Complex equipment multi-scale and multi-field digital twin model construction method

Abstract

The invention discloses a multi-scale and multi-field digital twin model construction method for complex equipment, and belongs to the technical field of intelligent manufacturing and engineering. The implementation method comprises the following steps of: aiming at the characteristic that machine tool equipment comprises multi-scale layers and multi-disciplinary fields, displaying the multi-scale layers and the multi-disciplinary fields of physical entity equipment in a unified modeling mode by adopting a Modelica modeling language through non-causal modeling and connector modeling, and breaking through the limitation of complex coupling characteristics of the equipment; taking the minimum error between the output of the machine tool digital twin model and the test of a machine tool test bed as an optimization target, considering the main shaft rigidity, the main shaft size, the processing temperature, the thermal conductivity and the thermal expansion coefficient correction variables, and constructing a machine tool digital twin model correction optimization problem model; an optimal parameter combination is searched by adopting an improved particle swarm algorithm of a model-elite learning strategy, correction of the machine tool digital twinborn model is realized, the consistency of the twinborn model and physical equipment is improved, and the accuracy of the machine tool equipment digital twinborn model is ensured.

Description

Technical Field

[0001] The present invention relates to a method for constructing a multi-scale and multi-domain digital twin model of complex equipment, and belongs to the field of intelligent manufacturing and engineering technology. Background Art

[0002] With the development of the new generation of network information technology, the integration of relevant information technology into physical equipment to achieve intelligent control and optimization of equipment has become the focus of attention of countries around the world. Digital Twin (DT) can effectively realize the interaction, integration and fusion between physical space and cyberspace, and is regarded as a key technology for realizing Cyber-Physical Systems (CPS). As the core and key prerequisite for realizing digital twin technology, the model builds a comprehensive, accurate and autonomously optimized model for the digital twin system, which plays a vital role in the development of related fields.

[0003] With the development of science and technology, physical equipment is becoming more and more complex. It covers multiple scales and multiple disciplines and has extremely complex coupling characteristics. Therefore, it is difficult to build a complete and comprehensive digital twin model for it. At present, the construction method of digital twin models only focuses on some characteristics of the equipment, ignoring the multi-scale dimensions and multi-disciplinary characteristics of the equipment, and cannot fully and comprehensively display the static equipment information and dynamic operation information of the physical equipment. At the same time, during the implementation of the digital twin model, since the physical equipment is affected by various actual operating conditions, the idealized digital twin model currently constructed has a large discrepancy with the actual equipment output results, and there is a lack of methods for real-time correction of the digital twin model, which cannot effectively support the reliable implementation of intelligent services related to the digital twin system and cannot meet the relevant needs of the development of intelligent manufacturing. Summary of the invention

[0004] The purpose of the present invention is to provide a method for constructing a multi-scale and multi-domain digital twin model of complex equipment, which adopts the Modelica modeling language to display the multi-scale levels and multi-disciplinary fields of physical entity equipment through a unified modeling method, breaking through the limitations of the complex coupling characteristics of the equipment; taking the minimum error between the output of the machine tool digital twin model and the test of the machine tool test bench as the optimization goal, considering the spindle stiffness, spindle size, processing temperature, thermal conductivity, and thermal expansion coefficient correction variables, constructing a machine tool digital twin model correction optimization problem model; using the improved particle swarm algorithm with the "role model-elite" learning strategy to find a set of optimal parameter combinations, realizing the correction of the machine tool digital twin model, improving the consistency of the twin model with the physical equipment, and improving the accuracy of the machine tool equipment model.

[0005] The objective of the present invention is achieved through the following technical solutions:

[0006] The present invention discloses a method for constructing a multi-scale and multi-domain digital twin model of complex equipment. For machine tool equipment including multi-scale levels (unit level, subsystem level, system level) and multi-disciplinary fields (electrical, control, mechanical), the Modelica modeling language is used to realize the construction of a multi-scale and multi-domain digital twin model of machine tool equipment through non-causal modeling and connector modeling, and the multi-scale level and multi-disciplinary field information of physical entity equipment are uniformly displayed. Based on the multi-scale and multi-domain digital twin model of machine tool equipment, the error between the output of the machine tool digital twin model and the test bench of the machine tool is minimized as the optimization goal, and the improved particle swarm algorithm of the "role model-elite" learning strategy is used to iteratively correct the spindle stiffness, spindle size, processing temperature, thermal conductivity, and thermal expansion coefficient parameters, and find a set of optimal parameter combinations to realize the correction of the digital twin model, improve the consistency of the twin model with the physical equipment, and ensure the accuracy of the digital twin model of the machine tool equipment.

[0007] The present invention discloses a method for constructing a multi-scale and multi-domain digital twin model of complex equipment, comprising the following steps:

[0008] Step 1: Consider the multi-scale and multi-domain characteristics of machine tool equipment, and divide the equipment hierarchy and subject areas. Divide the physical equipment into subsystems in different fields, including control subsystems, power subsystems, and mechanical subsystems, and then divide each subsystem into the smallest components and assemblies. The control subsystem includes PLC control system, PWM control system, and servo control system to control the position, speed, and current of the machine tool. The power subsystem includes motors, contactors, and relays, which are responsible for starting, stopping, and speed regulation of machine tools and motors. The mechanical subsystem includes drive mechanisms, transmission mechanisms, and actuators, which coordinate to complete the processing tasks of the machine tool.

[0009] Step 2: Analyze and determine the physical modeling elements in different disciplines, use physical model relationships to describe the physical phenomena and processes in different fields, and analyze the interaction between parameters in different disciplines of machine tool equipment.

[0010] There are three basic elements in an electrical subsystem: inductance, resistance, and capacitance. If only inductive loads exist in the electrical subsystem, the relationship between load current and voltage is expressed as:

[0011] U(t)=Ldi(t) / dt (1)

[0012] Where U is the voltage and L is the inductance.

[0013] If the circuit contains only resistive loads, the relationship between load current and drive voltage is expressed as:

[0014] U(t)=Ri(t) (2)

[0015] Where R is the load resistance.

[0016] If there is only capacitive load in the subsystem, the relationship between load current and drive voltage is expressed as:

[0017]

[0018] Where C is the capacitance.

[0019] The control subsystem includes position controller and speed controller. The position controller uses PID adjustment to enable the motor spindle to move accurately according to the instructions and reduce deviation:

[0020] w(t)=K p (θ-θ * ) (4)

[0021] Where Kp is the PID proportional adjustment factor and θ is the rotation angle.

[0022] The speed controller uses PID regulation to accurately control the motor speed according to the instructions and reduce the deviation:

[0023]

[0024] Where Ksp is the PID proportional integral adjustment factor, and w is the rotation angular velocity.

[0025] The mechanical subsystem is composed of mechanical elements, which are mainly divided into three categories: elastic elements, inertial elements and damping elements. If there is only one elastic load in the subsystem and one end of the system is fixed, the relationship between the displacement (angle) of the transmission mechanism and the driving force (driving torque) is expressed as:

[0026] F(t)=Kx(t)(T(t)=Gθ(t)) (6)

[0027] Where K is the elastic stiffness coefficient of the transmission component, G is the torsional stiffness coefficient of the transmission mechanism, x is the load displacement, θ is the load rotation angle, F is the driving force, and T is the driving torque.

[0028] If the subsystem has only viscous friction damping load, the relationship between displacement and the driving force of the transmission mechanism is expressed as:

[0029] F(t)=Bdx(t) / dt(T(t)=Bdθ(t) / dt) (7)

[0030] Where B is the viscous damping coefficient of the transmission mechanism and x is the load displacement.

[0031] When the subsystem has only inertial load, the relationship between displacement and driving force of the transmission mechanism is expressed as:

[0032] F(t)=md 2 x(t) / dt 2 (T(t)=Jd 2 θ(t) / dt 2 ) (8)

[0033] Where m is the mass of the load, J is the moment of inertia of the load, x is the load displacement, and θ is the load rotation angle.

[0034] The control system sends control signals to the power system to modify electrical parameters such as voltage and current, so that the power system equipment outputs different dynamic parameters to the mechanical system to control the machine tool processing tasks. In addition, the mechanical system and the power system will output feedback signals to the control system to adjust the control parameters of the control system. The dynamic parameters include rotational angular velocity and rotational torque.

[0035] Step 3: Based on the non-causal modeling characteristics of the Modelica language, the behavior and performance of the system or component are described through the physical model relationship. Based on the laws of physics and the constitutive formulas of components and parts, the Modelica language is used to describe the physical model relationship equations of components and parts, and the physical model relationship model of components and parts is constructed. The instance of the connector class is used to represent the component interface, and the information shared between components and the structure and properties of the interface are defined. The shared information includes flow variables and potential variables; components are connected by connectors to realize the flow of parameters in different disciplines between different component models. From the perspective of energy, through the constitutive equations of the combined components (Ohm's law) and the connectors based on the conservation equations (energy balance, momentum balance and mass balance) and the generalized Kirchhoff's law, the models of various disciplines (electrical, control, mechanical) are seamlessly integrated to obtain the equation group of the entire system. The models of various disciplines include electrical models, control models, and mechanical models. Through model assembly and model integration, the external coupling interfaces between unit models are connected in sequence to complete the construction of subsystem models in various fields. The external coupling interfaces between subsystems in various fields are connected in sequence to complete the construction of multi-domain system models of machine tools, and the construction of multi-scale and multi-domain machine tool models is realized in a unified modeling method.

[0036] Step 4: Taking the minimum error between the output of the machine tool digital twin model and the machine tool test bench test as the optimization goal, and considering the optimization design variables and their upper and lower boundary constraints, the parameters of the twin model are iteratively modified to find a set of optimal parameter combinations to realize the correction of the digital twin model.

[0037] The optimization correction variables include spindle stiffness, spindle size, processing temperature, thermal conductivity, and thermal expansion coefficient parameters.

[0038] According to the upper and lower boundary constraints of the design variables, the objective function is to minimize the error between the output of the machine tool digital twin model and the machine tool test bench test. The objective function is composed of multiple error or residual functions, and each function is relatively independent. The weighted sum of each objective function is performed to minimize the overall trend of the objective function, and then the multi-objective optimization problem is transformed into a single-objective optimization problem. The machine tool correction optimization problem model is constructed as follows:

[0039]

[0040] Where k represents spindle stiffness, l represents spindle size, t represents processing temperature, r represents thermal conductivity, Δq represents thermal expansion coefficient; wi represents the correction weight of different parameters, Psim represents twin model output, and Preal represents actual equipment output; x L and x U They represent the lower and upper bounds of the design variables respectively.

[0041] Step 5. Use the improved particle swarm intelligent optimization algorithm with the "role model-elite" learning strategy to improve the deficiency of the traditional particle swarm algorithm that is prone to premature convergence to the local optimum, effectively solve the optimization and correction problem of the machine tool digital twin model constructed in step 4, find a set of optimal parameter combinations to realize the correction of the digital twin model, improve the consistency between the twin model and the physical equipment, and ensure the accuracy of the digital twin model of the machine tool equipment.

[0042] Step 5.1: In x L ≤x≤x U N groups of model correction parameter combinations are randomly selected within the design interval, and each combination vector x = [k, l, t, r, Δq] is used as a particle to generate an initial population of size N.

[0043] Step 5.2: Import each particle into the machine tool digital twin model, calculate the fitness value of each particle according to formula (10), and record the historical optimal position pbest of each particle and the historical optimal position best of the entire particle group.

[0044]

[0045] Among them, wi represents the correction weights of different parameters, Psim represents the output of the twin model, and Preal represents the actual output of the equipment.

[0046] Step 5.3: Each particle adds a Gaussian perturbation term to the self-learning strategy part of the algorithm, and sets the elite center or model center for each particle in the group as the group learning strategy corresponding to the particle with a certain probability, and calculates the speed Vi of the particle group's advance in the next step, as well as the position Xi+1 after calculating the forward step.

[0047] V i =ω×V i +C1×rand()×(pbest i +ζ1ζ2Gaussian(μ,σ 2 )-X i )+C2×rand()×(Ecenter i -X i ) (11) X i+1 =X i +V i

[0048] Where w is called the inertia factor, C1 and C2 are called learning factors, and rand() and ζ represent random numbers between (0,1). 2 ) means the mean is μ and the variance is σ 2 The Gaussian perturbation factor of . Ecenter represents the particle group learning strategy, which is set to the elite center ELcenter or the model center EXcenter with a certain probability.

[0049]

[0050]

[0051] Step 5.4: Re-enter the new generation of particles x = [k, l, t, r, Δq] into the machine tool digital twin model, modify the model parameters and simulate, calculate the fitness value of each particle, and determine whether the stopping criteria are met. If not, return to step 5.3 and iterate to generate a new population; if satisfied, find a set of optimal correction variable combinations to realize the correction of the digital twin model, improve the consistency of the twin model and the physical equipment, and improve the accuracy of the machine tool equipment model.

[0052] Beneficial effects:

[0053] 1. The present invention discloses a method for constructing a multi-scale and multi-domain digital twin model of complex equipment. Based on the non-causal modeling characteristics of the Modelica language, the behavior and performance of the system or component are described through the physical model relationship to construct component and part models. The component interface is represented by an instance of the connector class, and the information shared between components (flow variables, potential variables) and the structure and properties of the interface are defined; components are connected to each other through connectors to realize the flow of parameters in different disciplines between different component models. Through the coupling connection between interfaces based on the generalized Kirchhoff's law, the assembly and integration between models are completed, and models of different levels (unit level, subsystem level, system level) are constructed, so as to realize the construction of a multi-scale and multi-domain digital twin model of machine tool equipment in a unified modeling manner.

[0054] 2. The present invention discloses a method for constructing a multi-scale and multi-domain digital twin model of complex equipment. Taking into account the error between the digital twin model of a machine tool and the machine tool equipment, the optimization goal is to minimize the error between the output of the digital twin model of the machine tool and the test of the machine tool test bench. An improved particle swarm intelligent optimization algorithm with an "example-elite" learning strategy is adopted. By iteratively modifying the parameters of the twin model, a set of optimal parameter combinations is found to realize the correction of the digital twin model, improve the consistency between the twin model and the physical equipment, and improve the accuracy of the machine tool equipment model.

[0055] 3. The present invention discloses a method for constructing a multi-scale and multi-domain digital twin model of complex equipment, which models the multi-scale dimensions and multi-disciplinary characteristics of machine tool equipment in a unified manner, breaks through the limitations of the complex coupling characteristics of the equipment, and constructs a complete digital twin model to display the comprehensive information of the machine tool equipment. By improving the iterative correction of the particle swarm algorithm, the consistency between the twin model and the physical equipment is improved. The present invention can construct a digital twin model for complex equipment with comprehensive corresponding information and real-time correction, thereby providing effective guarantees for various intelligent functions and services in the operation management and maintenance of complex equipment.

[0056] 4. The present invention discloses a method for constructing a multi-scale and multi-domain digital twin model of complex equipment. The Modelica modeling language is used to model the physical mathematical equations between the parameters of different disciplines (electrical, control, and mechanical) of machine tool equipment through its non-causal modeling characteristics, so as to realize the interaction and conversion between the parameters of different disciplines, and then model the interfaces of each component model. Through the coupling connection between the interfaces based on the generalized Kirchhoff's law, the assembly and integration between the models are completed, and models of different levels (unit level, subsystem level, and system level) are constructed, and finally the construction of a multi-scale and multi-domain digital twin model of machine tool equipment is realized. The multi-scale level and multi-disciplinary field of physical entity equipment are displayed through a unified modeling method, breaking through the limitations of the complex coupling characteristics of the equipment, and constructing a complete digital twin model to display the comprehensive information of the equipment. Taking the minimum error between the output of the machine tool digital twin model and the test of the machine tool test bench as the optimization goal, considering the spindle stiffness, spindle size, processing temperature, thermal conductivity, and thermal expansion coefficient correction variables, a machine tool digital twin model correction optimization problem model is constructed. The improved particle swarm algorithm with the "role model-elite" learning strategy is used to improve the shortcoming of the traditional particle swarm algorithm that it is easy to converge to the local optimum prematurely, and effectively solve the optimization problem. Finally, the improved particle swarm algorithm is used to find a set of optimal parameter combinations to realize the correction of the digital twin model of the machine tool, ensure the consistency of the twin model with the physical equipment, and improve the accuracy of the digital twin model of the machine tool equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is an overall flow chart of an embodiment of a method for constructing a multi-scale and multi-domain digital twin model of complex equipment disclosed in the present invention.

[0058] Figure 2 It is a schematic diagram of the multi-scale composition of the machine tool digital twin model.

[0059] Figure 3 It is a schematic diagram of the multi-domain composition of the machine tool digital twin model.

[0060] Figure 4 Construct a flow chart for the multi-scale, multi-domain digital twin model of machine tools.

[0061] Figure 5 Flowchart for improving particle swarm optimization algorithm implementation.

[0062] Figure 6 Develop an iterative correction and optimization flow chart for the digital twin model of machine tools. DETAILED DESCRIPTION

[0063] The technical solution of the present invention is further described in detail below in conjunction with the accompanying drawings:

[0064] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the design process of the present invention is described in detail below in conjunction with the accompanying drawings. Among them, the same or similar symbols throughout represent the same or similar functions.

[0065] Embodiment 1:

[0066] like Figure 1 As shown, this embodiment discloses a method for constructing a multi-scale and multi-domain digital twin model of complex equipment, and the specific implementation steps are as follows:

[0067] Step 1: Consider the multi-scale and multi-domain characteristics of machine tool equipment and divide the equipment hierarchy and subject areas. Divide the physical equipment into subsystems in different fields, including control subsystems, power subsystems, and mechanical subsystems, and then divide each subsystem into the smallest components and assemblies, such as Figure 2 As shown. The control subsystem includes PLC control system, PWM control system, servo control system, which controls the position, speed and current of the machine tool. The power subsystem includes motor, contactor and relay, which is responsible for starting, stopping and speed regulation of the machine tool and the motor. The mechanical subsystem includes drive mechanism, transmission mechanism and actuator, which coordinate to complete the processing task of the machine tool.

[0068] Step 2: Summarize the physical modeling elements in different disciplines, use mathematical formulas to describe the physical phenomena and processes in different fields, and analyze the interaction between the parameters in different disciplines of machine tool equipment.

[0069] There are three basic elements in an electrical subsystem: inductance, resistance, and capacitance. If only inductive loads exist in the electrical subsystem, the relationship between load current and voltage can be expressed as:

[0070] U(t)=Ldi(t) / dt (14)

[0071] Where U is the voltage and L is the inductance.

[0072] If the circuit contains only resistive loads, the relationship between load current and drive voltage can be expressed as:

[0073] U(t)=Ri(t) (15)

[0074] Where R is the load resistance.

[0075] If there is only capacitive load in the subsystem, the relationship between load current and drive voltage can be expressed as:

[0076]

[0077] Where C is the capacitance.

[0078] The control subsystem includes position controller and speed controller. The position controller uses PID adjustment to enable the motor spindle to move accurately according to the instructions and reduce deviation:

[0079] w(t)=K p (θ-θ * ) (17)

[0080] Where Kp is the PID proportional adjustment factor and θ is the rotation angle.

[0081] The speed controller uses PID regulation to accurately control the motor speed according to the instructions and reduce the deviation:

[0082]

[0083] Where Ksp is the PID proportional integral adjustment factor, and w is the rotation angular velocity.

[0084] The mechanical subsystem is composed of mechanical elements, which are mainly divided into three categories: elastic elements, inertial elements and damping elements. Assuming that there is only one elastic load in the subsystem and one end of the system is fixed, the relationship between the displacement (angle) of the transmission mechanism and the driving force (driving torque) can be expressed as:

[0085] F(t)=Kx(t)(T(t)=Gθ(t)) (19)

[0086] Where K is the elastic stiffness coefficient of the transmission component, G is the torsional stiffness coefficient of the transmission mechanism, x is the load displacement, θ is the load rotation angle, F is the driving force, and T is the driving torque.

[0087] If the subsystem has only viscous friction damping load, the relationship between displacement and the driving force of the transmission mechanism can be expressed as:

[0088] F(t)=Bdx(t) / dt(T(t)=Bdθ(t) / dt) (20)

[0089] Where B is the viscous damping coefficient of the transmission mechanism and x is the load displacement.

[0090] When the subsystem has only inertial load, the relationship between displacement and driving force of the transmission mechanism can be expressed as:

[0091] F(t)=md 2 x(t) / dt 2 (T(t)=Jd 2 θ(t) / dt 2 ) (twenty one)

[0092] Where m is the mass of the load, J is the moment of inertia of the load, x is the load displacement, and θ is the load rotation angle.

[0093] The control system sends control signals to the power system to modify electrical parameters such as voltage and current, so that power system equipment such as motors output different dynamic parameters to the mechanical system, such as rotational angular velocity and rotational torque, to control machine tool processing tasks. In addition, the mechanical system and power system will output feedback signals to the control system to adjust the control parameters of the control system, such as Figure 3 shown.

[0094] Step 3. Based on the non-causal modeling characteristics of the Modelica language, the behavior and performance of the system or component are described through physical mathematical equations. Based on the laws of physics and the constitutive formulas of components and parts, the Modelica language is used to describe the components and parts with mathematical equations to build component and part models. Instances of the connector class are used to represent component interfaces, and the information shared between components (flow variables, potential variables) and the structure and properties of the interface are defined; components are connected to each other through connectors to realize the flow of parameters in different disciplines between different component models. From the perspective of energy, through the constitutive equations of the combined components (Ohm's law) and connectors based on conservation equations (energy balance, momentum balance and mass balance) and generalized Kirchhoff's laws, the models of various disciplines (electrical, control, mechanical) are seamlessly integrated to obtain the equation set of the entire system. Through model assembly and model integration, the external coupling interfaces between the unit models are connected in sequence to complete the construction of the subsystem models of each field, and the external coupling interfaces between the subsystems of each field are connected in sequence to complete the construction of the multi-domain system model of the machine tool. Finally, the construction of the multi-scale and multi-domain machine tool model is realized in a unified modeling method. The modeling process is as follows: Figure 4 shown.

[0095] Step 4: Taking the minimum error between the output of the machine tool digital twin model and the machine tool test bench test as the optimization goal, and considering the optimization design variables and their upper and lower boundary constraints, find a set of optimal parameter combinations to realize the correction of the digital twin model by iteratively modifying the parameters of the simulation model.

[0096] The optimization correction variables include spindle stiffness, spindle size, processing temperature, thermal conductivity, and thermal expansion coefficient parameters.

[0097] According to the upper and lower boundary constraints of the design variables, the objective function is to minimize the error between the output of the machine tool digital twin model and the machine tool test bench test. The objective function is usually composed of multiple error or residual functions, and each function is generally relatively independent. The weighted sum of each objective function is used to minimize its overall trend, and then the multi-objective optimization problem is transformed into a single-objective optimization problem. The mathematical model of the machine tool correction optimization problem is constructed as follows:

[0098]

[0099] Where k represents spindle stiffness, l represents spindle size, t represents processing temperature, r represents thermal conductivity, Δq represents thermal expansion coefficient; wi represents the correction weight of different parameters, Psim represents twin model output, and Preal represents actual equipment output; x L and x U They represent the lower and upper bounds of the design variables respectively.

[0100] Step 5: Use the improved particle swarm intelligent optimization algorithm with the "role model-elite" learning strategy to improve the deficiency of the traditional particle swarm algorithm that is prone to premature convergence to the local optimum, effectively solve the optimization and correction problem of the machine tool digital twin model constructed in step 4, find a set of optimal parameter combinations to realize the correction of the digital twin model, ensure the consistency between the twin model and the physical equipment, improve the accuracy of the machine tool equipment model, and improve the implementation process of the particle swarm optimization algorithm. Figure 5 The specific implementation steps are as follows:

[0101] Step 5.1: In x L ≤x≤x U N = 50 combinations of model correction parameters are randomly selected within the design interval, and each combination vector x = [k, l, t, r, Δq] is used as a particle to generate an initial population of size N = 50.

[0102] Step 5.2: Import each particle into the machine tool digital twin model, calculate the fitness value of each particle according to formula (10), and record the historical optimal position pbest of each particle and the historical optimal position best of the entire particle group.

[0103]

[0104] Among them, wi represents the correction weights of different parameters, Psim represents the output of the twin model, and Preal represents the actual output of the equipment.

[0105] Step 5.3: Each particle adds a Gaussian perturbation term to the self-learning strategy part of the algorithm, and sets the elite center or model center for each particle in the group as the group learning strategy corresponding to the particle with a certain probability, and calculates the speed Vi of the particle group's advance in the next step, as well as the position Xi+1 after calculating the forward step.

[0106] V i =ω×V i +C1×rand()×(pbest i +ζ1ζ2Gaussian(μ,σ 2 )-X i )+C2×rand()×(Ecenter i -X i ) (24) X i+1 =X i +V i

[0107] Where w is called the inertia factor, C1 and C2 are called learning factors, and rand() and ζ represent random numbers between (0,1). 2 ) means the mean is μ and the variance is σ 2 The Gaussian perturbation factor of . Ecenter represents the particle group learning strategy, which is set to the elite center ELcenter or the model center EXcenter with a certain probability.

[0108]

[0109] Step 5.4: Re-enter the new generation of particles x = [k, l, t, r, Δq] into the machine tool digital twin model, modify the model parameters and simulate, calculate the fitness value of each particle, and determine whether the stopping criteria are met. If not, repeat step 5.3 and iteratively search for the best result to generate a new population; if satisfied, find a set of optimal correction variable combinations to implement digital twin model correction, ensure the consistency of the twin model with the physical equipment, and improve the accuracy of the machine tool equipment model. The iterative correction optimization process of the machine tool digital twin model is as follows: Figure 6 shown.

[0110] The present invention discloses a method for constructing a multi-scale and multi-domain digital twin model of complex equipment, which can model the multi-scale dimensions and multi-disciplinary characteristics of machine tool equipment in a unified manner, break through the limitations of the complex coupling characteristics of the equipment, and construct a complete digital twin model to display the comprehensive information of the equipment. By improving the iterative correction of the particle swarm algorithm, the consistency between the twin model and the physical equipment is improved. The present invention can construct a digital twin model with comprehensive and real-time correction of corresponding information for complex equipment, which is helpful for the realization of various intelligent functions and services in the operation management and maintenance of complex equipment.

[0111] The specific description above further illustrates the purpose, technical solutions and beneficial effects of the invention in detail. It should be understood that the above is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for constructing a multi-scale and multi-domain digital twin model of complex equipment, characterized by: The following steps are included: Step 1: Consider the multi-scale and multi-domain characteristics of machine tool equipment, and divide the equipment hierarchy and subject areas; divide the physical equipment into subsystems in different fields, including control subsystem, power subsystem, and mechanical subsystem, and then divide each subsystem into the smallest components and assemblies; the control subsystem includes PLC control system, PWM control system, and servo control system to control the position, speed, and current of the machine tool; the power subsystem includes motors, contactors, and relays, which are responsible for starting, stopping, and speed regulation of the machine tool and the motor; the mechanical subsystem includes drive mechanism, transmission mechanism, and actuator, which coordinate to complete the processing tasks of the machine tool; Step 2: Analyze and determine the physical modeling elements in different disciplines, use physical model relationships to describe the physical phenomena and processes in different fields, and analyze the interaction between parameters in different disciplines of machine tool equipment; Step 3: Based on the non-causal modeling characteristics of the Modelica language, the behavior and performance of the system or component are described through the physical model relationship. Based on the laws of physics and the constitutive formulas of components and parts, the Modelica language is used to describe the physical model relationship equations of components and parts, and the physical model relationship model of components and parts is constructed; the instance of the connector class is used to represent the component interface, and the information shared between components and the structure and properties of the interface are defined. The shared information includes flow variables and potential variables; the components are connected to each other through connectors to realize the flow of parameters in different subject areas between different component models; from the perspective of energy, the constitutive equations of the combined components and the connectors based on the conservation equations and the generalized Kirchhoff's law are used to seamlessly integrate the models of various disciplines to obtain the equation group of the entire system. The models of various disciplines include electrical models, control models, and mechanical models; through model assembly and model integration, the external coupling interfaces between the unit models are connected in sequence to complete the construction of the subsystem models of each field, and the external coupling interfaces between the subsystems of each field are connected in sequence to complete the construction of the multi-domain system model of the machine tool, and the construction of the multi-scale and multi-domain machine tool model is realized in a unified modeling manner; Step 4: Taking the minimum error between the output of the machine tool digital twin model and the machine tool test bench test as the optimization goal, and considering the optimization design variables and their upper and lower boundary constraints, the parameters of the twin model are iteratively modified to find a set of optimal parameter combinations to realize the correction of the digital twin model; The optimization correction variables include spindle stiffness, spindle size, processing temperature, thermal conductivity, and thermal expansion coefficient parameters; Step 5. Use the improved particle swarm intelligent optimization algorithm with the "role model-elite" learning strategy to effectively solve the optimization and correction problem of the machine tool digital twin model constructed in step 4, find a set of optimal parameter combinations to realize the correction of the digital twin model, improve the consistency between the twin model and the physical equipment, and improve the accuracy of the machine tool equipment model.

2. A method for constructing a multi-scale and multi-domain digital twin model of complex equipment according to claim 1, characterized in that: The implementation method of step 2 is: An electrical subsystem has three basic components: inductance, resistance, and capacitance; if only inductive loads exist in the electrical subsystem, the relationship between load current and voltage is expressed as: U(t)=Ldi(t) / dt (1) Where U is voltage and L is inductance; If the circuit contains only resistive loads, the relationship between load current and drive voltage is expressed as: U(t)=Ri(t) (2) Where R is the load resistance; If there is only capacitive load in the subsystem, the relationship between load current and drive voltage is expressed as: Where C is the capacitance; The control subsystem includes a position controller and a speed controller. The position controller uses PID regulation to enable the motor spindle to move accurately according to instructions and reduce deviations: w(t)=K p (θ-θ * ) (4) Where Kp is the PID proportional adjustment factor, and θ is the rotation angle; The speed controller uses PID regulation to accurately control the motor speed according to the instructions and reduce the deviation: Where Ksp is the PID proportional integral adjustment factor, w is the rotation angular velocity; The mechanical subsystem is composed of mechanical elements, which are mainly divided into three categories: elastic elements, inertial elements and damping elements. There is only one elastic load in the subsystem and one end of the system is fixed. The relationship between the displacement and driving force of the transmission mechanism is expressed as: F(t)=Kx(t)(T(t)=Gθ(t)) (6) Where K is the elastic stiffness coefficient of the transmission component, G is the torsional stiffness coefficient of the transmission mechanism; x is the load displacement, θ is the load rotation angle, F is the driving force, and T is the driving torque; If the subsystem has only viscous friction damping load, the relationship between displacement and the driving force of the transmission mechanism is expressed as: F(t)=Bdx(t) / dt(T(t)=Bdθ(t) / dt) (7) Where B is the viscous damping coefficient of the transmission mechanism, and x is the load displacement; When the subsystem has only inertial load, the relationship between displacement and driving force of the transmission mechanism is expressed as: F(t)=md 2 x(t) / dt 2 (T(t)=Jd 2 θ(t) / dt 2 ) (8) Where m is the mass of the load, J is the moment of inertia of the load, x is the load displacement, and θ is the load rotation angle; The control system sends control signals to the power system to modify electrical parameters such as voltage and current, so that the power system equipment outputs different dynamic parameters to the mechanical system to control the machine tool processing tasks; in addition, the mechanical system and the power system will output feedback signals to the control system to adjust the control parameters of the control system; the dynamic parameters include rotational angular velocity and rotational torque.

3. A method for constructing a multi-scale and multi-domain digital twin model of complex equipment as claimed in claim 2, characterized in that: In step four, According to the upper and lower boundary constraints of the design variables, the objective function is to minimize the error between the output of the machine tool digital twin model and the test bench test of the machine tool; The objective function is composed of multiple error or residual functions, and each function is relatively independent. The objective functions are weighted and summed to minimize the overall trend of the objective function, and then the multi-objective optimization problem is transformed into a single-objective optimization problem. The machine tool correction optimization problem model is constructed as follows: Where k represents spindle stiffness, l represents spindle size, t represents processing temperature, r represents thermal conductivity, Δq represents thermal expansion coefficient; wi represents the correction weight of different parameters, Psim represents twin model output, and Preal represents actual equipment output; x L and x U They represent the lower and upper bounds of the design variables respectively.

4. A method for constructing a multi-scale and multi-domain digital twin model of complex equipment as claimed in claim 3, characterized in that: Step 5 is implemented as follows: Step 5.1: In x L ≤x≤x U Randomly select N groups of model correction parameter combinations within the design interval, and each combination vector x = [k, l, t, r, Δq] is used as a particle to generate an initial population of size N; Step 5.2: Import each particle into the machine tool digital twin model, calculate the fitness value of each particle according to formula (10), and record the historical optimal position pbest of each particle and the historical optimal position best of the entire particle group; Where wi represents the correction weights of different parameters, Psim represents the output of the twin model, and Preal represents the actual output of the equipment; Step 5.3: Each particle adds a Gaussian perturbation term to the self-learning strategy part of the algorithm, and sets the elite center or model center as the group learning strategy corresponding to each particle in the group with a certain probability, and calculates the speed Vi of the particle group in the next step, as well as the position Xi+1 after calculating the forward step; V i =ω×V i +C1×rand()×(pbest i +ζ1ζ2Gaussian(μ,σ 2 )-X i )+C2×rand()×(Ecenter i -X i ) (11) X i+1 =X i +V i Among them, w is called the inertia factor, C1 and C2 are called learning factors, rand() and ζ represent random numbers between (0,1); Gaussian (μ,σ 2 ) means the mean is μ and the variance is σ 2 Gaussian perturbation factor; Ecenter represents the particle group learning strategy, which is set to the elite center ELcenter or the model center EXcenter with a certain probability; Step 5.4: Re-input the new generation of particles x=[k,l,t,r,Δq] into the digital twin model of the machine tool, modify the model parameters and simulate, calculate the fitness value of each particle, and determine whether the stopping criteria are met; if not, return to step 5.3 and iterate to generate a new population; if satisfied, find a set of optimal correction variable combinations to realize the correction of the digital twin model, improve the consistency of the twin model with the physical equipment, and ensure the accuracy of the digital twin model of the machine tool equipment.

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