Electro-hydraulic-electromagnetic excitation system cooperative control method and related equipment

Abstract

The embodiment of the invention provides an electro-hydraulic-electromagnetic excitation system cooperative control method and device. The electro-hydraulic-electromagnetic excitation system cooperative control method comprises the steps of obtaining target excitation spectrum data; determining an excitation simulation model based on the target excitation spectrum data; adjusting the excitation simulation model through a preset adaptive algorithm to obtain an optimized excitation simulation model; inputting the target excitation spectrum data into the optimized excitation simulation model for processing, and outputting optimized excitation simulation data; on the basis of the optimized excitation simulation data, an excitation report is generated, feedback control adjustment is conducted according to the excitation report, through the method, under common excitation of electro-hydraulic excitation and electromagnetic excitation, the corresponding excitation simulation model can be optimized, so that the optimized excitation simulation data can be output; therefore, the effect of feeding back, controlling and adjusting the excitation experiment by using the optimized excitation simulation data is achieved, and accurate experiment data can be ensured to be obtained.

Description

Technical Field

[0001] The present invention relates to the field of vibration environment simulation, and in particular to a coordinated control method and device of an electro-hydraulic-electromagnetic excitation system, an electronic device and a storage medium thereof. Background Art

[0002] With the development of industrial automation and precision manufacturing, vibration simulation plays a key role in many fields, especially in structural dynamics testing and equipment maintenance prediction. Vibration simulation technology enables engineers to predict and analyze the response of mechanical equipment when subjected to external excitation (such as vibration, pressure changes, etc.), so as to optimize the structure and prevent failures in advance.

[0003] Since the current excitation system requires manual adjustment and long-term experimental data correction, and when the excitation environment changes, it cannot promptly respond to changes in the actual situation to adjust the parameters of the simulation model, resulting in a large deviation between the simulation effect and the actual situation. Furthermore, the existing excitation system only provides excitation input based on the electromagnetic oscillator. Therefore, the existing excitation system has a single excitation input and cannot promptly adjust the parameters of the simulation model according to the changing excitation environment, resulting in poor simulation effects. Summary of the invention

[0004] The embodiment of the present invention provides a coordinated control method of an electro-hydraulic-electromagnetic excitation system to solve the problem that the existing excitation system has a single excitation input and cannot adjust the parameters of the simulation model in time according to the changing excitation environment, resulting in poor simulation effect.

[0005] In a first aspect, an embodiment of the present invention provides a method for coordinated control of an electro-hydraulic-electromagnetic excitation system, the method comprising the following steps: Acquire target excitation spectrum data; Determine the excitation simulation model based on the target excitation spectrum data; The vibration simulation model is adjusted by a preset adaptive algorithm to obtain an optimized vibration simulation model; Input the target excitation spectrum data into the optimized excitation simulation model for processing, and output the optimized excitation simulation data; Generate a vibration report based on the optimized vibration simulation data, and make feedback control adjustments based on the vibration report.

[0006] Optionally, obtain target excitation spectrum data, including: Collecting frequency spectra corresponding to the sinusoidal excitation spectrum and the random spectrum of the electromagnetic oscillator to determine the frequency of the first target sinusoidal excitation spectrum and the frequency of the first random spectrum of the electromagnetic oscillator; The frequency spectra corresponding to the sinusoidal excitation spectrum and the random spectrum of the electro-hydraulic oscillator are collected to determine the frequency of the second target sinusoidal excitation spectrum of the electro-hydraulic oscillator and the frequency of the second random spectrum; The target excitation spectrum data is determined based on the frequency of the first target sinusoidal excitation spectrum, the frequency of the first random spectrum pattern, the frequency of the second target sinusoidal excitation spectrum, and the frequency of the second random spectrum pattern.

[0007] Optionally, based on the target excitation spectrum data, a vibration simulation model is determined, including: Based on the target excitation spectrum data, target excitation spectrum characteristic data is determined, wherein the target excitation spectrum characteristic data includes a magnitude relationship between frequency data of the electromagnetic oscillator and the electro-hydraulic oscillator; Based on the magnitude relationship of the frequency data of the electromagnetic oscillator and the electro-hydraulic oscillator, the corresponding frequency domain model is determined; Based on the frequency domain model, the corresponding excitation simulation model is determined.

[0008] Optionally, determining target excitation spectrum feature data based on the target excitation spectrum data, and determining a corresponding frequency domain model based on the target excitation spectrum feature data includes: The target excitation spectrum data is processed by a frequency sweep signal to determine the frequency of the first target sinusoidal excitation spectrum, the frequency of the first random spectrum, the frequency of the second target sinusoidal excitation spectrum, and the frequency of the second random spectrum; By using the trained frequency domain recognition model, feature recognition processing is performed on the frequency of the first target sinusoidal excitation spectrum, the frequency of the first random spectrum, the frequency of the second target sinusoidal excitation spectrum, and the frequency of the second random spectrum to determine target excitation spectrum feature data; Based on the target excitation spectrum characteristic data, matching is performed in the frequency domain recognition database to determine the corresponding frequency domain model.

[0009] Optionally, before determining the target excitation spectrum feature data, the method further includes: performing feature recognition processing on the frequency of the first target sinusoidal excitation spectrum, the frequency of the first random spectrum, the frequency of the second target sinusoidal excitation spectrum, and the frequency of the second random spectrum through a trained frequency domain recognition model, and determining the target excitation spectrum feature data. Obtain a frequency domain recognition model to be trained and a frequency domain recognition sample set, wherein the frequency domain recognition sample set includes an excitation spectrum, frequency data corresponding to the excitation spectrum, and a frequency domain label corresponding to the frequency data; Based on the excitation spectrum, the frequency data corresponding to the excitation spectrum, and the frequency domain labels corresponding to the frequency data, the frequency domain recognition model to be trained is iteratively trained, and after the iterative training is completed, a trained frequency domain recognition model to be trained is obtained.

[0010] Optionally, the excitation simulation model is adjusted by a preset adaptive algorithm to obtain an optimized excitation simulation model, including: Determine the parameter optimization direction of the vibration simulation based on the target excitation spectrum data corresponding to the vibration simulation model; Based on the preset adaptive algorithm, the parameters are adjusted in the parameter optimization direction, and the optimized excitation simulation model is obtained after the parameter adjustment.

[0011] Optionally, the target excitation spectrum data is input into the optimized excitation simulation model for processing, and the optimized excitation simulation data is output, including: Determine the frequency response function and control algorithm for updating the optimized excitation simulation model; The target excitation spectrum data is processed based on the updated frequency response function to obtain optimized excitation simulation data.

[0012] In a second aspect, an embodiment of the present invention further provides an electro-hydraulic-electromagnetic excitation system coordinated control device, the electro-hydraulic-electromagnetic excitation system coordinated control device comprising: An acquisition module, used for acquiring target excitation spectrum data; A first determination module, used to determine an excitation simulation model based on the target excitation spectrum data; An optimization module is used to adjust the excitation simulation model through a preset adaptive algorithm to obtain an optimized excitation simulation model; A processing module, used for inputting target excitation spectrum data into the optimized excitation simulation model for processing, and outputting optimized excitation simulation data; The generation module is used to generate an excitation report based on the optimized excitation simulation data, and to make feedback control adjustments according to the excitation report.

[0013] In a third aspect, an embodiment of the present invention provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the electro-hydraulic-electromagnetic excitation system collaborative control method provided in an embodiment of the present invention are implemented.

[0014] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the electro-hydraulic-electromagnetic excitation system collaborative control method provided in the embodiment of the invention are implemented.

[0015] In an embodiment of the present invention, target excitation spectrum data is obtained; based on the target excitation spectrum data, an excitation simulation model is determined; through a preset adaptive algorithm, the excitation simulation model is adjusted to obtain an optimized excitation simulation model; the target excitation spectrum data is input into the optimized excitation simulation model for processing, and the optimized excitation simulation data is output; based on the optimized excitation simulation data, an excitation report is generated, and feedback control adjustment is performed according to the excitation report. Through the above method, the corresponding excitation simulation model can be optimized under the joint excitation of electro-hydraulic excitation and electromagnetic excitation, so that the optimized excitation simulation data can be output, so as to achieve the effect of using the optimized excitation simulation data to feedback control and adjust the excitation experiment, thereby ensuring that accurate experimental data can be obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 It is a flow chart of a coordinated control method of an electro-hydraulic-electromagnetic excitation system provided by an embodiment of the present invention; Figure 2 It is a structural schematic diagram of a method and device for cooperative control of an electro-hydraulic-electromagnetic excitation system provided in an embodiment of the present invention; Figure 3 It is a structural schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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.

[0019] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship. like Figure 1 As shown, Figure 1 1 is a flow chart of a method for coordinated control of an electro-hydraulic-electromagnetic excitation system provided by an embodiment of the present invention. The method for coordinated control of an electro-hydraulic-electromagnetic excitation system comprises the following steps: S101, obtaining target excitation spectrum data.

[0020] In an embodiment of the present invention, the above-mentioned electro-hydraulic-electromagnetic excitation system collaborative control method can be applied to an electro-hydraulic-electromagnetic excitation system collaborative control device. The above-mentioned electro-hydraulic-electromagnetic excitation system collaborative control device has functions such as excitation data processing, excitation data transmission and reception, and excitation data storage, and can be constructed based on a server or a server cluster. The above-mentioned server or server cluster can be an electronic device with excitation data processing capabilities.

[0021] The above-mentioned target excitation spectrum data may be composed of the target excitation spectrum data of the electromagnetic vibrator and the target excitation spectrum data of the electro-hydraulic vibrator. Specifically, it may include but is not limited to the frequency data corresponding to the sinusoidal excitation spectrum and the random spectrum generated by the electromagnetic vibrator, and the frequency data corresponding to the sinusoidal excitation spectrum and the random spectrum generated by the electro-hydraulic vibrator. It can be understood that the above-mentioned target excitation spectrum data may also be imported through external data, for example, by inputting a certain excitation into the simulation model, and by identifying the frequency and other characteristics of the excitation, the excitation spectrum data of the excitation is obtained.

[0022] S102: Determine an excitation simulation model based on target excitation spectrum data.

[0023] The above-mentioned excitation simulation model may include but is not limited to an electro-hydraulic excitation simulation model, an electromagnetic excitation simulation model and an electromagnetic-electro-hydraulic excitation simulation model. Specifically, for different excitation simulation models, corresponding frequency domain models may be established respectively, and different excitation inputs may be processed according to different frequency domain models.

[0024] It should be noted that when using an electro-hydraulic exciter, it can be divided into three situations: when the frequency of the electro-hydraulic exciter is less than 10Hz, the pressure signal and displacement signal of the electro-hydraulic exciter are collected; when the frequency of the electro-hydraulic exciter is greater than 10Hz, the acceleration signal of the excitation platform and the displacement signal of the electro-hydraulic exciter are collected; when the target excitation spectrum of the electro-hydraulic exciter is random or external data is imported, the acceleration signal of the excitation platform and the displacement signal of the electro-hydraulic exciter are collected.

[0025] When using an electromagnetic exciter, the acceleration signal of the excitation experiment and the displacement signal of the electromagnetic exciter are collected.

[0026] When using an electro-hydraulic-electromagnetic composite vibrator, the classification is as follows: when the target excitation spectrum data of the electromagnetic vibrator and the target excitation spectrum data of the electro-hydraulic vibrator are sinusoidal spectra of the same frequency, the frequency of the target sinusoidal excitation spectrum data of the electromagnetic vibrator is higher than the frequency of the target sinusoidal excitation spectrum data of the electro-hydraulic vibrator, or the frequency of the target sinusoidal excitation spectrum data of the electromagnetic vibrator is lower than the frequency of the target sinusoidal excitation spectrum data of the electro-hydraulic vibrator, if the frequency of the electro-hydraulic vibrator is less than 10Hz, collect the pressure signal of the electro-hydraulic vibrator, the displacement signal of the electro-hydraulic vibrator, the acceleration signal of the excitation platform and the displacement signal of the electromagnetic vibrator; if the frequency of the electro-hydraulic vibrator is greater than 10Hz, collect the acceleration signal of the excitation platform, the displacement signal of the electro-hydraulic vibrator and the displacement signal of the electromagnetic vibrator.

[0027] When the target excitation spectrum data of the electromagnetic vibrator and the target excitation spectrum data of the electro-hydraulic vibrator are random spectra or external data is imported to generate the target excitation spectrum data, the acceleration signal of the excitation platform, the displacement signal of the electro-hydraulic vibrator and the displacement signal of the electromagnetic vibrator are collected.

[0028] S103 , adjusting the excitation simulation model through a preset adaptive algorithm to obtain an optimized excitation simulation model.

[0029] The above-mentioned preset adaptive algorithm can be any control algorithm that can adjust the control parameters in real time according to the input data (such as target excitation spectrum data). The algorithm can automatically identify and adapt to the dynamic changes of the simulation experiment under different operating conditions, thereby ensuring that the excitation experiment can work effectively in various environments.

[0030] In a possible embodiment, the parameters corresponding to the selected excitation simulation model are adjusted by the preset adaptive algorithm to obtain an optimized excitation simulation model, and the optimized excitation simulation model can perform higher recognition or calculation on the current target excitation spectrum data.

[0031] S104, inputting the target excitation spectrum data into the optimized excitation simulation model for processing, and outputting the optimized excitation simulation data.

[0032] In a possible embodiment, the target excitation spectrum data is processed by the excitation simulation model after adjusting the parameters by the above-mentioned preset adaptive algorithm, and the frequency, frequency response and other data of the target excitation spectrum data are calculated based on the excitation environment set corresponding to the simulation model, so as to generate high-precision excitation simulation data such as excitation force and excitation displacement.

[0033] S105 . Generate a vibration report based on the optimized vibration simulation data, and perform feedback control adjustment according to the vibration report.

[0034] In an embodiment of the present invention, a corresponding excitation report is generated through the above-mentioned optimized excitation simulation data, such as excitation force data and excitation displacement data. The content of the excitation report may include but is not limited to a feedback control strategy and specific control parameters for adjusting the output of the electro-hydraulic oscillator and the electromagnetic oscillator according to the excitation simulation data.

[0035] In a possible embodiment, feedback control can be performed by the following steps: according to the target excitation spectrum, using the identified electromagnetic exciter frequency response function 、Frequency response function of electro-hydraulic exciter , inversely calculate the driving voltage of the target excitation spectrum, specifically: The driving voltage of the target excitation spectrum is the system input, and the corresponding frequency domain model is selected according to the target excitation spectrum. , frequency domain model and frequency domain model , and obtain the corresponding response signal; According to the target excitation spectrum, the response signal collected by the system is compared with the response signal obtained by the frequency domain model to obtain the error value between the actual output and the predicted output for feedback control; According to the error value between the observed actual output and the predicted output, the error value is taken as the control accuracy target, and the control parameters are optimized according to the classification of the target excitation spectrum; According to the test requirements of the test object, the corresponding target excitation spectrum is selected, and then the corresponding control parameters are selected according to the target excitation spectrum.

[0036] In an embodiment of the present invention, target excitation spectrum data is obtained; based on the target excitation spectrum data, an excitation simulation model is determined; through a preset adaptive algorithm, the excitation simulation model is adjusted to obtain an optimized excitation simulation model; the target excitation spectrum data is input into the optimized excitation simulation model for processing, and the optimized excitation simulation data is output; based on the optimized excitation simulation data, an excitation report is generated, and feedback control adjustment is performed according to the excitation report. Through the above method, the corresponding excitation simulation model can be optimized under the joint excitation of electro-hydraulic excitation and electromagnetic excitation, so that the optimized excitation simulation data can be output, so as to achieve the effect of using the optimized excitation simulation data to feedback control and adjust the excitation experiment, thereby ensuring that accurate experimental data can be obtained.

[0037] Optionally, the step of obtaining target excitation spectrum data also includes: collecting frequency spectra corresponding to the sinusoidal excitation spectrum and random spectrum of the electromagnetic oscillator, and determining the frequency of the first target sinusoidal excitation spectrum of the electromagnetic oscillator and the frequency of the first random spectrum; collecting frequency spectra corresponding to the sinusoidal excitation spectrum and random spectrum of the electro-hydraulic oscillator, and determining the frequency of the second target sinusoidal excitation spectrum of the electro-hydraulic oscillator and the frequency of the second random spectrum; determining the target excitation spectrum data based on the frequency of the first target sinusoidal excitation spectrum, the frequency of the first random spectrum, the frequency of the second target sinusoidal excitation spectrum and the frequency of the second random spectrum.

[0038] In one possible embodiment, a spectrum analyzer can be used to continuously monitor the excitation of an electromagnetic oscillator under different operating conditions to determine the frequency of its first target sinusoidal excitation spectrum and the main frequency distribution of the first random spectrum. Similarly, a spectrum analyzer can be used to continuously monitor an electro-hydraulic oscillator to obtain the frequency of its second target sinusoidal excitation spectrum and the frequency of the second random spectrum.

[0039] In another possible embodiment, by analyzing the frequency of the first target sinusoidal excitation spectrum, the frequency of the first random spectrum, the frequency of the second target sinusoidal excitation spectrum and the frequency of the second random spectrum, their corresponding size relationships, such as the high and low frequencies and the corresponding differences, are determined, and finally the target excitation spectrum data of the system is comprehensively determined based on the target excitation spectrum frequencies of the electromagnetic oscillator and the electro-hydraulic oscillator.

[0040] Optionally, the step of determining the excitation simulation model based on the target excitation spectrum data also includes determining target excitation spectrum characteristic data based on the target excitation spectrum data, determining a corresponding frequency domain model based on the frequency data size relationship between the electromagnetic oscillator and the electro-hydraulic oscillator, and determining a corresponding excitation simulation model based on the frequency domain model.

[0041] In an embodiment of the present invention, the target excitation spectrum characteristic data may include but is not limited to the frequency data magnitude relationship between the electromagnetic oscillator and the electro-hydraulic oscillator. Specifically, by determining the frequency data magnitude relationship between the electromagnetic oscillator and the electro-hydraulic oscillator, the target excitation spectrum characteristic data may be distinguished according to electro-hydraulic excitation characteristic data, electromagnetic excitation characteristic data, and electro-hydraulic-electromagnetic excitation characteristic data.

[0042] In a possible embodiment, the corresponding frequency domain models may be distinguished according to the electro-hydraulic excitation characteristic data, the electromagnetic excitation characteristic data, and the electro-hydraulic-electromagnetic excitation characteristic data. Specifically, the following frequency domain models may be established respectively: ; ; ; in, is the frequency domain model corresponding to the electromagnetic exciter, is the frequency domain model corresponding to the electro-hydraulic exciter, is the frequency domain model corresponding to the electro-hydraulic-electromagnetic exciter. Specifically, where is the frequency response function of the electromagnetic exciter; It is the frequency response function of the decoupled transition structure during electromagnetic excitation; is the frequency response function of the composite excitation platform; is the frequency response function of the test object; is the frequency response function of the electro-hydraulic exciter; It is the frequency response function of the decoupled transition structure during electro-hydraulic excitation; It is the frequency response function of the decoupled transition structure after electro-hydraulic-electromagnetic coupling; The frequency response function corresponding to the corresponding model can be determined through the above formula. According to the corresponding frequency response function, the corresponding model can analyze the input target excitation spectrum data and output the corresponding excitation simulation data.

[0043] In a possible embodiment, since different types of simulation models have different processing logics and emphases, the corresponding simulation model can be matched according to the corresponding frequency domain model, and the current target excitation spectrum data can be processed according to the matched simulation model.

[0044] Optionally, in the step of determining target excitation spectrum characteristic data based on the target excitation spectrum data, and determining the corresponding frequency domain model based on the target excitation spectrum characteristic data, the step also includes processing the target excitation spectrum data through a frequency sweeping signal to determine the frequency of the first target sinusoidal excitation spectrum, the frequency of the first random spectrum graph, the frequency of the second target sinusoidal excitation spectrum, and the frequency of the second random spectrum graph, performing feature recognition processing on the frequency of the first target sinusoidal excitation spectrum, the frequency of the first random spectrum graph, the frequency of the second target sinusoidal excitation spectrum, and the frequency of the second random spectrum graph through a trained frequency domain recognition model to determine the target excitation spectrum characteristic data, and matching in a frequency domain recognition database based on the target excitation spectrum characteristic data to determine the corresponding frequency domain model.

[0045] In the embodiment of the present invention, the frequency of the first target sinusoidal excitation spectrum may refer to the fundamental frequency of the first sinusoidal wave excitation used by the electromagnetic oscillator. Generally speaking, the sinusoidal excitation spectrum is usually used to simulate a vibration environment with a single or main frequency component.

[0046] The frequency of the first random spectrum mentioned above can be the main frequency component in the random vibration signal generated by the electromagnetic oscillator. Generally speaking, the random spectrum usually simulates the non-periodic, randomly distributed vibrations that occur in natural environments or complex mechanical operations. This spectrum contains multiple frequency components and is used to evaluate the overall stability and performance of the system when facing uncertain vibration sources.

[0047] The frequency of the second target sinusoidal excitation spectrum mentioned above may refer to the basic frequency of the second sinusoidal wave excitation used by the electro-hydraulic oscillator, which is similar to the first target sinusoidal excitation and is used for periodic vibration analysis of a specific frequency. Due to different test requirements or system characteristics, the frequency may be different from the frequency of the first target sinusoidal excitation spectrum.

[0048] The frequency of the second random spectrum may be the main frequency component of the second random vibration signal generated by the electro-hydraulic oscillator, and this signal simulates a wider random vibration environment.

[0049] The above-mentioned trained frequency domain recognition model can be any deep learning model that can perform frequency domain recognition on the frequency of the input excitation signal. Specifically, the frequency of the first target sinusoidal excitation spectrum, the frequency of the first random spectrum, the frequency of the second target sinusoidal excitation spectrum, and the frequency of the second random spectrum can be collected and acquired through a frequency sweep signal, and then the frequency of the first target sinusoidal excitation spectrum, the frequency of the first random spectrum, the frequency of the second target sinusoidal excitation spectrum, and the frequency of the second random spectrum are identified according to the trained frequency domain recognition model to determine the corresponding frequency characteristics, such as frequency composition, energy distribution and other data, and then the frequency domain model corresponding to the frequency characteristics is matched in the database to finally determine the required frequency domain model.

[0050] Through the above method, the accuracy and efficiency of system response are improved, the performance deviation caused by model mismatch is reduced, and the stability and reliability of the system in various operating environments are enhanced.

[0051] Optionally, in the step before performing feature recognition processing on the frequency of the first target sinusoidal excitation spectrum, the frequency of the first random spectrum, the frequency of the second target sinusoidal excitation spectrum and the frequency of the second random spectrum through the trained frequency domain recognition model, determining the characteristic data of the target excitation spectrum, it also includes obtaining the frequency domain recognition model to be trained and the frequency domain recognition sample set, iteratively training the frequency domain recognition model to be trained based on the excitation spectrum, the frequency data corresponding to the excitation spectrum, and the frequency domain label corresponding to the frequency data, and obtaining the trained frequency domain recognition model to be trained after the iterative training is completed.

[0052] In an embodiment of the present invention, the above-mentioned frequency domain identification sample set may include but is not limited to an excitation spectrum, frequency data corresponding to the excitation spectrum, and a frequency domain label corresponding to the frequency data. Specifically, the above-mentioned frequency domain label can be used to indicate a specific category or feature to which the frequency data belongs. It can be understood that by collecting a large amount of excitation spectrum data and determining through experiments or simulations the corresponding frequency domain components that match the frequency data corresponding to the current excitation spectrum data.

[0053] The frequency domain recognition model to be trained may be any deep learning model that is not yet able to perform frequency domain recognition on the frequency of the input excitation signal.

[0054] Specifically, in a possible embodiment, the input and response signals of the electro-hydraulic vibrator, the electromagnetic vibrator, and the electro-hydraulic-electromagnetic vibrator can be obtained respectively through the frequency sweep signal, and respectively used as training samples of the above-mentioned frequency domain recognition model to be trained, and the training samples are Fourier transformed to obtain the corresponding frequency domain training samples. More specifically, they can be identified according to the stochastic gradient least squares learning algorithm, and the process is as follows: According to the input and response signals of the electro-hydraulic vibrator, the electromagnetic vibrator, and the electro-hydraulic-electromagnetic vibrator, corresponding training samples are selected; The parameter vector of the trained frequency domain recognition model is treated by gradient descent To update, ; in, is the gradient descent step, is the training squared error gradient corresponding to the training sample with order i.

[0055] ; in, is the basis function, is a Gaussian kernel function model, then: ; ; In the formula, n represents the number of training samples; represents the jth factor of the parameter vector; represents the 2-norm, for example , T represents transpose; is the binary function of the kernel function; h is the bandwidth of the Gaussian kernel function; c is the mean of the Gaussian kernel function; Repeat the above steps until the final convergence accuracy reaches 95%, and then obtain the trained frequency domain recognition model.

[0056] Optionally, the step of adjusting the excitation simulation model through a preset adaptive algorithm to obtain an optimized excitation simulation model also includes determining a parameter optimization direction of the excitation simulation based on target excitation spectrum data corresponding to the excitation simulation model; adjusting parameters according to the parameter optimization direction based on the preset adaptive algorithm, and obtaining the optimized excitation simulation model after the parameter adjustment.

[0057] In an embodiment of the present invention, the above parameter optimization direction can be used to adjust the excitation balance of the system according to the adjusted parameters. Generally speaking, this direction can be determined according to the classification corresponding to the target excitation spectrum data, and the corresponding frequency domain model is selected for simulation according to the classification. Specifically, when the target excitation spectrum data of the electromagnetic exciter and the target excitation spectrum data of the electro-hydraulic exciter are sinusoidal spectra of the same frequency, and the target excitation spectrum data of the electromagnetic exciter and the target excitation spectrum data of the electro-hydraulic exciter are random spectra, select and ; When generating target excitation spectrum data for target sinusoidal excitation spectrum data of electromagnetic exciters of different frequencies and target sinusoidal excitation spectrum data of electro-hydraulic exciters and external data import, select .

[0058] Optionally, the step of inputting the target excitation spectrum data into the optimized excitation simulation model for processing and outputting the optimized excitation simulation data also includes determining an updated frequency response function and a control algorithm of the optimized excitation simulation model; processing the target excitation spectrum data based on the updated frequency response function to obtain the optimized excitation simulation data.

[0059] In an embodiment of the present invention, through a control algorithm, such as preset adaptive control or model predictive control (MPC), the model parameters are adjusted to match the dynamic characteristics of the actual device, and the target excitation spectrum data is input into the optimized model for processing, thereby generating optimized excitation simulation data, which accurately expresses the expected behavior and response of the device under the expected operating conditions.

[0060] The optimized excitation simulation data makes it possible to foresee the performance of the equipment in actual operation, so as to make necessary adjustments and optimizations. Through accurate simulation, potential structural defects or performance limitations can be discovered in the design stage, thus improving the performance of the equipment.

[0061] like Figure 2 As shown, the embodiment of the present invention further provides an electro-hydraulic-electromagnetic excitation system coordinated control device 200, the electro-hydraulic-electromagnetic excitation system coordinated control device comprising: An acquisition module 201 is used to acquire target excitation spectrum data; A first determination module 202 is used to determine an excitation simulation model based on the target excitation spectrum data; The optimization module 203 is used to adjust the excitation simulation model through a preset adaptive algorithm to obtain an optimized excitation simulation model; A processing module 204 is used to input the target excitation spectrum data into the optimized excitation simulation model for processing, and output the optimized excitation simulation data; The generating module 205 is used to generate an excitation report based on the optimized excitation simulation data, and perform feedback control adjustment according to the excitation report.

[0062] Optionally, the acquisition module 201 includes: A first determination submodule is used to collect frequency spectra corresponding to the sinusoidal excitation spectrum and the random spectrum of the electromagnetic oscillator, and determine the frequency of the first target sinusoidal excitation spectrum of the electromagnetic oscillator and the frequency of the first random spectrum; A second determination submodule is used to collect frequency spectra corresponding to the sinusoidal excitation spectrum and the random spectrum of the electro-hydraulic oscillator, and determine the frequency of the second target sinusoidal excitation spectrum and the frequency of the second random spectrum of the electro-hydraulic oscillator; The third determination submodule is used to determine the target excitation spectrum data based on the frequency of the first target sinusoidal excitation spectrum, the frequency of the first random spectrum, the frequency of the second target sinusoidal excitation spectrum and the frequency of the second random spectrum.

[0063] Optionally, the first determining module 202 includes: A fourth determination submodule is used to determine target excitation spectrum characteristic data based on the target excitation spectrum data, wherein the target excitation spectrum characteristic data includes a magnitude relationship between frequency data of the electromagnetic oscillator and the electro-hydraulic oscillator; A fifth determination submodule, used to determine a corresponding frequency domain model based on a magnitude relationship of frequency data of the electromagnetic oscillator and the electro-hydraulic oscillator; The sixth determination submodule is used to determine the corresponding excitation simulation model based on the frequency domain model.

[0064] Optionally, the above device includes: A second determination module is used to process the target excitation spectrum data through a frequency sweep signal to determine the frequency of the first target sinusoidal excitation spectrum, the frequency of the first random spectrum, the frequency of the second target sinusoidal excitation spectrum, and the frequency of the second random spectrum; The third determination module is used to perform feature recognition processing on the frequency of the first target sinusoidal excitation spectrum, the frequency of the first random spectrum, the frequency of the second target sinusoidal excitation spectrum and the frequency of the second random spectrum through the trained frequency domain recognition model to determine the target excitation spectrum feature data; The fourth determination module is used to match the target excitation spectrum feature data in the frequency domain recognition database to determine the corresponding frequency domain model.

[0065] Optionally, the above device further comprises: A training module, used to obtain a frequency domain recognition model to be trained and a frequency domain recognition sample set, wherein the frequency domain recognition sample set includes an excitation spectrum, frequency data corresponding to the excitation spectrum, and a frequency domain label corresponding to the frequency data; The iterative training module is used to iteratively train the frequency domain recognition model to be trained based on the excitation spectrum, the frequency data corresponding to the excitation spectrum, and the frequency domain label corresponding to the frequency data, and obtain the trained frequency domain recognition model to be trained after the iterative training is completed.

[0066] Optionally, the optimization module 203 includes: A seventh determination submodule is used to determine the parameter optimization direction of the excitation simulation based on the target excitation spectrum data corresponding to the excitation simulation model; The eighth determination submodule is used to adjust parameters in a parameter optimization direction based on a preset adaptive algorithm, and obtain an optimized excitation simulation model after the parameter adjustment.

[0067] Optionally, the optimization module 203 further includes: A ninth determination submodule is used to determine the frequency response function and control algorithm of the optimized excitation simulation model update; The tenth determination submodule is used to process the target excitation spectrum data based on the updated frequency response function to obtain optimized excitation simulation data.

[0068] like Figure 3 As shown, an embodiment of the present invention further provides an electronic device 300, including a processor, and the processor can execute any one of the above-mentioned electro-hydraulic-electromagnetic excitation system coordinated control methods.

[0069] Specifically, it includes a processor 301 and a memory 302, and a computer program for executing the electro-hydraulic-electromagnetic excitation system coordinated control method stored in the memory 302 and capable of running on the processor 301, wherein: The processor 301 runs the computer program of the electro-hydraulic-electromagnetic excitation system coordinated control method stored in the memory 302, and performs the following steps: Acquire target excitation spectrum data; Determining an excitation simulation model based on the target excitation spectrum data; The vibration simulation model is adjusted by a preset adaptive algorithm to obtain an optimized vibration simulation model; Input the target excitation spectrum data into the optimized excitation simulation model for processing, and output the optimized excitation simulation data; Generate a vibration report based on the optimized vibration simulation data, and make feedback control adjustments based on the vibration report.

[0070] Optionally, the processor 301 executes the step of acquiring target excitation spectrum data, including: Collecting frequency spectra corresponding to the sinusoidal excitation spectrum and the random spectrum of the electromagnetic oscillator to determine the frequency of the first target sinusoidal excitation spectrum and the frequency of the first random spectrum of the electromagnetic oscillator; The frequency spectra corresponding to the sinusoidal excitation spectrum and the random spectrum of the electro-hydraulic oscillator are collected to determine the frequency of the second target sinusoidal excitation spectrum of the electro-hydraulic oscillator and the frequency of the second random spectrum; The target excitation spectrum data is determined based on the frequency of the first target sinusoidal excitation spectrum, the frequency of the first random spectrum pattern, the frequency of the second target sinusoidal excitation spectrum, and the frequency of the second random spectrum pattern.

[0071] Optionally, the processor 301 performs the step of determining the excitation simulation model based on the target excitation spectrum data, including: Based on the target excitation spectrum data, determining target excitation spectrum characteristic data, wherein the target excitation spectrum characteristic data includes a magnitude relationship between frequency data of an electromagnetic oscillator and an electro-hydraulic oscillator; Determine a corresponding frequency domain model based on the magnitude relationship of the frequency data of the electromagnetic oscillator and the electro-hydraulic oscillator; Based on the frequency domain model, a corresponding excitation simulation model is determined.

[0072] Optionally, the processor 301 determines target excitation spectrum feature data based on the target excitation spectrum data, and determines a corresponding frequency domain model based on the target excitation spectrum feature data, including: Processing the target excitation spectrum data through a frequency sweep signal to determine the frequency of the first target sinusoidal excitation spectrum, the frequency of the first random spectrum, the frequency of the second target sinusoidal excitation spectrum, and the frequency of the second random spectrum; By using the trained frequency domain recognition model, feature recognition processing is performed on the frequency of the first target sinusoidal excitation spectrum, the frequency of the first random spectrum, the frequency of the second target sinusoidal excitation spectrum, and the frequency of the second random spectrum to determine target excitation spectrum feature data; Based on the target excitation spectrum characteristic data, matching is performed in a frequency domain recognition database to determine a corresponding frequency domain model.

[0073] Optionally, the processor 301 executes the trained frequency domain recognition model to perform feature recognition processing on the frequency of the first target sinusoidal excitation spectrum, the frequency of the first random spectrum, the frequency of the second target sinusoidal excitation spectrum, and the frequency of the second random spectrum, and before determining the target excitation spectrum feature data, the method further includes: Acquire a frequency domain recognition model to be trained and a frequency domain recognition sample set, wherein the frequency domain recognition sample set includes an excitation spectrum, frequency data corresponding to the excitation spectrum, and a frequency domain label corresponding to the frequency data; Based on the excitation spectrum, the frequency data corresponding to the excitation spectrum, and the frequency domain labels corresponding to the frequency data, the frequency domain recognition model to be trained is iteratively trained, and after the iterative training is completed, a trained frequency domain recognition model to be trained is obtained.

[0074] Optionally, the processor 301 further performs the adjustment of the excitation simulation model by using a preset adaptive algorithm to obtain an optimized excitation simulation model, including: Determine the parameter optimization direction of the vibration simulation based on the target excitation spectrum data corresponding to the vibration simulation model; Based on the preset adaptive algorithm, the parameters are adjusted in the parameter optimization direction, and the optimized excitation simulation model is obtained after the parameter adjustment.

[0075] Optionally, the processor 301 further executes the step of inputting the target excitation spectrum data into the optimized excitation simulation model for processing and outputting the optimized excitation simulation data, including: determining a frequency response function and a control algorithm updated by the optimized excitation simulation model; The target excitation spectrum data is processed based on the updated frequency response function to obtain optimized excitation simulation data.

[0076] An embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the various processes of the electro-hydraulic-electromagnetic excitation system collaborative control method or the application-end electro-hydraulic-electromagnetic excitation system collaborative control method provided by the embodiment of the present invention are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0077] Those skilled in the art can understand that the implementation of all or part of the processes in the above-mentioned embodiments is a program that can be completed by instructing related hardware through a computer program, and can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM).

[0078] The above disclosure is only the preferred embodiment of the present invention, which certainly cannot be used to limit the scope of the present invention. Therefore, equivalent changes made according to the claims of the present invention are still within the scope of the present invention.

Claims

1. A coordinated control method of an electro-hydraulic-electromagnetic excitation system, characterized in that: include: Acquire target excitation spectrum data; Determining an excitation simulation model based on the target excitation spectrum data; The vibration simulation model is adjusted by a preset adaptive algorithm to obtain an optimized vibration simulation model; Input the target excitation spectrum data into the optimized excitation simulation model for processing, and output the optimized excitation simulation data; Generate a vibration report based on the optimized vibration simulation data, and make feedback control adjustments based on the vibration report.

2. The electro-hydraulic-electromagnetic excitation system coordinated control method according to claim 1, characterized in that: The step of acquiring target excitation spectrum data comprises: The frequency spectra corresponding to the sinusoidal excitation spectrum and the random spectrum of the electromagnetic oscillator are collected to determine the frequency of the first target sinusoidal excitation spectrum and the frequency of the first random spectrum of the electromagnetic oscillator; The frequency spectra corresponding to the sinusoidal excitation spectrum and the random spectrum of the electro-hydraulic oscillator are collected to determine the frequency of the second target sinusoidal excitation spectrum of the electro-hydraulic oscillator and the frequency of the second random spectrum; The target excitation spectrum data is determined based on the frequency of the first target sinusoidal excitation spectrum, the frequency of the first random spectrum pattern, the frequency of the second target sinusoidal excitation spectrum, and the frequency of the second random spectrum pattern.

3. The electro-hydraulic-electromagnetic excitation system coordinated control method according to claim 1, characterized in that: The step of determining the excitation simulation model based on the target excitation spectrum data includes: Based on the target excitation spectrum data, determining target excitation spectrum characteristic data, wherein the target excitation spectrum characteristic data includes a magnitude relationship between frequency data of an electromagnetic oscillator and an electro-hydraulic oscillator; Determine a corresponding frequency domain model based on the magnitude relationship of the frequency data of the electromagnetic oscillator and the electro-hydraulic oscillator; Based on the frequency domain model, a corresponding excitation simulation model is determined.

4. The electro-hydraulic-electromagnetic excitation system coordinated control method according to claim 2 or 3, characterized in that: The step of determining target excitation spectrum characteristic data based on the target excitation spectrum data, and determining a corresponding frequency domain model based on the target excitation spectrum characteristic data, comprises: Processing the target excitation spectrum data through a frequency sweep signal to determine the frequency of the first target sinusoidal excitation spectrum, the frequency of the first random spectrum, the frequency of the second target sinusoidal excitation spectrum, and the frequency of the second random spectrum; By using the trained frequency domain recognition model, feature recognition processing is performed on the frequency of the first target sinusoidal excitation spectrum, the frequency of the first random spectrum, the frequency of the second target sinusoidal excitation spectrum, and the frequency of the second random spectrum to determine target excitation spectrum feature data; Based on the target excitation spectrum characteristic data, matching is performed in a frequency domain recognition database to determine a corresponding frequency domain model.

5. The electro-hydraulic-electromagnetic excitation system coordinated control method of claim 4, characterized in that: The method further comprises: performing feature recognition processing on the frequency of the first target sinusoidal excitation spectrum, the frequency of the first random spectrum, the frequency of the second target sinusoidal excitation spectrum and the frequency of the second random spectrum through the trained frequency domain recognition model, and determining the target excitation spectrum feature data before the method further comprises: Acquire a frequency domain recognition model to be trained and a frequency domain recognition sample set, wherein the frequency domain recognition sample set includes an excitation spectrum, frequency data corresponding to the excitation spectrum, and a frequency domain label corresponding to the frequency data; Based on the excitation spectrum, the frequency data corresponding to the excitation spectrum, and the frequency domain labels corresponding to the frequency data, the frequency domain recognition model to be trained is iteratively trained, and after the iterative training is completed, a trained frequency domain recognition model to be trained is obtained.

6. The electro-hydraulic-electromagnetic excitation system coordinated control method of claim 1, characterized in that: The method of adjusting the excitation simulation model by using a preset adaptive algorithm to obtain an optimized excitation simulation model includes: Based on the target excitation spectrum data corresponding to the excitation simulation model, determine the parameter optimization direction of the excitation simulation; Based on the preset adaptive algorithm, the parameters are adjusted in the parameter optimization direction, and the optimized excitation simulation model is obtained after the parameter adjustment.

7. The electro-hydraulic-electromagnetic excitation system coordinated control method of claim 1, characterized in that: The step of inputting the target excitation spectrum data into the optimized excitation simulation model for processing and outputting the optimized excitation simulation data comprises: Determine the frequency response function and control algorithm for updating the optimized excitation simulation model; The target excitation spectrum data is processed based on the updated frequency response function to obtain optimized excitation simulation data.

8. An electro-hydraulic-electromagnetic excitation system coordinated control device, characterized in that: include: An acquisition module, used for acquiring target excitation spectrum data; A first determination module, used to determine an excitation simulation model based on the target excitation spectrum data; An optimization module is used to adjust the excitation simulation model through a preset adaptive algorithm to obtain an optimized excitation simulation model; A processing module, used for inputting target excitation spectrum data into the optimized excitation simulation model for processing, and outputting optimized excitation simulation data; The generation module is used to generate an excitation report based on the optimized excitation simulation data, and to make feedback control adjustments according to the excitation report.

9. An electronic device, characterized in that: include: 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 steps in the electro-hydraulic-electromagnetic excitation system collaborative control method as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the steps in the electro-hydraulic-electromagnetic excitation system coordinated control method as described in any one of claims 1 to 7 are implemented.

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