Ring forging production line visualization method, system and equipment based on digital twinborn model

Abstract

The invention provides a ring forging production line visualization method, system and equipment based on a digital twin model, and is applied to the technical field of industrial equipment monitoring and visualization. Comprising the following steps: constructing three-dimensional geometric models corresponding to a plurality of ring forging devices on a ring forging production line; for each ring forging device, constructing a displacement-speed-acceleration curve according to the mass, rotational inertia and maximum torque of the ring forging device; constructing a vibration spectrum model according to the structural rigidity, the damping coefficient, the mass distribution and the inherent frequency of the ring forging equipment; constructing an instantaneous power curve and an energy efficiency model according to the friction coefficient, the transmission efficiency and the motor efficiency of the ring forging equipment; according to the heat capacity, the heat conductivity, the surface area and the heat dissipation coefficient of the ring forging equipment, a temperature rise-heat dissipation curve and a heat balance model are constructed; constructing a digital twinborn model according to the model in combination with a three-dimensional geometric model and real-time operation data; and performing rendering optimization on the plurality of digital twin models to obtain visual data. And millisecond level smooth visualization is realized.

Description

Technical Field

[0001] This application relates to the field of industrial equipment monitoring and visualization technology, and in particular to a visualization method, system and equipment for a ring forging production line based on a digital twin model. Background Technology

[0002] In the aerospace industry, while some manufacturers have introduced digital twin technology to monitor hot forging production lines (such as ring forging lines), the existing visualization monitoring systems suffer from significant processing bottlenecks, resulting in poor real-time performance. The core dynamics of industrial equipment in the physical workshop, such as mechanical movements, control signals, and process parameters, cannot be accurately mapped and synchronized in virtual space with millisecond-level precision, thus hindering efficient real-time visualization of industrial equipment data. Summary of the Invention

[0003] This application provides a visualization method, system, and equipment for a ring forging production line based on a digital twin model. By constructing a high-fidelity digital twin that integrates a multiphysics mechanism model and real-time operational data, abstract industrial data is transformed into concrete dynamic equipment behavior, achieving an intuitive mapping from numerical values ​​to phenomena. Furthermore, relying on multi-layered rendering optimization technology, the digital twin model is driven and efficiently rendered in real time, ultimately achieving millisecond-level smooth visualization of large-scale 3D scenes even under complex operating conditions.

[0004] This application provides a method for visualizing a ring forging production line based on a digital twin model, including: Construct three-dimensional geometric models of each of the multiple ring forging devices on the ring forging production line; For each ring forging machine, a displacement-velocity-acceleration curve is constructed based on the machine's mass, moment of inertia, and maximum torque; a vibration spectrum model is constructed based on the machine's structural stiffness, damping coefficient, mass distribution, and natural frequency; an instantaneous power curve and energy efficiency model are constructed based on the machine's friction coefficient, transmission efficiency, and motor efficiency; a temperature rise-heat dissipation curve and thermal balance model are constructed based on the machine's heat capacity, thermal conductivity, surface area, and heat dissipation coefficient; and a digital twin model of the ring forging machine is constructed based on the displacement-velocity-acceleration curve, the vibration spectrum model, the instantaneous power curve, the energy efficiency model, the temperature rise-heat dissipation curve, and the thermal balance model, combined with the corresponding three-dimensional geometric model and collected real-time operating data. Rendering optimization is performed on multiple digital twin models to obtain the visualization data corresponding to the ring forging production line.

[0005] This application also provides a visualization system for a ring forging production line based on a digital twin model, including: The model building module is used to construct three-dimensional geometric models corresponding to each of the multiple ring forging devices on the ring forging production line. For each ring forging device, a displacement-velocity-acceleration curve is constructed based on its mass, moment of inertia, and maximum torque. A vibration spectrum model is constructed based on the structural stiffness, damping coefficient, mass distribution, and natural frequency of the ring forging device. An instantaneous power curve and energy efficiency model are constructed based on the friction coefficient, transmission efficiency, and motor efficiency of the ring forging device. A temperature rise-heat dissipation curve and thermal balance model are constructed based on the heat capacity, thermal conductivity, surface area, and heat dissipation coefficient of the ring forging device. Based on the displacement-velocity-acceleration curve, the vibration spectrum model, the instantaneous power curve, the energy efficiency model, the temperature rise-heat dissipation curve, and the thermal balance model, combined with the corresponding three-dimensional geometric model and the collected real-time operating data, a digital twin model of the ring forging device is constructed. The visualization module is used to render and optimize multiple digital twin models to obtain the visualization data corresponding to the ring forging production line.

[0006] This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the visualization method for ring forging production line based on digital twin model as described above.

[0007] This application also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the visualization method for ring forging production line based on a digital twin model as described above.

[0008] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the visualization method for a ring forging production line based on a digital twin model as described above.

[0009] The visualization method, system, and equipment for a ring forging production line based on a digital twin model provided in this application embodiment construct three-dimensional geometric models corresponding to multiple ring forging devices on the ring forging production line. For each ring forging device, a displacement-velocity-acceleration curve is constructed based on the mass, moment of inertia, and maximum torque of the ring forging device. A vibration spectrum model is constructed based on the structural stiffness, damping coefficient, mass distribution, and natural frequency of the ring forging device. An instantaneous power curve and energy efficiency model are constructed based on the friction coefficient, transmission efficiency, and motor efficiency of the ring forging device. A temperature rise-heat dissipation curve and thermal balance model are constructed based on the heat capacity, thermal conductivity, surface area, and heat dissipation coefficient of the ring forging device. Based on the displacement-velocity-acceleration curve, the vibration spectrum model, the instantaneous power curve, the energy efficiency model, the temperature rise-heat dissipation curve, and the thermal balance model, combined with the corresponding three-dimensional geometric model and the collected real-time operating data, a digital twin model corresponding to the ring forging device is constructed. The multiple digital twin models are rendered and optimized to obtain the visualization data corresponding to the ring forging production line. This method constructs a high-fidelity digital twin that integrates a multiphysics mechanism model with real-time operational data, transforming abstract industrial data into concrete dynamic equipment behavior and achieving an intuitive mapping from numerical values ​​to phenomena. Based on this, and relying on multi-layered rendering optimization technology, the digital twin model is driven and efficiently rendered in real time, ultimately achieving millisecond-level smooth visualization of large-scale 3D scenes even under complex operating conditions. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in this application 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 this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a flowchart illustrating the visualization method for a ring forging production line based on a digital twin model provided in this application embodiment; Figure 2 This is a schematic diagram of the structure of the ring forging production line visualization system based on a digital twin model provided in this application embodiment; Figure 3 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0012] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0013] The visualization method for ring forging production lines based on digital twin models provided in this application can be applied not only to the aerospace industry, but also to high-precision technology fields such as high-end equipment manufacturing (e.g., automobile manufacturing, heavy machinery), transportation (e.g., rail transit, shipbuilding), and electronics and semiconductor manufacturing (e.g., chip manufacturing).

[0014] It should be noted that the execution subject involved in the embodiments of this application can be a visualization system for ring forging production line based on a digital twin model, or it can be an electronic device, and no specific limitation is made here.

[0015] Among them, the ring forging production line visualization system can also be called the ring forging production line visualization system.

[0016] Specifically, the hardware environment of the ring forging production line visualization system is as follows: 1. Central Processing Unit (CPU): Intel i7-10700K, or equivalent performance; 2. Graphics Processing Unit (GPU): RTX 4070, or equivalent performance; 3. Memory: 32GB of DDR4 (Double Data Rate 4) memory; 4. Storage: 512GB Non-Volatile Memory Express Solid-State Drive (NVMeSSD).

[0017] The software architecture of the ring forging production line visualization system is as follows: 1. Data Layer: (1) Industrial data acquisition unit: Through industrial communication protocols such as Modicon Bus (Modbus) and Open Platform Communications Unified Architecture (OPC UA), millisecond-level data transmission is achieved to collect real-time operating data of the ring forging equipment; (2) Manufacturing Execution System (MES) Integration Module: It interfaces with MES and receives business data such as production plans, work order information, and equipment status sent by MES through communication protocols such as Hypertext Transfer Protocol (HTTP) and Web Service. MES is a production management software that connects Enterprise Resource Planning (ERP) and the industrial automation layer. (3) Time-series database storage: High-performance time-series databases such as InfluxDB are used to achieve efficient storage and querying of massive industrial data (including the above-mentioned real-time operation data and business data).

[0018] 2. Technical layer: (1) Real-time data processing engine: Based on microservice architecture design, it cleans, verifies and preprocesses the collected raw data (i.e. the real-time running data mentioned above); (2) Data interpolation algorithm unit: adopts a high-precision real-time data interpolation algorithm to ensure accurate mapping between the digital twin and the physical entity (i.e., physical equipment, i.e. ring forging equipment); (3) Anomaly detection and early warning unit: Real-time monitoring and early warning of abnormal equipment status is achieved by setting thresholds and pattern recognition.

[0019] 3. Business Service Layer: (1) Digital twin mapping service: Establish a one-to-one correspondence between physical entities and digital twins to achieve state synchronization; (2) Visual rendering service: Based on the development engine (such as Unity 3D), it provides high-performance 3D scene rendering capabilities; (3) Work order management service: handles business logic related to the creation, allocation, and progress tracking of production work orders.

[0020] 4. Visual Presentation Layer: (1) 3D digital twin interface: a 3D visualization interface built on the Unity 3D engine, supporting scene roaming and device interaction; (2) Data dashboard interface: provides display functions for production data reports, key indicator statistics and other data; (3) Equipment monitoring interface: Displays the real-time status, operating parameters and historical trend charts of the equipment.

[0021] Secondly, the aforementioned electronic equipment is equipped with the aforementioned visualization system for the ring forging production line.

[0022] Alternatively, the electronic device may include: a computer, a mobile terminal, an industrial server, and a visual monitoring platform, etc.

[0023] The following section uses electronic devices as an example to illustrate in detail the visualization method for ring forging production lines based on digital twin models provided in this application: Figure 1 This is a flowchart illustrating the visualization method for a ring forging production line based on a digital twin model provided in an embodiment of this application. Figure 1 As shown, the method includes the following steps 101-103.

[0024] Step 101: Construct the three-dimensional geometric models of each of the multiple ring forging devices on the ring forging production line.

[0025] Among them, the ring forging production line refers to a complete set of automated production equipment and systems arranged in a specific process sequence for manufacturing ring forgings (such as bearing rings, gear rings, flanges, and aero-engine ring parts).

[0026] Ring forging equipment refers to an independent mechanical system on a ring forging production line that performs specific processing functions. Optionally, multiple ring forging equipment may include at least a heating furnace, a forging press (such as a 60MN press), a rolling mill (such as a 2500mm ring rolling mill), and a heat treatment furnace. The process involves several steps: First, a heating furnace heats the metal billet to the required high temperature (e.g., 1200°C) for forging, increasing its plasticity and making it easier to deform. Second, a forging press (usually a hydraulic press) performs preliminary forging on the heated billet, compacting it under immense pressure to form a near-ring-shaped preform (e.g., a disc or ring-shaped billet). Third, a rolling mill (specifically a ring rolling mill) precisely rolls the preform, using radial and axial rolling forces to thin the ring wall and increase its diameter, ultimately achieving the required precise dimensions and shape. Finally, a heat treatment furnace heats the rolled ring (e.g., quenching and tempering), controlling the heating, holding, and cooling processes to adjust its internal microstructure and obtain the required mechanical properties (e.g., hardness and strength).

[0027] A three-dimensional geometric model is a digital and visual representation of the ring forging equipment and its surrounding environment in virtual space. This three-dimensional geometric model consists of vertices, edges, and faces, and defines the three-dimensional shape, size, and structure of the ring forging equipment.

[0028] In step 101, the electronic device first identifies multiple ring forging devices on the ring forging production line. For each ring forging device, the electronic device can first construct the basic geometry of the part based on the mechanical drawings and measured data of the ring forging device. Then, the basic geometry of the part is refined into a detailed part structure, and assembly constraints are used to realize the linkage relationship of the moving structure of the equipment, so as to construct the corresponding three-dimensional geometric model, thereby providing data support for the subsequent construction of a digital twin model.

[0029] Optionally, the above assembly constraints include at least coincidence constraints and concentricity constraints.

[0030] Among them, coincidence constraint is used to make a geometric element (such as a plane, edge, point or axis) on one part coincide with a geometric element on another part; concentric constraint is used to make two cylindrical or arc-shaped surfaces share the same central axis.

[0031] Optionally, the electronic device constructs a three-dimensional geometric model corresponding to each of the multiple ring forging devices on the ring forging production line. This may include: the electronic device using a development engine to construct a three-dimensional geometric model corresponding to each of the multiple ring forging devices on the ring forging production line.

[0032] The entire process utilizes a development engine to efficiently and accurately construct a three-dimensional geometric model, laying a high-fidelity visualization core foundation for the digital twin system of the ring forging production line, and realizing the accurate mapping and intuitive presentation of physical entities in virtual space.

[0033] Step 102: For each ring forging machine, construct the displacement-velocity-acceleration curve based on the mass, moment of inertia, and maximum torque of the ring forging machine; construct the vibration spectrum model based on the structural stiffness, damping coefficient, mass distribution, and natural frequency of the ring forging machine; construct the instantaneous power curve and energy efficiency model based on the friction coefficient, transmission efficiency, and motor efficiency of the ring forging machine; construct the temperature rise-heat dissipation curve and thermal balance model based on the heat capacity, thermal conductivity, surface area, and heat dissipation coefficient of the ring forging machine; and construct the corresponding digital twin model of the ring forging machine based on the displacement-velocity-acceleration curve, vibration spectrum model, instantaneous power curve, energy efficiency model, temperature rise-heat dissipation curve, and thermal balance model, combined with the corresponding three-dimensional geometric model and the collected real-time operating data.

[0034] A digital twin model is a digital model created in virtual space using digital technology that corresponds to a physical device in the real world. This digital model can not only simulate the appearance of the real object, but also accurately reflect its behavior and performance. It provides a completely new way to monitor, analyze, and optimize various complex systems, thereby improving efficiency and reducing costs.

[0035] It should be noted that the physical properties parameters shared by multiple ring forging machines may include at least mass, moment of inertia, maximum torque, structural stiffness, damping coefficient, mass distribution, natural frequency, friction coefficient, transmission efficiency, motor efficiency, heat capacity, thermal conductivity, surface area, and heat dissipation coefficient; the operating characteristic models shared by these multiple ring forging machines may include at least displacement-velocity-acceleration curves, vibration spectrum models, instantaneous power curves, energy efficiency models, temperature rise-heat dissipation curves, and thermal balance models.

[0036] In step 102, the following operations are performed for each ring forging machine: Operation 1: Since the mass and moment of inertia of the ring forging equipment determine the ease of changing the motion state of the equipment (i.e., affecting acceleration and angular acceleration), and the maximum torque limits the acceleration limit, the displacement-velocity-acceleration curve is flat for ring forging equipment with high inertia and low power; conversely, the displacement-velocity-acceleration curve is steep. Therefore, electronic equipment can construct a motion envelope model of the displacement-velocity-acceleration curve based on the mass, moment of inertia, and maximum torque. Operation 2: Since the structural stiffness and damping coefficient of the ring forging equipment jointly determine the natural frequency, which is the vibration characteristic of the equipment itself, and the mass distribution of the ring forging equipment affects the inertial distribution of the equipment itself, they jointly determine the vibration response of the ring forging equipment after being excited. Therefore, the electronic equipment can construct a vibration spectrum model based on the structural stiffness, damping coefficient, mass distribution and natural frequency. This vibration spectrum model is used to describe the vibration frequency components and amplitude of the equipment. Changes in parameters such as structural stiffness and damping coefficient will directly lead to changes in the vibration spectrum. Operation 3: Since the friction coefficient and transmission efficiency of the ring forging equipment determine how much of the driving energy will be lost as heat energy in the process of converting driving energy into useful work, and the motor efficiency reflects the efficiency of energy conversion, an increase in the friction coefficient or a decrease in transmission efficiency will lead to an increase in the instantaneous power required to complete the same work, or a decrease in the overall energy efficiency. Therefore, electronic equipment can construct an instantaneous power curve and energy efficiency model based on the friction coefficient, transmission efficiency and motor efficiency. Abnormal peaks in the instantaneous power curve often point to abnormal frictional resistance. Operation 4: Since the heat capacity of the ring forging equipment represents the equipment's ability to store heat, the thermal conductivity determines the speed of heat transfer inside the equipment, the surface area affects the heat exchange area between the ring forging equipment and the surrounding environment, and the heat dissipation coefficient reflects the ring forging equipment's ability to dissipate heat to the surrounding environment, these factors together determine the temperature change and thermal equilibrium state of the ring forging equipment. Therefore, electronic equipment can construct temperature rise-heat dissipation curves and thermal equilibrium models based on heat capacity, thermal conductivity, surface area, and heat dissipation coefficient. Among these, for ring forging equipment with small heat capacity and poor heat dissipation, the corresponding temperature rise-heat dissipation curve is steeper, and the thermal equilibrium temperature that can be achieved is higher. Operation 5: The electronic device collects the real-time operating data corresponding to the ring forging equipment, and integrates the above-mentioned operating feature model with the real-time operating data and the corresponding three-dimensional geometric model pre-built to obtain the digital twin model corresponding to the ring forging equipment. Specifically, the real-time operating data is used as input to drive the operating feature model to calculate the motion state of the ring forging equipment at the current moment, and then mapped and synchronized to the corresponding three-dimensional geometric model, thereby generating a digital twin model that is synchronized with the physical equipment in real time and has the same state.

[0037] Based on operations 1-5, the electronic equipment can construct digital twin models corresponding to multiple ring forging devices. The entire process transforms the deep physical attributes of the ring forging device into a computable operational characteristic model, enabling the digital twin to possess high-fidelity prediction and diagnostic capabilities. It can fundamentally understand and simulate the operational behavior of the physical equipment. Furthermore, by integrating real-time operational data with the dynamic drivers of all operational characteristic models, a living twin (i.e., the aforementioned digital twin model) that is completely synchronized and consistent with the physical equipment is generated. This provides a unique and reliable data source and interactive environment for achieving precise monitoring, fault early warning, and optimization decision-making.

[0038] It should be noted that the execution sequence of operations 1-4 above is not limited. Furthermore, based on multiple ring forging machines and multiple digital twins, the electronic equipment is designed with a system architecture integrating data acquisition, processing, and visualization. This architecture addresses the problems of low integration and complex maintenance in existing industrial systems, offering significant advantages in system integration and maintainability. Simultaneously, it achieves unified management of the entire process, reducing system complexity by 50% and deployment costs by 60%.

[0039] Optionally, during the process of integrating the real-time operating data, operating feature model, and three-dimensional geometric model corresponding to the ring forging equipment, the electronic equipment can establish a mapping table between physical parameters and digital attributes to achieve automatic data conversion.

[0040] For example, when the ring forging equipment is a heating furnace, the electronic equipment can establish a mapping table between the temperature data collected by the temperature sensor and the color of the three-dimensional digital model: when the temperature T < 800℃, the three-dimensional digital model is displayed in blue (RGB:0,0,255); when 800℃ ≤ T < 1000℃, the three-dimensional digital model is displayed in green (RGB:0,255,0); when 1000℃ ≤ T < 1200℃, the three-dimensional digital model is displayed in yellow (RGB:255,255,0); when T ≥ 1200℃, the three-dimensional digital model is displayed in red (RGB:255,0,0).

[0041] When the ring forging equipment is a forging press, the electronic equipment can establish a mapping relationship table between pressure data and the degree of model deformation: when the pressure P < 50 MPa, the three-dimensional digital model maintains its original shape; when 50 MPa ≤ P < 80 MPa, the three-dimensional digital model undergoes slight deformation at the stress point; when P ≥ 80 MPa, the three-dimensional digital model undergoes significant deformation and is accompanied by crack texture display.

[0042] When the ring forging equipment is a rolling mill, the electronic equipment can establish a mapping table between radial rolling force and the color of the three-dimensional digital model: when the radial rolling force F < 60% of the rated value, the three-dimensional digital model is displayed in dark blue; when 60% of the rated value ≤ F < 90% of the rated value, the three-dimensional digital model is displayed in orange; when 90% of the rated value ≤ T, the three-dimensional digital model is displayed in red.

[0043] When the ring forging equipment is a heat treatment furnace, the electronic equipment can establish a mapping relationship table between the carbon potential of the atmosphere and the changes in the workpiece inside the furnace: when the carbon potential of the atmosphere Q < 0.05% of the set value, there is no change on the surface of the workpiece in the light blue atmosphere inside the furnace; when Q = ±0.03% of the set value, the workpiece with a transparent heat wave effect has a normal color temperature; when 0.05% ≤ Q of the set value, the workpiece with a gray carbon black cloud effect has carbon texture on its surface.

[0044] In summary, electronic devices can effectively and quickly achieve real-time synchronous mapping from physical devices to digital twins through the four mapping relationship tables shown in the examples above. Real-time synchronous mapping updates the 3D digital model with real-time running data, enabling the 3D digital model to dynamically reflect the current state of the physical device. The implementation mechanism of this real-time synchronous mapping mainly includes the steps of data acquisition, data transmission, data processing, and data display.

[0045] Optionally, the method may further include: electronic devices assigning identity identifiers to multiple ring forging devices and their corresponding digital twin models.

[0046] The identity identifier of the ring forging equipment is the same as that of its corresponding digital twin model.

[0047] This ensures a one-to-one correspondence between the ring forging equipment and its corresponding digital twin.

[0048] It should be noted that, in addition to having common physical property parameters and common operating characteristic models, multiple ring forging machines also have unique physical property parameters and unique operating characteristic models. Based on this, the digital twin models constructed by electronic equipment according to the common operating characteristic models and unique operating characteristic models of different ring forging machines are also different.

[0049] Optionally, when the ring forging equipment is a heating furnace, the unique physical property parameters of the heating furnace include at least the layout and power density of the heating elements, the furnace sealing and the capacity of the atmosphere system; the unique operating characteristic model includes at least the workpiece heating curve model and the furnace atmosphere recovery model.

[0050] Optionally, when the ring forging equipment is a forging press, the unique physical property parameters of the forging press include at least the rated pressure and flow rate of the hydraulic system, the clearance and accuracy between the slide and the guide rail, etc.; the unique operating characteristic model includes at least the forging force-displacement curve, the forging misalignment prediction model, etc.

[0051] Optionally, when the ring forging equipment is a rolling mill, the unique physical property parameters of the rolling mill include at least the size ratio of the main roll to the mandrel and the profile of the roll; the unique operating characteristic models include at least the wall thickness reduction model and the ring section filling forming model.

[0052] Optionally, when the ring forging equipment is a heat treatment furnace, the unique physical property parameters of the heat treatment furnace include at least the cooling system capacity, temperature control accuracy and uniformity; the unique operating characteristic model includes at least the workpiece cooling rate curve, process curve tracking and performance uniformity model.

[0053] In some embodiments, the electronic device constructs a digital twin model of the ring forging equipment based on the displacement-velocity-acceleration curve, vibration spectrum model, instantaneous power curve, energy efficiency model, temperature rise-heat dissipation curve, and thermal balance model, combined with the corresponding three-dimensional geometric model and the collected real-time operating data. This can include at least the following implementation methods: Implementation Method 1: When the ring forging equipment is a heating furnace, the electronic equipment constructs a workpiece heating curve model based on the layout and power density of the heating elements in the heating furnace; constructs a furnace atmosphere recovery model based on the furnace chamber sealing and atmosphere system capacity; and constructs a digital twin model of the ring forging equipment based on the workpiece heating curve model, furnace atmosphere recovery model, displacement-velocity-acceleration curve, vibration spectrum model, instantaneous power curve, energy efficiency model, temperature rise-heat dissipation curve, and thermal balance model, combined with the corresponding three-dimensional geometric model and the collected real-time operating data.

[0054] In implementation method 1, the power and distribution of the elements in the heating furnace directly determine the rate at which heat is transferred to various parts of the workpiece, which is the core of predicting the time required for the workpiece to rise from room temperature to the target temperature. Therefore, the electronic device can construct a workpiece heating curve model based on the layout and power density of the heating elements. Furthermore, the physical sealing of the furnace body and the atmosphere supply capacity determine the time required for the harmful oxygen content to drop to a safe level or for the protective atmosphere to recover after the furnace door is opened (i.e., loading and unloading). Therefore, the electronic device can construct a furnace atmosphere recovery model based on the furnace sealing and atmosphere system capacity. Then, based on the workpiece heating curve model, the furnace atmosphere recovery model, and the common operating characteristic model, combined with the corresponding three-dimensional geometric model and the collected real-time operating data, the electronic device constructs a digital twin model of the ring forging equipment.

[0055] Optionally, the real-time operating data corresponding to the heating furnace may include at least temperature data, atmosphere data, and equipment status data.

[0056] Implementation Method 2: When the ring forging equipment is a forging press, the electronic equipment constructs a forging force-displacement curve based on the rated pressure and flow rate of the hydraulic system of the forging press; constructs a forging misalignment prediction model based on the clearance and precision between the slider and the guide rail in the forging press; and constructs a digital twin model of the ring forging equipment based on the forging force-displacement curve, the forging misalignment prediction model, the displacement-velocity-acceleration curve, the vibration spectrum model, the instantaneous power curve, the energy efficiency model, the temperature rise-heat dissipation curve, and the thermal balance model, combined with the corresponding three-dimensional geometric model and the collected real-time operating data.

[0057] In implementation method 2, since the rated pressure of the hydraulic system determines the maximum forging force and the system flow rate determines the slide idle and return speeds, the electronic device can construct a forging force-displacement curve based on the rated pressure and flow rate of the hydraulic system. This forging force-displacement curve is the core of describing how the pressure changes with displacement during a complete forging process. Furthermore, due to the physical clearance and manufacturing precision of the mechanical structure, misalignment of the upper and lower dies directly leads to the direct cause of the critical quality defect of lateral misalignment in the forging. Therefore, the electronic device can construct a forging misalignment prediction model based on the clearance and precision between the slide and the guide rail in the forging press. Then, based on the forging force-displacement curve, the forging misalignment prediction model, and the common operating characteristic model, combined with the corresponding three-dimensional geometric model and the collected real-time operating data, the electronic device constructs a digital twin model corresponding to the ring forging equipment.

[0058] Optionally, the real-time operating data corresponding to the forging press may include at least mechanical data, motion data, and equipment status data.

[0059] Implementation Method 3: When the ring forging equipment is a rolling mill, the electronic equipment constructs a wall thickness reduction model based on the size ratio of the main roll to the mandrel in the rolling mill; constructs a ring section filling and forming model based on the profile of the rolls in the rolling mill; and constructs a digital twin model of the ring forging equipment based on the wall thickness reduction model, the ring section filling and forming model, the displacement-velocity-acceleration curve, the vibration spectrum model, the instantaneous power curve, the energy efficiency model, the temperature rise-heat dissipation curve, and the thermal balance model, combined with the corresponding three-dimensional geometric model and the collected real-time operating data.

[0060] In implementation method 3, since the physical size ratio of the rolls in the rolling mill is the geometric basis of the ring movement, directly determining the increase in diameter and the reduction in wall thickness per revolution of the ring, the electronic device can construct a wall thickness reduction model based on the size ratio of the main roll to the mandrel in the rolling mill. Furthermore, since the rolling process involves rolling irregularly shaped cross-section rings (such as L-shaped ones), the physical profile of the rolls directly determines how the metal fills the cavity, which is crucial for controlling the accuracy of the ring cross-sectional shape. Therefore, the electronic device can construct a ring cross-section filling and forming model based on the profile of the rolls. Then, based on the wall thickness reduction model, the ring cross-section filling and forming model, and the common operating characteristic model, combined with the corresponding three-dimensional geometric model and the collected real-time operating data, the electronic device constructs a digital twin model of the ring forging equipment.

[0061] Optionally, the real-time operating data corresponding to the rolling mill may include at least mechanical data, motion and position data, and equipment status data.

[0062] Implementation Method 4: When the ring forging equipment is a heat treatment furnace, the electronic equipment constructs a workpiece cooling rate curve based on the cooling system capacity of the heat treatment furnace; constructs a process curve tracking and performance uniformity model based on the temperature control accuracy and uniformity of the heat treatment furnace; and constructs a digital twin model of the ring forging equipment based on the workpiece cooling rate curve, process curve tracking and performance uniformity model, displacement-velocity-acceleration curve, vibration spectrum model, instantaneous power curve, energy efficiency model, temperature rise-heat dissipation curve, and thermal balance model, combined with the corresponding three-dimensional geometric model and the collected real-time operating data.

[0063] In implementation method 4, the physical capabilities of the cooling system in the heat treatment furnace determine the cooling rate that the workpiece can achieve within the furnace. This cooling rate must match the material phase transformation law to obtain the target hardness and microstructure. Therefore, the electronic device can construct a workpiece cooling rate curve based on the cooling system capabilities. Furthermore, the physical precision of furnace temperature control determines the deviation between the actual temperature curve and the ideal process curve, which directly affects the uniformity and stability of performance within a product batch. Therefore, the electronic device can construct a process curve tracking and performance uniformity model based on temperature control precision and uniformity. Then, the electronic device constructs a digital twin model corresponding to the ring forging equipment based on the workpiece cooling rate curve, the process curve tracking and performance uniformity model, and the common operating characteristic model, combined with the corresponding three-dimensional geometric model and the collected real-time operating data.

[0064] Optionally, the real-time operating data corresponding to the heat treatment furnace may include at least temperature data, process gas data, cooling system data, and process timing data.

[0065] In some embodiments, the method may further include: the electronic device acquiring the previous adjacent time and the next adjacent time; the electronic device calculating the difference result at the current time based on the previous adjacent time, the value of the first sampling point at the previous adjacent time, the next adjacent time, and the value of the second sampling point at the next adjacent time; and the electronic device using the difference result as real-time running data.

[0066] In this embodiment of the application, to ensure the accuracy of the subsequent digital twin model, the electronic device can use a data difference algorithm to collect real-time operating data of each ring forging device. Specifically, for each ring forging device, the electronic device first obtains the current time and the previous and next adjacent times. Then, the electronic device samples the collected continuous data to obtain the first sampling point value at the previous adjacent time and the second sampling point value at the next adjacent time. Next, the electronic device calculates the difference result at the current time based on the previous adjacent time, the first sampling point value, the next adjacent time, and the second sampling point value, and uses the difference result as real-time operating data.

[0067] Optionally, the above real-time operating data can be calculated using a difference formula, which is as follows: ; in, Indicates the current moment; Indicates the previous adjacent time; Indicates the next adjacent time; Indicates the current time The difference results below; Indicates the previous adjacent time. The value of the first sampling point below; This represents the value of the second sampling point at the next adjacent time.

[0068] It should be noted that the data difference algorithm involved in the embodiments of this application is used to solve the problem of mismatch between industrial data sampling frequency and display requirements. It can better handle nonlinear changes and sudden changes, thereby ensuring a high degree of consistency between the digital twin and the physical entity. The error can be controlled within 5%.

[0069] Optionally, different ring forging machines are equipped with corresponding status detection points and data sensors to collect real-time operating data of the corresponding ring forging machines.

[0070] Optionally, the method may further include: the electronic device receiving real-time operating data sent by status detection points and data sensors via an industrial communication protocol (such as Modbus).

[0071] The entire process reduces data transmission latency from 500 milliseconds to 50 milliseconds, achieving millisecond-level data transmission to meet real-time requirements and effectively improve transmission efficiency. Thus, the digital twin updates synchronously within 50 milliseconds when the equipment status of the ring forging equipment changes.

[0072] It should be noted that the aforementioned industrial communication protocols can employ technologies such as protocol optimization, multi-threaded concurrency, and priority management, which can respectively reduce communication overhead, improve data transmission efficiency, and ensure priority transmission of critical data.

[0073] Optionally, electronic devices can preprocess (e.g., data cleaning and repair, data standardization and formatting, data compression and aggregation) and convert protocols on received data (such as physical attribute parameters, real-time operational data, and business data) through deployed edge computing gateways.

[0074] The entire process uses an edge computing gateway to perform localized preprocessing and protocol unification of multi-source heterogeneous data, achieving data quality improvement, transmission burden reduction, and system efficiency enhancement from the field to the cloud, laying a solid data foundation for building a high-fidelity, low-latency digital twin.

[0075] Step 103: Render and optimize multiple digital twin models to obtain the visualization data corresponding to the ring forging production line.

[0076] In step 103, the electronic device uses a development engine and GPU acceleration technology to render and optimize multiple digital twin models, thereby obtaining the visualization data corresponding to the ring forging production line and achieving smooth 3D visualization.

[0077] The following section elaborates on how electronic devices optimize the rendering of multiple digital twin models to obtain the corresponding visualized data for the ring forging production line: In some embodiments, each digital twin model corresponds to a three-level detail model, namely a high-precision digital twin model, a medium-precision digital twin model, and a low-precision digital twin model.

[0078] Among them, the high-precision digital twin model contains 100,000+ faces; the medium-precision digital twin model contains 10,000+ faces; and the low-precision digital twin model contains 1,000+ faces.

[0079] Based on this, electronic devices perform rendering optimization on multiple digital twin models to obtain visualized data corresponding to the ring forging production line, which can include one of the following implementation methods: Implementation Method 1: The electronic equipment adopts a Level of Detail (LOD) rendering optimization algorithm. For each digital twin model, if the Euclidean distance between the virtual camera and the digital twin model is less than a first preset distance threshold, then the high-precision digital twin model corresponding to the ring forging equipment is activated; if the Euclidean distance is greater than or equal to the first preset distance threshold and less than the second preset distance threshold, then the medium-precision digital twin model corresponding to the ring forging equipment is activated; if the Euclidean distance is greater than or equal to the second preset distance threshold, then the low-precision digital twin model corresponding to the ring forging equipment is activated; based on all activated digital twin models, the visualization data corresponding to the ring forging production line is generated.

[0080] The virtual camera is the embodiment and sensory organ in the digital twin virtual world. Specifically, it is an invisible observation point defined by mathematics that determines how the three-dimensional scene (i.e., the digital twin model) is projected onto the interactive two-dimensional screen (i.e., the aforementioned three-dimensional digital twin interface).

[0081] In implementation method 1, the electronic device can use a LOD-level rendering optimization algorithm to optimize the rendering of multiple digital twin models. Specifically, for each digital twin model, the electronic device first obtains the Euclidean distance between the virtual camera and the digital twin model; then, the electronic device compares the Euclidean distance with a preset distance threshold: if the Euclidean distance is less than the first preset distance threshold (e.g., 50 meters), it means that the distance between the digital twin model and the virtual camera is very close, and it has entered the visual range that requires high-precision display. At this time, the high-precision digital twin model corresponding to the ring forging equipment is activated; if the Euclidean distance is greater than or equal to the first preset distance threshold and less than the second preset distance threshold (…), the electronic device can optimize the rendering of multiple digital twin models. If the distance is 200 meters, it indicates that the digital twin model is within the medium-range observation range of the virtual camera. The user can identify the basic structure and operating status of the digital twin model, but does not need to observe microscopic details. In this case, the medium-precision digital twin model corresponding to the ring forging equipment is activated. If the Euclidean distance is greater than or equal to the second preset distance threshold, it indicates that the digital twin model is in the far-range or edge field of view of the virtual camera. The user only needs to perceive the existence and approximate location of the digital twin model, without needing to identify any structural details. In this case, the low-precision digital twin model corresponding to the ring forging equipment is activated. Finally, the electronic device generates the visualization data corresponding to the ring forging production line based on all activated digital twin models. The entire process dynamically switches model resources of different precision by judging the Euclidean distance and the preset distance threshold set in real time, reducing the rendering overhead of far-range models.

[0082] Implementation Method 2: The electronic equipment adopts a multi-device collaborative LOD dynamic adjustment algorithm to construct an association matrix corresponding to multiple ring forging devices. The association matrix includes the process association degree between any two ring forging devices. For each ring forging device, the association degree vector of the ring forging device is determined according to the process association degree between the ring forging device and other ring forging devices. If the association degree vector is less than the first preset association degree threshold, the low-precision digital twin model corresponding to other ring forging devices is activated. If the association degree vector is greater than or equal to the first preset association degree threshold and less than the second preset association degree threshold, the medium-precision digital twin model corresponding to other ring forging devices is activated. If the association degree vector is greater than or equal to the second preset association degree threshold, the high-precision digital twin model corresponding to other ring forging devices is activated. Based on all activated digital twin models, the visualization data corresponding to the ring forging production line is generated.

[0083] In implementation method 2, the electronic device can use a multi-device collaborative LOD dynamic adjustment algorithm to render and optimize multiple digital twin models. Specifically, based on the ring forging production line, the electronic device pre-constructs an association matrix M corresponding to multiple ring forging devices. M[i][j] in the association matrix M represents the process association degree between ring forging device i and ring forging device j, and the process association degree is a value between 0 and 1. For each ring forging device, the electronic device can respond to the user's input selection operation, first obtain the process association degree between the ring forging device and other ring forging devices, and then calculate the association degree vector V of the ring forging device. The calculation formula of the association degree vector V is: V=[M[i][1],M[i][2],...,M[i][n]]. Then, the electronic device compares the association degree vector with the pre-constructed process association matrix M. A correlation threshold is used for comparison: if the correlation vector is less than the first preset correlation threshold (e.g., 0.3), it indicates that the other ring forging equipment is a low-correlation equipment, and a low-precision digital twin model corresponding to the other ring forging equipment is activated; if the correlation vector is greater than or equal to the first preset correlation threshold and less than the second preset correlation threshold (e.g., 0.7), it indicates that the other ring forging equipment is a medium-correlation equipment, and a medium-precision digital twin model corresponding to the other ring forging equipment is activated; if the correlation vector is greater than or equal to the second preset correlation threshold, it indicates that the other ring forging equipment is a high-correlation equipment, and a high-precision digital twin model corresponding to the other ring forging equipment is activated; finally, the electronic device generates visualized data corresponding to the ring forging production line based on all activated digital twin models. The entire process ensures that while the user focuses on the key equipment (i.e., the current ring forging equipment), the upstream and downstream related equipment (i.e., other ring forging equipment) of the key equipment also maintain appropriate visualization accuracy.

[0084] In some embodiments, the electronic device performs rendering optimization on multiple digital twin models to obtain visualization data corresponding to the ring forging production line. This may include: the electronic device using a CPU parallel rendering algorithm (hereinafter referred to as: parallel rendering algorithm) to obtain rendering tasks corresponding to multiple digital twin models, wherein the rendering task includes multiple subtasks; the electronic device using at least one computing unit in the CPU to process multiple subtasks in parallel and generate visualization data corresponding to the ring forging production line; wherein, if the similarity between any two subtasks is less than a preset similarity threshold, then the number of computing units is multiple, and the multiple computing units correspond one-to-one with the multiple subtasks; if the similarity between the first subtask and the second subtask among the multiple subtasks is greater than or equal to the preset similarity threshold, then the first subtask and the second subtask correspond to the same computing unit, and the same computing unit has the Single Instruction, Multiple Data (SIMD) characteristic.

[0085] The rendering task refers to a relatively independent and identifiable graphics computation task that an electronic device needs to complete in order to generate the final visual image / visual data (i.e., a frame of image). Optionally, the multiple subtasks may include at least geometry processing tasks (such as vertex transformation), lighting calculation tasks, texture sampling tasks, and post-processing tasks.

[0086] SIMD (Simultaneous Data Execution) allows a single instruction to operate on multiple data elements simultaneously. In other words, the same instruction can be executed on multiple subtasks at once, rather than processing them one by one. This significantly improves the throughput of large-scale data parallel computing and is a key technology for achieving real-time, smooth rendering of complex 3D scenes.

[0087] In this embodiment, the electronic device can use a parallel rendering algorithm to optimize the rendering of multiple digital twin models. Specifically, the electronic device first obtains the rendering task in the virtual scene where the multiple digital twin models are located, and determines the multiple sub-tasks included in the rendering task. Then, the electronic device judges the similarity between any two sub-tasks. If all similarities are less than a preset similarity threshold, it means that these multiple sub-tasks cannot be processed by the same computing unit. At this time, the electronic device uses multiple computing units to process multiple sub-tasks in parallel. Specifically, it uses the universal toolbox in the GPU (such as Compute Shader) to implement the parallel processing of computationally intensive operations such as vertex transformation, lighting calculation, and texture sampling to generate the visualization data corresponding to the ring forging production line. These multiple computing units correspond one-to-one with these multiple sub-tasks. If there is a target similarity among all similarities that is greater than the preset similarity threshold, it means that the processing of the two sub-tasks corresponding to the target similarity is similar. At this time, the same computing unit can be used for processing. Based on this, the electronic device can use at least one computing unit to process multiple sub-tasks in parallel, through Compute Shader. The shader enables parallel processing of computationally intensive operations such as vertex transformation, lighting calculation, and texture sampling, generating visualization data corresponding to the ring forging production line. The entire process leverages the GPU's SIMD capabilities to handle multiple similar rendering operations simultaneously, significantly improving rendering efficiency for large-scale scenes.

[0088] In some embodiments, the electronic device performs rendering optimization on multiple digital twin models to obtain visualized data corresponding to the ring forging production line. This may include: the electronic device employing a scene elimination optimization algorithm to construct an axially aligned bounding box that completely encloses each digital twin model; the electronic device constructing an axially aligned bounding box tree based on the multiple axially aligned bounding boxes, the axially aligned bounding box tree including multiple parent nodes, each parent node corresponding one-to-one with the multiple axially aligned bounding boxes, and each parent node including at least one child node; the electronic device performing a step-by-step analysis of the intersection relationship between the multiple parent nodes and the view frustum of the virtual camera. The system performs the following checks: If the intersection relationship is that the current parent node is inside the view frustum, then the digital twin model corresponding to the current parent node and its corresponding child node is retained; if the intersection relationship is that the current parent node is outside the view frustum, then the model data corresponding to the current parent node and its corresponding child node is discarded; if the intersection relationship is that the current parent node is located within the view frustum, then the system continues to check the intersection relationship between the current parent node's adjacent child nodes and the view frustum until the last child node is checked. The electronic device performs software rasterization preprocessing on all static occlusions in the retained digital twin models to generate a Potentially Visible Set (PVS). Based on the model data in the Potentially Visible Set, the electronic device generates visualization data corresponding to the ring forging production line.

[0089] Scene culling optimization algorithms include Hierarchical Frustum Culling and software rasterization occlusion culling.

[0090] The view frustum of a virtual camera defines the range of all three-dimensional space that the user can see, not just the plane in the viewfinder of the virtual camera, but the entire pyramid-shaped (or trapezoidal) space from in front of the lens to the furthest point the user can see.

[0091] PVS refers to the data set of all objects (or scene blocks) that may be seen by a virtual camera from a specific viewpoint (or area) in a virtual scene. It is a pre-computed data structure used to accelerate real-time rendering.

[0092] In this embodiment, the electronic device can use a scene culling optimization algorithm to optimize the rendering of multiple digital twin models. Specifically, the electronic device first uses a hierarchical frustum culling algorithm to construct an axially aligned bounding box that completely encloses each digital twin model. At this point, the electronic device can construct multiple axially aligned bounding boxes. Then, based on these multiple axially aligned bounding boxes, the electronic device constructs an axially aligned bounding box tree (such as an AABB bounding box tree) and recursively detects the intersection relationship between nodes and the frustum of the virtual camera. That is, it judges the intersection relationship between multiple parent nodes and the frustum one by one: if the intersection relationship is that the current parent node is inside the frustum, it means that the user can completely see the digital twin model corresponding to the current parent node and its corresponding child nodes. In this case, the digital twin model corresponding to the current parent node and its corresponding child nodes can be retained; if the intersection relationship is... If the current parent node is outside the view frustum, it means the user cannot see the model data (possibly the entire digital twin model) corresponding to the current parent node and its corresponding child nodes. In this case, the model data can be removed. If the intersection relationship is that the current parent node is located within the view frustum, the intersection relationship between the current parent node's adjacent child nodes and the view frustum is determined until the last child node is determined. This quickly preserves the model data within the user's field of view and excludes model data outside the user's field of view. Subsequently, the electronic device uses a software rasterization occlusion culling algorithm, specifically a depth buffer pre-rendering algorithm, to perform software rasterization preprocessing on static occluders in the virtual scene where all the retained digital twin models are located, generating a potential visible set. Then, at runtime, only the model objects (i.e., model data) in the potential visible set are rendered, significantly reducing the number of rendering calls and improving rendering efficiency.

[0093] In some embodiments, the electronic device performs rendering optimization on multiple digital twin models to obtain visualization data corresponding to the ring forging production line. This may include: the electronic device employing an adaptive rendering optimization algorithm to collect real-time operating data of each ring forging device, including pressure, temperature, rotational speed, and position coordinates; the electronic device constructing a state evaluation model of the ring forging device based on the pressure, temperature, rotational speed, and position coordinates, with the output of the state evaluation model being a device state index; if the device state index is greater than a preset index threshold, the rendering accuracy of the digital twin model corresponding to the ring forging device and within a preset virtual area where the digital twin model is located is increased, while the rendering accuracy outside the preset virtual area where the digital twin model is located is decreased, resulting in a new digital twin model; the electronic device obtaining visualization data corresponding to the ring forging production line based on all the new digital twin models.

[0094] The condition assessment model is trained based on operational data samples and equipment condition index samples.

[0095] In this embodiment, the electronic device can use an adaptive rendering optimization algorithm based on the device's operating status to optimize the rendering of multiple digital twin models. Specifically, for each ring forging device, the electronic device first collects the pressure P, temperature T, rotational speed R, and position coordinates L of the ring forging device, and then constructs a state evaluation model. The output of the state evaluation model is the device state index E=f(P,T,R,L). Then, the electronic device compares the device state index with a preset index threshold: if the device state index is less than or equal to the preset index threshold, no task operation is performed; if the device state index is greater than the preset index threshold, it indicates that the ring forging device is in a high-speed motion or critical working state. At this time, the electronic device can improve the rendering accuracy of the digital twin model corresponding to the ring forging device and the preset virtual area where the digital twin model is located (e.g., improve to the highest level of rendering accuracy), and reduce the rendering accuracy outside the preset virtual area where the digital twin model is located (e.g., reduce the rendering accuracy by 2-3 levels) to obtain a new digital twin model. Finally, the electronic device obtains the visualization data corresponding to the ring forging production line based on all the new digital twin models. The entire process dynamically adjusts the allocation of rendering resources to ensure sufficient visualization accuracy during critical operating phases of the ring forging equipment, while reducing system load during stable operation.

[0096] In some embodiments, the electronic device performs rendering optimization on multiple digital twin models to obtain visualization data corresponding to the ring forging production line. This may include: the electronic device employing an importance-based elimination method to collect real-time production data for each ring forging device and calculate an importance index based on the real-time production data; the electronic device determining a rendering elimination strategy for the digital twin models of the ring forging devices based on the index range of the importance index, wherein the magnitude of the index range is inversely proportional to the elimination degree of the rendering elimination strategy; the electronic device processing the digital twin models corresponding to the corresponding ring forging devices according to different rendering elimination strategies to obtain rendering-eliminated digital twin models; and the electronic device generating visualization data corresponding to the ring forging production line based on all rendering-eliminated digital twin models.

[0097] In the embodiments of the present application, the electronic device may adopt an importance - grading elimination method to optimize the rendering of multiple digital twin models. Specifically, for each ring forging device, the electronic device first calculates an importance index W based on the real - time production data of the ring forging device collected. The real - time production data may at least include output Q, qualified rate Y, and energy consumption E. The calculation formula of the importance index W is: W = α×Q / Qmax+β×Y / Ymax+γ×(1 - E / Emax), where α represents the first weight coefficient, β represents the second weight coefficient, γ represents the third weight coefficient, α + β + γ = 1, Qmax represents the historical maximum value of output, Ymax represents the historical maximum value of the qualified rate, and Emax represents the historical maximum value of energy consumption. Then, the electronic device determines the rendering elimination strategy for the digital twin model of the ring forging device according to the index range where the importance index W is located. Specifically, if the importance index W is in the first index range (e.g., >0.8), it indicates that the area where the ring forging device is located is a key area; if the importance index W is in the second index range (e.g., 0.6 < W ≤ 0.8), it indicates that the area where the ring forging device is located is an important area; if the importance index W is in the third index range (e.g., 0.3 < W ≤ 0.6), it indicates that the area where the ring forging device is located is a general area; if the importance index W is in the fourth index range (e.g., ≤0.3), it indicates that the area where the ring forging device is located is a secondary area. At this time, the electronic device adopts a differentiated rendering elimination strategy for different - level areas, implementing minimum rendering elimination for the key area and maximum rendering elimination for the secondary area. Finally, the electronic device processes the digital twin models corresponding to the corresponding ring forging devices according to different rendering elimination strategies to obtain the digital twin models after rendering elimination, and then generates the visualization data corresponding to the ring forging production line based on all the digital twin models after rendering elimination. The entire process optimizes the rendering resource allocation in a data - driven manner to ensure the visualization quality of key production information.

[0098] It should be noted that the embodiments of the present application adopt large - scale three - dimensional scene real - time rendering technologies based on GPU acceleration, such as LOD - level rendering optimization algorithm, multi - device collaborative LOD dynamic adjustment algorithm, parallel rendering algorithm, scene elimination optimization algorithm, adaptive rendering optimization algorithm, and importance - grading elimination method, etc., which can achieve smooth rendering of complex industrial scenes at 60FPS, support the simultaneous display of more than 1000 ring forging devices, and make the finally generated visualization data have obvious advantages in three - dimensional visualization effects and performance.

[0099] In some embodiments, the method may further include: for each digital twin model, the electronic device generates a multidimensional feature vector based on the temperature change rate, pressure fluctuation amplitude, device vibration frequency, and energy consumption efficiency in the digital twin model; the electronic device inputs the multidimensional feature vector into a support vector machine classifier to obtain the device operating status output by the support vector machine classifier, wherein the support vector machine classifier is trained based on multidimensional feature vector samples and device operating status samples; the electronic device automatically triggers color coding updates and early warning prompts for the visualized data based on the device operating status.

[0100] In this embodiment, the electronic device uses a data analysis algorithm to identify the device's operating status. Specifically, for each digital twin model, the electronic device can first obtain the temperature change rate ΔT / Δt, pressure fluctuation amplitude σ(P), device vibration frequency f(vibration), and energy efficiency η from the digital twin model. Then, the electronic device constructs a multidimensional feature vector containing the temperature change rate ΔT / Δt, pressure fluctuation amplitude σ(P), device vibration frequency f(vibration), and energy efficiency η. This multidimensional feature vector can be represented by X=[ΔT / Δt,σ(P),f(vibration),η]. Next, the electronic device inputs this multidimensional feature vector into a trained support vector machine classifier to obtain the device operating status output by the support vector machine classifier. Finally, the electronic device continuously collects the device operating status and updates the status judgment in real time through a sliding time window (e.g., 5 minutes) in the data analysis algorithm. When the status changes, it automatically triggers the color coding update of the visual data and an early warning prompt.

[0101] Optionally, the operating status of the above-mentioned equipment can be divided into five categories: normal operation status, preheating status, overload status, abnormal vibration status, and standby status.

[0102] The normal operating conditions are: ΔT / Δt < 5℃ / min, σ(P) < 2MPa, f(vibration) < 10Hz; Preheating conditions: ΔT / Δt > 15℃ / min, σ(P) < 1MPa; Overload conditions: σ(P) > 5MPa, η < 60%; The abnormal vibration state is defined as: f(vibration) > 50Hz; The standby state is: ΔT / Δt≈0, P≈0, f(vibration)≈0.

[0103] Optionally, the method may also include: for each digital twin model, electronic equipment calculates in real time the key performance indicators (KPIs) of the ring forging equipment, such as temperature, pressure, and operating efficiency.

[0104] Optionally, the method may also include: for each digital twin model, the electronic equipment uses time series analysis to analyze the abnormal operating trends of the ring forging equipment.

[0105] Optionally, the method may further include: electronic devices providing real-time early warning and visual prompts for abnormal vibration states of the digital twin model.

[0106] Optionally, after step 103, the method may further include: the electronic device updating the operating status and production progress of the ring forging equipment in real time based on the visualized data; the electronic device displaying the level of the equipment operating status (such as normal, abnormal, fault, maintenance, etc.) according to the visual data and color coding, with different color codes corresponding to different levels; the electronic device providing a detailed equipment information panel and historical trend chart display based on the visualized data, and supporting work order information query and production data statistical analysis interaction.

[0107] The entire ring forging production process is visualized and interactive. Through data-driven dynamic color coding and multi-dimensional information panels, the complex equipment status and production progress are transformed into a clear visual situation, realizing a cognitive leap from "passive monitoring" to "active perception". In addition, with the help of deeply integrated visualization data and interactive functions, a decision support platform integrating real-time monitoring, historical backtracking, work order management and statistical analysis has been built, which significantly improves the transparency, accuracy and intelligent operation level of the production process.

[0108] Optionally, after step 103, the method may further include: the electronic device establishing a database of association between work order information and material batches, tracking the flow status of materials in each processing stage in real time, statistically analyzing the number of processed items and quality data of materials in different processes, and providing material traceability query and quality anomaly location functions.

[0109] Throughout the entire ring forging production material traceability and statistical analysis phase, by establishing a full-process material-work order related data chain, one-way accurate traceability and two-way quality traceability from raw materials to finished products were achieved, making the production process completely transparent. In addition, relying on real-time flow status monitoring and multi-dimensional statistical analysis capabilities, a digital decision-making system covering output, quality, and efficiency was constructed, providing a data-driven scientific basis for continuous process optimization and rapid anomaly location.

[0110] In this embodiment, the technical solution described in steps 101-103 above constructs a high-fidelity digital twin that integrates a multiphysics mechanism model and real-time operational data, transforming abstract industrial data into concrete dynamic equipment behavior, thus achieving an intuitive mapping from numerical values ​​to phenomena. Based on this, relying on multi-layered rendering optimization technology, the digital twin model is driven and efficiently rendered in real time, ultimately achieving millisecond-level smooth visualization of large-scale 3D scenes even under complex working conditions.

[0111] The following describes the visualization system for a ring forging production line based on a digital twin model provided in the embodiments of this application. The visualization system for a ring forging production line based on a digital twin model described below and the visualization method for a ring forging production line based on a digital twin model described above can be referred to and correspond to each other.

[0112] Figure 2 This is a schematic diagram of the structure of a ring forging production line visualization system based on a digital twin model provided in an embodiment of this application. For example... Figure 2 As shown, the system includes: a model building module 201 and a visualization module 202.

[0113] The model building module 201 is used to construct three-dimensional geometric models corresponding to each of the multiple ring forging devices on the ring forging production line. For each ring forging device, a displacement-velocity-acceleration curve is constructed based on the mass, moment of inertia, and maximum torque of the ring forging device. A vibration spectrum model is constructed based on the structural stiffness, damping coefficient, mass distribution, and natural frequency of the ring forging device. An instantaneous power curve and energy efficiency model are constructed based on the friction coefficient, transmission efficiency, and motor efficiency of the ring forging device. A temperature rise-heat dissipation curve and thermal balance model are constructed based on the heat capacity, thermal conductivity, surface area, and heat dissipation coefficient of the ring forging device. Based on the displacement-velocity-acceleration curve, the vibration spectrum model, the instantaneous power curve, the energy efficiency model, the temperature rise-heat dissipation curve, and the thermal balance model, combined with the corresponding three-dimensional geometric model and the collected real-time operating data, a digital twin model corresponding to the ring forging device is constructed. The visualization module 202 is used to render and optimize multiple digital twin models to obtain the visualization data corresponding to the ring forging production line.

[0114] Optionally, the model building module 201 is specifically used to, when the ring forging equipment is a heating furnace, construct a workpiece heating curve model based on the layout and power density of the heating elements of the heating furnace; construct a furnace atmosphere recovery model based on the furnace chamber sealing and atmosphere system capacity of the heating furnace; and construct a digital twin model corresponding to the ring forging equipment based on the workpiece heating curve model, the furnace atmosphere recovery model, the displacement-velocity-acceleration curve, the vibration spectrum model, the instantaneous power curve, the energy efficiency model, the temperature rise-heat dissipation curve, and the thermal balance model, combined with the corresponding three-dimensional geometric model and the collected real-time operating data.

[0115] Optionally, the model building module 201 is specifically used to, when the ring forging equipment is a forging press, construct a forging force-displacement curve based on the rated pressure and flow rate of the hydraulic system of the forging press; construct a forging misalignment prediction model based on the clearance and precision between the slider and the guide rail in the forging press; and construct a digital twin model corresponding to the ring forging equipment based on the forging force-displacement curve, the forging misalignment prediction model, the displacement-velocity-acceleration curve, the vibration spectrum model, the instantaneous power curve, the energy efficiency model, the temperature rise-heat dissipation curve, and the thermal balance model, combined with the corresponding three-dimensional geometric model and the collected real-time operating data.

[0116] Optionally, the model building module 201 is specifically used to, when the ring forging equipment is a rolling mill, construct a wall thickness reduction model based on the size ratio of the main roll to the mandrel in the rolling mill; construct a ring section filling and forming model based on the profile of the rolls in the rolling mill; and construct a digital twin model corresponding to the ring forging equipment based on the wall thickness reduction model, the ring section filling and forming model, the displacement-velocity-acceleration curve, the vibration spectrum model, the instantaneous power curve, the energy efficiency model, the temperature rise-heat dissipation curve, and the thermal balance model, combined with the corresponding three-dimensional geometric model and the collected real-time operating data.

[0117] Optionally, the model building module 201 is specifically used to, when the ring forging equipment is a heat treatment furnace, construct a workpiece cooling rate curve based on the cooling system capacity of the heat treatment furnace; construct a process curve tracking and performance uniformity model based on the temperature control accuracy and uniformity of the heat treatment furnace; and construct a digital twin model corresponding to the ring forging equipment based on the workpiece cooling rate curve, the process curve tracking and performance uniformity model, the displacement-velocity-acceleration curve, the vibration spectrum model, the instantaneous power curve, the energy efficiency model, the temperature rise-heat dissipation curve, and the thermal balance model, combined with the corresponding three-dimensional geometric model and the collected real-time operating data.

[0118] Optionally, the model building module 201 is further configured to obtain the previous adjacent time and the next adjacent time of the current time; calculate the difference result at the current time based on the previous adjacent time, the first sampling point value at the previous adjacent time, the next adjacent time, and the second sampling point value at the next adjacent time; and use the difference result as the real-time running data.

[0119] Optionally, each digital twin model corresponds to a three-level detail model, namely a high-precision digital twin model, a medium-precision digital twin model, and a low-precision digital twin model; the visualization module 202 is specifically used to employ a level-of-detail (LOD) rendering optimization algorithm. For each digital twin model, if the Euclidean distance between the virtual camera and the digital twin model is less than a first preset distance threshold, then the high-precision digital twin model corresponding to the ring forging equipment is activated; if the Euclidean distance is greater than or equal to the first preset distance threshold and less than a second preset distance threshold, then the medium-precision digital twin model corresponding to the ring forging equipment is activated; if the Euclidean distance is greater than or equal to the second preset distance threshold, then the low-precision digital twin model corresponding to the ring forging equipment is activated; or, a multi-device collaborative LOD rendering algorithm is used. A dynamic adjustment algorithm is used to construct an association matrix corresponding to the multiple ring forging devices. The association matrix includes the process association degree between any two ring forging devices. For each ring forging device, an association degree vector is determined based on the process association degree between the ring forging device and other ring forging devices. If the association degree vector is less than a first preset association degree threshold, a low-precision digital twin model corresponding to the other ring forging devices is activated. If the association degree vector is greater than or equal to the first preset association degree threshold and less than a second preset association degree threshold, a medium-precision digital twin model corresponding to the other ring forging devices is activated. If the association degree vector is greater than or equal to the second preset association degree threshold, a high-precision digital twin model corresponding to the other ring forging devices is activated. Based on all activated digital twin models, visualized data corresponding to the ring forging production line is generated.

[0120] Optionally, the visualization module 202 is specifically used to acquire rendering tasks corresponding to the multiple digital twin models using a parallel rendering algorithm, wherein the rendering tasks include multiple sub-tasks; and to process the multiple sub-tasks in parallel using at least one computing unit in the image processor CPU to generate visualization data corresponding to the ring forging production line; wherein, if the similarity between any two sub-tasks is less than a preset similarity threshold, then the number of computing units is multiple, and the multiple computing units correspond one-to-one with the multiple sub-tasks; if the similarity between the first sub-task and the second sub-task among the multiple sub-tasks is greater than or equal to the preset similarity threshold, then the first sub-task and the second sub-task correspond to the same computing unit, and the same computing unit has Single Instruction Multiple Data (SIMD) characteristics.

[0121] Optionally, the visualization module 202 is specifically used to employ a scene culling optimization algorithm to construct an axially aligned bounding box that completely encloses each digital twin model; based on the multiple axially aligned bounding boxes, construct an axially aligned bounding box tree, the axially aligned bounding box tree including multiple parent nodes, each parent node corresponding one-to-one with the multiple axially aligned bounding boxes, each parent node including at least one child node; and determine the intersection relationship between the multiple parent nodes and the view frustum of the virtual camera one by one: if the intersection relationship is that the current parent node is located within the view frustum, then the current parent node and the view frustum are retained. The digital twin model corresponding to the child node is defined; if the intersection relationship is that the current parent node is located outside the view frustum, then the model data corresponding to the current parent node and its corresponding child node are removed; if the intersection relationship is that the current parent node is located within the view frustum, then the intersection relationship between the adjacent child nodes of the current parent node and the view frustum is determined until the last child node is determined; software rasterization preprocessing is performed on all static occlusions in the retained digital twin models to generate a potential visible set; based on the model data in the potential visible set, visualization data corresponding to the ring forging production line is generated.

[0122] Optionally, the visualization module 202 is specifically used to employ an adaptive rendering optimization algorithm to collect real-time operating data of each ring forging device, including pressure, temperature, rotational speed, and position coordinates; construct a state evaluation model for the ring forging device based on the pressure, temperature, rotational speed, and position, with the output of the state evaluation model being a device state index; if the device state index is greater than a preset index threshold, then the rendering accuracy of the digital twin model corresponding to the ring forging device and the preset virtual area where the digital twin model is located is increased, and the rendering accuracy outside the preset virtual area where the digital twin model is located is decreased, resulting in a new digital twin model; based on all the new digital twin models, the visualization data corresponding to the ring forging production line is obtained.

[0123] Optionally, the visualization module 202 is specifically used to employ an importance-based elimination method to collect real-time production data of each ring forging equipment and calculate an importance index based on the real-time production data; determine a rendering elimination strategy for the digital twin model of the ring forging equipment pair based on the index range of the importance index, wherein the numerical value of the index range is inversely proportional to the elimination degree of the rendering elimination strategy; process the digital twin model corresponding to the corresponding ring forging equipment according to different rendering elimination strategies to obtain a rendered eliminated digital twin model; and generate visualization data corresponding to the ring forging production line based on all rendered eliminated digital twin models.

[0124] Optionally, the visualization module 202 is further configured to generate multi-dimensional feature vectors for each digital twin model based on the temperature change rate, pressure fluctuation amplitude, equipment vibration frequency, and energy consumption efficiency in the digital twin model; input the multi-dimensional feature vectors into a support vector machine classifier to obtain the equipment operating status output by the support vector machine classifier, wherein the support vector machine classifier is trained based on multi-dimensional feature vector samples and equipment operating status samples; and automatically trigger color coding updates and early warning prompts for the visualization data based on the equipment operating status.

[0125] Figure 3 This is a schematic diagram of the structure of the electronic device provided in an embodiment of this application. For example... Figure 3As shown, the electronic device may include: a processor 310, a communications interface 320, a memory 330, and a communications bus 340, wherein the processor 310, the communications interface 320, and the memory 330 communicate with each other through the communications bus 340. The processor 310 can call logic instructions in the memory 330 to execute a visualization method for a ring forging production line based on a digital twin model. This method includes: constructing three-dimensional geometric models corresponding to multiple ring forging devices on the ring forging production line; for each ring forging device, constructing a displacement-velocity-acceleration curve based on the device's mass, moment of inertia, and maximum torque; constructing a vibration spectrum model based on the device's structural stiffness, damping coefficient, mass distribution, and natural frequency; constructing an instantaneous power curve and energy efficiency model based on the device's friction coefficient, transmission efficiency, and motor efficiency; constructing a temperature rise-heat dissipation curve and thermal balance model based on the device's heat capacity, thermal conductivity, surface area, and heat dissipation coefficient; constructing a digital twin model corresponding to the ring forging device based on the displacement-velocity-acceleration curve, the vibration spectrum model, the instantaneous power curve, the energy efficiency model, the temperature rise-heat dissipation curve, and the thermal balance model, combined with the corresponding three-dimensional geometric model and collected real-time operating data; and performing rendering optimization on multiple digital twin models to obtain visualization data corresponding to the ring forging production line.

[0126] Furthermore, the logical instructions in the aforementioned memory 330 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0127] On the other hand, this application also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the visualization method for a ring forging production line based on a digital twin model provided by the above methods. This method includes: constructing three-dimensional geometric models corresponding to multiple ring forging devices on the ring forging production line; constructing displacement-velocity-acceleration curves for each ring forging device based on its mass, moment of inertia, and maximum torque; and constructing displacement-velocity-acceleration curves based on the structural stiffness, damping coefficient, mass distribution, and natural frequency of the ring forging device. A vibration spectrum model is constructed; based on the friction coefficient, transmission efficiency, and motor efficiency of the ring forging equipment, an instantaneous power curve and energy efficiency model are constructed; based on the heat capacity, thermal conductivity, surface area, and heat dissipation coefficient of the ring forging equipment, a temperature rise-heat dissipation curve and a thermal balance model are constructed; based on the displacement-velocity-acceleration curve, the vibration spectrum model, the instantaneous power curve, the energy efficiency model, the temperature rise-heat dissipation curve, and the thermal balance model, combined with the corresponding three-dimensional geometric model and the collected real-time operating data, a digital twin model corresponding to the ring forging equipment is constructed; multiple digital twin models are rendered and optimized to obtain the visualization data corresponding to the ring forging production line.

[0128] In another aspect, embodiments of this application also provide a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, this computer program is implemented to perform the visualization method for a ring forging production line based on a digital twin model provided by the methods described above. This method includes: constructing three-dimensional geometric models corresponding to multiple ring forging devices on the ring forging production line; for each ring forging device, constructing a displacement-velocity-acceleration curve based on the mass, moment of inertia, and maximum torque of the ring forging device; constructing a vibration spectrum model based on the structural stiffness, damping coefficient, mass distribution, and natural frequency of the ring forging device; and constructing a vibration spectrum model based on the ring forging device's... Based on the friction coefficient, transmission efficiency, and motor efficiency of the equipment, an instantaneous power curve and energy efficiency model are constructed. Based on the heat capacity, thermal conductivity, surface area, and heat dissipation coefficient of the ring forging equipment, a temperature rise-heat dissipation curve and thermal balance model are constructed. Based on the displacement-velocity-acceleration curve, the vibration spectrum model, the instantaneous power curve, the energy efficiency model, the temperature rise-heat dissipation curve, and the thermal balance model, combined with the corresponding three-dimensional geometric model and collected real-time operating data, a digital twin model of the ring forging equipment is constructed. Multiple digital twin models are rendered and optimized to obtain the visualized data corresponding to the ring forging production line.

[0129] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0130] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0131] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application 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. Such 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 this application.

Claims

1. A visualization method for a ring forging production line based on a digital twin model, characterized in that, include: Construct three-dimensional geometric models of each of the multiple ring forging devices on the ring forging production line; For each ring forging machine, a displacement-velocity-acceleration curve is constructed based on the machine's mass, moment of inertia, and maximum torque; a vibration spectrum model is constructed based on the machine's structural stiffness, damping coefficient, mass distribution, and natural frequency; an instantaneous power curve and energy efficiency model are constructed based on the machine's friction coefficient, transmission efficiency, and motor efficiency; a temperature rise-heat dissipation curve and thermal balance model are constructed based on the machine's heat capacity, thermal conductivity, surface area, and heat dissipation coefficient; and a digital twin model of the ring forging machine is constructed based on the displacement-velocity-acceleration curve, the vibration spectrum model, the instantaneous power curve, the energy efficiency model, the temperature rise-heat dissipation curve, and the thermal balance model, combined with the corresponding three-dimensional geometric model and collected real-time operating data. Rendering optimization is performed on multiple digital twin models to obtain the visualization data corresponding to the ring forging production line.

2. The visualization method for ring forging production line based on digital twin model according to claim 1, characterized in that, The process involves constructing a digital twin model of the ring forging equipment based on the displacement-velocity-acceleration curve, the vibration spectrum model, the instantaneous power curve, the energy efficiency model, the temperature rise-heat dissipation curve, and the thermal balance model, combined with the corresponding three-dimensional geometric model and the collected real-time operating data. This includes: When the ring forging equipment is a heating furnace, a workpiece heating curve model is constructed based on the layout and power density of the heating elements in the heating furnace; a furnace atmosphere recovery model is constructed based on the furnace chamber sealing and atmosphere system capacity; and a digital twin model of the ring forging equipment is constructed based on the workpiece heating curve model, the furnace atmosphere recovery model, the displacement-velocity-acceleration curve, the vibration spectrum model, the instantaneous power curve, the energy efficiency model, the temperature rise-heat dissipation curve, and the thermal balance model, combined with the corresponding three-dimensional geometric model and the collected real-time operating data.

3. The visualization method for ring forging production line based on digital twin model according to claim 1, characterized in that, The process involves constructing a digital twin model of the ring forging equipment based on the displacement-velocity-acceleration curve, the vibration spectrum model, the instantaneous power curve, the energy efficiency model, the temperature rise-heat dissipation curve, and the thermal balance model, combined with the corresponding three-dimensional geometric model and the collected real-time operating data. This includes: When the ring forging equipment is a forging press, a forging force-displacement curve is constructed based on the rated pressure and flow rate of the hydraulic system of the forging press; a forging misalignment prediction model is constructed based on the clearance and precision between the slider and the guide rail in the forging press; and a digital twin model of the ring forging equipment is constructed based on the forging force-displacement curve, the forging misalignment prediction model, the displacement-velocity-acceleration curve, the vibration spectrum model, the instantaneous power curve, the energy efficiency model, the temperature rise-heat dissipation curve, and the thermal balance model, combined with the corresponding three-dimensional geometric model and the collected real-time operating data.

4. The visualization method for ring forging production line based on digital twin model according to claim 1, characterized in that, The process involves constructing a digital twin model of the ring forging equipment based on the displacement-velocity-acceleration curve, the vibration spectrum model, the instantaneous power curve, the energy efficiency model, the temperature rise-heat dissipation curve, and the thermal balance model, combined with the corresponding three-dimensional geometric model and the collected real-time operating data. This includes: When the ring forging equipment is a rolling mill, a wall thickness reduction model is constructed based on the size ratio of the main roll to the mandrel in the rolling mill; a ring section filling and forming model is constructed based on the profile of the rolls in the rolling mill; and a digital twin model of the ring forging equipment is constructed based on the wall thickness reduction model, the ring section filling and forming model, the displacement-velocity-acceleration curve, the vibration spectrum model, the instantaneous power curve, the energy efficiency model, the temperature rise-heat dissipation curve, and the thermal balance model, combined with the corresponding three-dimensional geometric model and the collected real-time operating data.

5. The visualization method for ring forging production line based on digital twin model according to claim 1, characterized in that, The process involves constructing a digital twin model of the ring forging equipment based on the displacement-velocity-acceleration curve, the vibration spectrum model, the instantaneous power curve, the energy efficiency model, the temperature rise-heat dissipation curve, and the thermal balance model, combined with the corresponding three-dimensional geometric model and the collected real-time operating data. This includes: When the ring forging equipment is a heat treatment furnace, a workpiece cooling rate curve is constructed based on the cooling system capacity of the heat treatment furnace; a process curve tracking and performance uniformity model is constructed based on the temperature control accuracy and uniformity of the heat treatment furnace; and a digital twin model of the ring forging equipment is constructed based on the workpiece cooling rate curve, the process curve tracking and performance uniformity model, the displacement-velocity-acceleration curve, the vibration spectrum model, the instantaneous power curve, the energy efficiency model, the temperature rise-heat dissipation curve, and the thermal balance model, combined with the corresponding three-dimensional geometric model and the collected real-time operating data.

6. The visualization method for ring forging production line based on digital twin model according to claim 1, characterized in that, The method further includes: Get the previous and next adjacent timestamps of the current time. The difference result at the current time is calculated based on the previous adjacent time, the value of the first sampling point at the previous adjacent time, the next adjacent time, and the value of the second sampling point at the next adjacent time. The difference result is used as the real-time running data.

7. The visualization method for ring forging production line based on digital twin model according to any one of claims 1-6, characterized in that, Each digital twin model corresponds to a three-level detail model: a high-precision digital twin model, a medium-precision digital twin model, and a low-precision digital twin model. The rendering optimization of multiple digital twin models yields the visualized data corresponding to the ring forging production line, including: A Level of Detail (LOD) rendering optimization algorithm is employed. For each digital twin model, if the Euclidean distance between the virtual camera and the digital twin model is less than a first preset distance threshold, then the high-precision digital twin model corresponding to the ring forging equipment is activated; if the Euclidean distance is greater than or equal to the first preset distance threshold and less than a second preset distance threshold, then the medium-precision digital twin model corresponding to the ring forging equipment is activated; if the Euclidean distance is greater than or equal to the second preset distance threshold, then the low-precision digital twin model corresponding to the ring forging equipment is activated; or... A multi-device collaborative LOD dynamic adjustment algorithm is employed to construct an association matrix corresponding to the multiple ring forging devices. This association matrix includes the process association degree between any two ring forging devices. For each ring forging device, an association degree vector is determined based on its process association degree with other ring forging devices. If the association degree vector is less than a first preset association degree threshold, a low-precision digital twin model corresponding to the other ring forging devices is activated. If the association degree vector is greater than or equal to the first preset association degree threshold and less than a second preset association degree threshold, a medium-precision digital twin model corresponding to the other ring forging devices is activated. If the association degree vector is greater than or equal to the second preset association degree threshold, a high-precision digital twin model corresponding to the other ring forging devices is activated. Based on all the initiated digital twin models, generate the corresponding visualization data for the ring forging production line.

8. The visualization method for ring forging production line based on digital twin model according to any one of claims 1-6, characterized in that, The process of rendering and optimizing multiple digital twin models to obtain the visualized data corresponding to the ring forging production line includes: A parallel rendering algorithm is used to obtain the rendering tasks corresponding to the multiple digital twin models, and the rendering tasks include multiple sub-tasks; At least one computing unit in the image processor CPU is used to process the multiple sub-tasks in parallel to generate visualization data corresponding to the ring forging production line. If the similarity between any two subtasks is less than a preset similarity threshold, then there are multiple computing units, and each computing unit corresponds to one of the multiple subtasks. If the similarity between a first subtask and a second subtask is greater than or equal to the preset similarity threshold, then the first subtask and the second subtask correspond to the same computing unit, and the same computing unit has Single Instruction Multiple Data (SIMD) characteristics.

9. The visualization method for a ring forging production line based on a digital twin model according to any one of claims 1-6, characterized in that, The process of rendering and optimizing multiple digital twin models to obtain the visualized data corresponding to the ring forging production line includes: Using a scene elimination optimization algorithm, an axially aligned bounding box is constructed to completely enclose each digital twin model. Based on multiple axially aligned bounding boxes, an axially aligned bounding box tree is constructed. The axially aligned bounding box tree includes multiple parent nodes, each of which corresponds to one of the multiple axially aligned bounding boxes. Each parent node includes at least one child node. The intersection relationships between the multiple parent nodes and the view frustum of the virtual camera are determined one by one: if the intersection relationship is that the current parent node is inside the view frustum, then the digital twin model corresponding to the current parent node and its corresponding child node is retained; if the intersection relationship is that the current parent node is outside the view frustum, then the model data corresponding to the current parent node and its corresponding child node is discarded; if the intersection relationship is that the current parent node is located within the view frustum, then the intersection relationships between the adjacent child nodes of the current parent node and the view frustum are determined, until the last child node is determined. Software rasterization preprocessing is performed on all static occlusions in the retained digital twin models to generate a potential visible set; Based on the model data in the potential visible set, the visualization data corresponding to the ring forging production line is generated.

10. A visualization system for a ring forging production line based on a digital twin model, characterized in that, include: The model building module is used to build the three-dimensional geometric models of each of the multiple ring forging devices on the ring forging production line. For each ring forging machine, a displacement-velocity-acceleration curve is constructed based on the machine's mass, moment of inertia, and maximum torque; a vibration spectrum model is constructed based on the machine's structural stiffness, damping coefficient, mass distribution, and natural frequency; an instantaneous power curve and energy efficiency model are constructed based on the machine's friction coefficient, transmission efficiency, and motor efficiency; a temperature rise-heat dissipation curve and thermal balance model are constructed based on the machine's heat capacity, thermal conductivity, surface area, and heat dissipation coefficient; and a digital twin model of the ring forging machine is constructed based on the displacement-velocity-acceleration curve, the vibration spectrum model, the instantaneous power curve, the energy efficiency model, the temperature rise-heat dissipation curve, and the thermal balance model, combined with the corresponding three-dimensional geometric model and collected real-time operating data. The visualization module is used to render and optimize multiple digital twin models to obtain the visualization data corresponding to the ring forging production line.

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