Internet of Things equipment control method based on digital twinning

Abstract

The invention relates to the technical field of industrial data processing, and provides an Internet of Things equipment control method based on digital twinning. Working parameters in industrial production are obtained through sensing equipment; modeling is conducted on the industrial production process of the production equipment based on the working parameters, a temperature field model representing heat conduction and a deformation model representing the mechanical vibration state are sequentially generated, and deformation compensation parameters are generated based on the deformation model; when the deformation compensation parameter is larger than a set threshold value, modeling is conducted on the production equipment based on the working parameters, a wear model representing the deformation condition of the production equipment is generated, the minimum value of the wear model is solved, and a solution corresponding to the minimum value is determined to serve as a wear factor; and generating a control instruction based on the deformation compensation parameter to control the production equipment to work, and controlling the production equipment to stop working when the wear factor is greater than a preset wear threshold value. And comprehensive monitoring and optimal control of the equipment are realized, the operation precision and stability are improved, and industrial digital transformation is promoted.

Description

Technical Field

[0001] This application relates to the technical field of industrial data processing, and more particularly, to an Internet of Things device control method based on digital twin. Background Art

[0002] With the rapid development of Industry 4.0 and intelligent manufacturing, the demand for intelligent, efficient, and precise control of equipment in the industrial production environment is increasing day by day. The wide application of Internet of Things technology enables real-time data interaction between production equipment and between equipment and control systems, providing a data foundation for equipment control. However, it is difficult to achieve comprehensive optimization control of equipment only relying on Internet of Things technology, because the equipment will be affected by various factors during operation, such as thermal deformation, mechanical vibration, wear, etc. These factors will cause the equipment performance to decline and even lead to failures.

[0003] However, in practical applications, in a high-speed rotating or high-temperature environment, direct measurement contact sensors may interfere with the processing process or cannot withstand extreme working conditions, and direct measurement usually can only obtain deformation data of local points. For example, a laser displacement sensor monitors a certain point, and at the same time, real-time measurement of high-frequency deformation requires an ultra-high sampling rate, which poses extremely high requirements for hardware costs and data processing. Therefore, there are problems in the prior art that the production control of production equipment is not precise and complete enough. Summary of the Invention

[0004] This application provides an Internet of Things device control method based on digital twin, which can at least to some extent solve the problem that the production control of production equipment is not precise and complete enough.

[0005] Other features and advantages of this application will become apparent through the following detailed description, or be learned in part through the practice of this application.

[0006] According to one aspect of this application, there is provided an Internet of Things device control method based on digital twin, including: obtaining working parameters in industrial production through sensing devices deployed in the Internet of Things; modeling the industrial production process of production equipment based on the working parameters, sequentially generating a temperature field model representing heat conduction and a deformation model representing the mechanical vibration state, and generating deformation compensation parameters based on the deformation model; when the deformation compensation parameters are greater than a set threshold, modeling the production equipment based on the working parameters, generating a wear model representing the deformation situation of the production equipment, obtaining the minimum value of the wear model, and determining the solution corresponding to the minimum value as the wear factor; generating a control instruction based on the deformation compensation parameters to control the operation of the production equipment, and controlling the production equipment to suspend operation when the wear factor is greater than a preset wear threshold.

[0007] In this application, based on the foregoing solution, the working parameters in industrial production are obtained by means of the sensing devices deployed in the Internet of Things, including: deploying sensing devices in the industrial production environment based on the Internet of Things architecture, and building a local area network among the sensing devices; collecting the working parameters in industrial production through the sensing devices, and transmitting the working parameters to the control terminal through the local area network.

[0008] In this application, based on the foregoing solution, the industrial production process of the production equipment is modeled based on the working parameters, and a temperature field model representing heat conduction and a deformation model representing the mechanical vibration state are generated in sequence. The deformation compensation parameters are generated based on the deformation model, including: determining the heat generation power of the production equipment in the working state based on the interaction force and relative speed between the production equipment and the workpiece in the industrial parameters; constructing a temperature field model representing heat conduction based on the heat generation power, material density and specific heat capacity of the production equipment; determining the thermal deformation force based on the temperature information of the production equipment and the contact area between the production equipment and the external environment in the industrial parameters; generating a deformation model representing the mechanical vibration state based on the thermal deformation force and the mechanical parameters in the industrial parameters, and generating deformation compensation parameters based on the deformation model.

[0009] In this application, based on the foregoing solution, the mechanical parameters include the mass matrix, damping matrix and stiffness matrix of the production equipment in the working state; wherein, the mass matrix represents the mass distribution of each degree of freedom in the production equipment; the damping matrix represents the energy dissipation mechanism of the production equipment; the stiffness matrix represents the ability of the production equipment to resist deformation.

[0010] In this application, based on the foregoing solution, the production equipment is modeled based on the working parameters to generate a wear model representing the deformation condition of the production equipment, including: obtaining the preset current harmonic calibration coefficient and acoustic emission calibration coefficient; modeling the production equipment based on the current harmonic calibration coefficient, acoustic emission calibration coefficient, current harmonic component and acoustic emission energy in the working parameters, and generating a wear model representing the deformation condition of the production equipment.

[0011] In this application, based on the foregoing solution, the minimum value of the wear model is obtained, and the solution corresponding to the minimum value is determined as the wear factor, including: inputting the working parameters into the wear model to determine the minimum value of the wear model; obtaining the solution of the wear model based on the minimum value as the wear factor.

[0012] In this application, based on the foregoing solution, generating a control instruction based on the deformation compensation parameter to control the operation of the production equipment, and controlling the production equipment to suspend operation when the wear factor is greater than a preset wear threshold includes: generating a control instruction based on the deformation compensation parameter, encapsulating the control instruction, and transmitting the encapsulated control instruction to a control system through a preset communication channel to control the operation of the production equipment; when it is detected that the wear factor is greater than the preset wear threshold, triggering an emergency control mechanism to control the production equipment to suspend operation.

[0013] In this application, based on the foregoing solution, after generating a control instruction based on the deformation compensation parameter to control the operation of the production equipment and controlling the production equipment to suspend operation when the wear factor is greater than a preset wear threshold, it further includes: sending an alarm message to production management personnel to inform them of abnormal equipment wear.

[0014] In this application, based on the foregoing solution, after generating a control instruction based on the deformation compensation parameter to control the operation of the production equipment and controlling the production equipment to suspend operation when the wear factor is greater than a preset wear threshold, it further includes: analyzing the wear factor and working parameters to generate a diagnostic report, and sending the diagnostic report to a management terminal.

[0015] In this application, based on the foregoing solution, the sensing device includes: a mechanical sensor, a strain sensor, and a temperature sensor.

[0016] According to one aspect of the present application, there is provided a digital twin-based Internet of Things device control device, including:

[0017] An acquisition unit for acquiring working parameters in industrial production through a sensing device deployed in the Internet of Things;

[0018] A deformation unit for modeling the industrial production process of a production equipment based on the working parameters, sequentially generating a temperature field model representing heat conduction and a deformation model representing the mechanical vibration state, and generating a deformation compensation parameter based on the deformation model;

[0019] A wear unit for, when the deformation compensation parameter is greater than a set threshold, modeling the production equipment based on the working parameters, generating a wear model representing the deformation condition of the production equipment, obtaining the minimum value of the wear model, and determining the solution corresponding to the minimum value as the wear factor;

[0020] A control unit for generating a control instruction based on the deformation compensation parameter to control the operation of the production equipment, and controlling the production equipment to suspend operation when the wear factor is greater than a preset wear threshold.

[0021] According to one aspect of the present application, there is provided a computer-readable medium having stored thereon a computer program, which when executed by a processor, implements the method for controlling an Internet of Things device based on digital twin as described in the above embodiments.

[0022] According to one aspect of the present application, there is provided an electronic device, including: one or more processors; a storage device for storing one or more programs, which when executed by the one or more processors, cause the one or more processors to implement the method for controlling an Internet of Things device based on digital twin as described in the above embodiments.

[0023] According to one aspect of the present application, there is provided a computer program product or a computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the method for controlling an Internet of Things device based on digital twin provided in the above various optional implementation manners.

[0024] In the technical solution of the present application, working parameters in industrial production are obtained through sensing devices deployed in the Internet of Things; based on the working parameters, a model of the industrial production process of production equipment is built, successively generating a temperature field model representing heat conduction and a deformation model representing the mechanical vibration state, and generating a deformation compensation parameter based on the deformation model; when the deformation compensation parameter is greater than a set threshold, a wear model representing the deformation condition of the production equipment is generated based on the working parameters in the production equipment, the minimum value of the wear model is obtained, and the solution corresponding to the minimum value is determined as the wear factor; a control instruction is generated based on the deformation compensation parameter to control the operation of the production equipment, and the production equipment is controlled to pause when the wear factor is greater than a preset wear threshold. By means of the Internet of Things sensing devices, working parameters are obtained, a temperature field model, a deformation model and wear are constructed, a deformation compensation parameter and a wear factor are generated. Based on these parameters and factors, a control instruction is generated to accurately control the device in real time, and the equipment is stopped in time when the wear exceeds the threshold. It realizes comprehensive monitoring and optimized control of the equipment, improves the operation accuracy and stability, reduces the failure rate and maintenance cost, ensures production safety, and promotes the digital transformation of industry.

[0025] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The accompanying drawings here are incorporated into the specification and form a part of this specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application. Obviously, the accompanying drawings in the following description are only some embodiments of the present application, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts.

[0027] Figure 1 Schematically shows a flowchart of a method for controlling an Internet of Things device based on digital twin in an embodiment of the present application.

[0028] Figure 2 Schematically shows a flowchart of generating deformation compensation parameters in an embodiment of the present application.

[0029] Figure 3 Schematically shows a schematic diagram of a device for controlling an Internet of Things device based on digital twin in an embodiment of the present application.

[0030] Figure 4 Shows a schematic diagram of the structure of a computer system of an electronic device suitable for implementing the embodiments of the present application. Detailed implementation manners

[0031] Now, example embodiments will be described more fully with reference to the accompanying drawings. However, the example embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be more complete and comprehensive, and will fully convey the concept of the example embodiments to those skilled in the art.

[0032] In addition, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of the embodiments of the present application. However, those skilled in the art will realize that the technical solutions of the present application can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of the present application.

[0033] The block diagrams shown in the accompanying drawings are only functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices.

[0034] The flowcharts shown in the accompanying drawings are merely illustrative and not necessarily include all content and operations / steps, nor are they necessarily executed in the described order. For example, some operations / steps can be decomposed, while some operations / steps can be combined or partially combined. Therefore, the actual execution order may change according to the actual situation.

[0035] This application is a method for controlling Internet of Things devices based on digital twins. Through the deep coupling of the digital twin model and Internet of Things devices, it realizes the real-time mapping, simulation optimization, and dynamic control of the production equipment status. Adopting a hierarchical collaborative computing architecture, combining the advantages of edge computing and cloud computing, it realizes the two-way real-time interaction between the physical device status and the digital twin model, forming a closed-loop control process of "perception - simulation - decision - execution". The overall system architecture includes the following key modules:

[0036] (1) Digital twin model construction module: Construct a high-precision digital twin model according to the physical characteristics, operating parameters, and sensor data of the Internet of Things devices. The model includes information such as the geometric structure, performance parameters, and operating status of the device. Through 3D modeling software and device parameter import, combined with real-time data to dynamically update the model. The digital twin model is not only used for the visualization of the device status, but also can directly generate control instructions through simulation optimization and act on the physical device in reverse.

[0037] (2) Data acquisition and transmission module: Real-time collect the operating data of Internet of Things devices, such as temperature, pressure, position, etc., and transmit it to the edge computing node and the cloud server through the network. Utilize the sensor network and wireless communication technology to achieve efficient data acquisition and transmission.

[0038] (3) Edge computing and real-time control module: Initially process the device data at the edge node to generate real-time control instructions, reduce the dependence on the cloud, and reduce latency. Deploy a lightweight edge computing framework, combined with real-time data processing algorithms, to quickly generate control instructions and send them to the device. Combining the advantages of edge computing and cloud computing, the edge node is responsible for real-time data processing and rapid control instruction generation, and the cloud is responsible for global optimization and model training.

[0039] (4) Cloud simulation and optimization module: Conduct simulation analysis on the digital twin model in the cloud, and optimize the control strategy using historical data and real-time data. Through machine learning algorithms and simulation software, predict and optimize the device status, and generate a globally optimal control strategy. It not only ensures the real-time nature of control, but also solves the problem of insufficient computing power in complex scenarios, and can adjust the control strategy in real time to adapt to the complex and changeable device operating environment.

[0040] (5) Bidirectional Data-Driven and Closed-Loop Control Module: Achieve bidirectional data interaction between the digital twin model and physical devices to form a closed-loop control. Through the interface and data synchronization mechanism, the optimized control instructions are sent from the cloud or edge nodes to the devices, and the device status is fed back to the digital twin model in real time. Analyze the historical operation data and real-time status of the devices, automatically generate optimized control strategies without manual intervention, significantly improving the intelligence level and adaptability of the system.

[0041] The implementation details of the technical solution of this application are elaborated in detail below:

[0042] Figure 1 The flowchart of the Internet of Things device control method based on digital twin according to an embodiment of this application is shown. Refer to Figure 1 As shown, the Internet of Things device control method based on digital twin at least includes steps S110 to S150, which are introduced in detail as follows:

[0043] In step S110, the working parameters in industrial production are obtained through the sensing devices deployed in the Internet of Things.

[0044] In this embodiment, the working parameters in industrial production are obtained through the sensing devices deployed in the Internet of Things. Sensing devices are installed at key parts of production equipment and the surrounding environment. Among them, various sensing devices such as mechanical sensors, strain sensors, temperature sensors, and image acquisition devices, and these sensing devices continuously collect data according to the preset sampling frequency and accuracy.

[0045] In an embodiment of this application, the working parameters in industrial production are obtained through the sensing devices deployed in the Internet of Things, including:

[0046] Deploy sensing devices in the industrial production environment based on the Internet of Things architecture, and build a local area network between the sensing devices;

[0047] Collect the working parameters in industrial production through the sensing devices, and transmit the working parameters to the control terminal through the local area network.

[0048] In an embodiment of this application, according to the technological process, equipment distribution, and monitoring requirements of industrial production, the installation positions of sensing devices are planned to ensure that various working parameters can be obtained comprehensively and accurately. These sensing devices have different functions, such as temperature sensors, pressure sensors, and flow sensors, and they are accurately installed at key parts of production equipment or specific nodes of the production line.

[0049] After installation, a stable and efficient local area network is constructed among the sensing devices using wireless communication technology or wired communication methods such as Ethernet. This local area network adopts a specific network protocol to ensure the communication quality and data security between devices.

[0050] After the local area network is constructed, the sensing devices start to collect the working parameters in industrial production according to the preset sampling frequency and rules. Each sensing device has an independent data acquisition module, which can measure the parameters at its location in real time and accurately, and convert the collected raw data into digital signals.

[0051] Exemplarily, mechanical sensors monitor mechanical parameters such as the rotational speed, torque, and pressure of the equipment in real time; temperature sensors record the temperature changes of various parts of the equipment; and image acquisition devices acquire the working images during the production process.

[0052] Subsequently, the collected working parameters are encapsulated and transmitted through the local area network according to the established communication protocol. During the transmission process, the network nodes perform routing and forwarding of the data, and through the low-power and high-reliability communication protocols of the Internet of Things, such as the Message Queuing Telemetry Transport Protocol, etc., for encapsulation and transmission, to ensure that the data can reach the control terminal quickly and reliably.

[0053] After receiving the data, the control terminal starts the data reception and processing program, parses, stores, and preliminarily analyzes the data, providing strong support for subsequent production decision-making and control.

[0054] Optionally, during the transmission process, to ensure the integrity and accuracy of the data, data encryption, checksum, and retransmission mechanisms are adopted. After the data is transmitted to the edge computing node or data center, preprocessing operations such as data cleaning, denoising, and format conversion are performed to remove invalid and error data and unify the data format for subsequent analysis and processing, and finally the working parameters available for industrial production analysis and decision-making are obtained.

[0055] In step S120, based on the working parameters, a model of the industrial production process of the production equipment is built, successively generating a temperature field model representing heat conduction and a deformation model representing the mechanical vibration state, and generating deformation compensation parameters based on the deformation model.

[0056] It should be noted that in practical applications, in a high-speed rotating or high-temperature environment, contact sensors for direct measurement may interfere with the processing process or encounter extreme working conditions that are intolerable. Moreover, direct measurement usually can only obtain deformation data of local points. For example, a laser displacement sensor monitors a certain point, while thermal deformation exhibits spatial distribution characteristics, such as the coupling of axial elongation and radial bending of the spindle. At the same time, real-time measurement of high-frequency deformation, such as instantaneous deformation caused by vibration, requires an ultra-high sampling rate, which poses extremely high requirements for hardware costs and data processing. Therefore, directly collecting the deformation of production equipment is impossible in many production environments.

[0057] In this embodiment, determining the heat generation power based on the interaction force and relative speed between the production equipment and the workpiece in the working state among the industrial parameters is the basis for subsequent modeling. First, through a force sensor and a speed sensor, collect the interaction force between the production equipment and the workpiece during operation. The force sensor can accurately measure the magnitude and direction of the force exerted by the equipment on the workpiece, and the speed sensor monitors the relative movement speed between the equipment and the workpiece in real time. After obtaining these data, using the principle of frictional heat generation in thermodynamics, combined with the interaction force and relative speed, determine the heat generation power of the production equipment in the working state through an energy conservation algorithm, so as to reflect the amount of heat generated by the equipment during operation due to factors such as friction through the heat generation power.

[0058] As Figure 2 shown, in an embodiment of the present application, based on the working parameters, model the industrial production process of the production equipment, and sequentially generate a temperature field model representing heat conduction and a deformation model representing the mechanical vibration state. Based on the deformation model, generate deformation compensation parameters, including:

[0059] S210, based on the interaction force and relative speed between the production equipment and the workpiece in the working state among the industrial parameters, determine the heat generation power of the production equipment in the working state;

[0060] S220, based on the heat generation power, material density, and specific heat capacity of the production equipment, construct a temperature field model representing heat conduction;

[0061] S230, based on the temperature information of the production equipment and the contact area between the production equipment and the external environment among the industrial parameters, determine the thermal deformation force;

[0062] S240, based on the thermal deformation force and the mechanical parameters among the industrial parameters, generate a deformation model representing the mechanical vibration state, and generate deformation compensation parameters based on the deformation model.

[0063] In this embodiment, based on the interaction force and relative speed between the production equipment and the workpiece in the working state among the industrial parameters, determine the heat generation power Q of the production equipment in the working statefric is:

[0064] Q fric = μ·vF c

[0065] Wherein, μ represents the friction coefficient, v represents the relative velocity, and F c represents the interaction force between the production equipment and the workpiece in the working state.

[0066] After that, based on the calculated heat generation power, combined with parameters such as the material density and specific heat capacity of the production equipment, a temperature field model characterizing heat conduction is constructed. For example, using the finite element analysis method, the production equipment is divided into multiple tiny units, and according to the heat conduction equation and boundary conditions, the heat conduction process inside the equipment is simulated. Through continuous iterative calculations, the temperature distribution of the equipment at different times and positions is obtained, thereby establishing an accurate temperature field model, which provides a basis for subsequent analysis of the thermal deformation and mechanical vibration of the equipment.

[0067] Specifically, after calculating the heat generation power, based on the heat generation power, the material density and specific heat capacity of the production equipment, the temperature field model characterizing heat conduction is constructed as follows:

[0068]

[0069] Wherein, T represents the temperature information of the production equipment in the working state, t represents time, α represents the thermal diffusivity of the production equipment, represents the Laplace operator of the temperature field, and Q fric represents the heat generation power, ρ represents the material density of the production equipment, and c p represents the specific heat capacity.

[0070] In the above process, by calculating the heat generation power of the production equipment in the working state, and then based on the heat generation power, the material density and specific heat capacity of the production equipment, a temperature field model characterizing heat conduction is constructed. Determining the heat generation power based on the interaction force and relative velocity between the industrial equipment and the workpiece in the working state, and then constructing the temperature field model, can accurately simulate the heat conduction process inside the equipment and understand the temperature distribution of the equipment at different positions. Since thermal deformation may cause a decrease in the accuracy of the equipment and the occurrence of failures, this is of great significance for analyzing the thermal deformation and thermal stress of the equipment.

[0071] After determining the heat generation power and constructing the temperature field model, the thermal deformation force is determined based on the temperature information of the production equipment in the industrial parameters and the contact area between the production equipment and the external environment. The temperature information is derived from the previously constructed temperature field model, which can provide the temperature values of various parts of the equipment at different times. The contact area is obtained through precise measuring equipment to ensure the accuracy of the data. Using the thermal expansion theory and mechanical principles, factors such as the thermal expansion coefficient and elastic modulus of the material are considered. According to the temperature change and the contact area, through thermal-mechanical coupling analysis, for example, the thermal deformation force generated by the equipment due to the temperature change is calculated. This thermal deformation force reflects the force exerted by the structural deformation of the equipment under thermal action on the surrounding environment, and it is one of the important factors affecting the mechanical vibration state of the equipment.

[0072] In this embodiment, based on the temperature information of the production equipment in the industrial parameters, the temperature change parameter per unit time is determined, and the contact area between the production equipment and the external environment is obtained. Then, based on the temperature change parameter and the contact area, the thermal deformation force F is determined. ther is:

[0073] F ther = βA·ΔT

[0074] where β represents the thermal expansion coefficient, A represents the contact area, and ΔT represents the temperature change parameter.

[0075] Based on the calculated thermal deformation force and the mechanical parameters in the industrial parameters, a deformation model characterizing the mechanical vibration state is generated. Among them, the mechanical parameters include the mass, stiffness, or damping of the equipment, etc., and these parameters are obtained through experimental tests or the design documents of the production equipment. Using the multi-body dynamics theory and vibration analysis methods, combined with the thermal deformation force and the mechanical parameters, the dynamic equation of the equipment is established and solved to simulate the vibration behavior of the equipment under different working conditions, and the deformation situation of the equipment is obtained, thereby generating a deformation model.

[0076] In this embodiment, the mechanical parameters in the industrial parameters include the mass matrix, damping matrix, and stiffness matrix of the production equipment in the working state. In addition, they also include the displacement and cutting force of the production equipment in the working state. Among them, the mass matrix describes the mass distribution and inertial characteristics of each degree of freedom in the production equipment, and each mass or moment of inertia corresponds to an element in the matrix; the damping matrix characterizes the energy dissipation mechanism of the production equipment, such as friction, air resistance, or internal dissipation of the material; the stiffness matrix reflects the ability of the production equipment to resist deformation, that is, the relationship between the elastic restoring force and the displacement.

[0077] After determining the thermal deformation force, based on the thermal deformation force and the mechanical parameters in the industrial parameters, the deformation model characterizing the mechanical vibration state is:

[0078]

[0079] Among them, M represents the mass matrix, C represents the damping matrix, K represents the stiffness matrix, q represents the displacement, represents the first derivative of the displacement, that is, the velocity; represents the second derivative of the displacement, that is, the acceleration, F c represents the interaction force between the production equipment and the workpiece in the working state.

[0080] In the above process, the thermal deformation force is determined by collecting the contact area and temperature change parameters in real time. At the same time, based on the mass matrix, damping matrix, and stiffness matrix of the production equipment in the working state, combined with the displacement and its derivative, the vibration and deformation of the production equipment are evaluated, and a deformation model is generated. After generating the deformation model, the genetic algorithm is used to optimize and adjust the model to generate deformation compensation parameters. The deformation compensation parameters can be used to adjust the operating state of the equipment, compensate for the deviation caused by thermal deformation and mechanical vibration, improve the operating accuracy and stability of the equipment, and ensure the smooth progress of the industrial production process.

[0081] In practical applications, the production equipment performs mechanical motion during the industrial production process. Heat is generated through friction, which causes temperature changes. As a result, the production equipment deforms under the influence of temperature changes and reacts on the mechanical system. Therefore, in this solution, the heat generation power is first determined by the friction coefficient and the relative velocity, and then the temperature information of the current production equipment is simulated based on the heat generation power and the properties of the production materials. Furthermore, the thermal deformation force of the production equipment is determined based on the temperature information, and finally, the possible deformation distance of the production equipment is predicted based on the thermal deformation force, and deformation compensation parameters are generated according to the deformation distance to compensate for the thermal deformation error.

[0082] The above process realizes the coupled modeling based on force and heat through the temperature field model and the deformation model, completely describes the interaction between the mechanical system and the thermal field, and can calculate the deformation trend under different working conditions in advance. For example, when the industrial parameters (cutting speed, feed rate) change, predict the future temperature field distribution and the corresponding deformation amount to achieve active compensation. In digital twin, the model can simulate the behavior of the equipment throughout its life cycle, can be used to predict the equipment state in real time, such as the change in the friction coefficient caused by tool wear, and improve the machining accuracy and energy efficiency through dynamic prediction and compensation to adapt to progressive degradation. To accurately simulate the heat conduction and mechanical vibration state of the equipment, timely discover the deformation problems of the equipment, and improve the operating accuracy and stability of the equipment through compensation parameters, reducing production errors and failures caused by deformation.

[0083] In step S130, when the deformation compensation parameter is greater than the set threshold, a wear model representing the deformation situation of the production equipment is generated based on the working parameters for the production equipment, the minimum value of the wear model is obtained, and the solution corresponding to the minimum value is determined as the wear factor.

[0084] When the deformation compensation parameter is monitored to be greater than the set threshold, start the process of modeling the production equipment based on the working parameters to generate a wear model. First, collect and integrate the current working parameters, which cover multiple key aspects of the equipment operation, such as current harmonic components, acoustic emission energy, temperature, pressure, etc. These parameters reflect the actual state of the equipment during operation from different dimensions and are the basis for constructing an accurate wear model.

[0085] Train and optimize according to the potential relationship between the working parameters and equipment wear, continuously adjust the parameters and structure of the model, and combine factors such as the structural characteristics of the production equipment, operating conditions, and historical wear data to construct a wear model for the deformation situation of the production equipment. The input of the wear model is the working parameters, and the output is the wear factor of equipment wear. By simulating the biological evolution process through the genetic algorithm, perform selection, crossover, and mutation operations on a group of candidate solutions, screen out better solutions, and finally find the minimum value of the wear model.

[0086] The wear factor represents the equipment wear situation under the current working parameters. Compare it with the historical wear data, safety threshold, etc. of the equipment, which provides an important basis for subsequent equipment maintenance decisions, production adjustments, and fault warnings, helping production managers take measures in a timely manner to ensure the stable operation and production efficiency of the production equipment.

[0087] In an embodiment of the present application, based on the working parameters, model the production equipment to generate a wear model representing the deformation situation of the production equipment, including:

[0088] Obtain the preset current harmonic calibration coefficient and acoustic emission calibration coefficient;

[0089] Based on the current harmonic calibration coefficient and acoustic emission calibration coefficient, as well as the current harmonic component and acoustic emission energy in the working parameters, model the production equipment to generate a wear model representing the deformation situation of the production equipment.

[0090] In this embodiment, obtain the preset current harmonic calibration coefficient and acoustic emission calibration coefficient. Among them, the current harmonic calibration coefficient represents the theoretical prediction value of mapping the wear factor to the current harmonic; the acoustic emission calibration coefficient represents the theoretical prediction value of mapping the wear factor to the acoustic emission energy. These calibration coefficients are obtained through a large number of experiments and data analyses and are stored in a special database or configuration file. By accessing these storage locations and following a specific data format and protocol, read the current harmonic calibration coefficient and acoustic emission calibration coefficient.

[0091] While obtaining the calibration coefficients, the current harmonic components and acoustic emission energy data in the working parameters are collected synchronously. The current harmonic components are collected in real time by current sensors, which can accurately capture the harmonic components in the current and convert them into digital signals for transmission to the computing system. The acoustic emission energy is obtained by acoustic emission sensors, which collect and convert the acoustic emission signals generated by the production equipment during operation. Among them, the current harmonic components reflect the harmonic distortion degree of the motor load and are related to the load fluctuation caused by bearing wear; the acoustic emission energy characterizes the high-frequency energy release generated by microcracks on the bearing surface.

[0092] Optionally, the collected current harmonic components and acoustic emission energy data are preprocessed, including operations such as data cleaning, denoising, and normalization. Outliers and incorrect data in the data are removed, and a filtering algorithm is used to eliminate the interference noise in the signal, and the data is adjusted to a unified scale range for subsequent calculation and analysis. The preprocessed data, together with the calibration coefficients, will be used as the input data for modeling.

[0093] During the modeling process, the current harmonic components and acoustic emission energy are used as input features, and the current harmonic calibration coefficients and acoustic emission calibration coefficients are used to weight and adjust the input features. The model is trained with historical data and real-time collected data, and the parameters and structure of the model are adjusted so that the model can accurately reflect the relationship between the current harmonic components, acoustic emission energy, and equipment wear conditions. During the training process, methods such as cross-validation and error analysis are used to evaluate the accuracy and reliability of the model.

[0094] Specifically, based on the current harmonic calibration coefficients and acoustic emission calibration coefficients, as well as the current harmonic components and acoustic emission energy in the working parameters, a wear model R(η) representing the deformation situation of the production equipment is generated for the production equipment as follows:

[0095]

[0096] where η represents the wear factor, I h represents the current harmonic component, E AE represents the acoustic emission energy, K I represents the current harmonic calibration coefficient, K E represents the acoustic emission calibration coefficient, and ‖·‖2 represents the Euclidean norm.

[0097] After the wear model is constructed, the wear model is used to calculate new working parameter data, and a wear prediction result representing the deformation situation of the production equipment is output. This wear model can reflect the wear degree of the equipment under different working conditions in real time, provide a decision-making basis for the maintenance and management of the equipment, help to detect potential problems of the equipment in time, and avoid equipment failures and production accidents.

[0098] In practical applications, bearing wear causes mechanical load fluctuations, an increase in the high-frequency harmonic components in the motor current, and the expansion and release of high-frequency elastic waves due to the propagation of surface microcracks caused by wear. By fusing current harmonics and acoustic emission energy data, a non-linear observation model is established between the wear factor and sensor data, and multi-physical quantity collaborative sensing is used to accurately quantify the health state of the equipment and reduce the false alarm rate of a single sensor, such as the impact of electromagnetic interference on the current.

[0099] Obtain the preset current harmonic calibration coefficient and acoustic emission calibration coefficient, and combine the current harmonic components and acoustic emission energy in the working parameters to model the production equipment and generate a wear model. Current harmonics and acoustic emission signals can reflect the internal friction, wear, etc. of the equipment, and the wear model established through these parameters can accurately characterize the wear state of the equipment.

[0100] In an embodiment of the present application, obtaining the minimum value of the wear model and determining the solution corresponding to the minimum value as the wear factor includes:

[0101] Input the working parameters into the wear model to determine the minimum value of the wear model;

[0102] Based on the minimum value, find the solution of the wear model as the wear factor.

[0103] In this embodiment, the preprocessed and integrated working parameters, including relevant data such as current harmonic components and acoustic emission energy, are sent one by one into the calculation framework of the wear model according to the input format required by the wear model. Taking the working parameters in the wear model as independent variables, numerical calculation methods, such as iterative optimization algorithms, are used to adjust the mapping relationship of the input parameters in the model to find the parameter combination that minimizes the wear evaluation value.

[0104] In each iteration process, calculate the wear evaluation value under the current parameter combination and compare it with the previous value. If it is found that the current value is smaller, update the minimum value record and continue to adjust the parameters for the next round of iteration until the preset convergence condition is met, such as the number of iterations reaches the upper limit or the change range of the wear evaluation value is less than a certain threshold. At this time, the determined value is the minimum value of the wear model.

[0105] After determining the minimum value, the corresponding solution can be directly obtained by an analytical method. By performing linear approximation and iterative approximation near the minimum value point, the exact parameter solution that makes the wear model reach the minimum value is gradually found. This solution represents a key characteristic value of the equipment wear condition under the current working parameter conditions, that is, the wear factor.

[0106] Optionally, the obtained wear factor is further verified and evaluated to ensure its accuracy and reliability. For example, it is compared with the wear situation in historical data to check for any abnormal deviations. Once the verification is passed, this wear factor is output for subsequent equipment status evaluation, maintenance decision-making, and optimization control of the production process, helping production managers promptly understand the wear degree of the equipment and take corresponding measures to ensure the normal operation of the equipment and production efficiency.

[0107] In step S140, a control instruction is generated based on the deformation compensation parameter to control the operation of the production equipment, and the production equipment is controlled to suspend operation when the wear factor is greater than a preset wear threshold.

[0108] In this embodiment, after generating the deformation compensation parameter, it is converted into a specific control signal. For example, when the production equipment is a numerically controlled machine tool, the deformation compensation parameter corresponds to the fine adjustment amount of the tool path, and then the control instruction will accurately calculate the movement increments of the tool in the X, Y, and Z axis directions, as well as the adjustment values of the feed speed and spindle speed.

[0109] In an embodiment of the present application, generating a control instruction based on the deformation compensation parameter to control the operation of the production equipment and controlling the production equipment to suspend operation when the wear factor is greater than a preset wear threshold includes:

[0110] Generating a control instruction based on the deformation compensation parameter, encapsulating the control instruction, and transmitting the encapsulated control instruction to the control system through a preset communication channel to control the operation of the production equipment;

[0111] When it is detected that the wear factor is greater than the preset wear threshold, an emergency control mechanism is triggered to control the production equipment to suspend operation.

[0112] During the process of generating the control instruction, the dynamic characteristics and response capabilities of the production equipment are combined to ensure the rationality and feasibility of the instruction. The generated control instruction will be encapsulated in a specific data format, such as using an industrial standard communication protocol, to ensure the accuracy and compatibility of the instruction during transmission. Subsequently, the encapsulated control instruction will be transmitted to the control system of the production equipment quickly and stably through a reliable communication channel, such as industrial Ethernet, fieldbus, etc. Generating a control instruction based on the deformation compensation parameter, encapsulating the instruction, and transmitting it to the control system through a preset communication channel can achieve real-time control of the equipment. The control instruction can be accurately adjusted according to the deformation situation of the equipment to ensure the normal operation of the equipment.

[0113] Optionally, the communication channel adopts an error detection and correction mechanism, such as cyclic redundancy check, to ensure that no data loss or error occurs during the transmission of instructions. After receiving the instructions, the control system of the production equipment immediately parses and executes the instructions, adjusts the operating state of the equipment, so as to achieve precise control of the equipment working process and effectively compensate for the influence brought by deformation.

[0114] In this embodiment, a wear threshold is preset in advance, which is comprehensively determined based on various factors such as the design life of the equipment, safety standards, and production quality requirements. During the monitoring process, the latest wear factor is regularly obtained from the wear model and compared with the set threshold. Once it is detected that the wear factor is greater than the set threshold, an emergency control mechanism is triggered. First, the system generates a special control instruction that clearly instructs the production equipment to suspend operation. This instruction is transmitted to the control system of the production equipment through the communication channel with the highest priority to ensure that the equipment can respond quickly. When it is detected that the wear factor is greater than the preset wear threshold, an emergency control mechanism is triggered to control the equipment to suspend operation. This can prevent the equipment from being further damaged due to excessive wear and ensure the safety of the equipment and personnel.

[0115] At the same time, an alarm message is sent to the production management personnel, informing them that the equipment is abnormally worn and immediate maintenance and repair are required. The alarm message will include detailed information such as the specific location of the equipment, the value of the wear factor, and the recommended maintenance measures.

[0116] During the period when the equipment is suspended, the operating data of the equipment can be further analyzed to provide a detailed diagnostic report for subsequent repair and maintenance, helping technicians quickly locate problems and formulate reasonable repair plans, so that the equipment can resume normal production as soon as possible after repair and reduce production losses caused by equipment failures. To achieve real-time and precise control of the equipment, improve production efficiency and quality; at the same time, stop the equipment operation in time when the equipment is severely worn to avoid accidents and reduce maintenance costs and production losses.

[0117] In the technical solution of this application, working parameters in industrial production are obtained through sensing devices deployed in the Internet of Things. Based on the working parameters, a model of the industrial production process of production equipment is built, successively generating a temperature field model representing heat conduction and a deformation model representing the mechanical vibration state. A deformation compensation parameter is generated based on the deformation model. When the deformation compensation parameter is greater than a set threshold, a wear model representing the deformation condition of the production equipment is generated based on the working parameters in the modeling of the production equipment. The minimum value of the wear model is obtained, and the solution corresponding to the minimum value is determined as the wear factor. A control instruction is generated based on the deformation compensation parameter to control the operation of the production equipment, and the production equipment is controlled to suspend operation when the wear factor is greater than a preset wear threshold. By means of the Internet of Things sensing devices, working parameters are obtained, a temperature field model, a deformation model and wear are constructed, and a deformation compensation parameter and a wear factor are generated. Based on these parameters and factors, control instructions are generated to accurately control the equipment in real time. When the wear exceeds the threshold, the equipment is stopped in time. It realizes comprehensive monitoring and optimized control of the equipment, improves the operation accuracy and stability, reduces the failure rate and maintenance cost, ensures production safety, and promotes the digital transformation of industry.

[0118] The following introduces the device embodiments of this application, which can be used to execute the method for controlling Internet of Things devices based on digital twins in the above embodiments of this application. It can be understood that the device can be a computer program (including program code) running in a computer device. For example, the device is an application software. The device can be used to execute the corresponding steps in the method provided in the embodiments of this application. For details not disclosed in the device embodiments of this application, please refer to the embodiments of the method for controlling Internet of Things devices based on digital twins in the above of this application.

[0119] Figure 3 The block diagram of a device for controlling Internet of Things devices based on digital twins according to an embodiment of this application is shown.

[0120] Referring to Figure 3 As shown, a device for controlling Internet of Things devices based on digital twins according to an embodiment of this application includes:

[0121] An acquisition unit 310, configured to obtain working parameters in industrial production through sensing devices deployed in the Internet of Things;

[0122] A deformation unit 320, configured to build a model of the industrial production process of production equipment based on the working parameters, successively generate a temperature field model representing heat conduction and a deformation model representing the mechanical vibration state, and generate a deformation compensation parameter based on the deformation model;

[0123] A wear unit 330, configured to, when the deformation compensation parameter is greater than a set threshold, generate a wear model characterizing the deformation condition of the production equipment based on the working parameters for modeling the production equipment, obtain the minimum value of the wear model, and determine the solution corresponding to the minimum value as the wear factor;

[0124] A control unit 340, configured to generate a control instruction based on the deformation compensation parameter to control the operation of the production equipment, and control the production equipment to suspend operation when the wear factor is greater than a preset wear threshold.

[0125] In this application, based on the foregoing solution, the working parameters in industrial production are obtained through the sensing devices deployed in the Internet of Things, including: deploying sensing devices in the industrial production environment based on the Internet of Things architecture, and building a local area network among the sensing devices; collecting the working parameters in industrial production through the sensing devices, and transmitting the working parameters to a control terminal through the local area network.

[0126] In this application, based on the foregoing solution, the industrial production process of the production equipment is modeled based on the working parameters, and a temperature field model characterizing heat conduction and a deformation model characterizing the mechanical vibration state are generated in sequence. The deformation compensation parameter is generated based on the deformation model, including: determining the heat generation power of the production equipment in the working state based on the interaction force and relative speed between the production equipment and the workpiece in the industrial parameters; constructing a temperature field model characterizing heat conduction based on the heat generation power, material density, and specific heat capacity of the production equipment; determining the thermal deformation force based on the temperature information of the production equipment and the contact area between the production equipment and the external environment in the industrial parameters; generating a deformation model characterizing the mechanical vibration state based on the thermal deformation force and the mechanical parameters in the industrial parameters, and generating a deformation compensation parameter based on the deformation model.

[0127] In this application, based on the foregoing solution, the mechanical parameters include the mass matrix, damping matrix, and stiffness matrix of the production equipment in the working state; wherein, the mass matrix characterizes the mass distribution of each degree of freedom in the production equipment; the damping matrix characterizes the energy dissipation mechanism of the production equipment; the stiffness matrix characterizes the ability of the production equipment to resist deformation.

[0128] In this application, based on the foregoing solution, the generation of a wear model characterizing the deformation condition of the production equipment based on the working parameters for modeling the production equipment includes: obtaining a preset current harmonic calibration coefficient and acoustic emission calibration coefficient; generating a wear model characterizing the deformation condition of the production equipment by modeling the production equipment based on the current harmonic calibration coefficient, acoustic emission calibration coefficient, current harmonic component, and acoustic emission energy in the working parameters.

[0129] In this application, based on the foregoing solution, obtaining the minimum value of the wear model and determining the solution corresponding to the minimum value as the wear factor includes: inputting the working parameters into the wear model to determine the minimum value of the wear model; and obtaining the solution of the wear model based on the minimum value as the wear factor.

[0130] In this application, based on the foregoing solution, generating a control instruction based on the deformation compensation parameter to control the operation of the production equipment and controlling the production equipment to pause when the wear factor is greater than a preset wear threshold includes: generating a control instruction based on the deformation compensation parameter, encapsulating the control instruction, and transmitting the encapsulated control instruction to the control system through a preset communication channel to control the operation of the production equipment; and triggering an emergency control mechanism to control the production equipment to pause when it is detected that the wear factor is greater than the preset wear threshold.

[0131] In this application, based on the foregoing solution, after generating a control instruction based on the deformation compensation parameter to control the operation of the production equipment and controlling the production equipment to pause when the wear factor is greater than a preset wear threshold, it further includes: sending an alarm message to the production management personnel to inform them of abnormal equipment wear.

[0132] In this application, based on the foregoing solution, after generating a control instruction based on the deformation compensation parameter to control the operation of the production equipment and controlling the production equipment to pause when the wear factor is greater than a preset wear threshold, it further includes: analyzing the wear factor and working parameters to generate a diagnostic report and sending the diagnostic report to the management terminal.

[0133] In this application, based on the foregoing solution, the sensing device includes: a mechanical sensor, a strain sensor, and a temperature sensor.

[0134] In the technical solution of this application, working parameters in industrial production are obtained through sensing devices deployed in the Internet of Things; based on the working parameters, an industrial production process of production equipment is modeled, and a temperature field model representing heat conduction and a deformation model representing the mechanical vibration state are sequentially generated. A deformation compensation parameter is generated based on the deformation model; when the deformation compensation parameter is greater than a set threshold, a wear model representing the deformation condition of the production equipment is generated based on the working parameters for modeling the production equipment, the minimum value of the wear model is obtained, and the solution corresponding to the minimum value is determined as the wear factor; a control instruction is generated based on the deformation compensation parameter to control the operation of the production equipment, and when the wear factor is greater than a preset wear threshold, the production equipment is controlled to suspend operation. By means of Internet of Things sensing devices, working parameters are obtained, a temperature field model, a deformation model, and wear are constructed, and a deformation compensation parameter and a wear factor are generated. Based on these parameters and factors, control instructions are generated to accurately control the equipment in real time, and the equipment is stopped in time when the wear exceeds the threshold. Comprehensive monitoring and optimized control of the equipment are realized, the operation accuracy and stability are improved, the failure rate and maintenance cost are reduced, production safety is guaranteed, and the digital transformation of industry is promoted.

[0135] Figure 4 The structural schematic diagram of a computer system of an electronic device suitable for implementing the embodiments of this application is shown.

[0136] It should be noted that the computer system of the electronic device in this embodiment is only an example and should not bring any limitation to the functions and usage scope of the embodiments of this application.

[0137] In this embodiment, the computer system includes a central processing unit 401, which can perform various appropriate actions and processes according to the program stored in the read-only memory 402 or the program loaded from the storage part 408 into the random access memory 403, such as executing the method for controlling Internet of Things devices based on digital twin described in the above embodiments. In the random access memory 403, various programs and data required for system operation are also stored. The central processing unit 401, the read-only memory 402, and the random access memory 403 are connected to each other through a bus 404. The input / output interface 405 is also connected to the bus 404.

[0138] The following components are connected to the input / output interface 405: an input section 406 including a keyboard, a mouse, etc.; an output section 407 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc. and a speaker, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the input / output interface 405 as needed. A removable medium 411 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc. is mounted on the drive 410 as needed so that a computer program read therefrom is installed into the storage section 408 as needed.

[0139] Specifically, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 409, and / or installed from the removable medium 411. When the computer program is executed by the central processing unit 401, various functions defined in the system of the present application are executed.

[0140] It should be noted that the computer-readable medium shown in the embodiments of the present application can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of a computer-readable storage medium can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, apparatus, or device. In the present application, a computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable computer program. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, and this computer-readable medium can send, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device. The computer program contained on a computer-readable medium can be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0141] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present application. Among them, each block in the flowchart or block diagram can represent a module, a program segment, or a part of code, and the above module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks shown can actually be executed substantially in parallel, and they can sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, and the combination of blocks in the block diagram or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0142] The units involved in the embodiments described in this application can be implemented in software or in hardware, and the described units can also be provided in a processor. Among them, the names of these units do not, in some cases, constitute a limitation on the unit itself.

[0143] According to one aspect of the present application, there is provided a computer program product or a computer program, which includes computer instructions stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the methods provided in the above various optional implementation manners.

[0144] As another aspect, the present application further provides a computer-readable medium, which may be included in the electronic device described in the above embodiments; or may exist alone without being assembled into the electronic device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by an electronic device, the electronic device implements the method for controlling an Internet of Things device based on digital twin described in the above embodiments.

[0145] It should be noted that although several modules or units of a device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of the two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0146] Through the description of the above embodiments, those skilled in the art can easily understand that the example embodiments described herein can be implemented in software or in a manner combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (such as a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiments of the present application.

[0147] After considering the specification and practicing the embodiments disclosed herein, those skilled in the art will readily conceive of other embodiments of the present application. The present application is intended to cover any variations, uses, or adaptations of the present application, which follow the general principles of the present application and include common general knowledge or conventional technical means in the technical field not disclosed in the present application.

[0148] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present application is only limited by the appended claims.

Claims

1. An Internet of Things device control method based on digital twin, characterized in that, Including: Obtaining working parameters in industrial production through sensing devices deployed in the Internet of Things; Modeling the industrial production process of production equipment based on the working parameters, successively generating a temperature field model representing heat conduction and a deformation model representing the mechanical vibration state, and generating a deformation compensation parameter based on the deformation model; When the deformation compensation parameter is greater than a set threshold, modeling the production equipment based on the working parameters to generate a wear model representing the deformation condition of the production equipment, obtaining the minimum value of the wear model, and determining the solution corresponding to the minimum value as the wear factor; Generating a control instruction based on the deformation compensation parameter to control the operation of the production equipment, and controlling the production equipment to suspend operation when the wear factor is greater than a preset wear threshold.

2. The method for controlling an Internet of Things device based on digital twin according to claim 1, wherein The obtaining of the working parameters in industrial production through sensing devices deployed in the Internet of Things includes: Deploying sensing devices in the industrial production environment based on the Internet of Things architecture and constructing a local area network among the sensing devices; Collecting the working parameters in industrial production through the sensing devices and transmitting the working parameters to a control terminal through the local area network.

3. The method for controlling an Internet of Things device based on digital twin according to claim 1, wherein Modeling the industrial production process of production equipment based on the working parameters, successively generating a temperature field model representing heat conduction and a deformation model representing the mechanical vibration state, and generating a deformation compensation parameter based on the deformation model, including: Determining the heat generation power of the production equipment in the working state based on the interaction force and relative speed between the production equipment and the workpiece in the working state among the industrial parameters; Constructing a temperature field model representing heat conduction based on the heat generation power, material density, and specific heat capacity of the production equipment; Determining the thermal deformation force based on the temperature information of the production equipment and the contact area between the production equipment and the external environment among the industrial parameters; Generating a deformation model representing the mechanical vibration state based on the thermal deformation force and the mechanical parameters in the industrial parameters, and generating a deformation compensation parameter based on the deformation model.

4. The method for controlling an Internet of Things device based on digital twin according to claim 3, wherein, The mechanical parameters include the mass matrix, damping matrix, and stiffness matrix of the production equipment in the working state; Among them, the mass matrix represents the mass distribution of each degree of freedom in the production equipment; the damping matrix represents the energy dissipation mechanism of the production equipment; the stiffness matrix represents the ability of the production equipment to resist deformation.

5. The method for controlling an Internet of Things device based on digital twin according to claim 1, characterized in that, Modeling the production equipment based on the working parameters to generate a wear model representing the deformation condition of the production equipment, including: Obtaining a preset current harmonic calibration coefficient and acoustic emission calibration coefficient; Modeling the production equipment based on the current harmonic calibration coefficient, acoustic emission calibration coefficient, and current harmonic component and acoustic emission energy in the working parameters to generate a wear model representing the deformation condition of the production equipment.

6. The method for controlling an Internet of Things device based on digital twin according to claim 5, wherein, Obtaining the minimum value of the wear model and determining the solution corresponding to the minimum value as the wear factor, including: Inputting the working parameters into the wear model to determine the minimum value of the wear model; Obtaining the solution of the wear model based on the minimum value as the wear factor.

7. The method for controlling an Internet of Things device based on digital twin according to claim 1, wherein, Generate a control instruction based on the deformation compensation parameter to control the operation of the production equipment, and control the production equipment to pause when the wear factor is greater than a preset wear threshold, including: Generate a control instruction based on the deformation compensation parameter, encapsulate the control instruction, and transmit the encapsulated control instruction to the control system through a preset communication channel to control the operation of the production equipment; If it is detected that the wear factor is greater than a preset wear threshold, trigger an emergency control mechanism to control the production equipment to pause.

8. The method for controlling an Internet of Things device based on digital twin according to claim 7, characterized in that, After generating a control instruction based on the deformation compensation parameter to control the operation of the production equipment and controlling the production equipment to pause when the wear factor is greater than a preset wear threshold, it further includes: Send an alarm message to the production management personnel to inform them of abnormal equipment wear.

9. The method for controlling an Internet of Things device based on digital twin according to claim 7, wherein After generating a control instruction based on the deformation compensation parameter to control the operation of the production equipment and controlling the production equipment to pause when the wear factor is greater than a preset wear threshold, it further includes: Analyze the wear factor and working parameters to generate a diagnostic report, and send the diagnostic report to the management terminal.

10. The method for controlling an Internet of Things device based on digital twin according to any one of claims 1-9, wherein the sensing device includes: Mechanical sensors, strain sensors, and temperature sensors.

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