Control method and device of PLC system and IOT equipment based on digital twinning

Abstract

The embodiment of the invention provides a control method for a PLC system and IOT equipment based on digital twinning, and the method comprises the steps: deploying a virtual machine and a plurality of virtual PLC instances in a server, managing different parts of the PLC system, building a digital twinning model according to the operation data of a physical assembly, and deploying the digital twinning model to the virtual PLC instances, a data transmission link of IOT equipment and a digital twin model is constructed through a network to realize real-time synchronization, collected data is preprocessed and then analyzed by a data analysis model, potential faults and maintenance requirements are predicted, a result is fed back through the link, and a virtual PLC instance and the model are adjusted and optimized based on the result and a model operation state. According to the method, virtualization and digital twinning technologies are integrated, so that a PLC system and IOT equipment are efficiently coordinated, the data flow is optimized, the control precision and the intelligent level are improved, the reliability and adaptability of the system are enhanced, the operation and maintenance cost is effectively reduced, and the production efficiency is improved.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the field of automation technology and related technology fields. Specifically, it relates to a control method and device for a PLC system and IOT devices based on digital twin. Background Art

[0002] With the development of a highly digital and intelligent industrial environment, in the field of industrial automation, the current PLC system, as a digital operation control electronic system specifically designed for industrial scenarios, is widely used in tasks such as logical operation, sequence control, timing, counting, and arithmetic operations. With the continuous evolution of IOT technology, the integration of PLC and IOT devices has created new opportunities for industrial automation, covering aspects such as device monitoring, data collection, and remote control.

[0003] However, the application of existing technologies in virtualization and digital twin technologies faces many difficulties. Its shortcomings are mainly reflected in: the lack of application of virtualization technology in the PLC control system, resulting in bottlenecks in system scalability and flexibility; the lack of application of digital twin technology, making it difficult to achieve accurate simulation and immediate optimization of physical systems; limitations in data processing and analysis efficiency, making it difficult to fully exploit the value of the massive data generated by IOT devices.

[0004] Therefore, there is an urgent need for a control method for a PLC system and IOT devices to solve the above technical problems. Summary of the Invention

[0005] The embodiments described herein provide a control method and device for a PLC system and IOT devices based on digital twin, which solve the problems existing in the prior art.

[0006] According to a first aspect of the present disclosure, there is provided a control method for a PLC system and IOT devices based on digital twin, including:

[0007] Deploy a virtual machine on a server and configure a plurality of virtual PLC instances on the virtual machine, where each of the virtual PLC instances manages different parts of the PLC system, and the virtual machine is used to run and manage each of the virtual PLC instances;

[0008] Create a digital twin model for each of the physical components according to the operation data of the physical components of the PLC system, and deploy the digital twin model to the virtual PLC instance;

[0009] Establish a data transmission link between the IOT device and the digital twin model using a network to achieve real-time data synchronization;

[0010] Collect data using the IOT device and the physical components, input it into a data analysis model for data analysis after preprocessing, obtain the analysis results, and predict potential faults and maintenance requirements based on the analysis results;

[0011] Use the data transmission link to feedback the analysis results to the digital twin model and the virtual PLC instance;

[0012] Adjust and optimize the virtual PLC instance and the digital twin model based on the analysis results and the operating status of the digital twin model.

[0013] In some embodiments of the present disclosure, the digital twin model is used to represent the behaviors and interactions of the physical components.

[0014] In some embodiments of the present disclosure, before the step of respectively creating digital twin models for multiple physical components of the PLC system, it includes:

[0015] Select the physical components from the PLC system as the objects of digital twins.

[0016] In some embodiments of the present disclosure, after the step of establishing a data transmission channel between the PLC system, the IOT device and the digital twin model to achieve real-time data synchronization, it includes:

[0017] Calibrate the digital twin model using the operation data.

[0018] In some embodiments of the present disclosure, after the step of obtaining the calibrated digital twin model, it further includes:

[0019] Simulate various operation scenarios in a virtual environment and predict the reactions of the virtual PLC instance under different conditions;

[0020] Adjust the configuration of the virtual PLC instance or the physical components according to the simulation results;

[0021] Collect the operating status of the digital twin model;

[0022] Monitor the operating status of the digital twin model in real time and detect anomalies in a timely manner.

[0023] In some embodiments of the present disclosure, before the step of inputting into a data analysis model for data analysis after preprocessing, it includes:

[0024] Select appropriate data analysis tools and techniques according to data characteristics and analysis objectives;

[0025] Develop and train the data analysis model.

[0026] In some embodiments of the present disclosure, the preprocessing step includes:

[0027] Performing cleaning processing on the data to remove invalid, incorrect, or incomplete data records;

[0028] Normalizing the cleaned data.

[0029] According to a second aspect of the present disclosure, there is provided a control device for a PLC system and IOT devices based on digital twins, including:

[0030] A deployment module for deploying a virtual machine on a server and configuring a plurality of virtual PLC instances on the virtual machine, wherein each of the virtual PLC instances manages different parts of the PLC system, and the virtual machine is used to run and manage each of the virtual PLC instances;

[0031] A creation module for creating a digital twin model for each of the physical components according to the operation data of the physical components of the PLC system and deploying the digital twin model to the virtual PLC instance;

[0032] A synchronization module for establishing a data transmission link between the IOT device and the digital twin model using a network to achieve real-time data synchronization;

[0033] An analysis module for collecting data using the IOT device and the physical components, inputting the preprocessed data into a data analysis model for data analysis to obtain an analysis result, and predicting potential faults and maintenance requirements based on the analysis result;

[0034] A feedback module for feeding back the analysis result to the digital twin model and the virtual PLC instance using the data transmission link;

[0035] An optimization module for adjusting and optimizing the virtual PLC instance and the digital twin model based on the analysis result and the operating state of the digital twin model.

[0036] In some embodiments of the present disclosure, the digital twin model is used to represent the behavior and interaction of the physical components.

[0037] In some embodiments of the present disclosure, it further includes:

[0038] A first selection module for selecting the physical components from the PLC system as the objects of digital twins.

[0039] In some embodiments of the present disclosure, it further includes:

[0040] A calibration module for calibrating the digital twin model using the operation data.

[0041] In some embodiments of the present disclosure, it includes:

[0042] A prediction module for simulating various operation scenarios in a virtual environment and predicting the responses of the virtual PLC instances under different conditions;

[0043] An adjustment module for adjusting the configuration of the virtual PLC instance or the physical component according to the simulation results;

[0044] A mobile phone module for collecting the operating status of the digital twin model;

[0045] A monitoring module for real-time monitoring of the operating status of the digital twin model to detect abnormalities in a timely manner.

[0046] In some embodiments of the present disclosure, it further includes:

[0047] A second selection module for selecting appropriate data analysis tools and techniques according to data characteristics and analysis objectives;

[0048] A training module for developing and training the data analysis model.

[0049] In some embodiments of the present disclosure, it further includes:

[0050] A preprocessing module for performing cleaning processing on the data, removing invalid, incorrect, or incomplete data records, and normalizing the cleaned data.

[0051] According to the third aspect of the present disclosure, a control method for a PLC system and IOT devices based on digital twin is provided, which is applied to the scenario where the PLC system is an intelligent automobile assembly line. The PLC system is an intelligent automobile assembly line, and sensors are installed on the assembly line. The method includes:

[0052] Deploy virtual machines in the data center, configure multiple virtual PLC instances on each virtual machine, and set the management scope for each virtual PLC system according to the functional partitions and process links of the assembly line, so that it controls different parts of the assembly line to achieve refined management of the assembly line and efficient utilization of resources;

[0053] For the key physical components in the automobile assembly line, use technical means such as 3D modeling and physical property simulation to construct digital twin models respectively. The digital twin models need to map multiple aspects of attributes such as the geometric shape, motion mode, and mechanical properties of the key components to reflect their actual operating status and interaction relationships in the assembly line;

[0054] Establish a data transmission link between the IOT device and the digital twin model using the network to achieve real-time data synchronization;

[0055] Install various types of sensors at key positions on the assembly line to collect operation data during the operation of the assembly line, build a machine learning model, analyze the collected sensor data, and through feature extraction, pattern recognition and trend prediction of the data, achieve early warning of equipment failures and accurate prediction of maintenance requirements;

[0056] Feed back the analysis results output by the machine learning model to the virtual PLC system and the digital twin model;

[0057] The virtual PLC system adjusts the control strategy for each part of the assembly line according to the data analysis results to optimize the production process, and the digital twin model optimizes its own parameters and updates the simulation scenario according to the data analysis results, thereby providing a more accurate basis for formulating preventive maintenance plans;

[0058] According to the fourth aspect of the present disclosure, a control method for a PLC system and an IOT device based on digital twin is provided, which is applied to a chemical plant scenario where the PLC system is installed with sensors on the assembly line. The method includes:

[0059] Select the reactor core sensors, namely pressure and temperature sensors, as the key data collection nodes;

[0060] Build a mathematical model based on the historical data information of the sensors, which is used to characterize the operating characteristics of the reactor under multiple pressure and temperature conditions;

[0061] Collect sensor data in real time and synchronize it to the virtual PLC system immediately to ensure data timeliness;

[0062] Calibrate and fine-tune the existing model using the actual operation data to ensure accurate prediction results;

[0063] Simulate various complex operating conditions in a virtual environment to estimate the performance of the reactor under extreme working conditions;

[0064] Adjust the control parameters of the reactor according to the simulation results to achieve optimal operating performance;

[0065] Carry out real-time monitoring of the reactor operating state to quickly detect abnormal conditions;

[0066] Deeply analyze the data change trend, predict potential faults and plan maintenance arrangements;

[0067] Collect feedback on the application effectiveness of the virtual PLC system from the operators;

[0068] Optimize the model according to the feedback information to enhance the prediction accuracy and system reliability.

[0069] According to a fifth aspect of the present disclosure, a control method for a PLC system and IOT devices based on digital twin is provided, which is applied to the scenario where the PLC system is for the production line of an intelligent manufacturing factory. The method includes:

[0070] Arrange temperature sensors, pressure sensors, and vibration sensors at key parts of the production line, collect physical quantity data according to the set frequency, and transmit it to the data center through wired or wireless means;

[0071] Preprocess the data. First, clean the data after it reaches the center. Use screening algorithms to remove abnormal, incorrect, and duplicate data, such as correcting over-temperature data. Then standardize the cleaned data, and use algorithms such as normalization to unify the dimensions for subsequent analysis;

[0072] Select machine learning algorithms such as LSTM or random forest to build a predictive maintenance model, divide the data into a training set and a test set, train and adjust the parameters to make the model have a high prediction accuracy for equipment failure probability, maintenance type, and time node, and provide a basis for maintenance decisions;

[0073] Integrate the output of the verified prediction model into the PLC system through a data interface, write a receiving and processing program. When a fault warning or maintenance suggestion is encountered, the PLC system issues a control instruction to adjust the parameters or arrange maintenance, monitor and manage the production line, improve efficiency and reduce downtime;

[0074] Collect the feedback of model prediction and actual equipment data, compare the differences to evaluate the performance, calculate indicators such as accuracy, increase the data dimension or adjust the feature weights according to the results, retrain the model, optimize the model, adapt to the changes in the production line, and improve the prediction accuracy.

[0075] According to a sixth aspect of the present disclosure, a control method for a PLC system and IOT devices based on digital twin is provided, which is applied to the scenario where the PLC system is for an automobile manufacturing line. The method includes:

[0076] Select the robot arm and assembly line on the automobile production line, and based on their physical characteristics and operation data, build a highly realistic digital twin model with the help of professional modeling software and algorithms to map the equipment form and working condition performance;

[0077] Build a high-speed network, use industrial Ethernet or 5G to connect the pressure and position of the production line sensors, motors, cylinders, and the digital twin system, and achieve real-time bidirectional synchronization and coordination of data according to the unified protocol standard to ensure that all data is updated in real time;

[0078] Use the digital twin model combined with CAE and VR technologies to simulate the assembly process, set multi-parameter scenarios, discover and solve problems such as rhythm, materials, and space, and optimize the layout, equipment, process, and quality control across departments.

[0079] Establish a monitoring platform based on digital twin to implement real-time monitoring, process sensor data with big data and AI algorithms, monitor and give early warnings in real time according to the fault prediction model, pre-arrange maintenance according to the predictive maintenance strategy, and reduce unplanned downtime.

[0080] Collect the feedback from the operators on the production line, and optimize and adjust the digital twin model in combination with the model data to improve the accuracy and application value.

[0081] According to the eighth aspect of the present disclosure, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps of the method in any one of the above embodiments are implemented.

[0082] According to the ninth aspect of the present disclosure, a computer-readable storage medium is provided. A computer program is stored on the computer-readable storage medium, and when the computer program is executed by the processor, the steps of the method in any one of the above embodiments are implemented.

[0083] The control method of the PLC system and IOT device based on digital twin provided by the embodiments of the present disclosure deploys virtual machines and virtual PLC instances in the server to manage each part of the PLC system, constructs a digital twin model based on the operation data of physical components and deploys it. The data link between the IOT device and the digital twin model is established through the network to achieve synchronization. The collected data is preprocessed and the results are obtained by the analysis model. After predicting faults and maintenance requirements, feedback is given, and then the instances and models are optimized according to the results and model status. This method combines two technologies, enables the PLC and IOT devices to cooperate efficiently, optimizes the data flow, improves the control level, enhances the reliability and adaptability of the system, reduces costs and improves efficiency. Among them, virtualization provides computing resources and flexibility to support the operation of the digital twin model; the digital twin provides a real-time source for data processing algorithms to achieve monitoring and optimization; the data processing results are used to optimize the configuration performance of the virtual PLC, make full use of IOT data to achieve efficient management and control, and jointly build a highly integrated intelligent manufacturing system.

[0084] The above description is only an overview of the technical solutions of the embodiments of the present application. In order to be able to understand the technical means of the embodiments of the present application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of the embodiments of the present application more obvious and understandable, the specific embodiments of the present application are specifically given below. Description of the Drawings

[0085] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings of the embodiments will be briefly described below. It should be understood that the following described drawings only relate to some embodiments of the present disclosure and do not limit the present disclosure, where:

[0086] Figure 1It is a schematic flowchart of a control method for a PLC system and IOT devices based on digital twin provided by an embodiment of the present disclosure;

[0087] Figure 2 It is a schematic structural diagram of a control device for a PLC system and IOT devices based on digital twin provided by an embodiment of the present disclosure;

[0088] Figure 3 It is a schematic flowchart of a control method for a PLC system and IOT devices based on digital twin provided by an example of the present disclosure;

[0089] Figure 4 It is a schematic flowchart of another control method for a PLC system and IOT devices based on digital twin provided by an example of the present disclosure;

[0090] Figure 5 It is a schematic flowchart of another control method for a PLC system and IOT devices based on digital twin provided by an example of the present disclosure;

[0091] Figure 6 It is a schematic flowchart of another control method for a PLC system and IOT devices based on digital twin provided by an example of the present disclosure;

[0092] Figure 7 It is a schematic structural diagram of a computer device provided by an embodiment of the present disclosure.

[0093] In the drawings, reference numerals with the same last two digits correspond to the same elements. It should be noted that the elements in the drawings are schematic and not drawn to scale. Detailed Embodiments

[0094] In order to make the objectives, technical solutions, and advantages of the embodiments of the present disclosure clearer, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings. Obviously, the described embodiments are some, but not all, of the embodiments of the present disclosure. All other embodiments obtained by those skilled in the art based on the described embodiments of the present disclosure without creative efforts shall also fall within the scope of protection of the present disclosure.

[0095] Reference to "embodiment" herein means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the present application. The phrase "embodiment" appearing in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0096] As used in this document, the term "and / or" merely describes the relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: the existence of A, the coexistence of A and B, and the existence of B. Additionally, in this document, the character " / " generally indicates that the associated objects before and after are in an "or" relationship.

[0097] In addition, in all embodiments of the present disclosure, terms such as "first" and "second" are only used to distinguish one component (or a part of a component) from another component (or another part of a component).

[0098] In the description of this application, unless otherwise specified, the meaning of "a plurality" refers to two or more (including two). Similarly, "a plurality of groups" refers to two or more groups (including two groups).

[0099] Glossary:

[0100] PLC: Programmable Logic Controller, used to control and manage mechanical equipment on an industrial automation production line.

[0101] IOT: Internet of Things, which connects various devices and sensors through the Internet to achieve information sharing and intelligent control.

[0102] Virtualization: A technology that allows multiple virtual machines to be created on a single physical server, and each virtual machine can run different operating systems and applications.

[0103] Digital Twin: A technology that enables real-time monitoring, simulation, and optimization of a system by creating an exact virtual copy of a physical system.

[0104] To enable those skilled in the art to better understand the solution of this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the accompanying drawings.

[0105] The application of existing technologies to virtualization and digital twin technologies faces many difficulties. Its shortcomings are mainly reflected in: the lack of application of virtualization technology in the PLC control system, resulting in bottlenecks in system scalability and flexibility; the lack of application of digital twin technology, making it difficult to achieve accurate simulation and immediate optimization of physical systems; the limited efficiency of data processing and analysis, making it difficult to fully exploit the value of the massive data generated by IOT devices.

[0106] Based on the problems existing in the prior art, the embodiments of the present disclosure provide a control method for a PLC system and IOT devices based on digital twin. Figure 1 It is a schematic flowchart of a control method for a PLC system and IOT devices based on digital twin provided by the embodiments of the present disclosure. As Figure 1 shown, the method includes:

[0107] S110. Deploy a virtual machine on the server and configure multiple virtual PLC instances on the virtual machine. Each of the virtual PLC instances manages different parts of the PLC system, and the virtual machine is used to run and manage each of the virtual PLC instances.

[0108] In a specific implementation manner, as an embodiment, configure parameters such as the input / output ports, communication protocols, and data storage formats of the virtual PLC instances to accurately map the functions of the PLC system. Through testing and debugging, ensure the accuracy and stability of the simulation.

[0109] S120. Create digital twin models for each of the physical components according to the operation data of the physical components of the PLC system, and deploy the digital twin models to the virtual PLC instances.

[0110] In a specific implementation manner, the digital twin model is used to represent the behaviors and interactions of the components.

[0111] The application of digital twin technology plays a core role in the integration of the PLC system and IOT devices. It can not only simulate the operating state of the physical system, but also predict future maintenance requirements and optimize the production process.

[0112] Specifically, first, conduct a comprehensive analysis of the physical components of the PLC system, including their structures, functions, operating parameters, and interrelationships. Use advanced 3D modeling software to build digital twin models based on the analyzed data, accurately restoring the geometric shapes and physical characteristics of each physical component. Then, deploy the built digital twin models to the virtual PLC system through specific data interfaces and conversion protocols to ensure the compatibility and interactivity of the models with the virtual environment. During the deployment process, strictly test the response characteristics and data transmission accuracy of the models in the virtual PLC system, and conduct repeated verification and optimization by simulating actual working conditions, so that the digital twin models can run stably in the virtual PLC system and accurately reflect the states of the physical components.

[0113] In a specific implementation manner, as an embodiment, before implementing step S120, it may further include:

[0114] Select physical components from the PLC system as the objects of digital twin, such as sensors, actuators, and controllers.

[0115] S130. Use the network to establish a data transmission link between the IOT device and the digital twin model to achieve real-time data synchronization, ensuring that the data flow from the IOT device to the digital twin model is stable and accurate.

[0116] In a specific embodiment, as an example, first select an appropriate network protocol, such as the low-latency MQTT protocol. Deploy communication modules at the IOT device end and the digital twin model end respectively, and set matching network parameters. Ensure data security and integrity through encryption and verification mechanisms, establish data caching and retransmission strategies to cope with network fluctuations, and achieve stable and accurate real-time data synchronization.

[0117] In addition, after implementing step S130, the following may also be included:

[0118] Calibrate the digital twin model using the operation data.

[0119] Simulate various operation scenarios in a virtual environment to predict the reactions of the virtual PLC instance under different conditions;

[0120] Adjust the configuration of the virtual PLC instance or the physical component according to the simulation results;

[0121] Collect the operating status of the digital twin model;

[0122] Monitor the operating status of the digital twin model in real time to detect anomalies in a timely manner.

[0123] In a specific embodiment, as an example, first, the received actual operation data is sorted, classified, and screened to eliminate obvious abnormal or incorrect data records, ensuring the accuracy and reliability of the data. For example, data points outside the normal physical parameter range (such as pressure, temperature, flow rate, etc.) are marked and excluded to prevent them from misleading the calibration process. Then, based on the standard operating parameters and performance indicators of the physical components of the PLC system, the key dimensions and target values for calibration are determined. For example, for a specific type of motor component, its rated speed, torque range, etc. are used as important reference bases for calibration, so that the motor model in the digital twin model highly matches the actual component in these key parameters. Next, algorithmic techniques such as data fitting and parameter optimization are used to gradually substitute the sorted actual operation data into the digital twin model for calculation and comparison. By continuously adjusting relevant parameters in the model, such as physical property coefficients, motion equation coefficients, etc., the output results of the model under different working conditions are made to be consistent with the actual operation data within a preset error range. For example, the least squares method is used to optimize and adjust the linear parameters in the model, or the genetic algorithm is used to globally search for and optimize complex non-linear parameters to achieve high-precision calibration of the model. During the process of adjusting parameters, multiple rounds of simulation verification are carried out to compare the differences between the model under the input of the same actual operation data and its uncalibrated state and the operating state of the actual physical system before. Visualization tools are used to intuitively display and analyze the simulation results, such as drawing a curve comparison graph of the model output and actual data, so as to more clearly evaluate the calibration effect and timely discover and correct possible deviations and problems. Finally, after repeated calculations, verifications, and adjustments, when the output results of the digital twin model under multiple typical working conditions are highly matched with the actual operation data and all performance indicators meet the preset calibration accuracy requirements, it is determined that the model calibration is completed, thereby obtaining a calibrated digital twin model that can accurately reflect the actual operating state and behavioral characteristics of the physical components of the PLC system.

[0124] S140. Use the IOT device and the physical component to collect data. After preprocessing, input the data into a data analysis model for data analysis to obtain an analysis result, and predict potential faults and maintenance requirements based on the analysis result.

[0125] In a specific embodiment, before collection, determine the IOT devices and sensors that need to collect data, such as temperature sensors, position sensors, speed sensors, etc. And establish a data collection mechanism for data collection to ensure the continuity and integrity of the data.

[0126] In a specific embodiment, as an example, the preprocessing step includes:

[0127] Perform cleaning processing on the data to remove invalid, incorrect, or incomplete data records;

[0128] Normalize the data so that it conforms to specific formats and standards.

[0129] In a specific implementation, as an example, it further includes:

[0130] Select appropriate data analysis tools and techniques according to data characteristics and analysis objectives, such as machine learning algorithms, statistical analysis, etc.;

[0131] Develop and train data analysis models, such as prediction models, classification models, etc.

[0132] S150. Use the data transmission link to feedback the analysis results to the digital twin model and the virtual PLC instance.

[0133] In a specific implementation, integrate the data analysis results into the PLC control system for real-time monitoring and control decision-making.

[0134] S160. Adjust and optimize the virtual PLC instance and the digital twin model based on the analysis results and the running status of the digital twin model.

[0135] In addition, after implementing step S160, it may further include:

[0136] Collect the running data and feedback information of the virtual PLC system;

[0137] Optimize the digital twin model based on the running data and feedback information of the virtual PLC system.

[0138] In addition, after implementing step S160, it may further include:

[0139] Regularly evaluate the performance of the data analysis model to ensure its accuracy and effectiveness.

[0140] Adjust and optimize the data analysis model according to the performance evaluation results and new business requirements.

[0141] Data processing and analysis algorithms play a crucial role in the integration of PLC and IOT devices, enabling the system to extract valuable information from the large amount of collected data, thus supporting better decision-making and system optimization.

[0142] The PLC system and IOT device control method based on virtualization and digital twin technology provided by the embodiments of the present disclosure deploys virtual machines and multiple virtual PLC instances on the server to manage different parts of the PLC system, creates a digital twin model according to the operation data of physical components and deploys it to the virtual PLC instances, and constructs a data transmission link between the IOT device and the digital twin model through the network to achieve real-time synchronization. The collected data is analyzed by the data analysis model after preprocessing to predict potential faults and maintenance requirements, and the results are fed back through the link. Then, the virtual PLC instances and the model are adjusted and optimized based on the results and the model operating status. This method integrates virtualization and digital twin technologies, enables the PLC system and IOT devices to cooperate efficiently, optimizes the data flow, improves the control accuracy and intelligence level, enhances the system reliability and adaptability, effectively reduces the operation and maintenance costs, and improves the production efficiency. The application of virtualization technology in the PLC control system, the creation of virtual replicas by digital twin technology, and the integration of data processing and analysis algorithms - together constitute a highly integrated intelligent manufacturing system. They are interdependent and jointly achieve the efficient operation and optimization of the system. Virtualization technology provides the computing resources and flexibility required to run the digital twin model, enabling the digital twin model to run in a highly controlled and scalable environment. Applying virtualization technology to the PLC control system realizes the flexible expansion and efficient management of the system. The digital twin model provides a rich real-time data source for data processing and analysis algorithms by synchronizing the data of the physical system in real time, enabling real-time monitoring and optimization. The results of data processing and analysis can be used to optimize the configuration and performance of the virtual PLC, make full use of the data generated by IOT devices, and achieve more efficient system management and control.

[0143] Based on the above embodiments, the embodiments of the present disclosure further provide a control device for a PLC system and IOT devices based on digital twin, as Figure 2 shown. The control device for the PLC system and IOT devices includes:

[0144] A construction module 210, configured to construct a digital twin model according to the physical components of the PLC system and deploy it to the virtual PLC system;

[0145] A synchronization module 220, configured to construct a data transmission channel by using network technology to achieve real-time data synchronization between the digital twin model and the IOT device;

[0146] A calibration module 230, configured to calibrate the digital twin model based on the received actual operation data to obtain a calibrated digital twin model;

[0147] A preprocessing module 240, configured to collect data from the IOT device and the physical components, and obtain preprocessed data after preprocessing;

[0148] A training module 250, configured to input the preprocessed data into a data analysis model for training to obtain a trained data analysis model, and deploy it to the virtual PLC system;

[0149] An analysis module 260, configured to send the actual operation data through the data transmission channel to the digital twin model for conversion processing to obtain conversion data and send it to the virtual PLC system. The virtual PLC system inputs the conversion data into the trained data analysis model for analysis, obtains an analysis result, and feeds it back to the virtual PLC system and the digital twin model.

[0150] In a specific embodiment, the digital twin model is used to represent the behaviors and interactions of the components.

[0151] In a specific embodiment, it further includes:

[0152] A selection module, configured to select a physical component from the PLC system as an object for digital twin.

[0153] In a specific embodiment, it further includes:

[0154] A deployment module, configured to deploy a virtual machine on a server, where the virtual machine is used to run and manage the virtual PLC system;

[0155] A configuration module, configured to configure the virtual PLC system to simulate the functions of the PLC system.

[0156] In a specific embodiment, it further includes:

[0157] A simulation module, configured to simulate various operation scenarios in a virtual environment and predict the reactions of the virtual PLC system under different conditions;

[0158] An adjustment module, configured to adjust the PLC program or the physical component configuration according to the simulation results;

[0159] A detection module, configured to monitor the running state of the virtual PLC system in real time and discover anomalies in a timely manner;

[0160] A prediction module, configured to analyze data trends and predict potential faults and maintenance requirements.

[0161] In a specific embodiment, it further includes:

[0162] A selection module, configured to select a matching data analysis tool and technology according to data characteristics and analysis objectives.

[0163] In a specific embodiment, it further includes:

[0164] A collection module for collecting the operation data and feedback information of the virtual PLC system;

[0165] An optimization module for optimizing the digital twin model based on the operation data and feedback information of the virtual PLC system.

[0166] In a specific embodiment, it further includes:

[0167] A preprocessing module for performing cleaning processing on the data, removing invalid, incorrect or incomplete data records, and normalizing the cleaned data.

[0168] The control device for the PLC system and IOT device based on digital twin provided by the embodiments of the present disclosure realizes the integrated control of the PLC system and IOT device.

[0169] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can refer to the partial descriptions of the method embodiments. The device embodiments described above are only illustrative. The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of the present invention. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0170] The following is illustrated with a specific example:

[0171] The PLC system in this embodiment is an automobile assembly line. To improve the efficiency and adaptability of the automobile assembly line and reduce the maintenance cost, a control method for the PLC system and IOT device based on virtualization and digital twin technology is adopted, which is applied to the chemical plant scenario of the PLC system. Sensors are installed on the assembly line, such as Figure 3 As shown, the method includes:

[0172] S301, Deploy virtual machines on the data center, configure multiple virtual PLC instances on each virtual machine, and set the management scope for each virtual PLC system according to the functional partitions and process links of the assembly line, so that it can control different parts of the assembly line to achieve refined management of the assembly line and efficient utilization of resources.

[0173] S302, For the key physical components in the automobile assembly line, such as robot arms, conveyor belts, etc., use technical means such as 3D modeling and physical property simulation to construct digital twin models respectively. The digital twin models need to map the geometric shapes, motion patterns, mechanical properties and other aspects of the key components to truly reflect their actual operating states and interaction relationships in the assembly line.

[0174] S303. Establish a data transmission link between the IOT device and the digital twin model using the network to achieve real-time data synchronization;

[0175] S304. Install various types of sensors at key positions on the assembly line, including but not limited to temperature sensors, pressure sensors, etc., to collect operation data during the operation of the assembly line. Build a machine learning model to analyze the collected sensor data. Through feature extraction, pattern recognition, and trend prediction of the data, achieve early warning of equipment failures and accurate prediction of maintenance requirements.

[0176] S305. Feed the analysis results output by the machine learning model back into the virtual PLC system and the digital twin model.

[0177] S306. The virtual PLC system adjusts the control strategy for each part of the assembly line according to the data analysis results to optimize the production process, such as adjusting the movement trajectory of the robotic arm, the running speed of the conveyor belt, etc. The digital twin model optimizes its own parameters and updates the simulation scenario according to the data analysis results, and then provides a more accurate basis for formulating preventive maintenance plans, such as determining the maintenance cycle and maintenance content of the equipment.

[0178] Through the organic integration and coordinated operation of the above virtualization technology, digital twin technology, and data processing algorithms, the production efficiency of the automobile assembly line has been effectively improved, the downtime caused by equipment failures has been reduced, and the maintenance cost has been lowered, significantly enhancing the overall efficiency and flexibility of the manufacturing system.

[0179] The following is illustrated with another specific example:

[0180] The PLC system in this embodiment is the reactor of a chemical plant. By monitoring the pressure and temperature data through the virtualized PLC system, pre-judgment and early warning of potential safety hazards are achieved, laying a solid foundation for chemical production safety. The PLC system and IOT device control method based on virtualization and digital twin technology is applied to the scenario of the production line of an intelligent manufacturing factory, such as Figure 4 As shown, the method includes:

[0181] S401. Select the core sensors of the reactor, namely pressure and temperature sensors, as the key nodes for data collection.

[0182] S402. According to the historical data information of the sensors, build a mathematical model, which is used to characterize the operating characteristics of the reactor under various pressure and temperature conditions.

[0183] S403. Real-time collect sensor data and immediately synchronize it to the virtual PLC system to ensure data timeliness.

[0184] S404. Calibrate and fine-tune the existing model using actual operation data to ensure accurate prediction results.

[0185] S405. Simulate various complex operating conditions in a virtual environment to estimate the performance of the reactor under extreme working conditions.

[0186] S406. Adjust the control parameters of the reactor according to the simulation results to optimize the operating performance.

[0187] S407. Real-time monitoring and early warning: Conduct real-time monitoring of the reactor operating status and quickly detect abnormal conditions.

[0188] S408. Deeply analyze the data change trend, predict potential faults and plan maintenance arrangements.

[0189] S409. Collect feedback on the application effectiveness of the virtual PLC system from the operators.

[0190] S410. Optimize the model according to the feedback information to enhance the prediction accuracy and system reliability.

[0191] In this embodiment, by virtualizing the PLC system, accidents can be pre-vented, the downtime and maintenance costs can be significantly reduced, and the overall chemical production safety and stability can be greatly improved.

[0192] The following is illustrated with another specific example:

[0193] The PLC system in this embodiment is the production line of an intelligent manufacturing factory. Through data processing and analysis algorithms, an intelligent production management system is constructed to improve the production line efficiency, ensure product quality and reduce operations. A PLC system and IOT device control method based on virtualization and digital twin technologies is applied to the scenario of the production line of an intelligent manufacturing factory, such as Figure 5 As shown, the method includes:

[0194] S501. Install temperature sensors, pressure sensors, vibration sensors, etc. at key parts of the production line, collect physical quantity data according to the set frequency, and transmit it to the data center via wired or wireless means.

[0195] S502. Preprocess the data. First, after the data reaches the center, it is cleaned. Abnormal, incorrect, and duplicate data are removed using screening algorithms, such as correcting over-temperature data. Then, the cleaned data is standardized, and the dimension is unified using algorithms such as normalization for subsequent analysis.

[0196] In S503, select machine learning algorithms such as LSTM or random forest to build a predictive maintenance model. Divide the data into a training set and a test set, train and tune the parameters to make the model have a high prediction accuracy for equipment failure probability, maintenance type, and time nodes, providing a basis for maintenance decisions.

[0197] In S504, integrate the output of the verified prediction model into the PLC system through a data interface, write a receiving and processing program. When a fault warning or maintenance suggestion is encountered, the PLC system issues a control instruction to adjust the parameters or arrange maintenance, monitor and manage the production line, improving efficiency and reducing downtime.

[0198] In S505, collect the feedback of model predictions and actual equipment data, compare the differences to evaluate the performance, calculate indicators such as accuracy, and according to the results, increase the data dimension or adjust the feature weights, retrain the model, optimize the model, adapt to the changes in the production line, and improve the prediction accuracy.

[0199] Through this embodiment, the production line of the intelligent manufacturing factory reduces unplanned downtime, accurately plans maintenance, improves the production line efficiency and product quality, controls the enterprise operation cost, and increases the production stability and controllability.

[0200] The following is illustrated with another specific example:

[0201] The PLC system in this embodiment is for an automobile manufacturing line. By using digital twin technology, it improves efficiency, reduces downtime, and realizes efficient and sustainable manufacturing. It adopts a PLC system and IOT device control method based on virtualization and digital twin technology, and applies it to the scenario of an automobile manufacturing line, such as Figure 6 As shown, the method includes:

[0202] In S601, select key equipment on the automobile production line, such as robotic arms and assembly lines. Based on their physical characteristics and operation data, build a highly realistic digital twin model with the help of professional modeling software and algorithms, which is used to map the equipment form and working conditions.

[0203] In S602, build a high-speed network, use industrial Ethernet or 5G to connect the production line sensors (pressure, position, etc.) and actuators (motors, cylinders, etc.) and the digital twin system. According to the unified protocol standard, achieve real-time two-way data synchronization and collaboration to ensure that all data is updated in real time.

[0204] In S603, use the digital twin model combined with CAE and VR technologies to simulate the assembly process, set multi-parameter scenarios, discover and solve potential bottlenecks, such as cycle time, materials, and space problems, and optimize the layout, equipment, process, and quality control across departments.

[0205] In S604, establish a digital twin-based monitoring platform for real-time monitoring, use big data and AI algorithms to process sensor data, monitor and give early warnings in real time according to the fault prediction model, and pre-arrange maintenance according to the predictive maintenance strategy to reduce unplanned downtime.

[0206] S605. Collect the feedback from the operators on the production line, and optimize and adjust the digital twin model in combination with the model data to improve the accuracy and application value.

[0207] Through the solution of this embodiment, production delays can be reduced, the operation efficiency of the production line can be improved, the output can be increased, the cycle can be shortened, the product quality can be improved, and the defective and waste product rates can be reduced.

[0208] The embodiment of the present application also provides a computer device. Specifically, please refer to Figure 7 , Figure 7 which is the basic structural block diagram of the computer device in this embodiment.

[0209] The computer device includes a memory 710 and a processor 720 that are communicatively connected to each other through a system bus. It should be noted that only the computer device with components 710 - 720 is shown in the figure, but it should be understood that it is not required to implement all the shown components, and more or fewer components can be implemented alternatively. Among them, those skilled in the art of the present technology can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0210] The computer device can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The computer device can perform human-computer interaction with the user through a keyboard, a mouse, a remote control, a touchpad or a voice control device, etc.

[0211] The memory 710 includes at least one type of readable storage medium, and the readable storage medium includes non-volatile memory or volatile memory, such as flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory, etc.), random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disc, etc. The RAM can include static RAM or dynamic RAM. In some embodiments, the memory 710 can be an internal storage unit of the computer device, for example, the hard disk or memory of the computer device. In other embodiments, the memory 710 can also be an external storage device of the computer device, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the computer device. Of course, the memory 710 can also include both the internal storage unit and the external storage device of the computer device. In this embodiment, the memory 710 is generally used to store the operating system and various application software installed on the computer device, such as the program code of the above method. In addition, the memory 710 can also be used to temporarily store various types of data that have been output or will be output.

[0212] The processor 720 is generally used to execute the overall operations of the computer device. In this embodiment, the memory 710 is used to store program code or instructions, and the program code includes computer operation instructions. The processor 720 is used to execute the program code or instructions stored in the memory 710 or process data, such as running the program code of the above method.

[0213] In this text, the bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, etc. This bus system can be divided into an address bus, a data bus, a control bus, etc. For the sake of convenience in representation, only a thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0214] Another embodiment of the present application further provides a computer-readable medium, which can be a computer-readable signal medium or a computer-readable storage medium. The processor in the computer reads the computer-readable program code stored in the computer-readable medium, so that the processor can execute the functional actions specified in each step or the combination of steps in the above method; and generate a device that implements the functional actions specified in each block or the combination of blocks in the block diagram.

[0215] The computer-readable medium includes but is not limited to electronic, magnetic, optical, electromagnetic, infrared memories or semiconductor systems, devices or apparatuses, or any suitable combination of the foregoing. The memory is used to store program codes or instructions, and the program codes include computer operation instructions. The processor is used to execute the program codes or instructions of the above method stored in the memory.

[0216] For the definitions of the memory and the processor, reference can be made to the description of the foregoing computer device embodiments, and details are not repeated here.

[0217] In several embodiments provided by the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of modules or units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of devices or units can be in electrical, mechanical or other forms.

[0218] In each embodiment of the present application, each functional unit or module can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0219] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the methods of various embodiments of this application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical discs.

[0220] Unless the context clearly indicates otherwise, the singular forms of the words used in this specification and the appended claims include the plural, and vice versa. Thus, when referring to the singular, the plural of the corresponding term is usually included. Similarly, the terms "comprising" and "including" will be interpreted as inclusive rather than exclusive. Likewise, the term "including" and "or" should be interpreted as inclusive, unless such an interpretation is explicitly prohibited in this specification. Where the term "example" is used in this specification, especially when it is located after a group of terms, the "example" is merely exemplary and illustrative, and should not be considered exclusive or extensive.

Claims

1. A control method for a PLC system and an IOT device based on digital twins, characterized in that: The PLC system is equipped with an IOT device, and the method includes: Deploy a virtual machine on a server, and configure multiple virtual PLC instances on the virtual machine, wherein each of the virtual PLC instances manages a different part of the PLC system, and the virtual machine is used to run and manage each of the virtual PLC instances; Creating a digital twin model for each physical component according to the operation data of the physical components of the PLC system, and deploying the digital twin model to the virtual PLC instance; Using the network to establish a data transmission link between the IOT device and the digital twin model to achieve real-time data synchronization; Collect data using the IOT device and the physical component, input data into a data analysis model after preprocessing to perform data analysis, obtain analysis results, and predict potential failures and maintenance requirements based on the analysis results; Feeding the analysis result back to the digital twin model and the virtual PLC instance using the data transmission link; The virtual PLC instance and the digital twin model are adjusted and optimized based on the analysis results and the operating status of the digital twin model.

2. The method according to claim 1, characterized in that The digital twin model is used to represent the behavior and interactions of the physical components.

3. The method according to claim 1, characterized in that Before the step of respectively creating digital twin models for the multiple physical components of the PLC system, the method includes: The physical component is selected from the PLC system as the object of the digital twin.

4. The method according to claim 1, characterized in that: After the step of establishing a data transmission channel between the PLC system, the IOT device and the digital twin model to achieve real-time data synchronization, the method further comprises: The digital twin model is calibrated using the operational data.

5. The method according to claim 1, characterized in that After the step of obtaining the calibrated digital twin model, the method further includes: Simulate various operating scenarios in a virtual environment to predict how the virtual PLC instance will react under different conditions; adjusting the configuration of the virtual PLC instance or the physical component according to the simulation result; Collecting the operating status of the digital twin model; Monitor the operating status of the digital twin model in real time and detect abnormalities in a timely manner.

6. The method according to claim 1, characterized in that Before the step of inputting the pre-processed data into the data analysis model for data analysis, the method includes: Select appropriate data analysis tools and techniques based on data characteristics and analysis objectives; Develop and train the data analysis model.

7. The method according to claim 1, characterized in that The pre-processing step comprises: Cleaning the data to remove invalid, erroneous or incomplete data records; The cleaned data is normalized.

8. A control device for a PLC system and an IOT device based on digital twins, characterized in that: include: A deployment module, used to deploy a virtual machine on a server, and configure multiple virtual PLC instances on the virtual machine, wherein each of the virtual PLC instances manages a different part of the PLC system, and the virtual machine is used to run and manage each of the virtual PLC instances; A creation module, configured to create a digital twin model for each physical component according to operation data of the physical components of the PLC system, and deploy the digital twin model to the virtual PLC instance; A synchronization module, used to establish a data transmission link between the IOT device and the digital twin model using a network to achieve real-time data synchronization; An analysis module is used to collect data using the IOT device and the physical component, input the data into a data analysis model for data analysis after preprocessing, obtain analysis results, and predict potential failures and maintenance requirements based on the analysis results; A feedback module, configured to feed back the analysis result to the digital twin model and the virtual PLC instance by using the data transmission link; An optimization module is used to adjust and optimize the virtual PLC instance and the digital twin model based on the analysis results and the operating status of the digital twin model.

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

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

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