Full-process digital twinborn simulation system and method for material mixing and electronic equipment

Abstract

The invention discloses a material mixing full-process digital twin simulation system and method and electronic equipment. The system comprises a simulation module which is used for constructing a full-process digital twin model based on physical parameters of physical equipment, material physical characteristics and process logic in a material mixing process; the interactive interface module is used for establishing communication connection between the full-process digital twin model and a target control system so as to realize interaction between a control instruction and a virtual equipment state signal; and the acquisition module is used for acquiring the whole-process configuration parameters, the batching mode and the working mode and providing the parameters to the simulation module for operation. According to the method, the high-fidelity digital twin model is constructed, the virtual-real interaction interface is established, multi-link parameter collaborative debugging of material preparation, material preparation and mixing is supported, the whole-process configuration parameters can be tested at a time, step-by-step debugging is not needed, the debugging time and the debugging cost are remarkably reduced, and the process control precision is improved.

Description

Technical Field

[0001] This application relates to the fields of industrial automation and digital technology, and in particular to a digital twin simulation system and method for the entire material mixing process, and electronic equipment. Background Technology

[0002] The cathode material of lithium batteries is a core determinant of their energy density and cycle performance, and the stability and precision of its production process directly affect the product quality and safety performance of downstream power batteries. Among these processes, the cathode material blending process is a critical step in the production flow, covering the entire process from precursor preparation, lithium salt metering, multi-material collaborative conveying, stirring and dispersion, and anomaly handling.

[0003] In related technologies, the cathode material blending process mainly relies on the traditional offline debugging mode, that is, repeated testing on the physical production line. This method has the following problems: 1. Disruptions in end-to-end control and high debugging costs: Traditional debugging requires step-by-step "material preparation-batch-mixing", which cannot simulate the coupled effects of parameters in multiple stages (such as the transmission of material preparation accuracy to mixing uniformity), and repeated shutdowns lead to debugging cycles of up to several weeks, resulting in huge material waste; 2. Lack of digital twins and fragmented data: The lack of a digital model that maps to the actual production line 1:1 makes it impossible to achieve closed-loop analysis and reverse optimization of data across all dimensions, which makes it difficult to meet the needs of intelligent manufacturing. Summary of the Invention

[0004] This invention aims to at least partially solve one of the technical problems in related technologies. To this end, the first objective of this invention is to propose a digital twin simulation system for the entire material blending process. By constructing a digital twin model that highly maps to the entire cathode material blending process and establishing a virtual-real interaction interface, it supports collaborative debugging of parameters across multiple stages of the "material preparation-blending-mixing" process. This allows for one-time testing of all process configuration parameters without the need for step-by-step debugging, thereby significantly reducing debugging time and costs and improving process control accuracy.

[0005] The second objective of this invention is to propose a digital twin simulation method for the entire material mixing process.

[0006] The third objective of this invention is to provide an electronic device.

[0007] To achieve the above objectives, a digital twin simulation system for the entire material blending process is proposed according to a first aspect of the present invention, comprising: a simulation module, used to construct a digital twin model of the entire process based on the physical parameters of physical equipment, material physical properties, and process logic in the material blending process, wherein the material blending process includes a material preparation process, a material batching process, and a material mixing process; and an interactive interface module, configured to establish a communication connection between the digital twin model of the entire process and a target control system, used to receive control commands from the target control system and to feed back virtual equipment status signals of the digital twin model of the entire process to the target control system. To form a virtual-real control closed loop; the acquisition module, connected to the simulation module, is used to acquire the full-process configuration parameters, batching mode, and working mode, and provides these parameters to the simulation module. The full-process digital twin model operates based on the virtual-real mapping mechanism to simulate the operation status of the entire material mixing process, outputs full-process operation data, and generates optimized process control parameters based on the full-process operation data. The optimized process control parameters are then fed back to the target control system through the interactive interface module to optimize the material mixing process.

[0008] The material blending process digital twin simulation system according to an embodiment of the present invention includes a simulation module, an interactive interface module, and an acquisition module. The simulation module constructs a high-fidelity digital twin model of the entire process, and the interactive interface module enables real-time data interaction with the target control system (such as a PLC) (e.g., closed-loop QW control signals and IW feedback signals). Therefore, by constructing a digital twin model consistent with the entire cathode material blending process, it supports multi-stage parameter collaborative debugging of "material preparation-blending-mixing." Compared to traditional step-by-step physical debugging, this system can shorten the process debugging cycle by more than 85% (e.g., from 21 days to 3 days), and simultaneously reduce material costs by more than 99% due to the absence of actual material consumption. Furthermore, through closed-loop optimization of the entire process data, it can significantly reduce quality fluctuations in actual production (e.g., drastically reducing the range of specific capacity differences).

[0009] According to one embodiment of the present invention, the full-process digital twin model is further used for: performing coupling verification on the full-process configuration parameters; adjusting the state of each virtual device in the full-process digital twin model to the initial state if the coupling verification is successful; performing four-dimensional startup verification according to preset startup conditions when the working mode is received, wherein the four-dimensional startup verification includes equipment readiness, parameter compliance, environmental compliance, and safety interlock; and running the full-process configuration parameters and batching mode if the four-dimensional startup verification is successful and a startup command sent by the acquisition module is received.

[0010] According to one embodiment of the present invention, the full-process digital twin model includes multiple raw material silo models, multiple reducing scale models, multiple batching and feeding models, a mixing silo model, and a mixing equipment model; wherein, each raw material silo model is used to simulate the storage of one material, and each batching and feeding model is configured at the discharge end of the corresponding reducing scale model; the full-process digital twin model is also used to: execute the material preparation process: control the discharge devices of multiple raw material silo models to activate, so as to simulate the process of each material being transported from the corresponding raw material silo model to the corresponding reducing scale model, and issue a preparation target value when the count value of each reducing scale model reaches the preparation target value in the full-process configuration parameters. Material completion message; Execute material batching process: Based on the batching mode and the feeding frequency of each batching feeding model in the full process configuration parameters, control the start of the corresponding batching feeding model to simulate the process of conveying materials from multiple reducing scale models to the mixing chamber model until the batching target value in the full process configuration parameters is reached; Execute material mixing process: Control the mixing equipment model according to the mixing speed in the full process configuration parameters, and calculate the material uniformity in the mixing chamber model based on the virtual sensors of the simulation module, and control the mixing equipment model to maintain the current speed for a preset time when the material uniformity reaches the target uniformity in the full process configuration parameters.

[0011] According to one embodiment of the present invention, the full-process digital twin model is also used to: in the material mixing process, obtain the virtual operating current of the stirring equipment model through the virtual current sensor of the simulation module, and when the virtual operating current is greater than the rated current, feed back an overload signal to the target control system to trigger the target control system to reduce the feeding frequency of multiple batching feeding models.

[0012] According to one embodiment of the present invention, the feeding frequency of each batching feeding model includes a first feeding frequency and a second feeding frequency, wherein the first feeding frequency is greater than the second feeding frequency; the full-process digital twin model is also used to: at the beginning of the material batching process, control the corresponding batching feeding model according to the first feeding frequency of each batching feeding model, calculate the remaining material value in the material batching process, and when the remaining material value is less than the material preparation lead in the full-process configuration parameters, switch the feeding frequency of the corresponding batching feeding model from the corresponding first feeding frequency to the corresponding second feeding frequency.

[0013] According to one embodiment of the present invention, the coupling verification includes: verifying the preparation target value and batching target value of each material in the whole process configuration parameters, verifying the matching of the feeding frequency of each batching feeding model in the whole process configuration parameters with the mixing speed in the whole process configuration parameters, and verifying the matching of the environmental parameters and the preparation lead amount in the whole process configuration parameters.

[0014] According to one embodiment of the present invention, the end-to-end digital twin model is constructed based on an industrial automation simulation platform; the interaction interface module is configured to support both software-in-the-loop (Software-in-the-Loop) and hardware-in-the-loop (HIL) interaction modes; wherein, in the software-in-the-loop interaction mode, the interaction interface module establishes communication with the virtual controller software of the target control system through a coupling connection, and the virtual controller software is used to simulate the actual control logic; in the hardware-in-the-loop interaction mode, the interaction interface module performs data interaction with the physical controller of the target control system through the OPC UA protocol or the PROFINET protocol; the data interaction includes: receiving the digital output and target frequency signal of the target control system, and feeding back the digital input status and analog process value of the virtual device to the target control system.

[0015] According to one embodiment of the present invention, the full-process digital twin model is built on the SIMIT simulation platform; in the software-in-the-loop interactive mode, the virtual controller software is PLCSIM Advanced, and the interactive interface module establishes a connection with PLCSIM Advanced through the coupling interface of the SIMIT simulation platform.

[0016] According to one embodiment of the present invention, the full-process configuration parameters include abnormal configuration parameters, which include at least one of environmental interference parameters, equipment fault parameters, and parameter logic errors; the full-process digital twin model is also used to: run the abnormal configuration parameters to simulate the transmission path of cross-stage interference in the entire material mixing process; trigger the corresponding abnormal response logic according to the transmission path, and record the adjustment strategy corresponding to the full-process digital twin model when it recovers to the normal operating range.

[0017] According to one embodiment of the present invention, the full-process operation data includes the time consumed in the material preparation process, the time consumed in the material batching process, the time consumed in the material mixing process, as well as the working parameters of each virtual equipment model in the full-process digital twin model, batching accuracy deviation data, and mixing uniformity variation curve.

[0018] To achieve the above objectives, a digital twin simulation method for the entire material blending process is proposed according to a second aspect of the present invention, comprising: acquiring full-process configuration parameters, batching mode, and operating mode; inputting the full-process configuration parameters, batching mode, and operating mode into a pre-constructed full-process digital twin model, wherein the full-process digital twin model operates the full-process configuration parameters, batching mode, and operating mode based on a virtual-real mapping mechanism to simulate the operating state of the entire material blending process, the full-process digital twin model being constructed based on the physical parameters of physical equipment, material physical properties, and process logic in the material blending process, the material blending process including a material preparation process, a material batching process, and a material mixing process; acquiring the full-process operating data output by the full-process digital twin model, and generating optimized process control parameters based on the full-process operating data to optimize the material blending process, wherein the optimized process control parameters are fed back to the target control system through an interactive interface module, the interactive interface module being configured to establish a communication connection between the full-process digital twin model and the target control system.

[0019] According to the material blending process digital twin simulation method of this invention, the entire process configuration parameters, batching mode, and operating mode are acquired. These parameters are then input into a pre-constructed digital twin model, which runs and outputs the entire process operation data. Thus, by constructing a digital twin model that highly maps to the entire cathode material blending process and establishing a virtual-real interaction interface, it supports multi-stage parameter collaborative debugging of "material preparation-batching-mixing." This allows for one-time testing of all process configuration parameters without step-by-step debugging, significantly reducing debugging time and costs, and improving process control accuracy.

[0020] To achieve the above objectives, an electronic device is provided according to a third aspect of the present invention, including a memory, a processor, and a digital twin simulation program for the entire process of material mixing stored in the memory and executable on the processor. When the processor executes the digital twin simulation program for the entire process of material mixing, the aforementioned digital twin simulation method for the entire process of material mixing is realized.

[0021] According to the present invention, the electronic device executes a computer program of the above-mentioned digital twin simulation method for the entire process of material mixing through a processor. By constructing a digital twin model that is highly mapped to the entire process of cathode material mixing and establishing a virtual-real interaction interface, it supports the coordinated debugging of parameters in multiple stages of "material preparation-material mixing". It can test the configuration parameters of the entire process at once without step-by-step debugging, thereby significantly reducing debugging time and cost and improving process control accuracy.

[0022] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the structure of a digital twin simulation system for the entire material mixing process according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of a digital twin simulation system for the entire material mixing process according to another embodiment of the present invention; Figure 3 This is a schematic diagram illustrating the data interaction principle between a full-process digital twin model and a target control system (PLC) according to an embodiment of the present invention. Figure 4 This is a flowchart of a digital twin simulation method for the entire material blending process according to an embodiment of the present invention; Figure 5 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0024] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0025] The cathode material blending process covers the entire process of precursor preparation, lithium salt metering, multi-material collaborative conveying, stirring and dispersion, and emergency handling of abnormalities. It plays an important role in uniformly mixing various powder raw materials such as lithium source, transition metal oxide, and dopants in a specific ratio and achieving precise control of particle dispersion and composition uniformity. It is widely used in the large-scale production of mainstream cathode materials such as lithium iron phosphate and ternary materials.

[0026] In related technologies, the cathode material compounding process mainly relies on the traditional offline debugging mode. This involves actually building production equipment and repeatedly testing different combinations of process parameters (such as stirring speed, mixing time, and material addition sequence) to optimize production results. The main problems are twofold: a "disruption in full-process control" and a "lack of digital twins." Specifically: 1. Long process debugging cycle and high cost, with significant waste due to step-by-step debugging throughout the entire process: Parameter optimization of the compounding process in related technologies requires physical experiments to be carried out step by step according to the "material preparation-material batching-mixing" stage. Each adjustment requires a local process of "shutdown-adjustment of single-stage parameters-startup testing-sampling analysis," which not only fails to simulate the coupled effects of parameters in multiple stages but also doubles the equipment resource occupation time due to step-by-step debugging throughout the entire process. Especially when developing new cathode material formulations, the debugging cycle can last for several weeks, making it difficult to quickly match requirements and generating a large amount of cross-stage experimental waste. 2. Lack of full-process data support for process optimization and absence of digital twin mapping make it difficult to achieve refined control: The lack of digital twin models in related technologies makes it impossible to quantify the coupling relationship between the weight change of raw material silos in the preparation stage and the feeding frequency in the batching stage, and it is also difficult to track the movement trajectory of powder particles in the whole process, resulting in "fragmented" adjustment of parameters throughout the process.

[0027] Therefore, there is an urgent need for a digital twin system that can be built to closely map the actual production line in order to support virtual collaborative debugging throughout the entire process.

[0028] The following description, with reference to the accompanying drawings, describes a digital twin simulation system and method for the entire material blending process, as well as electronic equipment, according to embodiments of the present invention.

[0029] Figure 1 This is a schematic diagram of a digital twin simulation system for the entire material blending process according to an embodiment of the present invention. Figure 1 As shown, the digital twin simulation system 100 for the entire material mixing process includes: a simulation module 10, an interactive interface module 30, and an acquisition module 20.

[0030] The simulation module 10 is used to build a full-process digital twin model based on the physical parameters of physical equipment, physical properties of materials and process logic in the material blending process. The material blending process includes the material preparation process, the material batching process and the material mixing process. The interactive interface module 30 is configured to establish a communication connection between the full-process digital twin model and the target control system 200, and to receive control commands from the target control system 200 and to feed back virtual device status signals of the full-process digital twin model to the target control system 200, so as to form a virtual-real control closed loop. The acquisition module 20 is connected to the simulation module 10. The acquisition module 20 is used to acquire the full-process configuration parameters, batching mode and working mode, and provide the full-process configuration parameters, batching mode and working mode to the simulation module 10. The full-process digital twin model runs the full-process configuration parameters, batching mode and working mode based on the virtual-real mapping mechanism to simulate the running state of the entire material mixing process, output the full-process running data, and generate optimized process control parameters based on the full-process running data. The optimized process control parameters are fed back to the target control system 200 through the interactive interface module 30 to optimize the material mixing process.

[0031] Specifically, the entire process of compounding cathode materials includes material preparation, material batching, and material mixing. Simulation module 10 converts the physical parameters of equipment (such as silo volume and impeller diameter), material physical properties (such as bulk density and flowability coefficient), and environmental parameters from the actual cathode material production line into digital twin parameters to construct a fully digital twin model that is highly consistent with the entire cathode material compounding process. Interaction interface module 30 is crucial for realizing the digital twin and is configured to support both software-in-the-loop interaction based on virtual controllers and hardware-in-the-loop interaction based on physical controllers. Acquisition module 20 acquires the full-process configuration parameters (such as material preparation target value, batching target value, and material preparation lead time), batching mode (such as simultaneous batching and priority batching), and operating mode (such as automatic mode and single run) input by engineers. The full-process digital twin model operates based on the received parameters and instructions from the target control system 200, and provides real-time feedback on the operating status of the virtual equipment (such as virtual weighing value and virtual operating current) to the target control system 200, forming a closed loop. After the operation is completed, the full-process digital twin model outputs full-process operation data including key indicators such as the raw material bin weight change rate, feeder frequency drift, and mixing bin uniformity. This generates parameter-performance correlation curves, providing data support for the fine optimization of the entire process, and ultimately achieving efficient optimization and digital upgrade of the cathode material compounding process.

[0032] Exemplary, in some embodiments, such as Figure 2As shown, the end-to-end digital twin model is built on an industrial automation simulation platform. The interaction interface module 30 is configured to support both software-in-the-loop (Software-in-the-Loop) and hardware-in-the-loop (HIL) interaction modes. In the Software-in-the-Loop (Software-in-the-Loop) interaction mode, the interaction interface module 30 establishes communication with the virtual controller software of the target control system 200 through a coupling connection. The virtual controller software is used to simulate the actual control logic. In the Hardware-in-the-Loop (HIL) interaction mode, the interaction interface module 30 interacts with the physical controller of the target control system 200 through the OPC UA protocol or the PROFINET protocol. The data interaction includes receiving the digital output and target frequency signal of the target control system 200, and feeding back the digital input status and analog process values ​​of the virtual device to the target control system 200.

[0033] Specifically, the end-to-end digital twin model is built on an industrial automation simulation platform, such as the SIMIT simulation platform. SIMIT (Simulation Framework for Industrial Automation and Control Systems) is Siemens' simulation platform, which plays the role of the "controlled object" in this embodiment and is used to build an end-to-end digital twin model that includes electrical behavior, IO (Input / Output) signals, motor drives, and process procedures.

[0034] The interactive interface module 30 is implemented in two ways: SiL (Software-in-the-Loop) and HiL (Hardware-in-the-Loop) interaction modes. In the Software-in-the-Loop (SiL) mode, PLCSIM Advanced (a simulation tool provided by Siemens) acts as a "virtual PLC (Programmable Logic Controller)," simulating the logic execution of a real PLC (such as S7-1500) and providing a communication interface. SIMIT connects to the API (Application Programming Interface) provided by PLCSIM Advanced through specific coupling settings, thereby achieving closed-loop interaction between "virtual control" and "virtual objects." In the Hardware-in-the-Loop (HiL) mode, it directly interacts with the physical PLC (i.e., the physical PLC) via the OPC UA (Open Platform Communications Unified Architecture, a communication protocol for data exchange between devices, systems, and services) protocol or the PROFINET (Ethernet-based automation bus standard) protocol. Figure 2Communication between the actual PLC in the system is used to achieve data interaction between the target control system 200 and the full-process digital twin model.

[0035] Figure 3 The diagram illustrates the data interaction principle between the end-to-end digital twin model and the target control system (PLC), such as... Figure 2 As shown, the actual data flow of the actual material blending system is as follows: The PLC controls the field frequency converter through digital output QW and obtains feedback through digital input IW; simultaneously, it collects analog signal AI and outputs analog signal AO. The data flow of the full-process digital twin simulation system for material blending is as follows: The full-process digital twin model receives the PLC's QW (equipment control signal) and AO (target frequency signal) in real time via the OPC UA protocol, simulates the operating behavior of the equipment throughout the entire process, and transmits the generated IW (virtual equipment status) and AI (virtual process values, such as virtual weighing and virtual current) back to the PLC in real time. This interactive mechanism allows for the verification of the PLC's full-process control logic without connecting to real field equipment, enabling early detection of cross-stage logic defects.

[0036] Furthermore, a "full-process monitoring interface" (including equipment status animation area, parameter input area, abnormal alarm area, and data curve area) can be built in WinCC (Windows Control Center) software to achieve real-time data interaction between SIMIT and WinCC HMI (Human Machine Interface). Engineers can input full-process configuration parameters, batching mode, and working mode into the full-process monitoring interface and send them to the SIMIT simulation platform. The full-process monitoring interface can then display the running status and full-process operation data of the full-process digital twin model in the SIMIT simulation platform.

[0037] In the above embodiments, by constructing a digital twin model that highly maps to the entire process of cathode material compounding and establishing a virtual-real interaction interface, it supports multi-stage parameter collaborative debugging of "material preparation-combination-mixing". This allows for one-time testing of all process configuration parameters, avoiding repeated downtime at multiple stages. Experimental data shows that the number of full-process debugging attempts has decreased from approximately 100 to approximately 8, the process finalization cycle has been shortened from 21 days to 3 days, and debugging efficiency has increased by more than 85.7%. Furthermore, since there is no actual material consumption and no cross-stage experimental waste, it saves approximately 200 kg of expensive raw materials (such as precursors and lithium salts) compared to traditional debugging methods. Only the electricity cost for computer operation needs to be covered, resulting in a material cost reduction of more than 99%, thus significantly reducing debugging time and costs.

[0038] In some embodiments, the end-to-end digital twin model is also used for: performing coupling verification on the end-to-end configuration parameters; adjusting the state of each virtual device in the end-to-end digital twin model to the initial state if the coupling verification is successful; performing four-dimensional startup verification according to preset startup conditions if the working mode is received, wherein the four-dimensional startup verification includes device readiness, parameter compliance, environmental compliance, and safety interlock; and running the end-to-end configuration parameters and batching mode if the four-dimensional startup verification is successful and the startup command sent by the acquisition module 20 is received.

[0039] The four-dimensional startup verification in this embodiment includes equipment readiness (e.g., no motor faults), parameter coupling compliance, environmental compliance, and safety interlocks (e.g., emergency stop release). In addition to verifying the initial state of the motor in related technologies, the verification of "environmental parameters compliance" (e.g., temperature 25±2℃, humidity ≤40%RH) and "cross-process safety interlocks" (e.g., the mixing silo feed valve must be closed before the batching feeder can start) are added.

[0040] In some embodiments, the coupling verification includes: verifying the preparation target value and batching target value of each material in the overall process configuration parameters; verifying the matching of the feeding frequency of each batching feeding model in the overall process configuration parameters with the mixing speed in the overall process configuration parameters; and verifying the matching of the environmental parameters and the preparation lead time in the overall process configuration parameters.

[0041] Understandably, in addition to verifying that "the prepared material value is greater than or equal to the target value" in the relevant technologies, new verifications are added for "the matching of feeding frequency and mixing speed" (e.g., 45Hz feeding requires a mixing speed of ≥1500r / min) and "the matching of ambient humidity and prepared material lead time" (e.g., 45%RH humidity requires a lead time of 20kg). If the coupling verification is compliant, the "parameter coupling indicator" will turn green; if it is abnormal (e.g., 50%RH humidity, but a lead time of 15kg), it will prompt "the lead time needs to be adjusted to 22kg or the humidity reduced to 40%RH".

[0042] It should be noted that key parameters of the entire process (such as precursor batching target value of 836.90 kg, lithium salt batching target value of 400.00 kg, and ambient humidity threshold of 40%RH) can be entered or modified in the full-process monitoring interface to calculate parameter compliance in real time and simulate the full-process interception mechanism when parameters are abnormal.

[0043] In the above embodiments, by verifying the coupling of the configuration parameters throughout the entire process, a full-process interception mechanism can be simulated when parameters are abnormal. Furthermore, by performing startup verification, it can be ensured that the startup conditions of the entire process are completely consistent with those of the actual production line.

[0044] In some embodiments, the full-process digital twin model includes multiple raw material silo models, multiple reducing scale models, multiple batching and feeding models, a mixing silo model, and a mixing equipment model. Each raw material silo model is used to simulate the storage of a material, and each batching and feeding model is configured at the discharge end of the corresponding reducing scale model. The full-process digital twin model is also used for: executing the material preparation process: controlling the opening of the discharge devices of multiple raw material silo models to simulate the process of each material being transported from the corresponding raw material silo model to the corresponding reducing scale model, and issuing a preparation completion message when the count value of each reducing scale model reaches the material preparation target value in the full-process configuration parameters; executing the material batching process: controlling the corresponding batching feeding model according to the batching mode and the feeding frequency of each batching feeding model in the full-process configuration parameters to simulate the process of transporting materials from multiple reducing scale models to the mixing silo model until the batching target value in the full-process configuration parameters is reached; executing the material mixing process: controlling the mixing equipment model according to the mixing speed in the full-process configuration parameters, and calculating the material uniformity in the mixing silo model based on the virtual sensors of the simulation module 10, and controlling the mixing equipment model to maintain the current speed for a preset time when the material uniformity reaches the target uniformity in the full-process configuration parameters.

[0045] Taking the cathode material, which includes the precursor and lithium salt, as an example, there are two models: a raw material silo model, a reducing scale model, and a batching and feeding model. These are one set of models corresponding to the precursor and another set corresponding to the lithium salt. The overall process configuration parameters are shown in Table 1. Table 1

[0046] Among them, the drop value is the core compensation parameter of the batching process. It specifically refers to the weight of material that has not fully entered the target container during the falling process after the batching and feeding device (such as a screw feeder) receives the "stop feeding" signal, due to the inertia of the material and the delay in equipment response.

[0047] The end-to-end digital twin model synchronously outputs key signals such as the weight of the raw material silo, the real-time weight of the reducing scale, and the cumulative weight of the mixing silo in real time. It not only restores the metering logic of a single device, but also realizes the weight linkage of the entire process from "weight reduction in the raw material silo → weight increase in the reducing scale → weight increase in the mixing silo". In addition, the timing of the actions of the feeding equipment, mixing equipment and valves is digitally twin-modeled to accurately reproduce the time logic of "opening the raw material silo discharge valve → starting the material feeder → metering the reducing scale → opening the mixing silo feed valve → starting the batching feeder → starting the mixing motor".

[0048] In some embodiments, the feeding frequency of each batching feeding model includes a first feeding frequency and a second feeding frequency, wherein the first feeding frequency is greater than the second feeding frequency; the full-process digital twin model is also used to: at the beginning of the material batching process, control the corresponding batching feeding model according to the first feeding frequency of each batching feeding model, and calculate the remaining material value in the material batching process; when the remaining material value is less than the material preparation lead in the full-process configuration parameters, switch the feeding frequency of the corresponding batching feeding model from the corresponding first feeding frequency to the corresponding second feeding frequency.

[0049] Taking the simultaneous batching mode as an example, clicking the "Full Process Start" button in the full process monitoring interface simultaneously sends precursor preparation instructions and lithium salt preparation instructions. When the count value of the reducing scale model reaches the preparation target value (e.g., 880kg and 500kg), the preparation is complete, and the batching process automatically begins. The precursor batching feeding model starts at the first feeding frequency (e.g., 45Hz), and the lithium salt batching feeding model starts at the first feeding frequency (e.g., 30Hz). The full-process digital twin model calculates the remaining material value in real time. When the remaining material value is ≤ the preparation advance amount (e.g., 15kg), it automatically switches to the second feeding frequency (e.g., 3.1Hz and 4.1Hz) for precise replenishment until the batching target value is reached. After batching is completed, the stirring equipment model starts at 1500r / min and calculates the change in material uniformity (e.g., from 92% to 96.2%) through the embedded virtual sensor. After reaching the target uniformity (95%), it maintains the rotation speed.

[0050] In the above embodiments, control logic that is completely consistent with the process logic of the material mixing process is constructed, and the timing matching degree with the actual equipment action reaches 100%, thereby realizing the full-process simulation of material mixing.

[0051] In some embodiments, the full-process digital twin model is also used to: in the material mixing process, obtain the virtual operating current of the stirring equipment model through the virtual current sensor of the simulation module 10, and when the virtual operating current is greater than the rated current, feed back an overload signal to the target control system 200 to trigger the target control system 200 to reduce the feeding frequency of multiple batching feeding models, so as to realize the simulation control of load linkage.

[0052] In other words, during the mixing process, the full-process digital twin model calculates the mixing load (i.e., virtual working current) in real time using a virtual current sensor. If the virtual working current is too large, it indicates that the material in the mixing chamber is accumulating or the viscosity is too high. The full-process digital twin model simulates an overload phenomenon and sends an overload signal back to the PLC of the target control system 200. The PLC executes logical judgment and issues a "reduce frequency" instruction to multiple batching and feeding models in the full-process digital twin model (for example, reducing the frequency of the batching and feeding models by 10%). The multiple batching and feeding models decelerate to achieve load-linked simulation control, thereby solving the problem of "virtual-real interaction" closed-loop control that is difficult to achieve in traditional offline simulation.

[0053] In some embodiments, the full-process configuration parameters include abnormal configuration parameters, which include at least one of environmental interference parameters, equipment fault parameters, and parameter logic errors; the full-process digital twin model is also used to: run the abnormal configuration parameters to simulate the transmission path of cross-stage interference in the entire material mixing process; trigger the corresponding abnormal response logic according to the transmission path, and record the adjustment strategy corresponding to the full-process digital twin model when it recovers to the normal operating range.

[0054] Specifically, the end-to-end digital twin model can simulate the transmission of environmental interference (such as humidity changes), equipment failure (such as frequency drift), and parameter error.

[0055] Taking the transmission of environmental interference as an example: Engineers can manually adjust the humidity of the raw material silo from 35%RH to 55%RH. The full-process digital twin model synchronously simulates the transmission path caused by powder agglomeration, which is "reduced raw material silo feeding speed → reduced batching flow rate → extended batching time → decreased mixing uniformity". The system's response strategy is verified (such as automatically prompting that the preparation amount needs to be adjusted from 15kg to 22kg).

[0056] Taking equipment failure propagation as an example: the frequency drift of the precursor feeding model (from 45Hz to 40Hz) is simulated. The model displays its impact on the idling time of the subsequent mixing stage in real time and prompts that the compensation frequency needs to be adjusted.

[0057] In the above embodiments, the end-to-end digital twin model can simulate the entire process of interference transmission, avoiding product scrapping caused by cross-stage anomalies in actual production. Based on this, by simulating cross-stage interference and optimizing parameters, the specific capacity difference of the same batch of materials in actual mass production was reduced from 5.8 mAh / g to 1.2 mAh / g, and the performance fluctuation was reduced by 79.3%; moreover, the anomaly handling time was shortened from 45 minutes to 8 minutes, and the scrap rate was significantly reduced.

[0058] In some embodiments, the full-process operation data includes the time consumed in the material preparation process, the time consumed in the material batching process, the time consumed in the material mixing process, as well as the working parameters of each virtual equipment model in the full-process digital twin model, batching accuracy deviation data, and mixing uniformity variation curve.

[0059] Specifically, after the full-process simulation is completed, the full-process running data is automatically exported, as shown in Table 2: Table 2

[0060] It should be noted that the system supports sending the optimal parameter combination after simulation optimization (such as material preparation advance amount of 22kg and mixing speed of 1600r / min) to the actual production line to realize the data closed loop of "simulation-optimization-production".

[0061] In summary, the digital twin simulation system for the entire material blending process according to embodiments of the present invention achieves collaborative debugging and cross-stage optimization throughout the entire process by constructing a high-fidelity digital twin model and a virtual-real interaction interface. Compared with traditional methods, it significantly shortens the debugging cycle, reduces costs, and effectively improves the consistency of product quality in actual production. Furthermore, the full-process digital twin model can simulate the entire process interference transmission path, avoiding product scrapping caused by the superposition of cross-stage anomalies in actual production. Based on this, after simulating cross-stage interference and optimizing parameters, the specific capacity difference of the same batch of materials in actual mass production was reduced from 5.8 mAh / g to 1.2 mAh / g, and the performance fluctuation amplitude was reduced by 79.3%; moreover, the anomaly handling time was shortened from 45 minutes to 8 minutes, and the scrap rate was significantly reduced.

[0062] Corresponding to the above embodiments, embodiments of the present invention also propose a digital twin simulation method for the entire material blending process. For example... Figure 3 As shown, the digital twin simulation method for the entire material blending process includes: S201, obtain full process configuration parameters, ingredient mixing mode and working mode.

[0063] S202, input the full-process configuration parameters, batching mode, and working mode into the pre-built full-process digital twin model. The full-process digital twin model runs the full-process configuration parameters, batching mode, and working mode based on the virtual-real mapping mechanism to simulate the operation status of the entire material mixing process. The full-process digital twin model is constructed based on the physical parameters of physical equipment, physical properties of materials, and process logic in the material mixing process. The material mixing process includes the material preparation process, the material batching process, and the material mixing process.

[0064] S203: Obtain the full-process operation data output by the full-process digital twin model, and generate optimized process control parameters based on the full-process operation data to optimize the material mixing process. The optimized process control parameters are fed back to the target control system through the interactive interface module, which is configured to establish a communication connection between the full-process digital twin model and the target control system.

[0065] In some embodiments, the end-to-end digital twin model is also used for: performing coupling verification on the end-to-end configuration parameters; adjusting the state of each virtual device in the end-to-end digital twin model to its initial state if the coupling verification is successful; performing four-dimensional startup verification according to preset startup conditions when a working mode is received, wherein the four-dimensional startup verification includes device readiness, parameter compliance, environmental compliance, and safety interlock; and running the end-to-end configuration parameters and batching mode if the four-dimensional startup verification is successful and a startup command sent by the acquisition module is received.

[0066] In some embodiments, the end-to-end digital twin model includes multiple raw material silo models, multiple reducing scale models, multiple batching and feeding models, a mixing silo model, and a mixing equipment model. Each raw material silo model is used to simulate the storage of one type of material, and each batching and feeding model is configured at the discharge end of the corresponding reducing scale model.

[0067] The end-to-end digital twin model is also used for: Execute the material preparation process: Control the discharge devices of multiple raw material silo models to simulate the process of each material being transported from its corresponding raw material silo model to its corresponding reducing scale model. Once the count value of each reducing scale model reaches the material preparation target value in the overall process configuration parameters, a material preparation completion message is issued. Execute the material batching process: Based on the batching mode and the feeding frequency of each batching feeding model in the overall process configuration parameters, control the start of the corresponding batching feeding model to simulate the process of transporting materials from multiple reducing scale models to the mixing silo model until the batching target value in the overall process configuration parameters is reached. Execute the material mixing process: Control the mixing equipment model according to the mixing speed in the overall process configuration parameters, calculate the material uniformity within the mixing silo model based on the virtual sensors in the simulation module, and control the mixing equipment model to maintain the current speed for a preset time once the material uniformity reaches the target uniformity in the overall process configuration parameters.

[0068] In some embodiments, the full-process digital twin model is also used to: obtain the virtual operating current of the stirring equipment model through a virtual current sensor in the material mixing process, and when the virtual operating current is greater than the rated current, feed back an overload signal to the target control system to trigger the target control system to reduce the feeding frequency of multiple batching feeding models.

[0069] In some embodiments, the feeding frequency of each batching feeding model includes a first feeding frequency and a second feeding frequency, wherein the first feeding frequency is greater than the second feeding frequency; the full-process digital twin model is also used to: at the beginning of the material batching process, control the corresponding batching feeding model according to the first feeding frequency of each batching feeding model, and calculate the remaining value of material feeding during the material batching process; when the remaining value of at least one material is less than the material preparation lead amount in the full-process configuration parameters, switch the feeding frequency of the corresponding batching feeding model from the corresponding first feeding frequency to the corresponding second feeding frequency.

[0070] In some embodiments, the coupling verification includes: performing logical verification on the preparation target value and batching target value of each material in the overall process configuration parameters, verifying the matching of the feeding frequency and mixing speed of each batching feeding model, and verifying the matching of environmental parameters and preparation lead time in the overall process configuration parameters.

[0071] In some embodiments, the end-to-end digital twin model is built on an industrial automation simulation platform; the interaction interface module is configured to support both software-in-the-loop (Software-in-the-Loop) and hardware-in-the-loop (HIL) interaction modes; in the Software-in-the-Loop (Software-in-the-Loop) interaction mode, the interaction interface module establishes communication with the virtual controller software of the target control system through a coupling connection, and the virtual controller software is used to simulate the actual control logic; in the Hardware-in-the-Loop (HIL) interaction mode, the interaction interface module interacts with the physical controller of the target control system through the OPC UA protocol or the PROFINET protocol; the data interaction includes: receiving the digital output and target frequency signal of the target control system, and feeding back the digital input status and analog process values ​​of the virtual device to the target control system.

[0072] In some embodiments, the end-to-end digital twin model is built on the SIMIT simulation platform; in the software-in-the-loop interactive mode, the virtual controller software is PLCSIM Advanced, and the interactive interface module establishes a connection with PLCSIM Advanced through the coupling interface of the SIMIT simulation platform.

[0073] In some embodiments, the full-process configuration parameters include abnormal configuration parameters, which include at least one of environmental interference parameters, equipment fault parameters, and parameter logic errors; the full-process digital twin model is also used to: run the abnormal configuration parameters to simulate the transmission path of cross-stage interference in the entire material mixing process; trigger the corresponding abnormal response logic according to the transmission path, and record the adjustment strategy corresponding to the full-process digital twin model when it recovers to the normal operating range.

[0074] In some embodiments, the full-process operation data includes the time consumed in the material preparation process, the time consumed in the material batching process, the time consumed in the material mixing process, as well as the working parameters of each virtual device model in the full-process digital twin model, batching accuracy error data, and mixing uniformity variation curve.

[0075] It should be noted that the specific implementation methods of the digital twin simulation method for the entire process of material blending in the embodiments of the present invention correspond one-to-one with the specific implementation methods of the digital twin simulation system for the entire process of material blending in the embodiments of the present invention described above, and will not be repeated here.

[0076] According to an embodiment of the present invention, a digital twin simulation method for the entire material blending process acquires the full-process configuration parameters, batching mode, and operating mode, and inputs these parameters into a pre-constructed full-process digital twin model. The full-process digital twin model then operates based on a virtual-real mapping, implementing the full-process configuration parameters, batching mode, and operating mode, and outputs full-process operation data. The full-process digital twin model is constructed based on the physical parameters and process logic of each production device in the material blending process, which includes material preparation, batching, and mixing. The full-process operation data is used to generate optimization strategies to optimize the actual material blending process. Therefore, by constructing a digital twin model highly consistent with the entire cathode material blending process, multi-stage parameter collaborative debugging of "material preparation-batching-mixing" is supported. The entire process configuration parameters can be tested at once, eliminating the need for step-by-step debugging, thus significantly reducing debugging time and costs.

[0077] Corresponding to the above embodiments, embodiments of the present invention also propose an electronic device. For example... Figure 5 As shown, the electronic device 300 includes a memory 310, a processor 320, and a digital twin simulation program for the entire material mixing process stored in the memory 310 and run on the processor 320. When the processor 320 executes the digital twin simulation program for the entire material mixing process, it implements the aforementioned digital twin simulation method for the entire material mixing process.

[0078] According to the present invention, the electronic device executes the computer program of the above-mentioned material mixing simulation method through the processor, constructs a digital twin model that is highly mapped to the entire process of cathode material mixing, and establishes a virtual-real interaction interface. It supports multi-stage parameter collaborative debugging of "material preparation-mixing-blending". It can test the entire process configuration parameters at once without step-by-step debugging, thereby significantly reducing debugging time and debugging cost and improving process control accuracy.

[0079] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0080] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0081] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0082] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," and "circumferential" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this invention and simplifying the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0083] Furthermore, the terms "first," "second," etc., used in the embodiments of this invention are for descriptive purposes only and should not be construed as indicating or implying relative importance, or implicitly specifying the number of technical features indicated in this embodiment. Therefore, features defined with terms such as "first" and "second" in the embodiments of this invention can explicitly or implicitly indicate that the embodiment includes at least one of those features. In the description of this invention, the word "multiple" means at least two or more, such as two, three, four, etc., unless otherwise explicitly specified in the embodiments.

[0084] In this invention, unless otherwise explicitly specified or limited in the embodiments, the terms "installation," "connection," "joining," and "fixing" appearing in the embodiments should be interpreted broadly. For example, a connection can be a fixed connection, a detachable connection, or an integral part; it can also be a mechanical connection, an electrical connection, etc. Of course, it can also be a direct connection, or an indirect connection through an intermediate medium, or it can be the internal communication of two components, or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific implementation.

[0085] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can mean that the first feature is in direct contact with the second feature, or that the first feature is in indirect contact with the second feature through an intermediate medium. Furthermore, "above," "over," and "on top" of the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply that the first feature is at a lower horizontal level than the second feature.

[0086] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A digital twin simulation system for the entire material blending process, characterized in that, include: The simulation module is used to construct a full-process digital twin model based on the physical parameters of physical equipment, physical properties of materials, and process logic in the material blending process. The material blending process includes a material preparation process, a material batching process, and a material mixing process. An interactive interface module is configured to establish a communication connection between the full-process digital twin model and the target control system, for receiving control commands from the target control system and feeding back virtual device status signals of the full-process digital twin model to the target control system, so as to form a virtual-real control closed loop. An acquisition module, connected to the simulation module, is used to acquire full-process configuration parameters, batching mode, and working mode, and provide these parameters to the simulation module. The full-process digital twin model operates based on a virtual-real mapping mechanism, using the full-process configuration parameters, batching mode, and working mode to simulate the entire process of material blending, outputting full-process operation data, and generating optimized process control parameters based on the data. These optimized process control parameters are then fed back to the target control system through the interactive interface module to optimize the material blending process.

2. The digital twin simulation system for the entire material blending process according to claim 1, characterized in that, The end-to-end digital twin model is also used for: The coupling of the entire process configuration parameters is verified; If the coupling verification is successful, the state of each virtual device in the full-process digital twin model will be adjusted to the initial state. Upon receiving the aforementioned working mode, a four-dimensional startup verification is performed based on preset startup conditions. The four-dimensional startup verification includes equipment readiness, parameter compliance, environmental compliance, and safety interlocks. If the four-dimensional startup verification is successful and a startup command is received, the full-process configuration parameters and the ingredient dispensing mode will be executed.

3. The digital twin simulation system for the entire material blending process according to claim 2, characterized in that, The full-process digital twin model includes multiple raw material silo models, multiple reducing scale models, multiple batching and feeding models, a mixing silo model, and a mixing equipment model; wherein, each raw material silo model is used to simulate the storage of one material, and each batching and feeding model is configured at the discharge end of the corresponding reducing scale model; The end-to-end digital twin model is also used for: Execute the material preparation process: control the discharge device of the multiple raw material silo models to start, so as to simulate the process of each material being transported from the corresponding raw material silo model to the corresponding reducing scale model, and issue a material preparation completion message when the count value of each reducing scale model reaches the material preparation target value in the full process configuration parameters; Execute the material batching process: Based on the batching mode and the feeding frequency of each batching feeding model in the full process configuration parameters, control the start of the corresponding batching feeding model to simulate the process of conveying materials from multiple reduction scale models to the mixing bin model until the batching target value in the full process configuration parameters is reached; Execute the material mixing process: control the mixing speed of the stirring equipment model according to the mixing speed in the full process configuration parameters, calculate the material uniformity in the mixing chamber model based on the virtual sensor of the simulation module, and control the stirring equipment model to maintain the current speed for a preset time when the material uniformity reaches the target uniformity in the full process configuration parameters.

4. The digital twin simulation system for the entire material blending process according to claim 3, characterized in that, The end-to-end digital twin model is also used for: In the material mixing process, the virtual operating current of the stirring equipment model is obtained through the virtual current sensor of the simulation module; When the virtual operating current is greater than the rated current, an overload signal is fed back to the target control system to trigger the target control system to reduce the feeding frequency of the multiple batching and feeding models.

5. The digital twin simulation system for the entire material blending process according to claim 3, characterized in that, The feeding frequency of each batching feeding model includes a first feeding frequency and a second feeding frequency, wherein the first feeding frequency is greater than the second feeding frequency; The end-to-end digital twin model is also used for: At the beginning of the material batching process, the corresponding batching feeding model is controlled according to the first feeding frequency of each batching feeding model, and the remaining material value in the material batching process is calculated. If the remaining value of at least one material is less than the material preparation lead time in the full process configuration parameters, the feeding frequency of the corresponding batching and feeding model will be switched from the corresponding first feeding frequency to the corresponding second feeding frequency.

6. The digital twin simulation system for the entire material blending process according to claim 5, characterized in that, The coupling verification includes: verifying the preparation target value and batching target value of each material in the full process configuration parameters; verifying the matching of the feeding frequency of each batching feeding model in the full process configuration parameters with the mixing speed in the full process configuration parameters; and verifying the matching of the environmental parameters in the full process configuration parameters with the preparation lead time.

7. The digital twin simulation system for the entire material blending process according to claim 1, characterized in that, The end-to-end digital twin model is built on an industrial automation simulation platform, and the interactive interface module is configured to support both software-in-the-loop and hardware-in-the-loop interaction modes; wherein, In the software-in-the-loop interactive mode, the interactive interface module establishes communication with the virtual controller software of the target control system through a coupling connection, and the virtual controller software is used to simulate the actual control logic; In the hardware-in-the-loop interaction mode, the interaction interface module interacts with the physical controller of the target control system via the OPC UA protocol or the PROFINET protocol. The data interaction includes receiving the digital output and target frequency signal of the target control system, and feeding back the digital input status and analog process value of the virtual device to the target control system.

8. The digital twin simulation system for the entire material blending process according to claim 7, characterized in that, The full-process digital twin model is built on the SIMIT simulation platform; in the software-in-the-loop interactive mode, the virtual controller software is PLCSIM Advanced, and the interactive interface module establishes a connection with PLCSIM Advanced through the coupling interface of the SIMIT simulation platform.

9. The digital twin simulation system for the entire material blending process according to any one of claims 1-8, characterized in that, The full-process configuration parameters include abnormal configuration parameters, which include at least one of environmental interference parameters, equipment fault parameters, and parameter logic errors. The end-to-end digital twin model is also used for: Run the abnormal configuration parameters to simulate the transmission path of cross-stage disturbances throughout the entire material blending process; The corresponding abnormal response logic is triggered according to the transmission path, and the adjustment strategy corresponding to the recovery of the full-process digital twin model to the normal operating range is recorded.

10. The digital twin simulation system for the entire material blending process according to any one of claims 1-8, characterized in that, The full-process operation data includes the time consumed in the material preparation process, the time consumed in the material batching process, the time consumed in the material mixing process, as well as the working parameters of each virtual equipment model in the full-process digital twin model, the batching accuracy deviation data, and the mixing uniformity change curve.

11. A digital twin simulation method for the entire material mixing process, characterized in that, include: Obtain full-process configuration parameters, ingredient mixing mode, and working mode; The full-process configuration parameters, the batching mode, and the working mode are input into a pre-built full-process digital twin model. The full-process digital twin model operates the full-process configuration parameters, the batching mode, and the working mode based on a virtual-real mapping mechanism to simulate the operation status of the entire material blending process. The full-process digital twin model is constructed based on the physical parameters of the physical equipment, the physical properties of the materials, and the process logic in the material blending process. The material blending process includes the material preparation process, the material batching process, and the material mixing process. The system acquires the full-process operation data output by the full-process digital twin model and generates optimized process control parameters based on the full-process operation data to optimize the material mixing process. The optimized process control parameters are fed back to the target control system through an interactive interface module, which is configured to establish a communication connection between the full-process digital twin model and the target control system.

12. An electronic device, characterized in that, The system includes a memory, a processor, and a digital twin simulation program for the entire material blending process stored in the memory and capable of running on the processor. When the processor executes the digital twin simulation program for the entire material blending process, it implements the digital twin simulation method for the entire material blending process according to claim 11.

Images