. While ensuring accuracy, the model structure should be optimized as much as possible to facilitate subsequent rendering and interaction.

[0175] During the export process, the system may also need to perform some optimization processing. For example, mesh simplification may be required to reduce the number of faces of the model and improve rendering efficiency. Or UV unwrapping may be required to better apply textures. For some particularly complex models, LOD (Level of Detail) processing may also be required to create models with different levels of accuracy to suit different viewing distances and rendering requirements.

[0176] These two steps solve several key problems faced in traditional virtual production line modeling. First, it greatly improves the authenticity and accuracy of the virtual production line model. Through precise physical parameter setting and simulation, the behavior of the model can be very close to the actual production line, which is crucial for subsequent analysis and optimization.

[0177] Secondly, this method improves the usability and flexibility of the virtual production line model. By exporting the model to a standard 3D format, the model can be used on a variety of different software platforms, not only for simulation analysis, but also for visualization, VR / AR applications and other scenarios.

[0178] Furthermore, this approach provides a powerful tool for virtual commissioning and optimization of production lines. Engineers can test various design solutions and operating strategies in a virtual environment without having to conduct high-risk experiments on the actual production line. This not only saves time and costs, but also improves safety.

[0179] In terms of technical effects, this method based on physical simulation and 3D visualization has brought significant improvements. First, it greatly improves the efficiency of production line design and optimization. Engineers can quickly create and modify virtual production line models and conduct various hypothetical analyses, greatly shortening the product development cycle.

[0180] Second, this approach improves the quality of decision-making. Through highly realistic virtual models, managers and engineers can more intuitively understand the operation of the production line and make more informed decisions. For example, virtual models can be used to evaluate the effects of different layout options or analyze potential safety hazards.

[0181] Finally, this approach provides an ideal platform for training and collaboration. Using a visual virtual production line model, interactive training courses can be created, allowing operators to learn and practice in a safe environment. At the same time, this visual model also facilitates communication and collaboration between different departments. For example, the design department can more intuitively show new design plans to the production department.

[0182] In one embodiment of the virtual platform simulation monitoring method of the present application, see Figure 6 , and can also include the following:

[0183] Step S601: publishing the virtual production line model and the corresponding message queue connection parameters in JSON format to the service on the web page through HTTP protocol;

[0184] Step S602: After receiving the virtual production line model and the message queue connection parameters, the web page establishes a connection channel with the message queue service according to the message queue connection parameters, and sends a ready status signal to the message queue service to indicate that the web page has successfully received and connected to the relevant resources.

[0185] Optionally, in this embodiment, steps S601 and S602 constitute a key data transmission and communication establishment process, which transfers the virtual production line model from the simulation environment to the web page and establishes a real-time communication channel. This process involves a variety of network communication technologies, data serialization methods, and message queue systems, and is the basis for realizing remote monitoring and interaction of virtual production lines.

[0186] In step S601, the system first packages the virtual production line model and the corresponding message queue connection parameters into JSON format. JSON (JavaScript Object Notation) is a lightweight data exchange format that is easy for people to read and write, and easy for machines to parse and generate. There are several advantages to choosing JSON format: first, it is a text format that can be easily transmitted through the HTTP protocol; second, it supports complex nested structures and can well represent the hierarchical relationship of the virtual production line model; finally, most programming languages ​​have mature JSON parsing libraries to facilitate data transmission between different systems.

[0187] When packaging data, the system needs to consider how to efficiently represent 3D models. A common method is to use glTF (GL Transmission Format), a 3D model format optimized for network transmission. glTF can be embedded in JSON, which not only retains the geometry, material, and animation information of the 3D model, but also ensures transmission efficiency.

[0188] Message queue connection parameters usually include server address, port number, topic name, user name, password, etc. These parameters are encoded into JSON so that the web page can correctly connect to the message queue service.

[0189] Next, the system sends the packaged JSON data to the web service through the HTTP protocol. HTTP is an application layer protocol widely used in web applications. There are several advantages to using the HTTP protocol: first, it is a stateless protocol suitable for large-scale concurrent access; second, it supports various authentication mechanisms to ensure the security of data transmission; finally, it can penetrate firewalls well and facilitate remote access.

[0190] When sending data, the system may need to consider the size of the data. If the amount of data is large, it may be necessary to use chunked transfer encoding or breakpoint resume to ensure that large 3D models can be reliably transmitted to the web page.

[0191] Step S602 describes the process of the web page receiving data and establishing a communication channel. After the web page receives the virtual production line model and the message queue connection parameters, it first needs to parse the JSON data to extract the 3D model information and connection parameters. Parsing JSON usually uses the browser's built-in JSON.parse() function or a third-party library such as JSON5.

[0192] After parsing is completed, the web page establishes a connection channel with the message queue service according to the message queue connection parameters. Message queue is an asynchronous communication method that can decouple producers and consumers and improve the scalability and reliability of the system. Common message queue systems include RabbitMQ, Apache Kafka, ActiveMQ, etc.

[0193] Establishing a connection usually involves the following steps: first, create a message queue client; then, initialize the client with connection parameters (such as server address, port number, username, and password); finally, establish a connection and subscribe to the relevant topic or queue. This process may require the use of a specific message queue client library, such as MQTT.js or Stomp.js for web environments.

[0194] After the connection is established successfully, the web page sends a ready status signal to the message queue service. This signal indicates that the web page has successfully received and connected to the relevant resources and can start receiving real-time data or performing interactive operations. This status signal is usually a simple message containing the web page identifier and the current status.

[0195] These two steps solve several key problems faced in traditional virtual production line remote monitoring. First, it realizes the efficient transmission and loading of virtual production line models. By using JSON format and HTTP protocol, the system can quickly and reliably transmit large 3D models to the web page, allowing remote users to quickly access and interact with the virtual production line.

[0196] Secondly, this approach establishes a reliable real-time communication channel. Through the message queue system, the web page can receive real-time status updates of the production line and send control instructions at the same time. This two-way communication mechanism provides the basis for remote monitoring and control.

[0197] Furthermore, this approach improves the scalability and fault tolerance of the system. The message queue system can cache messages, ensuring reliable data transmission even if there is a brief network interruption. At the same time, it also supports multiple clients connecting at the same time, facilitating multi-user collaboration.

[0198] In terms of technical effects, this web-based virtual production line remote monitoring method has brought significant improvements. First, it greatly improves the accessibility of the system. Users only need a web browser to access and control the virtual production line from anywhere, which greatly improves management efficiency and response speed.

[0199] Secondly, this approach reduces the deployment and maintenance costs of the system. Compared with traditional desktop applications, web-based systems are easier to update and maintain. Administrators only need to update the code on the server side, and all users can immediately access the latest version of the system.

[0200] Finally, this approach facilitates data analysis and decision support. Through the web interface, various data visualization tools and analysis modules can be easily integrated to help managers better understand the operation status of the production line and make more informed decisions.

[0201] In one embodiment of the virtual platform simulation monitoring method of the present application, see Figure 7 , and can also include the following:

[0202] Step S701: monitoring the ready state signal of the web page by periodically polling the message queue service, and when the ready state signal of the web page is detected, pushing the location information and state change information of the virtual production line model to the web page through the publish-subscribe mechanism of the message queue service;

[0203] Step S702: After receiving the position information and the status change information, the web page updates the display of the virtual production line model accordingly to reflect the operation status of the production line in real time.

[0204] Optionally, in this embodiment, steps S701 and S702 describe a process of real-time data synchronization and visualization update, which is the core of realizing remote monitoring of virtual production lines. These two steps involve the polling mechanism of the message queue, the publish-subscribe model, and the front-end real-time rendering technology, which together build an efficient and reliable real-time monitoring system.

[0205] In step S701, the system first monitors the ready state signal of the web page by periodically polling the message queue service. Polling is a simple and effective detection mechanism that checks whether there are new messages by periodically sending query requests to the message queue. The advantage of this method is that it is simple to implement and applicable to various message queue systems. However, it may also bring some additional network overhead, especially when the polling frequency is high.

[0206] To optimize performance, the system may adopt an adaptive polling strategy. For example, the polling interval is gradually increased when no new messages are detected, and the polling interval is immediately reduced after a new message is detected. This strategy can reduce unnecessary network requests while maintaining timely response.

[0207] When the system detects the ready signal from the web page, it pushes the location information and status change information of the virtual production line model to the web page through the publish-subscribe mechanism of the message queue service. The publish-subscribe model is a messaging paradigm that decouples the sender (publisher) of the message from the receiver (subscriber), allowing multiple receivers to subscribe to messages on the same topic. This model is very suitable for real-time data update scenarios because it can efficiently broadcast data changes to all relevant clients.

[0208] When pushing data, the system needs to consider the format and granularity of the data. Location information usually includes the three-dimensional coordinates, rotation angle, etc. of each device or component. State change information may include the operating status of the equipment (such as on, off, standby), production parameters (such as speed, temperature, pressure), etc. These data are usually encoded in a compact binary format or a lightweight JSON format to reduce the burden of network transmission.

[0209] In addition, the system also needs to consider the frequency of data updates. Different update strategies may be required for different types of information. For example, location information may require a higher update frequency to ensure the smoothness of animation, while some status information may only need to be updated when it changes.

[0210] Step S702 describes the process of the web page receiving data and updating the display of the virtual production line model. After the web page receives the location information and status change information, it needs to update the display of the 3D model in a timely manner to reflect the operating status of the production line in real time. This process involves complex 3D rendering technology and performance optimization strategies.

[0211] First, the web page needs to parse the received data and convert it into transformation operations in the 3D scene. This may include updating the object's position, rotation, scale and other properties, or changing the object's material, color and other visual properties to reflect the state change. These operations are usually implemented through 3D graphics libraries such as WebGL or Three.js.

[0212] Secondly, in order to ensure real-time and smoothness, the web page needs to adopt an efficient rendering strategy. This may include using requestAnimationFrame to synchronize updates, using object pools to reduce garbage collection overhead, or using instancing technology to optimize the rendering of a large number of similar objects.

[0213] Furthermore, the web page also needs to deal with possible data delays or loss. For example, an interpolation algorithm can be used to smooth the movement of objects, or a prediction algorithm can be used to estimate the value of lost data packets. These techniques can improve the user experience and make the animation look smoother and more natural.

[0214] These two steps solve several key problems faced by traditional remote monitoring systems. First, it realizes real-time visualization of the production line status. Through efficient data transmission and rendering mechanisms, the system can reflect the actual operating status of the production line at a near real-time speed, making remote monitoring more intuitive and effective.

[0215] Secondly, this approach improves the scalability of the system. By using message queues and publish-subscribe mode, the system can easily support multiple clients monitoring at the same time without significantly increasing the burden on the server. This is especially important for large-scale production environments.

[0216] Furthermore, this approach enhances the fault tolerance of the system. Even in the case of unstable network, the message queue can cache messages to ensure reliable data transmission. At the same time, the client's adaptive update strategy can also provide a good user experience under various network conditions.

[0217] In terms of technical effects, this remote monitoring method based on message queues and real-time 3D rendering has brought significant improvements. First, it greatly improves the real-time and accuracy of monitoring. Managers and engineers can observe the operation status of the production line in near real time and quickly discover and respond to abnormal situations.

[0218] Secondly, this approach greatly enhances the interactivity of the system. Users can not only passively receive information, but also conduct virtual inspections through the 3D interface, zoom in to view areas of interest, and even perform remote control operations. This rich interactive method greatly improves the effect of remote monitoring.

[0219] Finally, this approach provides a foundation for data analysis and prediction. By collecting and visualizing real-time data, the system can help identify patterns and trends in production, providing a basis for predictive maintenance and production optimization.

[0220] In order to effectively improve the automation and real-time interactivity of virtual platform simulation monitoring, the present application provides an embodiment of a virtual platform simulation monitoring device for implementing all or part of the contents of the virtual platform simulation monitoring method, see Figure 8 The virtual platform simulation monitoring device specifically includes the following contents:

[0221] The signal analysis module 10 is used to perform signal analysis on the received programmable logic controller signal of the actual production line by setting a code analysis model to obtain a corresponding virtual signal table, establish a motion trajectory of the simulation model in the simulation software according to the virtual signal table, and add simulation events at key control nodes;

[0222] The model determination module 20 is used to simulate the actual motion characteristics and dynamic behaviors of the production line equipment by adopting the modeling method of the physical computing engine according to the motion trajectory and the simulation event, output the virtual production line model in the 3D model file format, and start the web page service, publish the virtual production line model and the message queue connection parameters to the web page, so that the web page receives the virtual production line model and the message queue connection parameters and connects to the corresponding message queue service, and sends a ready status signal to the message queue service;

[0223] The web page interaction module 30 is used to start sending the location information and state change information of the virtual production line model to the message queue service after monitoring the ready status signal of the web page, so that the web page updates the corresponding model position and model status after receiving the location information and the state change information.

[0224] From the above description, it can be seen that the virtual platform simulation monitoring device provided in the embodiment of the present application can perform signal analysis on the received programmable logic controller signal of the actual production line by setting a code parsing model to obtain a corresponding virtual signal table, establish the motion trajectory of the simulation model in the simulation software according to the virtual signal table, and add simulation events at key control nodes; output the virtual production line model in the 3D model file format, and publish the virtual production line model and message queue connection parameters to the web page; after monitoring the ready status signal of the web page, start sending the location information and status change information of the virtual production line model to the message queue service, so that the web page receives the position information and status change information and updates the corresponding model position and model status, thereby effectively improving the automation and real-time interactivity of the virtual platform simulation monitoring.

[0225] From the hardware level, in order to effectively improve the automation and real-time interactivity of virtual platform simulation monitoring, the present application provides an embodiment of an electronic device for implementing all or part of the contents of the virtual platform simulation monitoring method, and the electronic device specifically includes the following contents:

[0226] Processor, memory, communication interface and bus; wherein the processor, memory and communication interface communicate with each other through the bus; the communication interface is used to realize information transmission between the virtual platform simulation monitoring device and related devices such as the core business system, user terminal and related database; the logic controller can be a desktop computer, a tablet computer and a mobile terminal, etc., but the present embodiment is not limited thereto. In the present embodiment, the logic controller can be implemented with reference to the embodiment of the virtual platform simulation monitoring method and the embodiment of the virtual platform simulation monitoring device in the embodiment, and the contents thereof are incorporated herein, and the repeated parts are not repeated.

[0227] It is understandable that the user terminal may include a smart phone, a tablet electronic device, a network set-top box, a portable computer, a desktop computer, a personal digital assistant (PDA), a vehicle-mounted device, a smart wearable device, etc. Among them, the smart wearable device may include smart glasses, a smart watch, a smart bracelet, etc.

[0228] In practical applications, part of the virtual platform simulation monitoring method can be executed on the electronic device side as described above, or all operations can be completed in the client device. The specific selection can be based on the processing capability of the client device and the limitations of the user's usage scenario. This application does not limit this. If all operations are completed in the client device, the client device may also include a processor.

[0229] The client device may have a communication module (i.e., a communication unit) that can communicate with a remote server to achieve data transmission with the server. The server may include a server on the task scheduling center side, and other implementation scenarios may also include a server on an intermediate platform, such as a server on a third-party server platform that has a communication link with the task scheduling center server. The server may include a single computer device, or a server cluster consisting of multiple servers, or a server structure of a distributed device.

[0230] Fig. 9 FIG. 9 is a schematic block diagram of the system structure of the electronic device 9600 according to an embodiment of the present application. Fig. 9As shown, the electronic device 9600 may include a central processor 9100 and a memory 9140; the memory 9140 is coupled to the central processor 9100. It is worth noting that Fig. 9 is exemplary; other types of structures may also be used to supplement or replace this structure to implement telecommunication functions or other functions.

[0231] In one embodiment, the virtual platform simulation monitoring method function may be integrated into the central processing unit 9100. The central processing unit 9100 may be configured to perform the following control:

[0232] Step S101: Perform signal analysis on the received programmable logic controller signal of the actual production line by setting a code parsing model to obtain a corresponding virtual signal table, establish a motion trajectory of the simulation model in the simulation software according to the virtual signal table, and add simulation events at key control nodes;

[0233] Step S102: According to the motion trajectory and the simulation event, the actual motion characteristics and dynamic behaviors of the production line equipment are simulated by using a modeling method of a physical computing engine, a virtual production line model in a 3D model file format is output, and a web page service is started, and the virtual production line model and message queue connection parameters are published to the web page, so that the web page receives the virtual production line model and the message queue connection parameters and connects to the corresponding message queue service, and sends a ready status signal to the message queue service;

[0234] Step S103: After monitoring the ready status signal of the web page, start sending the location information and status change information of the virtual production line model to the message queue service, so that the web page updates the corresponding model position and model status after receiving the location information and the status change information.

[0235] From the above description, it can be seen that the electronic device provided in the embodiment of the present application performs signal analysis on the programmable logic controller signal of the actual production line received by setting a code parsing model to obtain a corresponding virtual signal table, establishes the motion trajectory of the simulation model in the simulation software according to the virtual signal table, and adds simulation events at key control nodes; outputs the virtual production line model in the 3D model file format, and publishes the virtual production line model and message queue connection parameters to the web page; after monitoring the ready status signal of the web page, starts to send the location information and status change information of the virtual production line model to the message queue service, so that the web page receives the position information and status change information and updates the corresponding model position and model status, thereby effectively improving the automation and real-time interactivity of virtual platform simulation monitoring.

[0236] In another embodiment, the virtual platform simulation monitoring device can be configured separately from the central processing unit 9100. For example, the virtual platform simulation monitoring device can be configured as a chip connected to the central processing unit 9100, and the function of the virtual platform simulation monitoring method can be implemented through the control of the central processing unit.

[0237] like Fig. 9 As shown, the electronic device 9600 may also include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is worth noting that the electronic device 9600 does not necessarily have to include Fig. 9 In addition, the electronic device 9600 may also include Fig. 9 For components not shown, reference may be made to the prior art.

[0238] like Fig. 9 As shown, the central processing unit 9100 is sometimes also referred to as a controller or an operation control, and may include a microprocessor or other processor device and / or logic device. The central processing unit 9100 receives input and controls the operation of various components of the electronic device 9600.

[0239] The memory 9140 may be, for example, one or more of a cache, a flash memory, a hard drive, a removable medium, a volatile memory, a non-volatile memory or other suitable devices. The above-mentioned information related to the failure may be stored, and a program for executing the relevant information may also be stored. The CPU 9100 may execute the program stored in the memory 9140 to implement information storage or processing, etc.

[0240] The input unit 9120 provides input to the central processing unit 9100. The input unit 9120 is, for example, a key or a touch input device. The power supply 9170 is used to provide power to the electronic device 9600. The display 9160 is used to display display objects such as images and texts. The display may be, for example, an LCD display, but is not limited thereto.

[0241] The memory 9140 may be a solid-state memory, such as a read-only memory (ROM), a random access memory (RAM), a SIM card, etc. It may also be a memory that saves information even when the power is off, can be selectively erased, and is provided with more data, examples of which are sometimes referred to as EPROMs, etc. The memory 9140 may also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application / function storage unit 9142, which is used to store application programs and function programs or processes for executing the operation of the electronic device 9600 through the central processor 9100.

[0242] The memory 9140 may also include a data storage unit 9143 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 9144 of the memory 9140 may include various drivers for communication functions of the electronic device and / or for executing other functions of the electronic device (such as messaging applications, address book applications, etc.).

[0243] The communication module 9110 is a transmitter / receiver that sends and receives signals via the antenna 9111. The communication module 9110 (transmitter / receiver) is coupled to the central processor 9100 to provide input signals and receive output signals, which may be the same as the case of a conventional mobile communication terminal.

[0244] Based on different communication technologies, multiple communication modules 9110 may be provided in the same electronic device, such as a cellular network module, a Bluetooth module and / or a wireless LAN module, etc. The communication module 9110 (transmitter / receiver) is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide an audio output via the speaker 9131 and receive an audio input from the microphone 9132, thereby realizing a common telecommunication function. The audio processor 9130 may include any suitable buffer, decoder, amplifier, etc. In addition, the audio processor 9130 is also coupled to the central processor 9100, so that recording can be performed on the local machine through the microphone 9132, and the sound stored on the local machine can be played through the speaker 9131.

[0245] The embodiments of the present application also provide a computer-readable storage medium capable of implementing all the steps of the virtual platform simulation monitoring method in the above embodiment, where the execution subject is a server or a client. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, all the steps of the virtual platform simulation monitoring method in the above embodiment, where the execution subject is a server or a client, are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0246] Step S101: Perform signal analysis on the received programmable logic controller signal of the actual production line by setting a code parsing model to obtain a corresponding virtual signal table, establish a motion trajectory of the simulation model in the simulation software according to the virtual signal table, and add simulation events at key control nodes;

[0247] Step S102: According to the motion trajectory and the simulation event, the actual motion characteristics and dynamic behaviors of the production line equipment are simulated by using a modeling method of a physical computing engine, a virtual production line model in a 3D model file format is output, and a web page service is started, and the virtual production line model and message queue connection parameters are published to the web page, so that the web page receives the virtual production line model and the message queue connection parameters and connects to the corresponding message queue service, and sends a ready status signal to the message queue service;

[0248] Step S103: After monitoring the ready status signal of the web page, start sending the location information and status change information of the virtual production line model to the message queue service, so that the web page updates the corresponding model position and model status after receiving the location information and the status change information.

[0249] From the above description, it can be seen that the computer-readable storage medium provided in the embodiment of the present application performs signal analysis on the received programmable logic controller signal of the actual production line by setting a code parsing model to obtain a corresponding virtual signal table, establishes the motion trajectory of the simulation model in the simulation software according to the virtual signal table, and adds simulation events at key control nodes; outputs the virtual production line model in the 3D model file format, and publishes the virtual production line model and message queue connection parameters to the web page; after monitoring the ready status signal of the web page, starts to send the location information and status change information of the virtual production line model to the message queue service, so that the web page receives the position information and status change information and updates the corresponding model position and model status, thereby effectively improving the automation and real-time interactivity of virtual platform simulation monitoring.

[0250] The embodiments of the present application also provide a computer program product capable of implementing all the steps of the virtual platform simulation monitoring method in the above embodiments, where the execution subject is a server or a client. When the computer program / instruction is executed by a processor, the steps of the virtual platform simulation monitoring method are implemented. For example, the computer program / instruction implements the following steps:

[0251] Step S101: Perform signal analysis on the received programmable logic controller signal of the actual production line by setting a code parsing model to obtain a corresponding virtual signal table, establish a motion trajectory of the simulation model in the simulation software according to the virtual signal table, and add simulation events at key control nodes;

[0252] Step S102: According to the motion trajectory and the simulation event, the actual motion characteristics and dynamic behaviors of the production line equipment are simulated by using a modeling method of a physical computing engine, a virtual production line model in a 3D model file format is output, and a web page service is started, and the virtual production line model and message queue connection parameters are published to the web page, so that the web page receives the virtual production line model and the message queue connection parameters and connects to the corresponding message queue service, and sends a ready status signal to the message queue service;

[0253] Step S103: After monitoring the ready status signal of the web page, start sending the location information and status change information of the virtual production line model to the message queue service, so that the web page updates the corresponding model position and model status after receiving the location information and the status change information.

[0254] From the above description, it can be seen that the computer program product provided in the embodiment of the present application performs signal analysis on the received programmable logic controller signal of the actual production line by setting a code parsing model to obtain a corresponding virtual signal table, establishes the motion trajectory of the simulation model in the simulation software according to the virtual signal table, and adds simulation events at key control nodes; outputs the virtual production line model in the 3D model file format, and publishes the virtual production line model and message queue connection parameters to the web page; after monitoring the ready status signal of the web page, starts to send the location information and status change information of the virtual production line model to the message queue service, so that the web page receives the position information and status change information and updates the corresponding model position and model status, thereby effectively improving the automation and real-time interactivity of virtual platform simulation monitoring.

[0255] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0256] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (apparatus), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0257] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0258] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0259] The present invention uses specific embodiments to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A virtual platform simulation monitoring method, characterized in that: The method comprises: The received programmable logic controller signal of the actual production line is analyzed by setting a code parsing model to obtain a corresponding virtual signal table, and the motion trajectory of the simulation model is established in the simulation software according to the virtual signal table, and simulation events are added at key control nodes; According to the motion trajectory and the simulation event, the actual motion characteristics and dynamic behaviors of the production line equipment are simulated by using a modeling method of a physical computing engine, a virtual production line model in a 3D model file format is output, and a web page service is started, and the virtual production line model and message queue connection parameters are published to the web page, so that the web page receives the virtual production line model and the message queue connection parameters, and connects to the corresponding message queue service, and sends a ready status signal to the message queue service; After monitoring the ready status signal of the web page, the location information and status change information of the virtual production line model are sent to the message queue service, so that the web page updates the corresponding model position and model status after receiving the location information and the status change information.

2. The virtual platform simulation monitoring method according to claim 1, characterized in that: Before performing signal analysis on the received programmable logic controller signal of the actual production line by setting a code analysis model to obtain a corresponding virtual signal table, the method includes: Obtain historical PLC programming code samples from a PLC signal library corresponding to a preset industrial communication protocol as training data for the initial model; The training data is input into an initial model using a sequence-to-sequence architecture for model training, and parameters are tuned according to signal definitions and variable declarations output by the model to obtain a code parsing model.

3. The virtual platform simulation monitoring method according to claim 1, characterized in that: The received programmable logic controller signal of the actual production line is analyzed by setting a code analysis model to obtain a corresponding virtual signal table, including: Performing signal analysis on the programmable logic controller signal of the actual production line by setting a code parsing model, and extracting key signal features according to the signal analysis results, wherein the key signal features include at least one of naming, data type, usage frequency, and relevance; The key signal features are structurally integrated to obtain a corresponding virtual signal table.

4. The virtual platform simulation monitoring method according to claim 1, characterized in that: The step of establishing the motion trajectory of the simulation model in the simulation software according to the virtual signal table and adding simulation events at key control nodes includes: Convert various input / output signals defined in the virtual signal table into analog signal sources and load devices in the simulation software, and construct the motion trajectory and dynamic behavior of the production line equipment in the simulation environment according to the logical relationship between the signals; After establishing the basic simulation model, add corresponding simulation events to the key control nodes to simulate the execution process of the PLC control logic in the actual production line.

5. The virtual platform simulation monitoring method according to claim 1, characterized in that: The actual motion characteristics and dynamic behaviors of the production line equipment are simulated by using a physical computing engine modeling method according to the motion trajectory and the simulation event, and a virtual production line model in a 3D model file format is output, including: The preset physical engine is used to set the physical parameters of mass, inertia and friction of various production line equipment in the simulation software, and the actual motion process of the equipment under the action of motion trajectory and simulation events is simulated according to the calculation of the physical parameters; The virtual production line scene constructed in the simulation software is exported into a visual virtual production line model using a 3D model file format.

6. The virtual platform simulation monitoring method according to claim 1, characterized in that: The step of publishing the virtual production line model and the message queue connection parameters to the web page end, so that the web page end connects to the corresponding message queue service after receiving the virtual production line model and the message queue connection parameters, and sends a ready status signal to the message queue service, includes: Publishing the virtual production line model and the corresponding message queue connection parameters to the web service in JSON format via HTTP protocol; After receiving the virtual production line model and the message queue connection parameters, the web page establishes a connection channel with the message queue service according to the message queue connection parameters, and sends a ready status signal to the message queue service to indicate that the web page has successfully received and connected to the relevant resources.

7. The virtual platform simulation monitoring method according to claim 1, characterized in that: After the ready state signal of the web page is detected, the position information and state change information of the virtual production line model are sent to the message queue service, so that the web page updates the corresponding model position and model state after receiving the position information and the state change information, including: The ready state signal of the web page is monitored by periodically polling the message queue service, and when the ready state signal of the web page is detected, the location information and state change information of the virtual production line model is pushed to the web page through the publish-subscribe mechanism of the message queue service; After receiving the position information and status change information, the web page can update the display of the virtual production line model accordingly to reflect the operating status of the production line in real time.

8. A virtual platform simulation monitoring device, characterized in that: The device comprises: The signal analysis module is used to perform signal analysis on the programmable logic controller signal received from the actual production line by setting a code parsing model to obtain a corresponding virtual signal table, establish a motion trajectory of the simulation model in the simulation software according to the virtual signal table, and add simulation events at key control nodes; A model determination module is used to simulate the actual motion characteristics and dynamic behaviors of the production line equipment by adopting a modeling method of a physical computing engine according to the motion trajectory and the simulation event, output a virtual production line model in a 3D model file format, and start a web page service, publish the virtual production line model and message queue connection parameters to the web page, so that the web page receives the virtual production line model and the message queue connection parameters and connects to the corresponding message queue service, and sends a ready status signal to the message queue service; The web page interaction module is used to start sending the location information and status change information of the virtual production line model to the message queue service after monitoring the ready status signal of the web page, so that the web page updates the corresponding model position and model status after receiving the location information and the status change information.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the virtual platform simulation monitoring method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the virtual platform simulation monitoring method according to any one of claims 1 to 7 are implemented.

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