Multi-parameter optimization method and system for curved surface acoustic wave device

Abstract

The invention discloses a multi-parameter optimization method and system for a curved surface acoustic wave device. The method comprises the following steps: constructing theoretical models of the surface acoustic wave device under different strain conditions; solving the coupling wave equation to obtain the propagation velocity of the surface acoustic wave in the piezoelectric material under different strain conditions; acquiring at least one structural parameter of the curved surface acoustic wave device associated with the propagation speed; and performing global optimization on the at least one structure parameter according to a preset global topological optimization network to obtain a target structure parameter corresponding to the at least one structure parameter. A global topological optimization network is adopted, gradient back propagation is utilized, structural parameters of a device are intelligently designed, a quality factor value of the device is generated through finite element simulation calculation, and finally the quality factor distribution condition of the generated device is output. The problem that accurate real-time measurement of multiple parameters of the bent surface of the bearing cannot be achieved in the prior art can be solved.

Description

Technical Field

[0001] The invention belongs to the technical field of flexible surface acoustic wave devices, and in particular relates to a multi-parameter optimization method and system for a curved surface acoustic wave device. Background Art

[0002] In the fields of industrial machinery, aerospace, energy mining, etc., real-time measurement of temperature, strain, vibration and other parameters of key components is of great significance. For example, in rotating parts of industrial machinery equipment (bearings, gas turbine main shafts, etc.), the temperature of the bearings rises sharply due to friction during rotation, and the long-term load causes the bearing structure to deform. In severe cases, failures or even fractures may occur. Therefore, it is very important to realize real-time monitoring of temperature, strain and other parameters of rotating parts such as bearings. Because traditional sensors have a single structure and are mostly prepared on hard substrates, they cannot measure multiple parameters at the same time, and their insufficient bending ability makes them unsuitable for application on curved structures, which limits their application in harsh environments.

[0003] Currently, the condition monitoring of rotating devices, such as the fatigue condition monitoring of bearings, is carried out by collecting the temperature, strain, vibration and other information of the bearings through temperature, strain and vibration sensors. However, the relevant sensors installed on the bearings are mainly discrete structures, in which the packaged sensors are in contact with the bearings or brackets. This monitoring method is not only limited by the structure of the equipment, but also can only indirectly obtain the data that needs to be collected, and the measurement accuracy is low. Summary of the invention

[0004] The present invention provides a multi-parameter optimization method and system for a bending surface acoustic wave device, which are used to solve the technical problem that most of the data to be collected can only be obtained indirectly and the measurement accuracy is low.

[0005] In a first aspect, the present invention provides a multi-parameter optimization method for a flexural surface acoustic wave device, comprising: A theoretical model of a surface acoustic wave device under different strain conditions is constructed, wherein the coupled wave equation of the surface acoustic wave device under bending strain is: , In the formula, is the temperature-dependent initial stress, is the nonlinear coefficient, is the temperature-dependent elastic stiffness constant, is the piezoelectric stress constant, is the dielectric constant, is the external force field term, is the density, is the random term of material inhomogeneity, is the electric potential, is the mechanical displacement, is the coupling term, is the potential field related to the propagation of sound waves, , , The displacement field is The weight in direction, , , , Respectively The coordinates of the spatial directions, For time, is the temperature, is the spatial coordinate; Solving the coupled wave equation to obtain the propagation speed of the surface acoustic wave in the piezoelectric material under different strain conditions; acquiring at least one structural parameter of the flexural surface acoustic wave device associated with the propagation velocity; The at least one structural parameter is globally optimized according to a preset global topology optimization network to obtain a target structural parameter corresponding to the at least one structural parameter.

[0006] In a second aspect, the present invention provides a multi-parameter optimization system for a flexural surface acoustic wave device, comprising: A construction module is configured to construct a theoretical model of a surface acoustic wave device under different strain conditions, wherein the coupled wave equation when the surface acoustic wave device is subjected to bending strain is: , In the formula, is the temperature-dependent initial stress, is the nonlinear coefficient, is the temperature-dependent elastic stiffness constant, is the piezoelectric stress constant, is the dielectric constant, is the external force field term, is the density, is the random term of material inhomogeneity, is the electric potential, is the mechanical displacement, is the coupling term, is the potential field related to the propagation of sound waves, , , The displacement field is The weight in direction, , , , Respectively The coordinates of the spatial directions, For time, is the temperature, is the spatial coordinate; A solution module configured to solve the coupled wave equation to obtain the propagation speed of the surface acoustic wave in the piezoelectric material under different strain conditions; An acquisition module configured to acquire at least one structural parameter of the flexural surface acoustic wave device associated with the propagation velocity; The optimization module is configured to perform global optimization on the at least one structural parameter according to a preset global topology optimization network to obtain a target structural parameter corresponding to the at least one structural parameter.

[0007] According to a third aspect, an electronic device is provided, comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the steps of the multi-parameter optimization method for a bent surface acoustic wave device according to any embodiment of the present invention.

[0008] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor executes the steps of the multi-parameter optimization method for a curved surface acoustic wave device according to any embodiment of the present invention.

[0009] The multi-parameter optimization method and system of the curved surface acoustic wave device of the present application designs a flexible surface acoustic wave sensor, integrates temperature, humidity and strain sensing units, adopts a global topology optimization network to optimize the device structural parameters, optimizes key parameters such as interdigital electrodes and reflection gratings, and combines finite element simulation to calculate the quality factor of the device. This method can quickly generate the structural parameters of high-performance devices, significantly improves the accuracy of multi-parameter measurement of the sensor, solves the problem that the existing technology cannot accurately measure multiple parameters of curved surfaces in real time, and is suitable for real-time monitoring of complex curved surfaces such as bearings. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0011] Figure 1 A flowchart of a multi-parameter optimization method for a flexural surface acoustic wave device provided by one embodiment of the present invention; Figure 2A structural block diagram of a multi-parameter optimization system for a flexural surface acoustic wave device provided by an embodiment of the present invention; Figure 3 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0012] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0013] See also Figure 1 , which shows a flow chart of a multi-parameter optimization method for a curved surface acoustic wave device of the present application.

[0014] like Figure 1 As shown, the multi-parameter optimization method for a flexural surface acoustic wave device specifically includes the following steps: Step S101 , constructing a theoretical model of a surface acoustic wave device under different strain conditions.

[0015] In this step, a multifunctional sensing flexible surface acoustic wave sensor with bending characteristics is provided, the flexible surface acoustic wave sensor includes a flexible substrate, a piezoelectric film, and an interdigital transducer and a reflection grating arranged on the piezoelectric film, the relationship between the frequency of the surface acoustic wave and its wavelength and wave velocity is obtained, a non-intersecting design method within the sensing frequency range of a single sensing unit is adopted, the interdigital width of each parameter is adjusted within the design frequency measurement range, thereby adjusting the wavelength of the surface acoustic wave to separate the frequency of each sensing unit, corresponding to temperature, humidity and strain parameters respectively, and then distinguishing each sensitive unit, so as to realize a surface acoustic wave sensor integrating three sensing units of temperature, humidity and strain on the same substrate, and the surface acoustic wave sensor is adhered to the curved surface of the bearing by high-temperature structural adhesive to complete the fixation of the flexible film surface acoustic wave sensor; A theoretical model of the surface acoustic wave device under bending conditions is established. The flexible surface acoustic wave sensor bends under the action of elastic strain. Under the action of strain, the interdigital transducer of the surface acoustic wave device deforms in the propagation direction of the surface acoustic wave. The resonant frequency of the surface acoustic wave device changes with the bending deformation. The theoretical model is established using the coupled wave equation and boundary condition method to obtain the relationship between the frequency shift of the device and the bending strain, and analyze the bending characteristics of the flexible surface acoustic wave sensor under elastic strain. The coupled wave equation when the surface acoustic wave device is subjected to bending strain is: , In the formula, is the temperature-dependent initial stress, is the nonlinear coefficient, is the temperature-dependent elastic stiffness constant, is the piezoelectric stress constant, is the dielectric constant, is the external force field term, is the density, is the random term of material inhomogeneity, is the electric potential, is the mechanical displacement, is the coupling term, is the potential field related to the propagation of sound waves, , , The displacement field is The weight in direction, , , , Respectively The coordinates of the spatial directions, For time, is the temperature, is the spatial coordinate; Step S102, solving the coupled wave equation to obtain the propagation speed of the surface acoustic wave in the piezoelectric material under different strain conditions.

[0016] In this step, when the applied strain is a disturbance to the propagating surface acoustic wave, the applied strain not only modifies the equation of motion but also changes the material constants. There are three independent perturbation material constants that affect the acoustic wave velocity under stress, namely, initial stress, , elastic stiffness constant and density , based on perturbation theory, according to , obtain the elastic stiffness constant after strain perturbation , is the third-order elastic tensor of the piezoelectric material, is the Euler strain; After being perturbed by strain, according to Get the density change of the piezoelectric material, where, , and is the strain component. By substituting the changed material parameters into the coupled wave equation and solving the coupled wave equation, the propagation velocity of the surface acoustic wave in the piezoelectric material under different strain conditions can be obtained.

[0017] Step S103: acquiring at least one structural parameter of the flexural surface acoustic wave device associated with the propagation velocity.

[0018] In this step, for a single-port SAW resonator, some of its design parameters mainly include: the number of interdigital electrodes , interdigital electrode aperture , interdigital electrode period , the distance between the reflectors , number of reflective gate electrodes and the coating thickness when preparing surface acoustic wave devices The interdigital electrode periods of the temperature, humidity and strain sensitive units of the integrated device are fixed at 42 , 40 and 38 The width of each finger is 10.5 , 10 and 9.5 Since surface acoustic waves will experience diffraction loss during propagation, the loss is related to Inversely proportional to the square of, in order to reduce diffraction loss, the interdigital aperture of the designed device is taken as , corresponding to 4200 , 4000 and 3800 Then, the number of IDT electrodes of different sensing units of the integrated surface acoustic wave device was , the distance between the reflectors , the number of reflective gate electrodes And the coating thickness when preparing the device Optimize the process while taking into account the different bending angles when the device is bent Impact on devices.

[0019] Step S104: performing global optimization on the at least one structural parameter according to a preset global topology optimization network to obtain a target structural parameter corresponding to the at least one structural parameter.

[0020] In this step, input features are obtained according to the at least one structural parameter, and the input features include high-order features of each structural parameter and complex relationships between each structural parameter; the input features are input into a condition generator, and the condition generator outputs a minimum loss function; a fitness function is constructed according to the minimum loss function, and a balanced solution set is obtained among multiple objectives according to the fitness function, and the objective weights are dynamically adjusted to meet different optimization requirements; a search is performed in the solution set according to a preset adaptive neighborhood search to obtain an optimal solution, that is, to obtain the target structural parameters corresponding to the at least one structural parameter.

[0021] Acquiring input features according to the at least one structural parameter comprises: Construct high-order features based on various structural parameters, and the expression is: , , In the formula, is the newly constructed high-order feature, The original feature In time of Power, is the original feature, is the function of the design parameter changing with time, is the standard deviation of the Gaussian distribution, To adjust The parameters of influence, is a custom function based on the performance of the flexural surface acoustic wave device, is the number of IDT electrodes, is the distance between the reflectors, is the number of reflective gate electrodes, is the coating thickness, is the bending angle; Considering the interaction between design parameters, the relationship between various structural parameters is simulated through interactive feature construction, and the expression is: , In the formula, is the newly constructed interaction feature, is the number of original features, is the number of interaction terms for each feature, is the weight matrix selected based on information gain, For adjustment The parameters that affect is a function of the bending angle.

[0022] The loss function of the condition generator is expressed as: , In the formula, is the loss function of the conditional generator, is the first weight factor, is the performance loss function, is the structural loss function, is the bending loss function, is the first weight of the regularization term, is the total number of devices, is the total number of design parameters, For the Device No. The variance of the design parameters; , In the formula, is the number of categories of the target quality factor, is the second weight factor, For the Device prediction Quality factor, For the The quality factor of each target; , In the formula, is the bending loss coefficient, is the bending angle, is the number of reflective gate electrodes, is the maximum bending angle, is the regulating factor, is the second weight of the regularization term; , In the formula, For the The weights of the design parameters, For the The first device The predicted values ​​of the design parameters, For the ideal values ​​of the design parameters.

[0023] The expression of the fitness function is: , , In the formula, is the fitness function, is the structural parameter, For environmental adaptability, is a custom performance-based fitness function, is a custom cost-based fitness function, is the cost function, is the environmental adaptability function, are the design parameters of the encoding, is the manufacturing cost, For environmental adaptability; The solution set that achieves a balance between multiple objectives according to the fitness function and dynamically adjusts the objective weights to adapt to different optimization requirements includes: Through Pareto optimization, we find a solution set that balances multiple objectives, and dynamically adjust the objective weights to adapt to different optimization requirements. The expression is: , , In the formula, For each performance goal Promoted to weight The power of For each cost target Promoted to weight The power of is the number of objective functions, To adjust the performance and cost balance function The coefficient of influence, is a function that measures the balance between performance objectives and cost objectives. For the The value of a performance goal, For the The value of the cost target, To adjust the environmental adaptability function The coefficient of influence, is the environmental adaptability function, For the The amount by which the target weights are adjusted. For the The adjustment factor related to the target, is the total number of iterations, is a constant used for smoothing adjustment, is the function used for weight adjustment, Dynamically adjust the objective weight function The coefficient of influence, To dynamically adjust the target weight function, For the The initial weight of the target, is the weight after iterative update.

[0024] The searching in the solution set according to the preset adaptive neighborhood search to obtain the optimal solution includes: The temperature scheduling strategy is used to control the temperature of simulated annealing to balance the globality and locality of the search. The adaptive neighborhood search is used to search within the neighborhood of the solution set to obtain the optimal solution, which is expressed as:

[0025] , In the formula, For the The temperature of the iteration, For the The temperature of the iteration, is the cooling parameter, , Regarding the number of iterations The function of adjusting the temperature change, is the current solution, is the newly found solution, is the search step length, is a normal distribution, A function to control the search step size; The expression of the learning rate in the global topology optimization network is: , In the formula, is the learning rate, is the initial learning rate, is the current iteration number, is the total number of iterations, is the decay exponent, which is used to control the learning rate as the current number of iterations increases. The decay rate of is the Sigmoid function, is the loss change in iteration, , Regarding the number of iterations Functions of and functions for adjusting learning rate; It should be noted that in each training process, a batch of surface acoustic wave device structural parameters are generated, and the generated device structural parameters are input into the finite element simulation software. The dynamic characteristics of the generated devices are analyzed through finite element simulation, and the quality factor of each device is calculated. The values ​​and quality factor gradients of each device are back-propagated through the network to update the weights of neurons and deconvolution kernels, thereby modifying arrive Complete mapping of the structure parameters of a new batch of surface acoustic wave devices is regenerated and the iteration continues; Set a quality factor threshold. When the device quality factor calculated by finite element simulation is higher than the set value, the quality factor of the device is output. values ​​and structural parameters, i.e. the quality factor distribution , according to the preparation conditions, try to select high The structural parameters of the value device are designed, and this method is used to intelligently design the structure of the surface acoustic wave sensor, so as to quickly obtain the structural parameters of higher performance devices.

[0026] In summary, the method of the present application adopts a global topology optimization network, uses the back propagation of gradients, intelligently designs the structural parameters of the device, and generates the quality factor of the device through finite element simulation calculation. The present invention can solve the problem that the prior art cannot realize accurate real-time measurement of multiple parameters of the curved surface of a bearing.

[0027] See also Figure 2 , which shows a structural block diagram of a multi-parameter optimization system for a bending surface acoustic wave device of the present application.

[0028] like Figure 2 As shown, the multi-parameter optimization system 200 includes a construction module 210 , a solution module 220 , an acquisition module 230 and an optimization module 240 .

[0029] The construction module 210 is configured to construct a theoretical model of a surface acoustic wave device under different strain conditions, wherein the coupled wave equation when the surface acoustic wave device is subjected to bending strain is: , In the formula, is the temperature-dependent initial stress, is the nonlinear coefficient, is the temperature-dependent elastic stiffness constant, is the piezoelectric stress constant, is the dielectric constant, is the external force field term, is the density, is the random term of material inhomogeneity, is the electric potential, is the mechanical displacement, is the coupling term, is the potential field related to the propagation of sound waves, , , The displacement field is The weight in direction, , , , Respectively The coordinates of the spatial directions, For time, is the temperature, is the spatial coordinate; A solution module 220, configured to solve the coupled wave equation to obtain the propagation speed of the surface acoustic wave in the piezoelectric material under different strain conditions; An acquisition module 230, configured to acquire at least one structural parameter of the flexural surface acoustic wave device associated with the propagation velocity; The optimization module 240 is configured to perform global optimization on the at least one structural parameter according to a preset global topology optimization network to obtain a target structural parameter corresponding to the at least one structural parameter.

[0030] It should be understood that Figure 2 Modules and references documented in Figure 1 Therefore, the operations and features described above for the method and the corresponding technical effects are also applicable to Figure 2 The modules in it will not be described in detail here.

[0031] In some other embodiments, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor executes the multi-parameter optimization method for a flexural surface acoustic wave device in any of the above method embodiments; As an implementation mode, the computer-readable storage medium of the present invention stores computer-executable instructions, and the computer-executable instructions are configured as follows: A theoretical model of a surface acoustic wave device under different strain conditions is constructed, wherein the coupled wave equation of the surface acoustic wave device under bending strain is: , In the formula, is the temperature-dependent initial stress, is the nonlinear coefficient, is the temperature-dependent elastic stiffness constant, is the piezoelectric stress constant, is the dielectric constant, is the external force field term, is the density, is the random term of material inhomogeneity, is the electric potential, is the mechanical displacement, is the coupling term, is the potential field related to the propagation of sound waves, , , The displacement field is The weight in direction, , , , Respectively The coordinates of the spatial directions, For time, is the temperature, is the spatial coordinate; Solving the coupled wave equation to obtain the propagation speed of the surface acoustic wave in the piezoelectric material under different strain conditions; acquiring at least one structural parameter of the flexural surface acoustic wave device associated with the propagation velocity; The at least one structural parameter is globally optimized according to a preset global topology optimization network to obtain a target structural parameter corresponding to the at least one structural parameter.

[0032] The computer-readable storage medium may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required by at least one function; the data storage area may store data created according to the use of the multi-parameter optimization system of the curved surface acoustic wave device, etc. In addition, the computer-readable storage medium may include a high-speed random access memory, and may also include a memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer-readable storage medium may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the multi-parameter optimization system of the curved surface acoustic wave device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0033] Figure 3 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention, such as Figure 3 As shown, the device includes: a processor 310 and a memory 320. The electronic device may also include: an input device 330 and an output device 340. The processor 310, the memory 320, the input device 330 and the output device 340 may be connected via a bus or other means. Figure 3 In the example, the connection via bus is taken. The memory 320 is the above-mentioned computer-readable storage medium. The processor 310 executes various functional applications and data processing of the server by running the non-volatile software programs, instructions and modules stored in the memory 320, that is, the multi-parameter optimization method of the curved surface acoustic wave device in the above-mentioned method embodiment is realized. The input device 330 can receive input digital or character information, and generate key signal input related to user settings and function control of the multi-parameter optimization system of the curved surface acoustic wave device. The output device 340 may include display devices such as display screens.

[0034] The electronic device can execute the method provided by the embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the method provided by the embodiment of the present invention.

[0035] As an implementation mode, the electronic device is applied to a multi-parameter optimization system of a curved surface acoustic wave device, and is used for a client, and includes: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can: A theoretical model of a surface acoustic wave device under different strain conditions is constructed, wherein the coupled wave equation of the surface acoustic wave device under bending strain is: , In the formula, is the temperature-dependent initial stress, is the nonlinear coefficient, is the temperature-dependent elastic stiffness constant, is the piezoelectric stress constant, is the dielectric constant, is the external force field term, is the density, is the random term of material inhomogeneity, is the electric potential, is the mechanical displacement, is the coupling term, is the potential field related to the propagation of sound waves, , , The displacement field is The weight in direction, , , , Respectively The coordinates of the spatial directions, For time, is the temperature, is the spatial coordinate; Solving the coupled wave equation to obtain the propagation speed of the surface acoustic wave in the piezoelectric material under different strain conditions; acquiring at least one structural parameter of the flexural surface acoustic wave device associated with the propagation velocity; The at least one structural parameter is globally optimized according to a preset global topology optimization network to obtain a target structural parameter corresponding to the at least one structural parameter.

[0036] Through the description of the above implementation modes, those skilled in the art can clearly understand that each implementation mode can be implemented by means of software plus a necessary general hardware platform, or of course by hardware. Based on such an understanding, the above technical solution can essentially or in other words be embodied in the form of a software product that contributes to the prior art. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of each embodiment or some parts of the embodiment.

[0037] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-parameter optimization method for a flexural surface acoustic wave device, characterized in that: include: A theoretical model of a surface acoustic wave device under different strain conditions is constructed, wherein the coupled wave equation of the surface acoustic wave device under bending strain is: ， In the formula, is the temperature-dependent initial stress, is the nonlinear coefficient, is the temperature-dependent elastic stiffness constant, is the piezoelectric stress constant, is the dielectric constant, is the external force field term, is the density, is the random term of material inhomogeneity, is the electric potential, is the mechanical displacement, is the coupling term, is the potential field related to the propagation of sound waves, , , The displacement field is The weight in direction, , , , Respectively The coordinates of the spatial directions, For time, is the temperature, is the spatial coordinate; Solving the coupled wave equation to obtain the propagation speed of the surface acoustic wave in the piezoelectric material under different strain conditions; acquiring at least one structural parameter of the flexural surface acoustic wave device associated with the propagation velocity; The at least one structural parameter is globally optimized according to a preset global topology optimization network to obtain a target structural parameter corresponding to the at least one structural parameter.

2. The multi-parameter optimization method for a flexural surface acoustic wave device according to claim 1, characterized in that: The step of performing global optimization on the at least one structural parameter according to a preset global topology optimization network to obtain a target structural parameter corresponding to the at least one structural parameter includes: Acquire input features according to the at least one structural parameter, wherein the input features include high-order features of each structural parameter and complex relationships between each structural parameter; Input the input features to the condition generator, and the condition generator outputs the minimum loss function; Constructing a fitness function according to the minimum loss function, and obtaining a balanced solution set among multiple objectives according to the fitness function, and dynamically adjusting the objective weights to adapt to different optimization requirements; The solution set is searched according to a preset adaptive neighborhood search to obtain an optimal solution, that is, to obtain a target structural parameter corresponding to the at least one structural parameter.

3. The multi-parameter optimization method for a flexural surface acoustic wave device according to claim 2, characterized in that: The acquiring of input features according to the at least one structural parameter comprises: Construct high-order features based on various structural parameters, and the expression is: ， ， In the formula, is the newly constructed high-order feature, The original feature In time of Power, is the original feature, is the function of the design parameter changing with time, is the standard deviation of the Gaussian distribution, To adjust The parameters of influence, is a custom function based on the performance of the flexural surface acoustic wave device, is the number of IDT electrodes, is the distance between the reflectors, is the number of reflective gate electrodes, is the coating thickness, is the bending angle; Considering the interaction between design parameters, the relationship between various structural parameters is simulated through interactive feature construction, and the expression is: ， In the formula, is the newly constructed interaction feature, is the number of original features, is the number of interaction terms for each feature, is the weight matrix selected based on information gain, For adjustment The parameters that affect is a function of the bending angle.

4. The multi-parameter optimization method for a flexural surface acoustic wave device according to claim 2, characterized in that: The loss function of the condition generator is expressed as: ， In the formula, is the loss function of the conditional generator, is the first weight factor, is the performance loss function, is the structural loss function, is the bending loss function, is the first weight of the regularization term, is the total number of devices, is the total number of design parameters, For the Device No. The variance of the design parameters; ， In the formula, is the number of categories of the target quality factor, is the second weight factor, For the Device prediction Quality factor, For the The quality factor of each target; ， In the formula, is the bending loss coefficient, is the bending angle, is the number of reflective gate electrodes, is the maximum bending angle, is the regulating factor, is the second weight of the regularization term; ， In the formula, For the The weights of the design parameters, For the The first device The predicted values ​​of the design parameters, For the ideal values ​​of the design parameters.

5. The multi-parameter optimization method for a flexural surface acoustic wave device according to claim 4, characterized in that: The expression of the fitness function is: ， ， In the formula, is the fitness function, is the structural parameter, For environmental adaptability, is a custom performance-based fitness function, is a custom cost-based fitness function, is the cost function, is the environmental adaptability function, are the design parameters of the encoding, is the manufacturing cost, For environmental adaptability; The solution set that achieves a balance between multiple objectives according to the fitness function and dynamically adjusts the objective weights to adapt to different optimization requirements includes: Through Pareto optimization, we find a solution set that balances multiple objectives, and dynamically adjust the objective weights to adapt to different optimization requirements. The expression is: ， ， In the formula, For each performance goal Promoted to weight The power of For each cost target Promoted to weight The power of is the number of objective functions, To adjust the performance and cost balance function The coefficient of influence, is a function that measures the balance between performance objectives and cost objectives. For the The value of a performance goal, For the The value of the cost target, To adjust the environmental adaptability function The coefficient of influence, is the environmental adaptability function, For the The amount by which the target weights are adjusted. For the The adjustment factor related to the target, is the total number of iterations, is a constant used for smoothing adjustment, is the function used for weight adjustment, Dynamically adjust the objective weight function The coefficient of influence, To dynamically adjust the target weight function, For the The initial weight of the target, is the weight after iterative update.

6. The multi-parameter optimization method for a flexural surface acoustic wave device according to claim 2, characterized in that: The searching in the solution set according to the preset adaptive neighborhood search to obtain the optimal solution includes: The temperature scheduling strategy is used to control the temperature of simulated annealing to balance the globality and locality of the search. The adaptive neighborhood search is used to search within the neighborhood of the solution set to obtain the optimal solution, which is expressed as: ， ， In the formula, For the The temperature of the iteration, For the The temperature of the iteration, is the cooling parameter, , Regarding the number of iterations The function of adjusting the temperature change, is the current solution, is the newly found solution, is the search step length, is a normal distribution, A function that controls the search step size.

7. The multi-parameter optimization method for a flexural surface acoustic wave device according to claim 1, characterized in that: The expression of the learning rate in the global topology optimization network is: ， In the formula, is the learning rate, is the initial learning rate, is the current iteration number, is the total number of iterations, is the decay exponent, which is used to control the learning rate as the current number of iterations increases. The decay rate of is the Sigmoid function, is the loss change in iteration, , Regarding the number of iterations The function of and the function of adjusting the learning rate.

8. A multi-parameter optimization system for a flexural surface acoustic wave device, characterized in that: include: A construction module is configured to construct a theoretical model of a surface acoustic wave device under different strain conditions, wherein the coupled wave equation when the surface acoustic wave device is subjected to bending strain is: ， In the formula, is the temperature-dependent initial stress, is the nonlinear coefficient, is the temperature-dependent elastic stiffness constant, is the piezoelectric stress constant, is the dielectric constant, is the external force field term, is the density, is the random term of material inhomogeneity, is the electric potential, is the mechanical displacement, is the coupling term, is the potential field related to the propagation of sound waves, , , The displacement field is The weight in direction, , , , Respectively The coordinates of the spatial directions, For time, is the temperature, is the spatial coordinate; A solution module configured to solve the coupled wave equation to obtain the propagation speed of the surface acoustic wave in the piezoelectric material under different strain conditions; An acquisition module configured to acquire at least one structural parameter of the flexural surface acoustic wave device associated with the propagation velocity; The optimization module is configured to perform global optimization on the at least one structural parameter according to a preset global topology optimization network to obtain a target structural parameter corresponding to the at least one structural parameter.

9. An electronic device, characterized in that: include: At least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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