Standardized electrical full-element generation and operation and maintenance system and method based on model information

Abstract

The present application relates to a standardized electrical full-element generation and operation and maintenance system and method based on model information. The system comprises: an automatic generation kernel for automatically generating standardized electrical full-element function units required for device operation according to imported device information and device three-dimensional models; a running control kernel for controlling the operation of the corresponding physical device and synchronously driving the digital twin to update the real-time state; a predictive operation kernel for realizing forward simulation prediction and analysis of the future operation state of the physical device in a virtual environment by advancing the speed of simulation execution to the current operation speed of the physical device; and a three-dimensional visualization platform for displaying the real-time operation state and future prediction state of the digital twin. Through the cooperation of the above modules, the system constitutes a complete closed loop of "design-generation-control-prediction-visualization", greatly improving the development efficiency, debugging safety and operation predictability of non-standard automated equipment.

Description

Technical Field

[0001] This invention relates to the field of industrial automation control technology, and in particular to a standardized electrical full-element generation and operation and maintenance system and method based on model information. Background Technology

[0002] Non-standard automated equipment, due to its high flexibility and adaptability, is widely used in modern manufacturing industries such as consumer electronics, auto parts, and new energy batteries, and is a key piece of equipment for achieving rapid product changeover and intelligent production.

[0003] Currently, the development of control processes for non-standard automated equipment generally adopts the traditional model: First, mechanical engineers use 3D software to draw a 3D model of the equipment according to process requirements; then, electrical engineers write wiring diagrams and control programs; after the program is written, the points of each action need to be set on the equipment site in accordance with the process; then, repeated debugging, verification and modification are carried out on the equipment site before it can be put into production.

[0004] The aforementioned traditional development model suffers from the following deep-seated technical flaws: 1. High commissioning risks and costs: Program logic errors, improper parameter settings, or mechanical interference problems can only be exposed during the on-site commissioning phase after equipment assembly. This can easily lead to damage to expensive mechanical parts, sensor failure, or even safety accidents, resulting in direct economic losses and project delays.

[0005] 2. Poor program reusability and maintainability: Non-standard equipment control programs are highly dependent on specific hardware selection and engineers' personal programming habits, resulting in a large amount of "one-time code." When equipment needs to be modified, upgraded, or similar processes are transferred to new equipment, it is almost necessary to start programming from scratch, making it impossible to effectively accumulate and reuse design knowledge.

[0006] 3. Slow response to process changes: Process adjustments are common on the production floor. In the traditional model, any modification to process parameters (such as speed, position) or process sequence requires electrical engineers to re-understand the code, modify the program, and perform tedious on-site debugging again. The response cycle can take several days or even weeks, severely restricting production flexibility.

[0007] 4. The design and debugging processes are disconnected, making collaboration difficult: Mechanical, electrical, and process engineers use different tools (CAD, programming software, debugging terminals), and information transmission relies on drawings, documents, and verbal communication, which can easily lead to misunderstandings and cause the design intent to deviate during the implementation process. Repeated communication and confirmation are required during the debugging phase, resulting in low efficiency.

[0008] Therefore, there is an urgent need for a new solution that enables the rapid, safe, standardized development and intelligent operation and maintenance of electrical control systems for non-standard automated equipment. Summary of the Invention

[0009] This invention provides a standardized electrical full-element generation and operation and maintenance system and method based on model information, in order to overcome at least one of the above-mentioned technical problems existing in the prior art.

[0010] To achieve the above objectives, the embodiments of the present invention adopt the following technical solutions: In a first aspect, the present invention provides a standardized electrical full-element generation and operation and maintenance system based on model information, comprising: An automatic generation kernel is used to automatically generate standardized electrical full-element functional units required for equipment operation based on imported equipment information and equipment 3D models. The full-element functional units include at least the equipment operation process stack, digital twin, equipment wiring diagram, hardware configuration configuration file and human-machine interface. The equipment information includes at least the equipment behavior logic and hardware composition information. The operation control core is used to parse and execute the device operation process stack, realize the control of the operation of the corresponding physical device, and synchronously drive the digital twin to perform real-time status updates. The predictive execution core is used to parse and execute the device operation process stack, achieve control logic isomorphism with the operation control core, and realize positive simulation prediction and analysis of the future operation state of the physical device by simulating the execution speed ahead of the current operation speed of the physical device in the virtual environment. A 3D visualization platform is used to display the real-time operating status and future predicted status of the digital twin.

[0011] In one possible implementation of the first aspect, the automatically generated kernel includes: The parsing module is used to parse the device behavior logic and automatically assemble and generate the device operation process stack, wherein the device operation process stack is a standard control instruction sequence.

[0012] The hardware module is used to automatically generate a device wiring diagram and a hardware configuration file and parameters for driving the physical device based on the hardware composition information. The digital model module is used to construct a digital twin of the physical device based on the three-dimensional model of the device and the hardware composition information; The interaction module is used to automatically generate a human-machine interface that includes manual operation, automated operation status display, parameter setting, and data monitoring based on the device operation process stack.

[0013] In one possible implementation of the first aspect, the parsing module is specifically used for: The device behavior logic is parsed and its control elements are mapped to predefined atomic action metaphrases or business process metaphrases in the system's built-in device behavior metaphrase library. The mapped atomic action metaphrases or business process metaphrases are then instantiated with parameters to generate the device operation process stack.

[0014] In one possible implementation of the first aspect, the runtime control core includes: The data module is used to collect real-time operating data of the physical device and drive the digital twin to perform synchronous updates. The strategy module is used to analyze and make decisions based on the currently executed instructions in the device's operation process stack and the system's real-time status data to generate a first device execution strategy; and to monitor key status indicators and event streams, and automatically generate a second device execution strategy when preset thresholds, rules or patterns are met. The execution module is used to generate general execution instructions according to the first device execution strategy or the second device execution strategy, and to control the physical device through the device control interface.

[0015] In one possible implementation of the first aspect, the execution module is configured with a unified device control interface, which is used to convert the general execution instructions into low-level drive signals compatible with a specific physical control unit.

[0016] In one possible implementation of the first aspect, the prediction runtime kernel includes: The cloning module is used to copy the digital twin generated by the automatic generation core and the device operation process stack. When the simulation task is initialized, the cloning module synchronously captures the current execution state of the device operation process stack in the operation control core and loads the state data into the clone as the initial state of the simulation. The simulation operation module, as an isomorphic entity of the operation control core, controls the digital twin to perform the same functions as the operation control core in a virtual environment; The simulation execution module is used to respond to the control commands of the simulation operation module, simulate the operation of the actuator in the physical device, and adjust the simulation execution speed to several times the operating speed of the physical device; The predictive analysis module is used to record and analyze the equipment status in the simulation task, generate predictive analysis conclusions including the future state trajectory of the equipment, potential conflict points and performance indicators, generate early warning information based on the predictive analysis conclusions, and feed it back to the human-computer interaction interface.

[0017] In one possible implementation of the first aspect, the 3D visualization platform includes: A real-time rendering window is used to display the real-time operating status of the digital twin; A prediction rendering window is used to display the predicted future state of the digital twin; The storage module is used to store a first preset number of historical running state snapshot data and a second preset number of future predicted state snapshot data.

[0018] The timeline interaction module, which is associated with the real-time rendering window and the prediction rendering window, provides a draggable timeline control for replaying the historical running state of the digital twin at any time or previewing the future predicted state of the digital twin simulation.

[0019] Compared with the prior art, the present invention has at least the following beneficial effects: This invention provides a standardized electrical full-element generation and operation and maintenance system based on model information. Through an automatic generation kernel, it automatically transforms design intent into executable standardized electrical full elements; through an operation control kernel, it achieves precise control of physical equipment and real-time synchronization with digital twins; through a predictive operation kernel, it performs advanced simulation in virtual space, exposing risks and optimization points in advance; and finally, through a 3D visualization platform, it provides users with an intuitive interface for monitoring, debugging, and decision support. Through the synergy of these modules, the system forms a complete closed loop of "design-generation-control-prediction-visualization," greatly improving the development efficiency, debugging safety, and operational predictability of non-standard automated equipment.

[0020] Furthermore, the standardized electrical element generation and operation and maintenance system based on model information provided by this invention only requires the original equipment model and equipment information as input to automatically generate standardized electrical elements. In particular, the electrical element generation achieved by this invention is not a rough simulation at the level of logical illustration or appearance, but a 1:1 holographic restoration driven by model information. The generated virtual twin maintains a high degree of consistency with the real physical equipment in four dimensions: geometric structure, electrical parameters, behavioral logic, and physical response. This high-fidelity mapping relationship makes the subsequent simulation prediction and operation control highly reliable, providing a reliable foundation for virtual debugging and physical operation and maintenance to "control the real with the virtual and predict the real with the virtual".

[0021] Secondly, this invention provides a standardized electrical full-element generation and operation and maintenance method based on model information, applicable to a standardized electrical full-element generation and operation and maintenance system based on model information in any implementation of the first aspect, the method comprising: Input steps: Obtain the device's 3D model and device information, including device behavior logic and hardware composition information; Automatic generation steps: Based on the imported equipment information and the equipment 3D model, the automatic generation kernel automatically generates standardized electrical full-element functional units required for equipment operation. The full-element functional units include at least the equipment operation process stack, digital twin, equipment wiring diagram, hardware configuration configuration file and human-machine interface. Operation and synchronization steps: The operation control kernel is used to parse and execute the device operation process stack to control the operation of the corresponding physical device and synchronously drive the digital twin to perform real-time status updates; Predictive simulation steps: The predictive runtime kernel synchronously parses and executes the device's runtime stack, and in the virtual environment, the simulation execution speed is ahead of the current running speed of the physical device to achieve positive simulation prediction and analysis of the future running state of the physical device; Visualization steps: The real-time operating status and future predicted status of the digital twin are displayed through a 3D visualization platform.

[0022] In one possible implementation of the second aspect, the predictive simulation step specifically includes: State synchronization steps: Copy the digital twin generated by the automatic generation core and the device operation process stack, and synchronously capture the current execution state of the device operation process stack in the operation control core, and load the state data into the clone as the initial state of the simulation; Forward simulation steps: In the virtual environment, starting from the initial state, sequentially execute the subsequent control instruction sequence that has not yet been executed in the device operation process stack, and adjust the simulation execution speed to run at several times the speed of the physical device, thereby driving the digital twin to complete the forward business process simulation in the virtual environment; Analysis and early warning steps: During the forward business process simulation, the state changes of the digital twin caused by the execution of the subsequent control command sequence are monitored and recorded in real time. The state changes are matched and analyzed with a preset logical rule base to deduce specific events, production indicator trends or abnormal states that the physical device will encounter at future time points, and early warning information is generated accordingly.

[0023] Thirdly, the present invention provides an electronic device, comprising: at least one processor and at least one memory, wherein the memory stores computer-readable instructions; the computer-readable instructions are executed by one or more of the processors, causing the electronic device to implement the standardized electrical full-element generation and operation and maintenance method based on model information as in any implementation of the second aspect.

[0024] Fourthly, the present invention provides a storage medium having a computer-executable program stored thereon, the computer-executable program being used to cause a computer to execute a standardized electrical full-element generation and operation and maintenance method based on model information as in any implementation of the second aspect.

[0025] Understandably, the beneficial effects of the second aspect method, the third aspect electronic device, and the fourth aspect storage medium provided above can be referenced to the beneficial effects of the first aspect and any of its possible design embodiments, which will not be repeated here. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of 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.

[0027] Figure 1 A structural block diagram of a standardized electrical full-element generation and operation and maintenance system based on model information provided in an embodiment of the present invention; Figure 2 A flowchart illustrating a standardized electrical full-element generation and operation and maintenance method based on model information provided in this embodiment of the invention; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0028] The following description, in conjunction with the accompanying drawings, illustrates the standardized electrical full-element generation and operation and maintenance system based on model information provided in the embodiments of the present invention.

[0029] It should be noted that, in the embodiments of the present invention: 1. The term "standardization" primarily emphasizes the automatic generation of all electrical functional units (including equipment operation stacks, digital twins, etc.) that adhere to predefined, unified generation rules and data specifications within the system. These functional units are characterized by complete structure, unified format, and consistent interfaces, allowing engineers to directly use them for control, simulation, and monitoring without secondary adaptation or manual integration. This significantly reduces the integration complexity between different functional units and lowers the learning and operational barriers for personnel.

[0030] 2. The “Electrical All Elements” cover the entire equipment lifecycle control process, from design (wiring diagrams, configuration), programming (process stack), simulation (digital twin) to operation monitoring (human-machine interface), forming a complete solution.

[0031] like Figure 1 As shown, this embodiment of the invention provides a standardized electrical full-element generation and operation and maintenance system based on model information, including: The automatic generation core 110 is used to automatically generate standardized electrical full-element functional units required for equipment operation based on the imported equipment information and equipment 3D model. The full-element functional units include at least the equipment operation process stack, digital twin, equipment wiring diagram, hardware configuration configuration file and human-machine interface. The equipment information includes at least the equipment behavior logic and hardware composition information. The operation control core 120 is used to parse and execute the device operation process stack, realize the control of the operation of the corresponding physical device, and synchronously drive the digital twin to perform real-time status updates. The predictive execution core 130 is used to parse and execute the device operation process stack, achieve control logic isomorphism with the operation control core, and realize positive simulation prediction and analysis of the future operation state of the physical device by simulating the execution speed ahead of the current operation speed of the physical device in the virtual environment. A 3D visualization platform 140 is used to display the real-time operating status and future predicted status of the digital twin.

[0032] The operation flow of the standardized electrical full-element generation and operation and maintenance system based on model information provided in this embodiment of the invention is as follows: I. Model Import: Users can import 3D models and equipment information of non-standard automated equipment through the system's front-end interface.

[0033] The 3D model of the equipment in the embodiments of the present invention may be, but is not limited to, a 3D model of the equipment and its components drawn using drawing software such as SolidWorks, Inventor, and AutoCAD. The equipment information in the embodiments of the present invention may include, but is not limited to, kinematic pair definitions, point mapping configurations, process information, operation configurations, section tasks, equipment behavior logic, and hardware composition information, etc., which are not limited here.

[0034] In one feasible implementation, the device information in this embodiment of the invention can be, but is not limited to, stored in the device 3D model. By writing the device information into the device 3D model through a plugin, the device 3D model and device information are integrated and connected, which can save verification time and improve development efficiency.

[0035] Furthermore, the device information in the embodiments of the present invention may be stored in the form of a single general file. The system can obtain the model of the corresponding device and its components by obtaining the names of the device components in the file and searching in the directory where the file is located according to the specified name.

[0036] II. Automatic kernel generation and execution: The automatic generation process of the core 110 starts automatically. Based on the imported equipment information and the equipment 3D model, it automatically generates standardized electrical full-element functional units required for equipment operation. The full-element functional units include at least the equipment operation process stack, digital twin, equipment wiring diagram, hardware configuration configuration file and human-machine interface.

[0037] In specific implementation, the automatically generated kernel 110 in this embodiment of the invention may include, but is not limited to: The parsing module is used to parse the device behavior logic and automatically assemble and generate the device operation process stack, wherein the device operation process stack is a standard control instruction sequence.

[0038] The hardware module is used to automatically generate a device wiring diagram and a hardware configuration file and parameters for driving the physical device based on the hardware composition information. The digital model module is used to construct a digital twin of the physical device based on the three-dimensional model of the device and the hardware composition information; The interaction module is used to automatically generate a human-machine interface that includes manual operation, automated operation status display, parameter setting, and data monitoring based on the device operation process stack.

[0039] In one feasible implementation, the parsing module in this embodiment of the invention is specifically used for: The device behavior logic is parsed, and its control elements are mapped to predefined atomic action metaphrases or business process metaphrases in the system's built-in device behavior metaphrase library. The mapped atomic action metaphrases or business process metaphrases are then instantiated with parameters to generate the device operation process stack.

[0040] To more clearly illustrate how the parsing module transforms device behavior logic into executable instructions, a specific example will be used as an example below.

[0041] Suppose the input device behavior logic is described as: "The loading unit goes to coordinate point (X100, Y200, Z-50) to perform a material picking operation." After receiving the above logical description, the parsing module will perform the following steps: Step 1: Parse to obtain business process metawords The parsing module identifies the core intent of "performing a material picking operation" and maps it to a pre-defined PickAndPlace business process metaphrase. This metaphrase itself is associated with a series of ordered atomic action metaphrs and necessary safety and decision logic.

[0042] Step 2: Business Process Metaphrase Deconstruction and Instantiation The system instantiates the PickAndPlace meta-phrase into a specific sequence of control instructions based on the specific equipment parameters (in this example, the loading unit and its vacuum suction cup) and input coordinates. This process automatically inserts standardized safety checks and exception handling logic, generating a preliminary, parameterized process stack skeleton. An example of the logical steps included is shown below: (1) Safety check: Check whether the Z-axis of the loading sub is at a safe height (to avoid collisions during horizontal movement). If it is safe, jump to (3); if it is not safe, continue to execute (2).

[0043] (2) The Z-axis of the loading unit rises to a safe height.

[0044] (3) Coordinated movement of the feeding pair along the X and Y axes: Move to above the target point (X100, Y200).

[0045] (4) The Z-axis of the feeding sub moves down: move to the material picking height (Z-50).

[0046] (5) Perform material handling action: Open the vacuum valve of the feeding unit (e.g., valve number "VAC_1").

[0047] (6) Material verification: After the delay, check the status of the vacuum sensor (such as sensor "`PS_1") of the feeding unit to confirm that the material has been adsorbed.

[0048] (6) Lifting the Z-axis of the loading unit: Lifting the material to a safe transport height.

[0049] (7) [Branch Logic] If the material verification fails, the exception handling sub-process will be executed.

[0050] Step 3: Parameterization of Atomic Action Metaphrs The parsing module further maps each of the above steps to lower-level atomic action metawords, performs precise parameter assignment and hardware binding, and ultimately generates a standardized sequence of instructions that can be directly interpreted by the execution module. For example: Step (3), “XY axis cooperative motion”, is deconstructed into two independent Axis_MoveTo atomic action meta-words and bound to specific hardware and parameters: Axis_MoveTo (Axis_ID: "X_Axis" Position: 100.0mm, Velocity: 500mm / s, Acceleration: 1000mm / s²) Axis_MoveTo (Axis_ID: "Y_Axis", Position: 200.0mm, Velocity: 500mm / s, Acceleration: 1000mm / s²) Step (5), “Open the vacuum valve”, is mapped to the IO_Set atomic action metaphrase: IO_Set(IO_ID: "VAC_1", State: ON, Duration: 0ms) Step (6), “material verification”, is mapped to the Sensor_WaitFor atomic action metaphrase: Sensor_WaitFor(Sensor_ID: "PS_1", Expected_State: ON, Timeout: 1000ms) Step 4: Form the final executable device operation process stack.

[0051] After the above mapping, parameterization, instruction conversion, and security logic injection, a rigorously structured device operation process stack is finally generated, containing conditional judgments and branches, and capable of directly driving hardware. This device operation process stack is a collection of control instructions, status checks, and logical judgments, delivered to the operation control core for controlling the physical device.

[0052] In specific implementation, the hardware module in this embodiment of the invention includes: The hardware composition information parsing unit is used to parse the electrical component information of the equipment based on the part names of each component and the assembly connection relationship between the components in the hardware composition information.

[0053] The configuration unit, based on the electrical component information of the device, queries a preset electrical resource database containing information on various electrical components to obtain the channel occupancy data of the required electrical components. Based on a predefined electrical component sorting rule library, it assigns a unique physical I / O address to each component, configures motion control parameters or communication parameters, and obtains general hardware configuration information. Finally, according to the rules of each brand, it generates a hardware configuration file that can be directly imported into the target programmable controller programming environment.

[0054] The drawing generation unit, based on the electrical component information of the equipment, queries a preset electrical component library containing standardized symbol graphics and electrical parameters to obtain the wire number rules, terminal arrangement rules, and drawing layout templates for the electrical component; and, in conjunction with the hardware configuration information, automatically instantiates electrical symbols, performs logical connections, and lays out the drawings through the interface of the electrical drawing software to generate equipment electrical schematic diagrams and / or wiring diagrams that conform to industry standards; the electrical drawing software includes, but is not limited to, Eplan and AutoCAD.

[0055] The parameter configuration generation unit, based on the electrical component information of the device, integrates hardware from different brands through a configurable driver adaptation layer, unifying them to a common execution standard, thereby decoupling the control logic from the execution hardware and providing support for device operation.

[0056] In a feasible implementation, the parameter configuration generation unit in the embodiment of the present invention decouples the control logic from the execution hardware in the following manner: according to the device electrical component information, query the driver library to determine the hardware mechanism model; and configure the parameters of the mechanism model according to the device electrical component information, so as to unify the mechanism to the general execution standard. The kinematic parameter models of different types of actuators (such as servo motor + ball screw, stepper motor + synchronous belt, cylinder, etc.) are stored in the driver library.

[0057] To more clearly illustrate how the hardware module automatically generates the device wiring diagram and the hardware configuration file for driving the physical device according to the hardware composition information, the following will be elaborated with a specific example.

[0058] Suppose the input device hardware composition information segment is: “<Feeding sub - type = "XY axis"> <Axis name = "X" type = "linear" direction = "X"> <Motor model = "MS1H1 - 40B30CB" / > <Screw rod model = "SFU01605 - 4" / > <Reducer model = "PS60 - 010" / > <Positive limit sensor model = "SME - 8M - 24V"> <Negative limit sensor model = "SME - 8M - 24V" / > < / Axis> <Axis name = "Y" type = "linear" direction = "Y"> <Motor model = "MS1H1 - 40B30CB" / > <Screw rod model = "SFU01610 - 4" / > <Reducer model = "PS60 - 010" / > <Positive limit sensor model = "SME - 8M - 24V" / > <Negative limit sensor model = "SME - 8M - 24V" / > < / Axis> < / Feeding sub>” 1. The hardware composition information parsing unit parses to obtain the electrical component information, including: (1) Query the component library according to the model name to obtain the key information: (2) Further count to obtain the control requirements: Electrical components: 2 400W servo motors Digital input (DI) requirements: X positive limit, X negative limit, Y positive limit, Y negative limit (4 sensors in total, NPN type) Digital output (DO) requirement: 0 Motion control requirements: 2-axis servo control (EtherCAT bus) 2. The configuration unit executes based on the electrical component information: (1) Query the electrical resource database: The SV660N servo driver is found to be compatible with a target 400W motor.

[0059] (2) Allocate physical I / O addresses: The positive limit of X -> %I0.0; The negative limit of X -> %I0.1; The positive limit of Y is %I0.2; The negative limit of Y is -> %I0.3.

[0060] (3) Configure motion control parameters: X-axis (axis number 1); Y-axis (axis number 2).

[0061] (4) Generate general hardware configuration information: Two SV660N units are connected via EtherCAT bus (1: X-axis, 2: Y-axis).

[0062] IO module (DI: 4 points, 1: positive limit of X, 2: negative limit of X, 3: positive limit of Y, 4: negative limit of Y; DO: 0 points).

[0063] 3. Execution of the drawing generation unit: (1) Search the electrical component database based on electrical component information: Retrieve the symbol macros for the SV660N servo drive and the limit switch (SME-8M-24V) from the EPLAN macro library.

[0064] (2) Automatic drawing based on hardware configuration information: Power supply diagram: Power supply for the main circuit and control circuit of the servo driver.

[0065] The PLC wiring diagram, instantiated from the (SME-8M-24V) symbol macro, yields: X+ limit → PLC I0.0; X-limit → PLC I0.1; Y+ limit → PLC I0.2; Y-limit → PLC I0.3.

[0066] The servo control diagram is obtained by instantiating the symbol macro of the SV660N servo driver, which shows the control connection between the PLC and the servo driver.

[0067] 4. Parameter configuration generation unit execution: (1) Query the driver library and match the hardware mechanism model. The parameter configuration generation unit queries the drive library one by one based on the electrical component information to determine the hardware mechanism model type for each motion axis: (2) Configure the kinematic parameter model (rotation → displacement transformation) For the "servo motor + reducer + ball screw" model, configure the key parameters to convert rotary motion into linear displacement: Step 1: Determine the relationship between the motor rotation and the lead screw rotation (reduction stage) Gearbox model: PS60-010, reduction ratio i=10:1; Physical meaning: For every 10 revolutions of the motor, the output shaft (i.e., the lead screw) of the reducer rotates 1 revolution.

[0068] Step 2: Determine the relationship between the lead screw rotation and linear displacement (conversion step) X-axis lead screw: SFU01605-4, lead Ph=5mm; Physical meaning: For every revolution of the lead screw, the nut (load) moves linearly by 5mm; Y-axis lead screw: SFU01610-4, lead Ph=10mm; Physical meaning: For every revolution of the lead screw, the nut moves linearly by 10mm.

[0069] Step 3: Establish a complete kinematic transformation model For the X-axis: Motor rotates N revolutions → Lead screw rotates N / 10 revolutions → Linear displacement = (N / 10) × 5mm; That is: linear displacement = number of motor rotations × (lead ÷ reduction ratio); X-axis displacement coefficient K X =5mm÷10=0.5mm / motor revolution; For the Y-axis: Motor rotates N revolutions → Lead screw rotates N / 10 revolutions → Linear displacement = (N / 10) × 10mm; Y-axis displacement coefficient K y =10mm÷10=1mm / motor revolution.

[0070] Step 4: Establish pulse-to-displacement mapping based on encoder resolution. Motor encoder resolution: 2500 lines / revolution, 10000 pulses / revolution after quadruple frequency multiplication; X-axis: 10,000 pulses → 1 motor revolution → 0.5mm linear displacement; Pulse equivalent = 0.5mm ÷ 10000 = 0.00005mm / pulse; 1mm displacement = 20000 pulses; Y-axis: 10000 pulses → 1 motor revolution → 1mm linear displacement; Pulse equivalent = 1mm ÷ 10000 = 0.0001mm / pulse; 1mm displacement = 10000 pulses.

[0071] The above automatically generates the electrical drawings, configuration files, and configuration parameters required to control the "feeding unit".

[0072] In one feasible implementation, the digital model module in this embodiment of the invention is used to parse the three-dimensional model of the device and device information, and to reconstruct the virtual model of the device in different target environments, so as to meet different application requirements such as visual monitoring, motion simulation, and path planning. 1. Analyze the relationship between equipment structure and motion. The digital modeling module first divides the imported 3D equipment model into structural parts. Based on the kinematic pair definitions and assembly connections in the equipment information, it identifies the 3D models of moving parts (such as motor shafts, cylinder pistons, guide rails, sliders, doors, conveyor belts, etc.) and fixed parts (such as frames, bases, and protective covers). Simultaneously, it analyzes the motion type (rotation, linear) and range of motion of each moving part.

[0073] 2. Model reconstruction for 3D visualization platforms To address the needs of visual monitoring, the digital modeling module maps the parsed equipment structure, motion relationships, and 3D models of components to a 3D visualization platform (such as Unity, Unreal Engine, Three.js, etc.): Model import and rendering: Import the 3D model of the parts into the target platform to restore the visual features of the equipment, such as geometry, material, and color.

[0074] Motion Component Binding: Binds the identified motion components to the corresponding transformation components in the visualization platform, enabling the motion components to move in the virtual environment.

[0075] Data source association: Establish association between each moving part and the corresponding real-time data source (such as axis position and cylinder position signal in PLC) so that the motion of the virtual model can map the real-time state of the physical equipment.

[0076] Results: A visualized digital twin of the device was generated that can run in real time in the monitoring interface for use in scenarios such as device status monitoring, operation demonstration, and remote inspection.

[0077] 3. Model Reconstruction for Algorithm Platforms To address the algorithm requirements for motion simulation, path planning, and collision detection, the numerical modeling module maps the analyzed equipment structure, motion relationships, and 3D models of components to the algorithm simulation platform (such as MATLAB / Simulink, CoppeliaSim, ROS / Gazebo, and a self-made motion planning engine). Kinematic model construction: Based on the definition of kinematic pairs and mechanism types (such as rotary joints, prismatic joints, and linkages), construct the kinematic model of the device (including forward kinematics and inverse kinematics) in the algorithm platform.

[0078] Workspace definition: Based on the range of motion of each axis, define the reachable workspace and obstacle area of ​​each axis.

[0079] Collision model simplification: To improve algorithm efficiency, complex geometric models are simplified into bounding box, convex hull, or mesh models required for collision detection.

[0080] Results: A virtual simulation model that can run in an algorithm environment is generated for scenarios such as offline programming, path planning verification, beat analysis, and collision detection.

[0081] 4. Model Association and Synchronization The digital modeling module ensures that both models are generated based on the same set of equipment description information, maintaining consistency in kinematic definitions, range of motion, and joint relationships. When equipment information changes (such as replacing different parts), the digital modeling module can incrementally update the relevant models without rebuilding them.

[0082] In one feasible implementation, the interaction module in this embodiment of the invention automatically generates a human-computer interaction interface based on the device operation process stack. The human-computer interaction interface may include, but is not limited to, a manual interface, an automated interface, a debugging interface, and a monitoring interface.

[0083] Manual interface The manual interface is used by operators to manually control the various execution units of the equipment. Based on the equipment's operational flow stack, the interaction module automatically identifies the types of execution elements (motion shafts, cylinders, vacuum suction cups, material trays, etc.) included in each section and dynamically generates corresponding manual operation components for each element type. Motion pair operation component: Includes controls for jog forward / reverse rotation, absolute position movement input, speed setting, homing, and enable / de-enable. The component simultaneously displays real-time information such as current position, position status, and limit status.

[0084] Cylinder operating components: include an extend / retract button and display the current position status.

[0085] Vacuum operation component: includes an on / off button and displays the vacuum establishment status.

[0086] Material tray operation components: include customized operation buttons such as push, collect, and reset.

[0087] The aforementioned operating components are automatically arranged into the interface area of ​​their respective work sections according to the division of work sections. The arrangement order is based on the topological connection relationship in the hardware composition information (such as arrangement along the material flow direction), so that the interface layout corresponds to the physical structure of the equipment and improves the intuitiveness of operation.

[0088] Automation Interface The automation interface displays status information of the equipment during automatic operation. Based on process information and work section task configuration, the interaction module automatically generates the following display areas on the interface: Section operation status: Displays the current start / stop status, mode selection, alarm words and other control word information of each section.

[0089] Current execution process: Highlight the process steps that the device is currently executing (e.g., "material picking → material handling → material placement").

[0090] Current status: Display the key status of each section in text or graphics (such as "Running", "Waiting for materials", "Fault").

[0091] Current process information: Displays the target output, quantity completed, cycle time, and estimated remaining time for the current batch.

[0092] Debugging interface The debugging interface is used by engineers to debug equipment, adjust parameters, and troubleshoot faults. The interaction module dynamically generates the following debugging functions based on the point mapping configuration and behavioral logic: Position correction: Supports online adjustment of parameters such as target position offset of each motion axis, cylinder action delay, and sensor trigger threshold, and can save the correction values ​​to the configuration file.

[0093] External task testing: Supports manually triggering external tasks (such as MES issuing work orders, vision system sending photo results, host computer issuing recipe switching) to simulate external signals to test the equipment's response logic.

[0094] Signal forcing: Supports forcibly setting / resetting PLC input / output points for troubleshooting wiring and logic problems (with safety lock protection).

[0095] Single-step execution: Supports breaking down automated processes into single steps, executing each step sequentially to verify the logical correctness of each step.

[0096] Monitoring interface The monitoring interface is used to monitor the overall operating status of the device in real time, and may include, but is not limited to: 3D visualization view: A visualized digital twin generated by embedding the digital model module, which maps device actions in real time.

[0097] Key Parameter Dashboard: Displays analog parameters such as shaft load, temperature, air pressure, and speed in the form of gauges, progress bars, and trend graphs.

[0098] Alarm information list: Displays current alarms and historical alarms, and supports filtering by time and type.

[0099] The aforementioned automatic kernel generation mechanism has revolutionized development efficiency. Engineers no longer need to manually draw blueprints, write code, configure hardware, or design interfaces. They only need to provide standardized descriptions, and the system can generate a complete set of usable electrical elements within hours. This reduces the development cycle from weeks or even months in the traditional model by several orders of magnitude, while ensuring the structured and standardized nature of the output, laying the foundation for subsequent reuse.

[0100] III. The operation control core performs operation control: The operation control core 120 parses and executes the device operation process stack to realize the operation control of the physical device and synchronously drives the digital twin to perform real-time status updates.

[0101] In specific implementation, the operation control core 120 in this embodiment of the invention may include, but is not limited to: The data module is used to collect real-time operating data of the physical device and drive the digital twin to perform synchronous updates. The strategy module is used to analyze and make decisions based on the currently executed instructions in the device's operation process stack and the system's real-time status data to generate a first device execution strategy; and to monitor key status indicators and event streams, and automatically generate a second device execution strategy when preset thresholds, rules or patterns are met. The execution module is used to generate general execution instructions according to the first device execution strategy or the second device execution strategy, and to control the physical device through the device control interface.

[0102] In one feasible implementation, the data module in this embodiment of the invention may, but is not limited to, read the position data of each motion mechanism every 1ms (one control cycle), and then adjust the coordinate position transformation of the corresponding actuator in the digital twin according to the position data, including translation, rotation and other motion forms, to achieve synchronization between the digital twin and the physical device.

[0103] In one feasible implementation, the strategy module in this embodiment of the invention possesses an "adaptive closed-loop control" function, which is manifested in its ability to proactively intervene in the control process based on real-time data and preset rules. A specific example is as follows: 1. Real-time collision avoidance protection (for manual operation): When an operator performs manual jogging operations through the human-machine interface, the strategy module not only generates a device execution strategy but also monitors the position feedback of the digital twin in real time. It continuously calculates the shortest distance between the moving part and surrounding fixed parts in the twin space. Once it detects that this distance is less than a preset safety threshold (e.g., 10mm), it immediately and automatically generates a high-priority "Emergency Stop (E-Stop)" or "Pause" execution strategy and transmits it to the execution module, thereby forcibly interrupting the movement before a physical collision occurs, forming a real-time safety closed loop.

[0104] 2. Automatic path planning and obstacle avoidance (for automated operation): When the equipment operation stack issues a target point instruction (such as "move the loading unit to position A"), the strategy module does not simply drive the loading unit to move in a straight line. It first queries the current virtual environment state represented by the digital twin to identify potential obstacles on the movement path (such as fixed components or interference zones from other mechanisms). Then, based on a built-in path planning algorithm (such as the RRT algorithm), the strategy module calculates a collision-free optimal or suboptimal path in real time in the virtual space. This path is then decomposed into a series of safe intermediate points, dynamically replacing or correcting the original simple straight-line movement instruction before being transmitted to the execution module. This forms a closed-loop motion planning system based on environment awareness, fundamentally avoiding the risk of collisions during automatic operation.

[0105] The strategy module in this embodiment of the invention achieves a shift from passively executing instructions to actively monitoring, predicting, and intervening through the above mechanism, effectively improving the ability to control and safely operate equipment.

[0106] In the specific implementation process, the execution module in the embodiment of the present invention is configured with a unified device control interface. The unified device control interface is used to convert the general instructions issued by the upper layer into the underlying drive signals compatible with the specific physical control unit.

[0107] In one feasible implementation, embodiments of the present invention define a unified control API based on device function types. The execution module, based on the kinematic parameters provided by the parameter configuration generation unit, establishes a mapping relationship from general instructions to physical actions for each logical control object. 1. Control logic standardization: The control logic of each execution element is abstracted into a unified standardized interface, so that upper-layer applications do not need to care about the differences in the form and type of the underlying hardware.

[0108] 2. General command conversion: When a general command (such as "move to coordinate 100mm") is received from the upper layer, the execution module converts the target position into the number of revolutions or pulses required by the motor based on the kinematic parameters (such as the number of pulses per millimeter, displacement coefficient, etc.) provided by the parameter configuration generation unit.

[0109] 3. Encapsulation of underlying logic: The execution module encapsulates underlying logic such as servo enable, alarm reset, homing process, and limit switch handling. Upper-layer applications only need to call standardized interfaces (such as EnableAxis(), Home(), MoveTo()) and do not need to pay attention to the implementation details of these underlying operations to achieve precise control of physical devices.

[0110] Through the above mechanism, the execution module achieves decoupling between control logic and hardware: when the hardware configuration changes (such as replacing a motor or lead screw of a different brand), only the kinematic parameters provided by the parameter configuration generation unit need to be updated, and the standardized control logic of the execution module and the upper-level application code do not need to be modified. This embodiment of the invention separates abstract motion control commands (such as how many millimeters to move) from specific mechanical parameters (transmission ratio, lead) and electrical parameters (encoder resolution), thereby achieving decoupling between control logic and physical execution mechanisms, significantly improving software reusability and flexibility during the modification or upgrading of the mechanical parts of the equipment.

[0111] IV. Predictive runtime kernel simulation: The predictive runtime core parses and executes the device's runtime stack, achieving isomorphism with the control logic of the runtime control core. In the virtual environment, it achieves positive simulation prediction and analysis of the future operating state of the physical device by simulating the execution speed ahead of the current operating speed of the physical device.

[0112] In specific implementation, the prediction kernel 130 in this embodiment of the invention may include, but is not limited to: The cloning module is used to copy the digital twin generated by the automatic generation core and the device operation process stack. When the simulation task is initialized, the cloning module synchronously captures the current execution state of the device operation process stack in the operation control core and loads the state data into the clone as the initial state of the simulation. The simulation operation module, as an isomorphic entity of the operation control core, controls the digital twin to perform the same functions as the operation control core in a virtual environment; The simulation execution module is used to respond to the control commands of the simulation operation module, simulate the operation of the actuator in the physical device, and adjust the simulation execution speed to several times the operating speed of the physical device; The predictive analysis module is used to record and analyze the equipment status in the simulation task, generate predictive analysis conclusions including the future state trajectory of the equipment, potential conflict points and performance indicators, generate early warning information based on the predictive analysis conclusions, and feed it back to the human-computer interaction interface.

[0113] In one feasible implementation, the cloning module in this embodiment of the invention can, but is not limited to, completely copy the real-time memory image (including program counter, stack, and current values ​​of all variables) of the device execution process stack in the running control core 120 through underlying data synchronization mechanisms such as memory mapping or inter-process communication. This is used as the initial state of the simulation of the predicting running core, ensuring accurate synchronization between the simulation environment and the current running state of the physical device, and laying the foundation for high-fidelity positive prediction.

[0114] In one feasible implementation, the simulation execution module is used to respond to the control commands of the simulation operation module and model the physical behavior of different execution elements to simulate the response characteristics of physical devices in a real environment. The specific simulation behavior is as follows: 1. Simulation behavior of servo motors / motion axes When the simulation execution module receives an axis motion command (such as "move to 100mm"): Motion process simulation: Based on the kinematic parameters of the axis (maximum speed, acceleration, number of pulses per millimeter), the simulation simulates the continuous motion of the axis from the current position to the target position and updates the virtual coordinates in real time.

[0115] Position signal simulation: When the virtual coordinates reach the target position (or enter the allowable error range), the simulation execution module automatically generates a position signal (such as an axis movement completion flag) for the simulation operation module to query.

[0116] Limit trigger simulation: When the virtual coordinate touches the set positive / negative limit position, the limit switch signal is automatically triggered and the movement stops.

[0117] 2. Simulation behavior of cylinders When the simulation execution module receives the cylinder extension command: Action delay simulation: Start timing based on the cylinder action time set in the parameter configuration (e.g., 0.3 seconds).

[0118] Intermediate state simulation: During the delay period, the cylinder state can be set to "extending".

[0119] Positioning signal simulation: After the delay ends, the cylinder status is automatically updated to "extended in position" and an extended in position signal is generated (such as when the reed switch is turned on).

[0120] Retraction Simulation: Upon receiving a retraction command, the retraction delay and arrival signal are simulated similarly.

[0121] 3. Simulation behavior of other actuators Vacuum suction cup: When it receives an open command, it generates a "vacuum establishment complete" signal after a certain delay.

[0122] Rotary motor: simulates rotational speed and direction of rotation, and generates a positioning signal after reaching a set number of revolutions.

[0123] Indicator lights: respond instantly to on / off commands without delay.

[0124] 4. Accelerate simulation The simulation execution module can accelerate all the above motion processes and delay processes by a set multiple (such as 10 times or 50 times), and quickly verify the correctness of the control program without changing the logical timing relationship.

[0125] In one feasible implementation, the predictive analysis module in this embodiment of the invention can predict the stability of equipment operation during operation. Through forward simulation results, it can accurately calculate the theoretical production capacity (e.g., the number of products that can be completed in 24 hours based on the current cycle time), bottleneck workstations (e.g., the cumulative time of each workstation in the simulation when it is in the "waiting" or "blocked" state, with the longest time being the bottleneck), potential conflict warnings (e.g., when the simulation finds that the planning path fails and a collision warning is given when the feeding unit moves to position A at 2:15 pm, the system will mark this time point and the conflict object), etc., without limitation.

[0126] This invention, through the setting of a predictive runtime kernel for advanced simulation and prediction, allows serious risks such as mechanical interference and logic deadlock to be exposed and resolved in advance in a virtual environment. This transforms on-site debugging from "high-risk trial and error" to "low-risk verification," significantly reducing the probability of equipment damage and personal safety risks. Simultaneously, simulation-based predictive analysis provides precise data-driven decision-making support for production scheduling optimization and preventative maintenance.

[0127] V. 3D Visualization: The 3D visualization platform 140 displays the real-time operating status and future predicted status of the digital twin.

[0128] In specific implementation, the 3D visualization platform 140 can be built using existing 3D rendering engines or development frameworks (such as Unity, Unreal Engine, Three.js, etc.). As a specific implementation method, the platform may include: A real-time rendering window is used to display the real-time operating status of the digital twin; A prediction rendering window is used to display the predicted future state of the digital twin; The storage module is used to store a first preset number of historical running state snapshot data and a second preset number of future predicted state snapshot data.

[0129] The timeline interaction module, which is associated with the real-time rendering window and the prediction rendering window, provides a draggable timeline control for replaying the historical running state of the digital twin at any time or previewing the future predicted state of the digital twin simulation.

[0130] In one feasible implementation, the real-time rendering window in this embodiment of the invention communicates with the runtime control core 120 via protocols such as OPC UA and TCP; the prediction rendering window communicates with the prediction runtime core 130 via protocols such as OPC UA and TCP.

[0131] In one feasible implementation, the storage module in this embodiment of the invention is used to periodically or event-triggeredly record key states during device operation to support historical backtracking, fault analysis, and simulation comparison.

[0132] Specifically, the storage module can store at least 100,000 historical running status snapshots and 500,000 simulation prediction status snapshots.

[0133] The status snapshot data includes the following information: Timing information: timestamp of the snapshot record (accurate to milliseconds), and the current cycle count of the device.

[0134] Actuator status: current position coordinates, movement speed, and operating status (enabled, moving, in position, alarm, etc.) of each axis / motor; current position (extended / retracted) and position signal status of each cylinder; vacuum establishment status of the vacuum suction cup, etc.

[0135] Input / output signal status: Real-time on / off status of each digital input point of the PLC (such as limit sensors, buttons, photoelectric switches); Real-time status of digital output points (such as solenoid valves, indicator lights, relays).

[0136] Controller internal variables: currently executing program segment, control mode (manual / automatic), alarm code, fault information, cumulative running time, etc.

[0137] Process parameters: Target position, speed setpoint, acceleration, pressure setpoint, and other process-related parameters for the current batch.

[0138] Simulation Identifier: (For simulation prediction snapshots) Identifies that this snapshot is simulation data and records information such as simulation speedup and prediction time point.

[0139] Through the aforementioned multi-dimensional state snapshot records, the storage module supports the following application scenarios: Historical review: Replay the equipment operation process and analyze whether the timing coordination of each actuator was normal before the failure occurred.

[0140] Performance analysis: Statistics on the actual performance of each axis, such as movement speed, positioning accuracy, and cylinder action time.

[0141] Simulation Comparison: The simulation prediction snapshot is compared with the actual running snapshot to verify the accuracy of the simulation model.

[0142] In one feasible implementation, the timeline control in this embodiment of the invention can span the bottom of the platform, with the actual time or simulation time marked on it. For the real-time rendering window, the left side of the timeline represents the "past," and the user drags the slider to an earlier moment, at which point the system reads the device status data and "replays" that historical instant in the 3D scene. For the prediction window, the right side of the timeline represents the "future." The user can drag the slider to preview the predicted device status at any future moment in the simulation.

[0143] The 3D visualization platform 140 of this invention integrates intuitive visualization and interaction methods for real-time, historical, and future states, greatly improving the transparency of equipment management and the efficiency of operation training. Managers can 'see through' the equipment operation and accurately assess production capacity; maintenance personnel can 'rehearse' malfunctions and prepare contingency plans in advance; operators can conduct risk-free training in a virtual environment, significantly reducing learning costs and the risk of misoperation.

[0144] The standardized electrical full-element generation and operation and maintenance system based on model information provided in the embodiments of the present invention has the following advantages or beneficial effects throughout the entire life cycle of non-standard automated equipment: During the program development phase, the business logic is automatically converted into standardized full-element functional units by automatically generating kernels, which completely eliminates a large amount of repetitive work in manual programming, drawing, configuration and interface design, and shortens the development time of the electrical control part from weeks to hours.

[0145] During the debugging and verification phase, the advanced simulation capabilities provided by the predictive runtime kernel enable most mechanical interference risks, logical errors, and cycle time bottlenecks to be exposed and resolved in advance in the virtual environment, greatly reducing the number of on-site trials and the risk of equipment damage, thereby significantly compressing the on-site debugging cycle.

[0146] During the on-site modification and optimization phase, thanks to the decoupling of control logic and hardware execution, as well as the intuitiveness of digital twins, program adjustments after process changes or equipment upgrades do not need to be redeveloped from the bottom up. They can be rapidly iterated and verified based on existing elements, reducing response time from several days to several hours.

[0147] During the customer training and operation phase, the integrated 3D visualization interface and digital twin generated in real time and synchronized with the equipment provide operators and maintenance personnel with an intuitive and safe virtual training environment and operation guidance, significantly reducing training costs and the risk of misoperation.

[0148] Based on the aforementioned standardized electrical full-element generation and operation and maintenance system based on model information, this invention also provides a standardized electrical full-element generation and operation and maintenance method based on model information, such as... Figure 2 As shown, the method may include, but is not limited to: Input step S1: Obtain the device's 3D model and device information, including device behavior logic and hardware composition information; Automatic generation step S2: Using the automatic generation kernel, based on the imported equipment information and the equipment 3D model, automatically generate standardized electrical full-element functional units required for equipment operation. The full-element functional units include at least the equipment operation process stack, digital twin, equipment wiring diagram, hardware configuration configuration file and human-machine interface. Operation and synchronization step S3: Use the operation control kernel to parse and execute the device operation process stack to control the operation of the corresponding physical device and synchronously drive the digital twin to perform real-time status updates; Predictive simulation step S4: Use the predictive running kernel to synchronously parse and execute the device operation process stack, and in the virtual environment, achieve positive simulation prediction and analysis of the future operating state of the physical device by advancing the simulation execution speed ahead of the current operating speed of the physical device; Visualization step S5: Display the real-time operating status and future predicted status of the digital twin through a 3D visualization platform.

[0149] In one feasible implementation, the prediction simulation step S3 in this embodiment of the invention specifically includes: State synchronization steps: Copy the digital twin generated by the automatic generation core and the device operation process stack, and synchronously capture the current execution state of the device operation process stack in the operation control core, and load the state data into the clone as the initial state of the simulation; Forward simulation steps: In the virtual environment, starting from the initial state, sequentially execute the subsequent control instruction sequence that has not yet been executed in the device operation process stack, and adjust the simulation execution speed to run at several times the speed of the physical device, thereby driving the digital twin to complete the forward business process simulation in the virtual environment; Analysis and early warning steps: During the forward business process simulation, the state changes of the digital twin caused by the execution of the subsequent control command sequence are monitored and recorded in real time. The state changes are matched and analyzed with a preset logical rule base (including physical constraint rules, process indicator rules, time sequence rules, etc.) to deduce specific events, production indicator trends or abnormal states that the physical equipment will encounter at future time points, and early warning information is generated accordingly.

[0150] The standardized electrical full-element generation and operation and maintenance method based on model information provided in the embodiments of the present invention can be executed by any electronic device 20 with data processing capabilities, such as a general-purpose computer, personal computer, laptop computer, switch or tablet computer, etc. The specific implementation method of the electronic device 20 is not limited here.

[0151] Figure 3 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present invention is shown. The electronic device 20 includes a processor 210, a memory 220, and a communication interface 230.

[0152] Processor 210 may include one or more processing cores. Processor 210 connects to various parts within electronic device 200 using various interfaces and lines, and performs various functions and processes data of electronic device 200 by running or executing instructions, programs, code sets, or instruction sets stored in memory 220, and by calling data stored in memory 220. Optionally, processor 210 may be implemented using at least one of the following hardware forms: Central Processing Unit (CPU), Graphics Processing Unit (GPU), Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA).

[0153] The memory 220 may include random access memory (RAM) or read-only memory (ROM). Optionally, the memory 220 may include a non-transitory computer-readable storage medium. The memory 220 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 220 may include a program storage area. This program storage area may store instructions for implementing an operating system, instructions for implementing at least one function, instructions for implementing the various method embodiments described above, etc.

[0154] Communication interface 230 is used to communicate with other devices, equipment or communication networks, such as data storage devices, image processing devices or Ethernet, wireless access network (RAN), wireless local area network (WLAN), etc.

[0155] In terms of physical implementation, the aforementioned devices (such as processor 210, memory 220, and communication interface 230) can each be devices within the same device (such as a laptop computer). Alternatively, at least two of these devices can be located within the same device, i.e., as different devices within the same device, similar to the deployment of devices or components in a distributed system.

[0156] It is understood that the structure illustrated in this embodiment does not constitute a specific limitation on the electronic device 20. In other embodiments of the present invention, the electronic device 20 may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0157] Based on the above-described method for generating and maintaining standardized electrical elements based on model information, this embodiment of the invention also provides a storage medium storing a computer-executable program. This computer-executable program is used to cause a computer to execute the above-described method for generating and maintaining standardized electrical elements based on model information. Explanations of the relevant content and descriptions of the beneficial effects of any of the computer-readable storage media provided above can be found in the corresponding embodiments described above, and will not be repeated here.

[0158] Those skilled in the art will understand that the program for implementing all or part of the steps of the above embodiments, which can be executed by a program instructing related hardware, can be stored in a computer-readable storage medium. The storage medium mentioned above can be a read-only memory, a random access memory, etc. The processing unit or processor mentioned above can be a central processing unit, a general-purpose processor, an application-specific integrated circuit (ASIC), a microprocessor (DSP), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof.

[0159] This invention also provides a computer program product containing instructions that, when executed on a computer, cause the computer to perform any of the methods described in the above embodiments. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this invention is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., SSD), etc.

[0160] It should be noted that the devices for storing computer instructions or computer programs provided in the embodiments of the present invention, such as, but not limited to, the aforementioned memory, computer-readable storage medium, and communication chip, are all non-transitory. Those skilled in the art should recognize that the functions described in the embodiments of the present invention in one or more of the above examples can be implemented using hardware, software, firmware, or any combination thereof. When implemented using software, these functions can be stored in a computer-readable storage medium or transmitted as one or more instructions or code on a computer-readable storage medium. Computer-readable storage media include computer storage media and communication media, wherein communication media include any medium that facilitates the transmission of computer programs from one place to another. Storage media can be any available medium accessible to general-purpose or special-purpose computers.

[0161] 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 standardized electrical full-element generation and operation and maintenance system based on model information, characterized in that, include: An automatic generation kernel is used to automatically generate standardized electrical full-element functional units required for equipment operation based on imported equipment information and equipment 3D models. The full-element functional units include at least the equipment operation process stack, digital twin, equipment wiring diagram, hardware configuration configuration file and human-machine interface. The equipment information includes at least the equipment behavior logic and hardware composition information. The operation control core is used to parse and execute the device operation process stack, realize the control of the operation of the corresponding physical device, and synchronously drive the digital twin to perform real-time status updates. The predictive execution core is used to parse and execute the device operation process stack, achieve control logic isomorphism with the operation control core, and realize positive simulation prediction and analysis of the future operation state of the physical device by simulating the execution speed ahead of the current operation speed of the physical device in the virtual environment. A 3D visualization platform is used to display the real-time operating status and future predicted status of the digital twin.

2. The standardized electrical full-element generation and operation and maintenance system based on model information according to claim 1, characterized in that, The automatically generated kernel includes: The parsing module is used to parse the device behavior logic and automatically assemble and generate the device operation process stack, wherein the device operation process stack is a standard control instruction sequence; The hardware module is used to automatically generate a device wiring diagram and a hardware configuration file and parameters for driving the physical device based on the hardware composition information. The digital model module is used to construct a digital twin of the physical device based on the three-dimensional model of the device and the hardware composition information; The interaction module is used to automatically generate a human-machine interface that includes manual operation, automated operation status display, parameter setting, and data monitoring based on the device operation process stack.

3. The standardized electrical full-element generation and operation and maintenance system based on model information according to claim 2, characterized in that, The parsing module is specifically used for: The device behavior logic is parsed and its control elements are mapped to predefined atomic action metaphrases or business process metaphrases in the system's built-in device behavior metaphrase library. The mapped atomic action metaphrases or business process metaphrases are then instantiated with parameters to generate the device operation process stack.

4. The standardized electrical full-element generation and operation and maintenance system based on model information according to claim 1, characterized in that, The operation control core includes: The data module is used to collect real-time operating data of the physical device and drive the digital twin to perform synchronous updates. The strategy module is used to analyze and make decisions based on the currently executed instructions in the device's operation process stack and the system's real-time status data to generate a first device execution strategy; and to monitor key status indicators and event streams, and automatically generate a second device execution strategy when preset thresholds, rules or patterns are met. The execution module is used to generate general execution instructions according to the first device execution strategy or the second device execution strategy, and to control the physical device through the device control interface.

5. The standardized electrical full-element generation and operation and maintenance system based on model information according to claim 4, characterized in that, The execution module is configured with a unified device control interface, which is used to convert the general execution instructions into low-level drive signals compatible with specific physical control units.

6. The standardized electrical full-element generation and operation and maintenance system based on model information according to claim 1, characterized in that, The prediction runtime kernel includes: The cloning module is used to copy the digital twin generated by the automatic generation core and the device operation process stack. When the simulation task is initialized, the cloning module synchronously captures the current execution state of the device operation process stack in the operation control core and loads the state data into the clone as the initial state of the simulation. The simulation operation module, as an isomorphic entity of the operation control core, controls the digital twin to perform the same functions as the operation control core in a virtual environment; The simulation execution module is used to respond to the control commands of the simulation operation module, simulate the operation of the actuator in the physical device, and adjust the simulation execution speed to several times the operating speed of the physical device; The predictive analysis module is used to record and analyze the equipment status in the simulation task, generate predictive analysis conclusions including the future state trajectory of the equipment, potential conflict points and performance indicators, generate early warning information based on the predictive analysis conclusions, and feed it back to the human-computer interaction interface.

7. The standardized electrical full-element generation and operation and maintenance system based on model information according to claim 1, characterized in that, The 3D visualization platform includes: A real-time rendering window is used to display the real-time operating status of the digital twin; A prediction rendering window is used to display the predicted future state of the digital twin; The storage module is used to store a first preset number of historical running state snapshot data and a second preset number of future predicted state snapshot data; The timeline interaction module, which is associated with the real-time rendering window and the prediction rendering window, provides a draggable timeline control for replaying the historical running state of the digital twin at any time or previewing the future predicted state of the digital twin simulation.

8. A standardized electrical full-element generation and operation and maintenance method based on model information, applied to the standardized electrical full-element generation and operation and maintenance system based on model information as described in any one of claims 1-7, characterized in that, include: Input steps: Obtain the device's 3D model and device information, including device behavior logic and hardware composition information; Automatic generation steps: Based on the imported equipment information and the equipment 3D model, the automatic generation kernel automatically generates standardized electrical full-element functional units required for equipment operation. The full-element functional units include at least the equipment operation process stack, digital twin, equipment wiring diagram, hardware configuration configuration file and human-machine interface. Operation and synchronization steps: The operation control kernel is used to parse and execute the device operation process stack to control the operation of the corresponding physical device and synchronously drive the digital twin to perform real-time status updates; Predictive simulation steps: The predictive runtime kernel synchronously parses and executes the device's runtime stack, and in the virtual environment, the simulation execution speed is ahead of the current running speed of the physical device to achieve positive simulation prediction and analysis of the future running state of the physical device; Visualization steps: The real-time operating status and future predicted status of the digital twin are displayed through a 3D visualization platform.

9. The standardized electrical full-element generation and operation and maintenance method based on model information according to claim 8, characterized in that, The prediction simulation steps specifically include: State synchronization steps: Copy the digital twin generated by the automatic generation core and the device operation process stack, and synchronously capture the current execution state of the device operation process stack in the operation control core, and load the state data into the clone as the initial state of the simulation; Forward simulation steps: In the virtual environment, starting from the initial state, sequentially execute the subsequent control instruction sequence that has not yet been executed in the device operation process stack, and adjust the simulation execution speed to run at several times the speed of the physical device, thereby driving the digital twin to complete the forward business process simulation in the virtual environment; Analysis and early warning steps: During the forward business process simulation, the state changes of the digital twin caused by the execution of the subsequent control command sequence are monitored and recorded in real time. The state changes are matched and analyzed with a preset logical rule base to deduce specific events, production indicator trends or abnormal states that the physical device will encounter at future time points, and early warning information is generated accordingly.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the standardized electrical full-element generation and operation and maintenance method based on model information as described in any one of claims 8 to 9.

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