Cloud filter chain full life cycle digitization method and system based on digital twinning
By constructing a cloud-based digital twin production line model and combining virtual reality with traditional simulation, real-time monitoring and fault diagnosis of production equipment are achieved, solving product quality and equipment failure issues, improving product quality and production efficiency, and reducing energy consumption and equipment idle rate.
Patent Information
- Application Number
- CN202510988552.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-17
- Publication Date
- 2025-11-14
AI Technical Summary
The existing system cannot guarantee the product qualification rate, which can easily lead to product loss. Furthermore, it cannot detect and handle equipment failures in a timely manner, affecting the normal operation of the equipment.
A cloud filter chain digital twin production line model is constructed using object-oriented modeling methods, enabling real-time monitoring and data acquisition. Combining virtual reality with traditional simulation, interactive control between the physical and virtual workshops is achieved through PLC, enabling equipment fault diagnosis and predictive analysis, and collaborative optimization throughout the entire lifecycle.
By optimizing process parameters through virtual-real fusion simulation, product losses can be reduced, product quality and pass rate can be improved, equipment idle rate and energy consumption can be reduced, and real-time monitoring and early warning of faults can be achieved to ensure production continuity and optimize production decisions.
Smart Images
Figure CN120952379A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of automotive parts, and in particular to a cloud-based digital filtering chain full lifecycle digitization method and system based on digital twins. Background Technology
[0002] The existing system cannot guarantee the product qualification rate and is prone to product damage, resulting in economic losses. If the production equipment malfunctions, it cannot be detected and dealt with in a timely manner, which can easily affect the normal operation of the equipment. Summary of the Invention
[0003] To address the existing problems, this invention provides a method and system for digitizing the entire lifecycle of a cloud filter chain based on digital twins, the specific solution of which is as follows:
[0004] A digital twin-based approach to the entire lifecycle digitization of a cloud filter chain includes the following steps:
[0005] S1 uses an object-oriented modeling approach to model equipment, processes, and production lines, forming a cloud filter chain digital twin production line model. It uses a unified modeling language to establish a physical data model, and according to modeling rules, it abstracts the physical data of the production line into a production line physical data class. It also performs real-time monitoring and data collection on the cloud filter chain digital twin production line model, and divides the collected physical data into resource dimension, task dimension, and process dimension.
[0006] S2 performs digital twin full lifecycle analysis on cloud filter chain devices and integrates it into traditional simulation demonstration and data analysis to form a visualized virtual three-dimensional scene model. It also debugs the cloud filter chain's ingredients and optimizes its quality.
[0007] S3 constructs a cloud filter chain digital twin workshop model, simulates physical characteristics, restores equipment performance and physical characteristics, and integrates the model into a visualized virtual 3D scene model after real-time monitoring and data acquisition. The interaction control between the physical workshop and the virtual workshop is completed through PLC.
[0008] S4 uses an equipment fault diagnosis and prediction analysis system to diagnose and predict equipment faults in both the physical and virtual workshops, while simultaneously optimizing the entire lifecycle production process of the cloud filter chain.
[0009] Preferably, a system based on a digital twin-based cloud filter chain full lifecycle digitization method includes a cloud filter chain digital twin production line system and a cloud filter chain digital twin workshop system connected via a network. The cloud filter chain digital twin workshop system includes a physical workshop, a virtual workshop, and a workshop control module connected via a network. The workshop control module includes a workshop real-time monitoring unit and a data acquisition unit connected via a network.
[0010] The cloud filter chain twin production line system includes an image acquisition unit, a production equipment information acquisition unit, and a database management unit; the image acquisition unit and the production equipment information acquisition unit upload the acquired data to the database management unit.
[0011] The beneficial effects of this invention are as follows:
[0012] In a virtual simulation environment of the production line, the system simulates and analyzes the workshop's operation based on real-time feedback data from the production site. Through multiple rounds of simulation and state prediction, it minimizes product loss, ensures product quality, optimizes equipment processing cycle time, reduces equipment idle time, and achieves energy reduction, providing crucial data for workshop operation optimization and production decision-making. Simultaneously, the data acquisition and real-time monitoring of this invention enables the collection and monitoring of the operating status of automated equipment in the workshop, providing monitoring and early warning of production equipment operation status. When equipment malfunctions, the system automatically alarms and displays possible fault causes according to weight on the HMI interface, ensuring the normal operation of the workshop. Attached Figure Description
[0013] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0014] Figure 1 This is a flowchart of the method of the present invention;
[0015] Figure 2 This is a block diagram of the physical data classification of the production line according to the present invention;
[0016] Figure 3 This is a system principle block diagram of the present invention. Detailed Implementation
[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0018] To address the technical problems of existing systems that cannot guarantee product qualification rates and are prone to product loss leading to economic losses, and that equipment malfunctions cannot be detected and addressed promptly, thus affecting normal equipment operation, this embodiment provides the following technical solution:
[0019] A digital twin-based approach to the entire lifecycle digitization of a cloud filter chain includes the following steps:
[0020] S1 employs an object-oriented modeling approach to model equipment, processes, and production lines, forming a cloud-based digital twin production line model. A physical data model is established using a unified modeling language. Based on modeling rules, the physical data of the production line is abstracted into production line physical data classes. Real-time monitoring and data collection are performed on the cloud-based digital twin production line model. During the production process, the collected physical data is categorized into resource, task, and process dimensions. The resource dimension represents the general and extended attributes of production factor information; the task dimension relates to production task data; and the process dimension describes data related to the production process.
[0021] Specifically, the resource dimension primarily focuses on production factor information, dividing it into general attributes and extended attributes. General attributes are possessed by all factors and have universal applicability, such as basic information and status information; they are not limited to a specific factor. Extended attributes are unique to each factor, possessing specificity and scalability.
[0022] Task dimension refers to information related to issuing tasks.
[0023] Process dimension describes relevant data in manufacturing and production. By classifying and analyzing production line resources and data, it lays the foundation for the subsequent establishment of a physical data model of the cloud filter chain production line.
[0024] S2. After modeling is completed, a digital twin full lifecycle analysis is performed on the cloud filter chain components. Virtual reality is integrated into traditional simulation demonstrations and data analysis to enhance the fusion of virtual and real spaces, forming a visualized virtual 3D scene model. The material preparation of the cloud filter chain is then adjusted, and quality is optimized. The cloud filter chain components are products from the cloud filter chain production line, including oil filters, fuel filters, diesel filters, oil filter elements, filter housings, and related automotive valves.
[0025] S3 constructs a digital twin workshop model for the cloud filter chain. By simulating the physical characteristics of friction, gravity, and resistance, it recreates the equipment's performance and physical properties. After real-time monitoring and data acquisition of this model, it is integrated into a visualized virtual 3D scene model. Interactive control between the physical and virtual workshops is achieved through a PLC. Utilizing .NET library functions and integration with an external real-time database, changes in the visualized virtual 3D scene are reflected in real-time based on data changes, integrating virtual reality into traditional simulation demonstrations and data analysis, thus strengthening the fusion of virtual and real spaces.
[0026] The physical workshop is an objectively existing collection of physical entities, consisting of automated processing, testing, and logistics equipment, as well as people, auxiliary resources, and the environment.
[0027] The virtual workshop is a collection of physical models and behavioral rules for the production elements of people, machines, materials, and environment in a workshop. Establishing a virtual space includes the following steps:
[0028] S1' uses the Unity 3D virtual reality engine software to render and model the production elements and interactive actions of the virtual workshop. Through the particle effects, 3D roaming, collision detection and scene switching function modules in the Unity 3D engine, the virtual presentation of the factory and human-computer interaction functions are realized. The physical workshop is modeled from three levels: elements, behavior and rules, and the established model is stored in the database.
[0029] S2' reads and stores data by connecting to a database via a webpage, forming a collection of data generated during the production process based on the virtual space of the webpage, thereby establishing a virtual workshop; through the integration of workshop twin data, it opens up the entire process information link, providing data-driven support for the virtual-physical integration of the workshop.
[0030] Real-time monitoring in step S3 includes the following steps:
[0031] S31, based on the cloud filter chain digital twin workshop model, constructs a virtual workshop including a scheduling model, quality control model, product resource model, process database, and resource library.
[0032] S32 uses MES to collect real-time data on physical workshop production plans, material consumption, production progress, product inspection, parts flow, resource usage, and personnel information.
[0033] S33. Based on the data collected in step S32, perform real-time simulation analysis of logistics, production efficiency, product quality, and plan achievement rate in the virtual workshop.
[0034] S34 optimizes the scheduling of personnel, materials, equipment, tooling, and tools in the physical workshop based on the simulation results of the virtual workshop.
[0035] S4 uses an equipment fault diagnosis and prediction analysis system to diagnose and predict equipment faults in both the physical and virtual workshops, while simultaneously optimizing the entire lifecycle production process of the cloud filter chain.
[0036] Specifically, the diagnostic analysis of the equipment fault diagnosis and prediction analysis system includes the following steps:
[0037] S41, establishing a closed-loop device management system based on a unit-level CPS architecture:
[0038] The unit-level CPS architecture is a foundational layer in the layered architecture of Cyber-Physical Systems (CPS), specifically referring to an intelligent control system built for a single device or independent production unit. Its core logic is to achieve deep interaction between physical workshop equipment and virtual workshop information models through data closed-loop.
[0039] Specifically, by collecting and analyzing the operating status of equipment, a fault database and operating status model are constructed, and big data analysis is used to provide management and control services for production equipment. At the same time, the control commands executed by the equipment are transmitted to the equipment control system through the information shell, so that the control system can achieve precise control of the manufacturing execution of equipment in the physical workshop. The equipment is interconnected through an industrial ring network.
[0040] S42, Real-time Data Acquisition:
[0041] The ModBus / OPC UA protocol is used to collect real-time data on equipment status, process parameters, and test results in the physical workshop, including: power on / off status, fault codes, spindle load, spindle speed, cutting force, and geometric dimensions.
[0042] S43, Twin Data Fusion and Simulation:
[0043] The collected data is stored in a real-time database, and the collection frequency is set according to the criticality of the equipment. The real-time collected data is processed before being stored in a historical database to reduce data redundancy and response lag. At the same time, equipment mechanism models, fault models and data analysis models are built in the virtual workshop, and virtual real-time simulation is performed based on the equipment collection data. The equipment start-up and shutdown, manufacturing actuators and process parameters are controlled in real time.
[0044] S44, Intelligent Monitoring and Early Warning:
[0045] On the production site, the equipment is displayed in three dimensions through a human-machine interface (HMI); the operating status of the production equipment is monitored and warned based on the workshop IoT system and equipment fault database. When the equipment is abnormal, the Andon system will automatically alarm and display possible fault causes on the HMI interface according to weight, so as to support the rapid troubleshooting on the workshop site.
[0046] Step S4 involves collaborative optimization of the entire lifecycle production process of the cloud filter chain, including product quality optimization, energy consumption optimization, production cycle optimization, and product qualification rate optimization.
[0047] Among them, based on the cloud filter chain digital twin production line model, the product qualification rate is optimized by linking the information interaction between the physical workshop and the virtual workshop through twin data. Specifically, there are two parallel paths. Path one: The cloud filter chain digital twin production line model reads and parses the twin data through the Jean framework, discovers potential process defects through reasoning, and dynamically adjusts the model parameters. Path two: The visualized virtual 3D scene model interacts with the PLC controller and real-time database of the physical workshop through a standardized data interface, and feeds back the optimization instructions of the virtual workshop to the equipment in the physical workshop in real time to execute the optimization instructions.
[0048] A system based on a digital twin-based digital twin approach for the entire lifecycle of a cloud filter supply chain includes a cloud filter supply chain digital twin production line system and a cloud filter supply chain digital twin workshop system connected via a network. The cloud filter supply chain digital twin workshop system includes a physical workshop, a virtual workshop, and a workshop control module connected via a network. The workshop control module includes a workshop real-time monitoring unit and a data acquisition unit connected via a network.
[0049] The cloud filter chain twin production line system includes an image acquisition unit, a production equipment information acquisition unit, and a database management unit; the image acquisition unit and the production equipment information acquisition unit upload the acquired data to the database management unit.
[0050] The image acquisition unit includes several 3D scanners, and the production equipment includes a stamping machine, a folding machine, a strip clamping machine, a roller machine, a punching machine, a glue injection machine, a packaging machine, and a warehousing and transportation machine. The production equipment information acquisition unit includes several sensors, a wireless gateway, and a wireless router.
[0051] The image acquisition unit is located at the inlet of the electric kiln, the tile mill, and the cleaning and drying machine, and is used to acquire images of the cloud filter chain at different processes and upload them to the database management unit.
[0052] The sensors in the production equipment information acquisition unit are installed on the production equipment to collect the operating parameters of the production equipment, and then transmit the data to the database management unit sequentially through a wireless gateway and a wireless router. The sensors include photoelectric sensors, pressure sensors, temperature sensors, and gravity sensors. The photoelectric sensor is installed on one side of the ball mill to record the number of milling cycles. The pressure sensor is installed on the pressure head of the forming press to record the pressing pressure of the forming press. The temperature sensor is installed on the electric kiln to record the sintering temperature. The gravity sensor is installed at the rear end of the cleaning and drying machine to record the weight of the cloud filter chain.
[0053] The database management unit is used to manage data related to the cloud filter chain twin production line system.
[0054] In summary, this invention uses a digital twin workshop to feed the results of virtual manufacturing execution back to the ERP system for production progress and delivery date feedback. It also issues production plans and optimized simulation execution strategies to the manufacturing execution system (MES). The MES completes real manufacturing execution based on the execution strategies and production plans transmitted from the digital twin. Various driving signals and data acquired from the physical workshop, including workshop production tasks, manufacturing resources, production capacity, logistics routes, and inventory status, effectively drive various levels in the digital space. In the virtual simulation environment of the production line, the system simulates and analyzes the workshop's operation based on real-time feedback data from the production site. Based on the virtual simulation results, the digital twin workshop system promptly formulates production strategies, resource scheduling workshop control instructions, and inputs them into the MES system, dynamically driving the delivery of production materials and adjusting workstation operation plans. Through statistical analysis of historical data, it supports workshop efficiency evaluation, quality statistical process control analysis, and equipment comprehensive efficiency analysis, providing crucial information for workshop operation optimization and production decision-making.
[0055] This invention dynamically optimizes process parameters through virtual-real fusion simulation, reducing product loss, ensuring quality consistency, and improving product quality and yield. Furthermore, by optimizing equipment cycle time and resource scheduling, it reduces equipment idle time, lowers energy consumption, and improves efficiency. Simultaneously, this invention enhances fault early warning capabilities, monitors equipment status in real time, automatically alarms and locates the cause of faults when anomalies occur, ensuring production continuity. Moreover, this invention can perform end-to-end collaborative optimization, achieving global collaborative optimization of production cycle time, energy consumption, and quality based on twin data, improving decision-making accuracy.
[0056] Those skilled in the art will further appreciate that the various illustrative logic blocks, modules, circuits, and algorithm steps described in conjunction with the embodiments disclosed herein can be implemented as electronic hardware, computer software, or a combination of both. To clearly illustrate this interchangeability between hardware and software, the various components, modules, circuits, and steps are described above in a generalized manner in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. Those skilled in the art may implement the described functionality in different ways for each specific application, but such implementation decisions should not be construed as departing from the scope of the invention.
[0057] The prior description of this disclosure is provided to enable any person skilled in the art to make or use this disclosure. Various modifications to this disclosure will be apparent to those skilled in the art, and the general principles defined herein may be applied to other variations without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not intended to be limited to the examples and designs described herein, but should be accorded the widest scope consistent with the principles and novel features disclosed herein.
[0058] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims
1. A cloud filter chain full lifecycle digitization method based on digital twins, characterized in that, Includes the following steps: S1 uses an object-oriented modeling approach to model equipment, processes, and production lines, forming a cloud filter chain digital twin production line model. It uses a unified modeling language to establish a physical data model, and according to modeling rules, it abstracts the physical data of the production line into a production line physical data class. It also performs real-time monitoring and data collection on the cloud filter chain digital twin production line model, and divides the collected physical data into resource dimension, task dimension, and process dimension. S2 performs digital twin full lifecycle analysis on cloud filter chain devices and integrates it into traditional simulation demonstration and data analysis to form a visualized virtual three-dimensional scene model. It also debugs the cloud filter chain's ingredients and optimizes its quality. S3 constructs a cloud filter chain digital twin workshop model, simulates physical characteristics, restores equipment performance and physical characteristics, and integrates the model into a visualized virtual 3D scene model after real-time monitoring and data acquisition. The interaction control between the physical workshop and the virtual workshop is completed through PLC. S4 performs fault diagnosis and predictive analysis on equipment in both physical and virtual workshops, and simultaneously optimizes the entire lifecycle production process of the cloud filter chain.
2. The method according to claim 1, characterized in that: The resource dimension in step S1 refers to the general and extended attributes of production factor information, the task dimension is associated with production task data, and the process dimension describes production process-related data.
3. The method according to claim 1, characterized in that: The physical workshop mentioned in step S3 is an objectively existing collection of physical workshops, consisting of processing, testing, and logistics automation equipment, as well as people, auxiliary resources, and the environment.
4. The method according to claim 1, characterized in that, The virtual workshop mentioned in step S3 is a collection of physical models and behavioral rules for the production elements of people, machines, materials, and environment in the workshop. Establishing the virtual space includes the following steps: S1' uses the Unity 3D virtual reality engine software to render and model the production elements and interactive actions of the virtual workshop. Through the particle effects, 3D roaming, collision detection and scene switching function modules in the Unity 3D engine, the virtual presentation of the factory and human-computer interaction functions are realized. The physical workshop is modeled from three levels: elements, behavior and rules, and the established model is stored in the database. S2' reads and stores data by connecting to a database via a webpage, forming a collection of data generated during the production process based on the virtual space of the webpage, thereby establishing a virtual workshop; through the integration of workshop twin data, it opens up the entire process information link, providing data-driven support for the virtual-physical integration of the workshop.
5. The method according to claim 1, characterized in that, Real-time monitoring in step S3 includes the following steps: S31, based on the cloud filter chain digital twin workshop model, constructs a virtual workshop including a scheduling model, a quality control model, a product resource model, a process database, and a resource library; S32 collects real-time data on production plans, material consumption, production progress, product inspection, parts circulation, resource usage, and personnel information from the physical workshop through MES. S33, Based on the data collected in step S32, perform real-time simulation analysis of logistics, production efficiency, product quality, and plan achievement rate in the virtual workshop; S34 optimizes the scheduling of personnel, materials, equipment, tooling, and tools in the physical workshop based on the simulation results of the virtual workshop.
6. The method according to claim 1, characterized in that: Step S4 of the equipment fault diagnosis and prediction analysis system includes the following steps: S41 establishes a closed-loop device management system based on a unit-level CPS architecture; Specifically, the system collects equipment operating status data, builds an equipment fault database and operating status model, and provides management and control services for production equipment based on big data analysis; it sends instructions to the equipment control system through an information shell to drive the equipment to perform manufacturing actions, with the equipment interconnected through an industrial ring network. S42, Real-time data acquisition; The equipment status, process parameters, and test results are obtained through the ModBus / OPC UA protocol, including: power on / off status, fault codes, spindle load, spindle speed, cutting force, and geometric dimensions. S43, Twin Data Fusion and Simulation; The data collection frequency is set according to the criticality level of the equipment. The collected data is stored in the real-time database and then transferred to the historical database after processing. Simultaneously build equipment mechanism models, fault prediction models, and data analysis models in a virtual workshop; Virtual real-time simulation is driven by equipment acquisition data, and the PLC provides real-time feedback control of equipment start-up and shutdown, actuators, and process parameters. S44, Intelligent Monitoring and Early Warning; The equipment status is displayed on the production site through the HMI 3D virtual interface; the operating status is monitored based on IoT data and fault database, and system alarms are triggered when abnormalities occur; and possible fault causes are displayed on the HMI 3D virtual interface according to weight to support rapid troubleshooting on the production site.
7. The method according to claim 1, characterized in that, Step S4 involves collaborative optimization of the entire lifecycle production process of the cloud filter chain, including: product quality optimization, energy consumption optimization, production cycle optimization, and product qualification rate optimization. Among them, based on the cloud filter chain digital twin production line model, the product qualification rate is optimized by linking the information interaction between the physical workshop and the virtual workshop through twin data. Specifically, there are two parallel paths. Path one: The cloud filter chain digital twin production line model reads and parses the twin data through the Jean framework, discovers potential process defects through reasoning, and dynamically adjusts the model parameters. Path two: The visualized virtual 3D scene model interacts with the PLC controller and real-time database of the physical workshop through a standardized data interface, and feeds back the optimization instructions of the virtual workshop to the equipment in the physical workshop in real time to execute the optimization instructions.
8. A system based on the method of any one of claims 1-7, characterized in that: It includes a cloud-connected digital twin production line system and a cloud-connected digital twin workshop system. The cloud-connected digital twin workshop system includes a physical workshop, a virtual workshop, and a workshop control module connected via the network. The workshop control module includes a real-time monitoring unit and a data acquisition unit connected via the network.
9. The system according to claim 8, characterized in that: The cloud filter chain twin production line system includes an image acquisition unit, a production equipment information acquisition unit, and a database management unit; the image acquisition unit and the production equipment information acquisition unit upload the acquired data to the database management unit.
10. The system according to claim 9, characterized in that: The image acquisition unit includes several 3D scanners, and the production equipment includes a punching machine, a paper folding machine, a strip clamping machine, a roller machine, a punching machine, a glue injection machine, a packaging machine, and a warehousing and transportation machine. The production equipment information acquisition unit includes several sensors, a wireless gateway, and a wireless router. The image acquisition unit is located at the inlet of the electric kiln, the tile mill, and the washing and drying machine, and is used to acquire images of the cloud filter chain at different processes and upload them to the database management unit. The sensors in the production equipment information acquisition unit are installed on the production equipment to collect the operating parameters of the production equipment, and then transmit the data to the database management unit sequentially through a wireless gateway and a wireless router. The sensors include photoelectric sensors, pressure sensors, temperature sensors, and gravity sensors. The photoelectric sensor is installed on one side of the ball mill to record the number of milling cycles. The pressure sensor is installed on the pressure head of the forming press to record the pressing pressure of the forming press. The temperature sensor is installed on the electric kiln to record the sintering temperature. The gravity sensor is installed at the rear end of the cleaning and drying machine to record the weight of the cloud filter chain. The database management unit is used to manage data related to the cloud filter chain twin production line system.