A method and system for adjusting resonant frequency

Abstract

The present invention discloses a method and system for adjusting the resonance frequency. The surface acoustic wave filtering device and method with temperature compensation include: the temperature-sensitive layer of the surface acoustic wave filtering device performs temperature sensing and signal acquisition; the signal preprocessing circuit amplifies and filters the acquired signal and inputs it into the temperature-frequency algorithm chip; the algorithm model based on neural network inside the temperature-frequency algorithm chip calculates the required compensation parameters; the compensation parameters are output to the variable capacitor to adjust the resonance frequency of the surface acoustic wave filtering device; through dynamically adjusting the resonance frequency of the surface acoustic wave filtering device, a closed-loop control of temperature compensation is achieved; the present application realizes the temperature compensation control of the surface acoustic wave filtering device, ensuring that the surface acoustic wave filter can maintain a stable resonance frequency in different temperature environments.

Description

Technical Field

[0001] The present invention relates to temperature compensation technology, and particularly to a method and system for adjusting the resonant frequency. Background Art

[0002] Surface Acoustic Wave (SAW) filters are widely used in wireless communication, radar systems, Radio Frequency Identification (RFID), satellite navigation and other fields due to their advantages of small size, low power consumption and stable performance. However, since the center frequency of the SAW filter is sensitive to temperature changes, temperature fluctuations may cause frequency drift of the filter, affecting the signal processing accuracy and communication quality of the system. Especially in application scenarios with high-precision and high-stability requirements, such as 5G communication, satellite communication and industrial control systems, the problem of frequency offset caused by temperature is particularly prominent.

[0003] In view of the above problems, the present invention proposes a SAW filtering device and method with temperature compensation function, which realizes adaptive compensation for temperature changes by optimizing the structural design of the SAW filter, adopting specific material combinations, integrating temperature compensation mechanisms, etc. The technical solution of the present invention can effectively reduce the influence of temperature changes on the filter performance without increasing additional power consumption and system complexity, improve the frequency stability of the filter, and thus meet the requirements of modern wireless communication and precision electronic systems for high-reliability and high-stability filters. Summary of the Invention

[0004] The present invention provides a method for adjusting the resonant frequency, including:

[0005] S110, the temperature-sensitive layer of the SAW filtering device performs temperature sensing and signal acquisition;

[0006] S120, the signal preprocessing circuit amplifies and filters the acquired signal and inputs it into the temperature-frequency algorithm chip;

[0007] S130, the algorithm model based on neural network inside the temperature-frequency algorithm chip calculates the required compensation parameters;

[0008] S140, outputs the compensation parameters to the variable capacitor to adjust the resonant frequency of the SAW filtering device;

[0009] S150, realizes the closed-loop control of temperature compensation by dynamically adjusting the resonant frequency of the SAW filtering device.

[0010] For a method for adjusting the resonant frequency as described above, the method for the temperature-sensitive layer of the SAW filtering device to perform temperature sensing and signal acquisition specifically includes the following sub-steps:

[0011] Set the temperature-sensitive layer;

[0012] The temperature - sensitive layer monitors the real - time temperature and captures electrical signals;

[0013] Collect and transmit the captured electrical signals.

[0014] A method and system for adjusting the resonance frequency as described above, wherein the signal pre - processing circuit amplifies and filters the temperature signal. First, it performs low - noise amplification through a pre - amplifier to amplify the signal to a level range suitable for subsequent processing, and then passes through a band - pass filter to remove high - frequency and low - frequency noise in the signal, only retaining the effective signal frequency band related to temperature changes, obtaining a more stable temperature signal.

[0015] A method for adjusting the resonance frequency as described above, wherein the pre - processed temperature signal is input into the temperature - frequency compensation algorithm chip. The neural network algorithm model inside the temperature - frequency compensation algorithm chip receives this temperature signal as input, and the neurons in the model perform layer - by - layer calculations on the input signal according to the trained weights. After the calculation of the neural network model, the temperature - frequency compensation algorithm chip outputs the required compensation parameters.

[0016] A method for adjusting the resonance frequency as described above, wherein the compensation parameters are output to a variable capacitor. The method for adjusting the resonance frequency of the surface acoustic wave filtering device specifically includes the following sub - steps:

[0017] Generate a corresponding control voltage signal according to the calculated compensation parameters;

[0018] Adjust the resonance frequency of the surface acoustic wave filtering device according to the control voltage signal.

[0019] The present invention also provides a system for adjusting the resonance frequency, including:

[0020] Temperature perception and signal acquisition module: The temperature - sensitive layer of the surface acoustic wave filtering device performs temperature perception and signal acquisition;

[0021] Signal pre - processing circuit module: The signal pre - processing circuit amplifies and filters the acquired signal and inputs it into the temperature - frequency algorithm chip;

[0022] Temperature - frequency algorithm chip module: The algorithm model based on neural network inside the temperature - frequency algorithm chip calculates the required compensation parameters;

[0023] Resonance frequency adjustment module: Output the compensation parameters to a variable capacitor to adjust the resonance frequency of the surface acoustic wave filtering device;

[0024] Closed - loop control module: Achieve closed - loop control of temperature compensation by dynamically adjusting the resonance frequency of the surface acoustic wave filtering device.

[0025] A resonance frequency adjustment system as described above, wherein the method for the temperature-sensitive layer of the surface acoustic wave filtering device to sense temperature and collect signals specifically includes the following sub-steps:

[0026] Set the temperature-sensitive layer;

[0027] The temperature-sensitive layer performs real-time temperature monitoring and captures electrical signals;

[0028] Collect and transmit the captured electrical signals.

[0029] A resonance frequency adjustment system as described above, wherein the signal preprocessing circuit amplifies and filters the temperature signal. First, it performs low-noise amplification through a preamplifier to amplify the signal to a level range suitable for subsequent processing, and then passes through a band-pass filter to remove high-frequency and low-frequency noises in the signal, only retaining the effective signal frequency band related to temperature changes, obtaining a more stable temperature signal.

[0030] A resonance frequency adjustment system as described above, wherein the preprocessed temperature signal is input into the temperature-frequency compensation algorithm chip. The neural network algorithm model inside the temperature-frequency compensation algorithm chip receives this temperature signal as input, and the neurons in the model perform layer-by-layer calculations on the input signal according to the trained weights. After the calculation by the neural network model, the temperature-frequency compensation algorithm chip outputs the required compensation parameters.

[0031] A resonance frequency adjustment system as described above, wherein the method for outputting the compensation parameters to the variable capacitor and adjusting the resonance frequency of the surface acoustic wave filtering device specifically includes the following sub-steps:

[0032] Generate a corresponding control voltage signal according to the calculated compensation parameters;

[0033] Adjust the resonance frequency of the surface acoustic wave filtering device according to the control voltage signal.

[0034] The beneficial effects achieved by the present invention are as follows: It realizes the temperature compensation control of the surface acoustic wave filtering device, ensuring that the surface acoustic wave filter can maintain a stable resonance frequency in different temperature environments. Description of the Drawings

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0036] Figure 1 It is a flowchart of a method and system for adjusting resonance frequency provided in Embodiment 1 of the present application;

[0037] Figure 2 It is a schematic diagram of a resonant frequency adjustment system provided in the second embodiment of the present application; Specific implementation manners

[0038] The following combines the accompanying 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 part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts fall within the protection scope of the present invention.

[0039] Embodiment 1

[0040] As Figure 1 shown, Embodiment 1 of the present application provides a method for adjusting the resonant frequency, including:

[0041] Step S110: The temperature-sensitive layer of the surface acoustic wave filtering device performs temperature perception and signal acquisition;

[0042] The method for the temperature-sensitive layer of the surface acoustic wave filtering device to perform temperature perception and signal acquisition specifically includes the following sub-steps:

[0043] Step S111: Set the temperature-sensitive layer;

[0044] A temperature-sensitive layer composed of polyvinylidene fluoride (PVDF) and carbon nanotubes (CNT) is covered on the surface of the surface acoustic wave filtering device body. PVDF has piezoelectricity and pyroelectricity, can sense temperature changes and generate corresponding electrical signal changes. The carbon nanotubes are evenly dispersed in PVDF. On the one hand, it enhances the mechanical properties of PVDF, and on the other hand, it improves its electrical conductivity, enabling the electrical signal changes caused by temperature changes to be transmitted more quickly and effectively. The thickness of the temperature-sensitive layer is precisely controlled and set to 500 nanometers to ensure high sensitivity to temperature changes and minimal interference to surface acoustic wave propagation.

[0045] Step S112: The temperature-sensitive layer performs real-time temperature monitoring and captures electrical signals;

[0046] After the surface acoustic wave filtering device is started, the temperature-sensitive layer continuously senses the temperature changes around the surface acoustic wave filter body. Due to the pyroelectric characteristics of PVDF, a small change in temperature will cause a corresponding charge change, thereby forming an electrical signal. Specifically, the formula: represents the formed electrical signal, where represents the pyroelectric voltage generated by PVDF, represents the pyroelectric coefficient, represents the temperature change rate.

[0047] Step S113: Collect and transmit the captured electrical signal;

[0048] The electrical signal generated by the temperature-sensitive layer is quickly conducted through the internal carbon nanotube network to the signal preprocessing circuit. Carbon nanotubes are tubular nanostructures composed of carbon atoms, with high electrical conductivity, high thermal conductivity, and high mechanical strength. In the surface acoustic wave filtering device, the carbon nanotubes are connected to the PVDF sensing layer and the signal preprocessing circuit in a network form to form an efficient signal transmission path. The signal preprocessing circuit is composed of multiple high-performance operational amplifiers and precision resistors and capacitors, and this circuit receives the weak electrical signal from the temperature-sensitive layer.

[0049] Step S120: The signal preprocessing circuit amplifies and filters the collected signal and inputs it into the temperature-frequency algorithm chip;

[0050] The temperature-frequency algorithm chip internally integrates a temperature-frequency mapping model based on the neural network algorithm. This model is trained with a large amount of experimental data, which covers the frequency offset of the surface acoustic wave filter at different temperatures and the corresponding output electrical signals of the temperature-sensitive layer. During actual operation, the temperature-frequency algorithm chip quickly calculates the parameter values required to adjust the frequency of the surface acoustic wave filter according to the received preprocessed temperature signal.

[0051] Since the temperature signal is weak, the signal preprocessing circuit needs to amplify and filter it to obtain a more stable temperature signal. First, it is low-noise amplified by a preamplifier to amplify the signal to a level range suitable for subsequent processing. Then, through a band-pass filter, the high-frequency and low-frequency noises in the signal are removed, and only the effective signal frequency band related to the temperature change is retained. The operational amplifier is selected with a model having an extremely low offset voltage and a high common-mode rejection ratio to ensure the accuracy of signal processing. Specifically, the formula: is used for amplification and filtering processing, where represents the amplified signal, represents the signal amplification gain, represents the noise component removed by filtering.

[0052] Step S130: The algorithm model based on the neural network inside the temperature-frequency algorithm chip calculates the required compensation parameters;

[0053] The preprocessed temperature signal is input into the temperature-frequency compensation algorithm chip. The neural network algorithm model inside the temperature-frequency compensation algorithm chip receives this temperature signal as input, and the neurons in the model perform layer-by-layer calculations on the input signal according to the weights obtained through training. This neural network model has multiple hidden layers, and each hidden layer contains multiple neurons. The weights obtained through training with a large amount of experimental data can accurately map the relationship between the temperature signal and the frequency offset of the surface acoustic wave filter.

[0054] After the calculation by the neural network model, the temperature-frequency compensation algorithm chip outputs the required compensation parameters. The compensation parameters are closely related to the temperature change and the frequency offset characteristics of the surface acoustic wave filter. When the temperature rises or falls, the model calculates the frequency by which the resonant frequency of the surface acoustic wave filter needs to be lowered or raised. The temperature-frequency compensation algorithm chip outputs the corresponding control parameters to achieve this frequency adjustment. Specifically, the formula is used: Calculate the compensation parameters, represents the compensation parameter, L represents the number of layers of the neural network, represents the number of neurons in the layer, represents the weight parameter, represents the input temperature signal, represents the bias,

[0055] Step S140: Output the compensation parameters to the variable capacitor to adjust the resonant frequency of the surface acoustic wave filtering device;

[0056] The method of outputting the compensation parameters to the variable capacitor to adjust the resonant frequency of the surface acoustic wave filtering device specifically includes the following sub-steps:

[0057] Step S141: Generate a corresponding control voltage signal according to the calculated compensation parameters;

[0058] According to the calculated compensation parameters, a corresponding control voltage signal is generated. This control voltage signal is transmitted to the frequency fine-tuning actuator. The frequency fine-tuning actuator consists of multiple microelectromechanical system (MEMS) variable capacitors, and these MEMS variable capacitors are connected to the resonant circuit of the surface acoustic wave filter. The MEMS variable capacitors work based on the electrostatic drive principle. When the control voltage output by the temperature-frequency compensation algorithm chip is applied to the MEMS variable capacitors, their capacitance values will change precisely. Since the resonant frequency of the resonant circuit is closely related to the capacitance value, by adjusting the capacitance value of the MEMS variable capacitors, the fine-tuning of the frequency of the surface acoustic wave filter can be achieved, thereby compensating for the frequency offset caused by temperature changes. Specifically, the formula is used: Calculate the control voltage signal, where represents the control voltage signal, represents the control gain coefficient, which is used to adjust the amplitude of the compensation signal. represents the compensation parameter. represents the offset compensation term to ensure that the output signal matches the operating range of the MEMS variable capacitor.

[0059] Step S142: Adjust the resonant frequency of the surface acoustic wave filtering device according to the control voltage signal.

[0060] Transmit the calculated control voltage signal to the frequency fine-tuning actuator. The MEMS component adjusts its own capacitance value according to the control voltage, and then adjusts the resonant frequency of the surface acoustic wave filtering device. Specifically, the formula is used: Calculate the resonant frequency, where represents the resonant frequency. represents the value of the variable capacitance. represents the initial capacitance. represents the voltage regulation coefficient. represents the control voltage signal, L and represent the equivalent inductance and inherent capacitance of the filter.

[0061] Step S150: Achieve closed-loop control of temperature compensation by dynamically adjusting the resonant frequency of the surface acoustic wave filtering device.

[0062] Achieve closed-loop control of temperature compensation by dynamically adjusting the resonant frequency of the surface acoustic wave filtering device. The temperature-frequency compensation algorithm chip continuously collects the temperature signal of the temperature-sensitive layer, calculates the frequency compensation parameter, and dynamically adjusts the capacitance value of the MEMS to form a closed-loop compensation control system, so that the surface acoustic wave filtering device is always stable at the target resonant frequency. Specifically, the formula is used: represents the closed-loop control of temperature compensation, where represents the real-time resonant frequency. represents the target resonant frequency. represents the frequency tolerance.

[0063] Embodiment 2

[0064] As Figure 2 shown, Embodiment 2 of the present application provides an adjustment system for the resonant frequency, including:

[0065] Temperature sensing and signal acquisition module 21: Perform temperature sensing and signal acquisition on the temperature-sensitive layer of the surface acoustic wave filtering device.

[0066] The method for performing temperature sensing and signal acquisition on the temperature-sensitive layer of the surface acoustic wave filtering device specifically includes the following sub-steps:

[0067] Setting module: Set the temperature-sensitive layer.

[0068] Cover a temperature-sensitive layer composed of polyvinylidene fluoride (PVDF) and carbon nanotubes (CNT) on the surface of the surface acoustic wave filtering device body. PVDF has piezoelectricity and pyroelectricity, can sense temperature changes and generate corresponding electrical signal changes. The carbon nanotubes are evenly dispersed in PVDF. On the one hand, it enhances the mechanical properties of PVDF, and on the other hand, it improves its conductivity, enabling the electrical signal changes caused by temperature changes to be transmitted more quickly and effectively. The thickness of the temperature-sensitive layer is precisely controlled and set to 500 nanometers to ensure high sensitivity to temperature changes and minimal interference to surface acoustic wave propagation.

[0069] Capture module: The temperature-sensitive layer monitors the temperature in real time and captures electrical signals;

[0070] After the surface acoustic wave filtering device is started, the temperature-sensitive layer continuously senses the temperature changes around the surface acoustic wave filter body. Due to the pyroelectric characteristics of PVDF, a slight temperature change will cause corresponding charge changes in it, thereby forming electrical signals. Specifically, the formula is used: represents the formed electrical signal, where, represents the pyroelectric voltage generated by PVDF, represents the pyroelectric coefficient, represents the temperature change rate.

[0071] Acquisition and transmission module: Acquire and transmit the captured electrical signals;

[0072] The electrical signals generated by the temperature-sensitive layer are quickly conducted to the signal preprocessing circuit through the internal carbon nanotube network. Carbon nanotubes are a kind of tubular nanostructure composed of carbon atoms, with high conductivity, high thermal conductivity and high mechanical strength. In the surface acoustic wave filtering device, the carbon nanotubes connect the PVDF sensing layer and the signal preprocessing circuit in a network form, forming an efficient signal transmission path. The signal preprocessing circuit is composed of multiple high-performance operational amplifiers and precision resistors and capacitors, and this circuit receives the weak electrical signals from the temperature-sensitive layer.

[0073] Signal preprocessing circuit module 22: The signal preprocessing circuit amplifies and filters the acquired signals and inputs them into the temperature-frequency algorithm chip;

[0074] The temperature-frequency algorithm chip internally integrates a temperature-frequency mapping model based on the neural network algorithm. This model is trained through a large amount of experimental data, which cover the frequency offset of the surface acoustic wave filter at different temperatures and the corresponding output electrical signals of the temperature-sensitive layer. During actual operation, the temperature-frequency algorithm chip quickly calculates the parameter values required to adjust the frequency of the surface acoustic wave filter according to the received preprocessed temperature signals.

[0075] Since the temperature signal is weak, the signal preprocessing circuit needs to amplify and filter it to obtain a more stable temperature signal. First, it is low-noise amplified by a preamplifier to amplify the signal to a level range suitable for subsequent processing. Then, through a band-pass filter, high-frequency and low-frequency noises in the signal are removed, and only the effective signal frequency band related to temperature changes is retained. An operational amplifier with extremely low offset voltage and high common-mode rejection ratio is selected to ensure the accuracy of signal processing. Specifically, the formula: is used for amplification and filtering processing, where represents the amplified signal, represents the signal amplification gain, represents the noise component removed by filtering.

[0076] Temperature-frequency algorithm chip module 23: The algorithm model based on neural network inside the temperature-frequency algorithm chip calculates the required compensation parameters;

[0077] The preprocessed temperature signal is input into the temperature-frequency compensation algorithm chip. The neural network algorithm model inside the temperature-frequency compensation algorithm chip receives this temperature signal as input, and the neurons in the model perform layer-by-layer calculations on the input signal according to the weights obtained through training. This neural network model has multiple hidden layers, each hidden layer contains multiple neurons, and the weights obtained through training with a large amount of experimental data can accurately map the relationship between the temperature signal and the frequency offset of the surface acoustic wave filter.

[0078] After the calculation by the neural network model, the temperature-frequency compensation algorithm chip outputs the required compensation parameters. The compensation parameters are closely related to temperature changes and the frequency offset characteristics of the surface acoustic wave filter. When the temperature rises or falls, the model calculates the frequency at which the resonant frequency of the surface acoustic wave filter needs to be lowered or raised, and the temperature-frequency compensation algorithm chip outputs the corresponding control parameters to achieve this frequency adjustment. Specifically, the formula: is used to calculate the compensation parameters, represents the compensation parameters, L represents the number of layers of the neural network, represents the number of neurons in the th layer, represents the weight parameter, represents the input temperature signal, represents the bias,

[0079] Resonant frequency adjustment module 24: Outputs the compensation parameters to the variable capacitor to adjust the resonant frequency of the surface acoustic wave filtering device;

[0080] The method of outputting the compensation parameters to the variable capacitor to adjust the resonant frequency of the surface acoustic wave filtering device specifically includes the following sub-steps:

[0081] Control voltage signal module: Generates corresponding control voltage signals according to the calculated compensation parameters;

[0082] According to the calculated compensation parameters, corresponding control voltage signals are generated. These control voltage signals are transmitted to the frequency fine-tuning actuator, which consists of multiple microelectromechanical system (MEMS) variable capacitors. These MEMS variable capacitors are connected to the resonant circuit of the surface acoustic wave filter, and the MEMS variable capacitors work based on the principle of electrostatic drive. When the control voltage output by the temperature-frequency compensation algorithm chip is applied to the MEMS variable capacitors, their capacitance values will change precisely. Since the resonant frequency of the resonant circuit is closely related to the capacitance value, by adjusting the capacitance value of the MEMS variable capacitors, the frequency of the surface acoustic wave filter can be fine-tuned, thereby compensating for the frequency shift caused by temperature changes. Specifically, the formula: is used to calculate the control voltage signal, where represents the control voltage signal, represents the control gain coefficient, which is used to adjust the amplitude of the compensation signal, represents the compensation parameter, represents the offset compensation term to ensure that the output signal matches the operating range of the MEMS variable capacitors.

[0083] Resonant frequency adjustment module: Adjusts the resonant frequency of the surface acoustic wave filtering device according to the control voltage signal;

[0084] The calculated control voltage signal is transmitted to the frequency fine-tuning actuator. The MEMS components adjust their own capacitance values according to the control voltage, and then adjust the resonant frequency of the surface acoustic wave filtering device. Specifically, the formula: is used to calculate the resonant frequency, where represents the resonant frequency, represents the value of the variable capacitor, represents the initial capacitance, represents the voltage regulation coefficient, represents the control voltage signal, L and represent the equivalent inductance and inherent capacitance of the filter.

[0085] Closed-loop control module 25: Achieves closed-loop control of temperature compensation by dynamically adjusting the resonant frequency of the surface acoustic wave filtering device;

[0086] By dynamically adjusting the resonant frequency of the surface acoustic wave filtering device, a closed-loop control for temperature compensation is achieved. The temperature-frequency compensation algorithm chip continuously collects the temperature signals of the temperature-sensitive layer, calculates the frequency compensation parameters, and dynamically adjusts the capacitance value of the MEMS to form a closed-loop compensation control system, enabling the surface acoustic wave filtering device to always be stable at the target resonant frequency. Specifically, the formula is used: represents the closed-loop control for temperature compensation, where represents the real-time resonant frequency, represents the target resonant frequency, represents the frequency tolerance.

[0087] The specific embodiments described above further elaborate on the purpose, technical solutions, and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made on the basis of the technical solutions of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for adjusting a resonant frequency, characterized in that: include: S110, the temperature sensitive layer of the surface acoustic wave filter device performs temperature sensing and signal collection; S120, the signal preprocessing circuit amplifies and filters the collected signal, and inputs it into the temperature-frequency algorithm chip; S130, the temperature-frequency algorithm chip calculates the required compensation parameters based on the neural network algorithm model; S140, outputting the compensation parameter to the variable capacitor to adjust the resonant frequency of the surface acoustic wave filter device; S150, realizing closed-loop control of temperature compensation by dynamically adjusting the resonant frequency of the surface acoustic wave filter device.

2. A method for adjusting the resonant frequency as claimed in claim 1, characterized in that: The method for temperature sensing and signal acquisition of the temperature sensitive layer of the surface acoustic wave filter device specifically comprises the following sub-steps: Setting temperature sensitive layer; The temperature-sensitive layer performs real-time temperature monitoring and captures electrical signals; The captured electrical signals are collected and transmitted.

3. A method for adjusting the resonant frequency as claimed in claim 1, characterized in that: The signal preprocessing circuit amplifies and filters the temperature signal. It first performs low-noise amplification through a preamplifier to amplify the signal to a level range suitable for subsequent processing. Then it passes through a bandpass filter to remove high-frequency and low-frequency noise in the signal, retaining only the effective signal frequency band related to temperature changes to obtain a more stable temperature signal.

4. A method for adjusting the resonant frequency as claimed in claim 3, characterized in that: The preprocessed temperature signal is input into the temperature-frequency compensation algorithm chip. The neural network algorithm model inside the temperature-frequency compensation algorithm chip receives the temperature signal as input. The neurons in the model calculate the input signal layer by layer according to the trained weights. After calculation by the neural network model, the temperature-frequency compensation algorithm chip outputs the required compensation parameters.

5. A method for adjusting the resonant frequency as claimed in claim 1, characterized in that: The method of outputting the compensation parameter to the variable capacitor to adjust the resonant frequency of the surface acoustic wave filter device specifically comprises the following sub-steps: Generate a corresponding control voltage signal according to the calculated compensation parameter; The resonant frequency of the surface acoustic wave filter device is adjusted according to the control voltage signal.

6. A resonant frequency adjustment system, characterized in that: include: Temperature sensing and signal acquisition module: The temperature sensitive layer of the surface acoustic wave filter device performs temperature sensing and signal acquisition; Signal preprocessing circuit module: The signal preprocessing circuit amplifies and filters the collected signal and inputs it into the temperature-frequency algorithm chip; Temperature-frequency algorithm chip module: The neural network-based algorithm model inside the temperature-frequency algorithm chip calculates the required compensation parameters; Resonance frequency adjustment module: outputs the compensation parameters to the variable capacitor to adjust the resonant frequency of the surface acoustic wave filter device; Closed-loop control module: A closed-loop control of temperature compensation is achieved by dynamically adjusting the resonant frequency of the surface acoustic wave filter device.

7. A resonant frequency adjustment system as claimed in claim 6, characterized in that: The method for temperature sensing and signal acquisition of the temperature sensitive layer of the surface acoustic wave filter device specifically comprises the following sub-steps: Setting temperature sensitive layer; The temperature-sensitive layer performs real-time temperature monitoring and captures electrical signals; The captured electrical signals are collected and transmitted.

8. A resonant frequency adjustment system as claimed in claim 6, characterized in that: The signal preprocessing circuit amplifies and filters the temperature signal. It first performs low-noise amplification through a preamplifier to amplify the signal to a level range suitable for subsequent processing. Then it passes through a bandpass filter to remove high-frequency and low-frequency noise in the signal, retaining only the effective signal frequency band related to temperature changes to obtain a more stable temperature signal.

9. A resonant frequency adjustment system as claimed in claim 8, characterized in that: The preprocessed temperature signal is input into the temperature-frequency compensation algorithm chip. The neural network algorithm model inside the temperature-frequency compensation algorithm chip receives the temperature signal as input. The neurons in the model calculate the input signal layer by layer according to the trained weights. After calculation by the neural network model, the temperature-frequency compensation algorithm chip outputs the required compensation parameters.

10. A resonant frequency adjustment system as claimed in claim 6, characterized in that: The method of outputting the compensation parameter to the variable capacitor to adjust the resonant frequency of the surface acoustic wave filter device specifically comprises the following sub-steps: Generate a corresponding control voltage signal according to the calculated compensation parameter; The resonant frequency of the surface acoustic wave filter device is adjusted according to the control voltage signal.

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