Intelligent vibration suppression method and system for multi-physics field cooperative control

Abstract

The invention relates to the technical field of vibration suppression, and discloses a multi-physics field cooperative control intelligent vibration suppression method and system, and the method comprises the following steps: S1, building a multi-physics field digital twinborn model based on a finite element analysis method, the multi-physical field at least comprises a coupling relationship among a pneumatic field, an electromagnetic field and a structural field, and the modeling of the fluid-structure interaction effect is included; and S2, based on the digital twin model, an improved particle swarm optimization algorithm is adopted to generate a control weight distribution scheme, and the control weight distribution scheme is used for dynamically adjusting the output proportion of a pneumatic actuator and an electromagnetic actuator. According to the method, the multi-physical field digital twinborn model containing the coupling relation of the pneumatic field, the electromagnetic field and the structural field and taking into account the fluid-solid coupling effect is established, so that the problem that the control precision is limited due to the fact that a single physical field control strategy is mostly adopted in a traditional vibration suppression method is solved.

Description

Technical Field

[0001] The present invention relates to the field of vibration suppression technology, and in particular to an intelligent vibration suppression method and system for collaborative control of multiple physical fields. Background Art

[0002] Vibration suppression is an engineering technique that controls the vibration amplitude, frequency, or energy of mechanical systems through technical means to ensure stable equipment operation. In fields such as mechanical engineering, aerospace, and vehicle manufacturing, excessive vibration can lead to structural fatigue, reduced accuracy, and even safety accidents. Therefore, vibration suppression is a key technology for ensuring system reliability.

[0003] Most traditional vibration suppression methods adopt a single physical field control strategy. Since the single physical field control strategy fails to take into account multi-field coupling and fluid-solid interaction, it results in limited control accuracy. Summary of the Invention

[0004] In order to make up for the above shortcomings, the present invention provides an intelligent vibration suppression method and system with multi-physical field collaborative control, aiming to improve the problem that most traditional vibration suppression methods adopt a single physical field control strategy. Since the single physical field control strategy does not take into account multi-field coupling and fluid-solid interaction, it causes limited control accuracy.

[0005] In a first aspect, the present invention provides the following technical solution: an intelligent vibration suppression method for multi-physics field collaborative control, comprising the following steps:

[0006] S1. Establish a multi-physics digital twin model based on the finite element analysis method, wherein the multi-physics field includes at least the coupling relationship between the aerodynamic field, the electromagnetic field, and the structural field, and takes into account the modeling of the fluid-structure coupling effect;

[0007] S2. Based on the digital twin model, an improved particle swarm optimization algorithm is used to generate a control weight distribution scheme, where the control weight distribution scheme is used to dynamically adjust the output ratio of the pneumatic actuator and the electromagnetic actuator;

[0008] S3. Real-time vibration state data is collected through a sensor array, the data is pre-processed by Kalman filtering, and the data is fed back to the digital twin model for iterative optimization of the control weight distribution scheme;

[0009] S4. Based on the optimized control weight distribution scheme, each physical field actuator is driven to output reverse restraining force in a coordinated manner through a synchronous control mechanism.

[0010] By adopting the above technical solution, a multi-physics field digital twin model that includes the coupling relationship of aerodynamic field, electromagnetic field and structural field and takes into account the fluid-solid coupling effect is established, and an improved particle swarm optimization algorithm is used to generate a control weight distribution scheme. Combined with real-time vibration data feedback, iterative optimization is carried out and the actuator is driven to collaboratively output reverse suppression force, thereby achieving precise vibration suppression in multi-physics field collaboration, thereby improving the traditional vibration suppression method that mostly adopts a single physical field control strategy. Since the single physical field control strategy does not take into account multi-field coupling and fluid-solid interaction, the control accuracy is limited.

[0011] Preferably, the multi-physics field digital twin model is established in step S1, including:

[0012] Construct a three-dimensional geometric model of the vibration system and map the material parameters, dynamic boundary conditions, and field distribution characteristics of each physical field;

[0013] A coupling equation including the Navier-Stokes equations and the Maxwell equations is established, wherein the coupling equation is used to characterize the interaction relationship between fluid dynamics, electromagnetic force and structural vibration.

[0014] Preferably, the iterative process of the improved particle swarm optimization algorithm in step S2 includes: initializing the position and velocity of the particle swarm, where the position corresponds to the control weight vector of each physical field; evaluating the particles using a fitness function based on the vibration suppression effect, where the fitness function is expressed as: Where α is the weight coefficient, e i is the vibration error of the i-th sampling point;

[0015] Execute the velocity iteration formula and the position iteration formula until the convergence condition is met, the formulas include:

[0016] Speed ​​iteration:

[0017] Position iteration:

[0018] Among them, v id is the particle velocity, x id is the particle position, w is the inertia weight, c1 and c2 are learning factors, r1 and r2 are random numbers, and p id is the optimal solution for individual particles, p gd is the global optimal solution, and k is the number of iterations.

[0019] Preferably, the vibration state data in step S3 includes vibration amplitude, frequency, phase and modal parameters, the sensor array includes acceleration sensors and displacement sensors, and the real-time acquisition period is 0.1ms-10ms.

[0020] Preferably, in step S4, driving each physical field actuator to cooperatively output a reverse restraining force includes:

[0021] When the vibration amplitude exceeds a preset threshold, the airflow pressure of the pneumatic actuator and the excitation current of the electromagnetic actuator are adjusted according to the weight distribution scheme, and the actuator response delay is compensated through the PID (Proportional-Integral-Derivative Controller) controller adjustment mechanism.

[0022] Preferably, the vibration state data collected by the sensor array in step S3 is processed in real time by an edge computing node, and the edge computing node communicates with the cloud server to update the parameter library of the digital twin model.

[0023] In a second aspect, the present invention provides the following technical solution: an intelligent vibration suppression system for collaborative control of multiple physical fields, comprising: a model building module, an algorithm processing module, a data feedback module, an execution control module, and a storage module;

[0024] The model building module is used to establish a multi-physics field digital twin model based on finite element analysis, and the model includes the coupling relationship between aerodynamic, electromagnetic and structural fields and the modeling of fluid-solid coupling effects;

[0025] The algorithm processing module is in communication with the model building module and is used to generate a control weight distribution scheme using an improved particle swarm optimization algorithm;

[0026] The data feedback module is in communication with the model building module and is used to collect vibration data through the sensor array and transmit it to the model building module after pre-processing by Kalman filtering;

[0027] The execution control module is in communication with the algorithm processing module, is used to drive the actuator through a synchronous control mechanism, and includes a PID compensation unit for adjusting the execution delay;

[0028] The storage module is used to save historical vibration data, optimized weighting schemes and iterative versions of the digital twin model.

[0029] Preferably, the algorithm processing module includes: a particle swarm optimization unit and a weight adjustment unit, the particle swarm optimization unit is used to perform the initialization, fitness evaluation and iterative optimization process of the improved particle swarm optimization algorithm to generate the control weight distribution scheme; the weight adjustment unit is used to dynamically adjust the aerodynamic field weight coefficient β1 and the electromagnetic field weight coefficient β2 according to real-time vibration data, where β1+β2=1.

[0030] In the third aspect, the invention provides the following technical solution: a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned intelligent vibration suppression method for collaborative control of multiple physical fields when executing the computer program.

[0031] In a fourth aspect, the present invention provides the following technical solution: a readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the above-mentioned intelligent vibration suppression method of multi-physical field collaborative control.

[0032] The present invention has the following beneficial effects:

[0033] 1. In the present invention, a multi-physics field digital twin model is established, which includes the coupling relationship of aerodynamic field, electromagnetic field and structural field and takes into account the fluid-solid coupling effect. An improved particle swarm optimization algorithm is used to generate a control weight distribution scheme. The iterative optimization is combined with real-time vibration data feedback to drive the actuator to collaboratively output the reverse suppression force, thereby realizing precise vibration suppression in the coordination of multiple physical fields. This improves the problem that most traditional vibration suppression methods adopt a single physical field control strategy. Since the single physical field control strategy does not take into account multi-field coupling and fluid-solid interaction, the control accuracy is limited.

[0034] 2. In the present invention, an improved particle swarm optimization algorithm is used based on a digital twin model to generate a control weight distribution scheme for dynamically adjusting the output ratio of pneumatic and electromagnetic actuators, thereby realizing intelligent optimization and adaptation of the control strategy, thereby improving the problem that most traditional vibration suppression methods use fixed control parameters and cannot be dynamically adjusted according to changes in working conditions, resulting in poor suppression effect under complex working conditions.

[0035] 3. In the present invention, vibration state data is collected in real time through a sensor array and fed back to the digital twin model after Kalman filter preprocessing to iteratively optimize the control weight distribution scheme, thereby realizing real-time dynamic iteration of the control strategy, thereby improving the problem that most traditional vibration suppression methods lack a real-time data feedback mechanism and cannot update the control strategy according to the actual vibration state, resulting in control lag. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a schematic diagram of the steps of the intelligent vibration suppression method for multi-physics field coordinated control proposed by the present invention;

[0037] Figure 2 This is a system architecture diagram of the intelligent vibration suppression system with multi-physical field collaborative control proposed in the present invention. DETAILED DESCRIPTION

[0038] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0039] Example 1

[0040] Reference Figure 1 In a first embodiment of the present invention, the present invention provides an intelligent vibration suppression method for multi-physics field coordinated control, comprising the following steps:

[0041] S1. Establish a multi-physics digital twin model based on the finite element analysis method. The multi-physics field includes at least the coupling relationship between the aerodynamic field, electromagnetic field and structural field, and takes into account the modeling of fluid-structure coupling effects;

[0042] S2. Based on the digital twin model, an improved particle swarm optimization algorithm is used to generate a control weight allocation scheme, which is used to dynamically adjust the output ratio of the pneumatic actuator and the electromagnetic actuator;

[0043] S3. Vibration state data is collected in real time through a sensor array. After Kalman filter preprocessing, the data is fed back to the digital twin model for iterative optimization of the control weight allocation scheme.

[0044] S4. Based on the optimized control weight distribution scheme, each physical field actuator is driven to output reverse restraining force in a coordinated manner through a synchronous control mechanism.

[0045] Specifically, through S1, a digital twin model including the coupling relationship of aerodynamic field, electromagnetic field, structural field and fluid-solid coupling effect is established based on finite element analysis, and the geometric structure, material parameters, boundary conditions and field distribution characteristics of the vibration system under multi-field interaction are accurately mapped, providing accurate physical model support for cross-field coupling for vibration suppression; through S2, an improved particle swarm optimization algorithm is adopted to initialize the particle swarm position (corresponding to the control weight vector), evaluate the fitness function based on the vibration error and the speed-position iteration formula to generate a control weight distribution scheme for dynamically adjusting the output ratio of the pneumatic actuator and the electromagnetic actuator, thereby realizing intelligent optimization of the control weight of the multi-physical field actuator; through S3, the vibration amplitude, frequency, phase and modal parameters are collected in real time at a period of 0.1ms-10ms through the sensor array (including acceleration and displacement sensors), and the noise is pre-processed by Kalman filtering and fed back to the digital twin model to form an iterative optimization closed loop of the control weight distribution scheme to ensure dynamic matching between the model and the actual vibration state; through S4, when the vibration amplitude exceeds the threshold, the optimal value is obtained according to the optimal value. The optimized weight distribution scheme coordinates the airflow pressure of the pneumatic actuator and the excitation current regulation of the electromagnetic actuator through a synchronous control mechanism, and uses PID regulation to compensate for the execution delay, driving the multi-physics field actuators to collaboratively output the reverse inhibition force, thereby realizing active cancellation and dynamic suppression of vibration; various technical features work together to construct a "modeling-optimization-feedback-execution" intelligent control closed loop to solve the problem of precise vibration suppression in a multi-physics field coupling environment, which is suitable for high-frequency response and high-precision scenarios such as precision instrument shock absorption and spacecraft attitude control; by establishing a multi-physics field digital twin model that includes the coupling relationship of aerodynamic fields, electromagnetic fields and structural fields and takes into account the fluid-solid coupling effect, an improved particle swarm optimization algorithm is used to generate a control weight distribution scheme, combined with real-time vibration data feedback iterative optimization and drive the actuators to collaboratively output the reverse inhibition force, thereby realizing precise vibration suppression in multi-physics field collaboration, thereby improving the traditional vibration suppression method that mostly adopts a single physical field control strategy. Since the single physical field control strategy does not take into account multi-field coupling and fluid-solid interaction, the control accuracy is limited.

[0046] In step S1, a multi-physics digital twin model is established, including:

[0047] Construct a three-dimensional geometric model of the vibration system and map the material parameters, dynamic boundary conditions, and field distribution characteristics of each physical field;

[0048] The coupling equations including Navier-Stokes equations and Maxwell equations are established, and the coupling equations are used to characterize the interaction relationship between fluid dynamics, electromagnetic force and structural vibration.

[0049] Specifically, a three-dimensional geometric model of the vibration system is constructed, and the material parameters, dynamic boundary conditions and field distribution characteristics of each physical field are mapped, which can accurately restore the physical structure and multi-field environment of the vibration system, and provide an accurate basic model for subsequent analysis; a coupling equation including the Navier-Stokes equations and the Maxwell equations is established, and the interaction between fluid dynamics, electromagnetic force and structural vibration is expressed in the form of mathematical equations, realizing a quantitative description of the coupling relationship between aerodynamic field, electromagnetic field and structural field, accurately characterizing the vibration characteristics under the synergistic action of multiple physical fields, and providing a theoretical basis for the formulation of control strategies.

[0050] The iterative process of the improved particle swarm optimization algorithm in step S2 includes: initializing the position and velocity of the particle swarm, where the position corresponds to the control weight vector of each physical field; evaluating the particles using a fitness function based on the vibration suppression effect, and the fitness function expression is: Where α is the weight coefficient, e i is the vibration error of the i-th sampling point;

[0051] Execute the velocity iteration formula and the position iteration formula until the convergence conditions are met. The formulas include:

[0052] Speed ​​iteration:

[0053] Position iteration:

[0054] Among them, v id is the particle velocity, x id is the particle position, w is the inertia weight, c1 and c2 are learning factors, r1 and r2 are random numbers, and p id is the optimal solution for individual particles, p gd is the global optimal solution, and k is the number of iterations.

[0055] Specifically, the position of the particle swarm is initialized to the control weight vector of each physical field to provide starting parameters for subsequent optimization, establish a mapping relationship between the control weight and the particle position, and determine the starting point of the optimization search; adopt a fitness function based on the vibration suppression effect The vibration error of the sampling point is e i As the core parameter, combined with the weight coefficient α, the vibration suppression effect is quantified into a numerical index, providing a clear standard for particle quality evaluation and guiding the optimization direction; through the speed iteration formula and position iteration formula Using the inertia weight w, learning factors c1 and c2, random numbers r1 and r2, and the individual optimal solution p id and the global optimal solution p gd, so that the particles continuously adjust their positions in the solution space, and the value range of the inertia weight w is controlled in 0.4-0.9. Through iterative search, the optimal control weight distribution scheme for vibration suppression is gradually approached until the convergence condition is met, realizing intelligent optimization of the output proportional control weights of the pneumatic actuator and electromagnetic actuator.

[0056] The vibration state data in step S3 includes vibration amplitude, frequency, phase and modal parameters. The sensor array includes acceleration sensors and displacement sensors. The real-time acquisition period is 0.1ms-10ms.

[0057] Specifically, through a sensor array composed of acceleration sensors and displacement sensors, vibration amplitude, frequency, phase and modal parameters are collected, and vibration status information is obtained from multiple dimensions such as vibration intensity, change frequency, phase relationship and vibration mode, which comprehensively reflects the vibration characteristics of the system; data is acquired in real time with an acquisition cycle of 0.1ms-10ms to ensure that high-frequency vibration signals and rapidly changing vibration states can be captured, meeting the needs of real-time vibration monitoring and rapid response control, and providing an accurate data basis for timely adjustment of control strategies.

[0058] In step S4, driving each physical field actuator to collaboratively output a reverse restraining force includes:

[0059] When the vibration amplitude exceeds the preset threshold, the airflow pressure of the pneumatic actuator and the excitation current of the electromagnetic actuator are adjusted according to the weight distribution scheme, and the actuator response delay is compensated through the PID adjustment mechanism.

[0060] Specifically, the preset threshold of the vibration amplitude is used as the trigger condition, and the control is started when it exceeds 50μm. The control process is only started when the vibration exceeds the set range to avoid invalid intervention, ensure that the system accurately intervenes in vibration suppression when necessary, and improve the pertinence and efficiency of the control strategy; according to the weight distribution scheme, the airflow pressure of the pneumatic actuator and the excitation current of the electromagnetic actuator are accurately adjusted to achieve the coordinated operation of the pneumatic field and the electromagnetic field actuator, and the vibration source excitation is offset by the output reverse suppression force to form a combined force of multiple physical fields to jointly suppress vibration; with the help of the PID adjustment mechanism, the actuator response delay is monitored and compensated in real time, and the control parameters are dynamically adjusted to ensure that the reverse suppression force output by each actuator accurately matches the actual vibration state, avoid control lag caused by delay, and improve the real-time and effectiveness of vibration suppression.

[0061] In step S3, the vibration state data collected by the sensor array is processed in real time by the edge computing node, and the edge computing node communicates with the cloud server to update the parameter library of the digital twin model.

[0062] Specifically, edge computing nodes are used to process the vibration state data collected by the sensor array in real time to avoid delays in data transmission to the cloud, achieve rapid analysis and preprocessing of vibration data, and meet the vibration suppression system's requirements for data processing timeliness; the edge computing nodes communicate the processed data with the cloud server to update the parameter library of the digital twin model, so that the digital twin model can reflect the vibration state changes of the actual system in real time, ensure the consistency of the model with the actual working conditions, improve the accuracy and effectiveness of the model, and provide a reliable basis for the optimization of the vibration suppression strategy.

[0063] Example 2:

[0064] Reference Figure 2 , in a second embodiment of the present invention, the present invention provides an intelligent vibration suppression system for multi-physical field collaborative control, comprising: a model building module, an algorithm processing module, a data feedback module, an execution control module and a storage module;

[0065] The model building module is used to establish a multi-physics digital twin model based on finite element analysis. The model includes the coupling relationship between aerodynamics, electromagnetics and structural fields, as well as the modeling of fluid-structure coupling effects.

[0066] The algorithm processing module is in communication with the model building module and is used to generate a control weight distribution scheme using an improved particle swarm optimization algorithm;

[0067] The data feedback module is in communication with the model building module and is used to collect vibration data through the sensor array and transmit it to the model building module after pre-processing by Kalman filtering;

[0068] The execution control module is in communication with the algorithm processing module, and is used to drive the actuator through a synchronous control mechanism, and includes a PID compensation unit for adjusting the execution delay;

[0069] The storage module is used to save historical vibration data, optimized weighting schemes, and iterative versions of the digital twin model.

[0070] Specifically, the model building module constructs a multi-physics field digital twin model based on finite element analysis, which includes the coupling relationship between pneumatic, electromagnetic, and structural fields, as well as fluid-structure coupling effects. It maps the system's geometric structure, material parameters, and field distribution characteristics, providing an accurate physical model foundation for vibration suppression. The algorithm processing module communicates with the model building module and uses an improved particle swarm optimization algorithm to generate a control weight distribution scheme that dynamically adjusts the output ratio of the pneumatic and electromagnetic actuators, thereby realizing intelligent optimization of the control strategy. The data feedback module collects vibration data through a sensor array and transmits it to the model building module after Kalman filter preprocessing, forming a real-time closed loop from data acquisition and processing to model updating, ensuring that the system iteratively optimizes the control scheme based on the actual vibration state. The execution control module receives the weight scheme from the algorithm processing module, drives the actuators to coordinately output the reverse suppression force through a synchronous control mechanism, and uses the PID compensation unit to adjust the execution delay to ensure real-time matching between control instructions and actual vibration. The storage module saves historical vibration data, optimized weight schemes, and iterative versions of the digital twin model, providing data support for model optimization, while facilitating the tracing of the evolution of the control strategy and supporting continuous improvement of the system. Each module works together through communication connections to build a complete control system of "modeling-optimization-feedback-execution-storage" to achieve precise vibration suppression in a multi-physical field coupling environment.

[0071] The algorithm processing module includes: a particle swarm optimization unit and a weight adjustment unit. The particle swarm optimization unit is used to perform the initialization, fitness evaluation and iterative optimization process of the improved particle swarm optimization algorithm to generate a control weight distribution scheme; the weight adjustment unit is used to dynamically adjust the aerodynamic field weight coefficient β1 and the electromagnetic field weight coefficient β2 according to real-time vibration data, where β1+β2=1.

[0072] Specifically, the particle swarm optimization unit performs the initialization, fitness evaluation and iterative optimization process of the improved particle swarm optimization algorithm, takes the control weight vector of each physical field as the particle position for iterative calculation, evaluates the quality of the particles based on the fitness function of the vibration suppression effect, and uses the speed and position iterative formula to gradually search for the optimal solution, thereby generating a control weight distribution scheme for the pneumatic actuator and the electromagnetic actuator, providing initial control parameters for vibration suppression; the weight adjustment unit dynamically adjusts the pneumatic field weight coefficient β1 and the electromagnetic field weight coefficient β2 based on the real-time collected vibration data, and flexibly changes the output ratio of the pneumatic and electromagnetic actuators under the constraint of β1+β2=1, so that the system can adapt to the vibration changes under different working conditions, and realize real-time optimization and precise control of the vibration suppression strategy.

[0073] Example 3

[0074] The third embodiment of the present invention is based on the same inventive concept. The present invention proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the intelligent vibration suppression method of multi-physical field collaborative control of the above-mentioned embodiment.

[0075] Example 4

[0076] The fourth embodiment of the present invention is based on the same inventive concept. The present invention proposes a computer device, including: a processor and a memory; the processor and the memory communicate with each other; the memory is used to store instructions; the processor is used to execute the instructions in the memory to execute the intelligent vibration suppression method of multi-physical field collaborative control of the above embodiment.

[0077] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0078] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or make equivalent substitutions for some of the technical features therein. 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. An intelligent vibration suppression method based on multi-physics field collaborative control, characterized in that: The following steps are involved: S1. Establish a multi-physics digital twin model based on the finite element analysis method, wherein the multi-physics field includes at least the coupling relationship between the aerodynamic field, the electromagnetic field, and the structural field, and takes into account the modeling of the fluid-structure coupling effect; S2. Based on the digital twin model, an improved particle swarm optimization algorithm is used to generate a control weight distribution scheme, where the control weight distribution scheme is used to dynamically adjust the output ratio of the pneumatic actuator and the electromagnetic actuator; S3. Real-time vibration state data is collected through a sensor array, the data is pre-processed by Kalman filtering, and the data is fed back to the digital twin model for iterative optimization of the control weight distribution scheme; S4. Based on the optimized control weight distribution scheme, each physical field actuator is driven to output reverse restraining force in a coordinated manner through a synchronous control mechanism.

2. The intelligent vibration suppression method for multi-physics field coordinated control according to claim 1 is characterized in that: The multi-physics field digital twin model is established in step S1, including: Construct a three-dimensional geometric model of the vibration system and map the material parameters, dynamic boundary conditions, and field distribution characteristics of each physical field; A coupling equation including the Navier-Stokes equations and the Maxwell equations is established, wherein the coupling equation is used to characterize the interaction relationship between fluid dynamics, electromagnetic force and structural vibration.

3. The intelligent vibration suppression method for multi-physics field coordinated control according to claim 1 is characterized in that: The iterative process of the improved particle swarm optimization algorithm in step S2 includes: initializing the position and velocity of the particle swarm, where the position corresponds to the control weight vector of each physical field; and evaluating the particles using a fitness function based on the vibration suppression effect, where the fitness function is expressed as: Where α is the weight coefficient, e i is the vibration error of the i-th sampling point; Execute the velocity iteration formula and the position iteration formula until the convergence condition is met, the formulas include: Speed ​​iteration: Position iteration: Among them, v id is the particle velocity, x id is the particle position, w is the inertia weight, c1 and c2 are learning factors, r1 and r2 are random numbers, and p id is the optimal solution for individual particles, p gd is the global optimal solution, and k is the number of iterations.

4. The intelligent vibration suppression method for multi-physics field coordinated control according to claim 1, characterized in that: The vibration state data in step S3 includes vibration amplitude, frequency, phase and modal parameters. The sensor array includes acceleration sensors and displacement sensors. The period of real-time acquisition is 0.1ms-10ms.

5. The intelligent vibration suppression method of multi-physics field coordinated control according to claim 1 is characterized in that: Driving each physical field actuator to cooperatively output a reverse restraining force in step S4 includes: When the vibration amplitude exceeds a preset threshold, the airflow pressure of the pneumatic actuator and the excitation current of the electromagnetic actuator are adjusted according to the weight distribution scheme, and the actuator response delay is compensated through the PID adjustment mechanism.

6. The intelligent vibration suppression method for multi-physics field coordinated control according to any one of claims 1 to 5, characterized in that: The vibration state data collected by the sensor array in step S3 is processed in real time by the edge computing node, and the edge computing node communicates with the cloud server to update the parameter library of the digital twin model.

7. Intelligent vibration suppression system with multi-physics field coordinated control, characterized by: include: Model building module, algorithm processing module, data feedback module, execution control module and storage module; The model building module is used to establish a multi-physics field digital twin model based on finite element analysis, and the model includes the coupling relationship between aerodynamic, electromagnetic and structural fields and the modeling of fluid-solid coupling effects; The algorithm processing module is in communication with the model building module and is used to generate a control weight distribution scheme using an improved particle swarm optimization algorithm; The data feedback module is in communication with the model building module and is used to collect vibration data through the sensor array and transmit it to the model building module after pre-processing by Kalman filtering; The execution control module is in communication with the algorithm processing module, is used to drive the actuator through a synchronous control mechanism, and includes a PID compensation unit for adjusting the execution delay; The storage module is used to save historical vibration data, optimized weighting schemes and iterative versions of the digital twin model.

8. The intelligent vibration suppression system with multi-physics field coordinated control according to claim 7, characterized in that: The algorithm processing module includes: a particle swarm optimization unit and a weight adjustment unit. The particle swarm optimization unit is used to perform the initialization, fitness evaluation and iterative optimization process of the improved particle swarm optimization algorithm to generate the control weight distribution scheme; the weight adjustment unit is used to dynamically adjust the aerodynamic field weight coefficient β1 and the electromagnetic field weight coefficient β2 according to real-time vibration data, where β1+β2=1.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the intelligent vibration suppression method for multi-physical field collaborative control as described in claims 1-6 is implemented.

10. A readable storage medium, characterized in that: The readable storage medium stores a computer program, which, when executed by a processor, implements the intelligent vibration suppression method for multi-physical field collaborative control as described in claims 1-6.

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