Plasmon-based control optimization method for narrow-band filter with low standing-wave ratio

Abstract

The invention discloses a control optimization method of a low standing-wave ratio narrow-band filter based on plasmon, and relates to the field of filter plasmon optimization control. According to the method, numerical solution is carried out on the second-order nonlinear differential equation through the fourth-order Runge-Kutta method, the dynamic coupling relation between the plasmon filter and the nonlinear resonator can be accurately described, and therefore accurate regulation and control over frequency response are achieved. In addition, by means of the optimization method, the standing-wave ratio of the narrow-band filter can be effectively reduced, signal reflection is reduced, and the transmission efficiency of the system is improved.

Description

Technical Field

[0001] The present invention relates to the field of filter plasmon optimization control, and in particular to a control optimization method for a low standing wave ratio narrowband filter based on plasmon. Background Art

[0002] In modern wireless communication and optical communication systems, filters are one of the most important core components, especially in narrowband filtering scenarios, where the performance of filters directly determines the transmission quality and signal stability of the system. Under the existing technical background, plasmon-based filtering technology has become a widely used filtering method. Plasmon filters use the plasmon effect between metal nanostructures and dielectric materials to achieve strong coupling between light and matter on a subwavelength scale, which can greatly improve the selectivity and response speed of the filter. Traditional plasmon filters achieve narrowband filtering of specific frequencies by adjusting structural parameters. Such filters usually have good frequency selectivity, can effectively suppress unnecessary frequency band signals and retain signals in the target frequency band, and are therefore widely used in optical communications, sensor networks, and wireless communication equipment. Plasmon filters have gradually become one of the key technologies for achieving high-performance filtering due to their small size, fast response, and easy integration.

[0003] In addition, by designing a special geometric structure, plasmon filters can achieve local field enhancement on a subwavelength scale, allowing them to work effectively in low-frequency bands, medium-frequency bands, and even high-frequency bands. Most existing plasmon filters are designed using linear resonators or quasi-linear resonators, and the frequency response characteristics of the filter are adjusted by changing the resonant frequency of the resonator. The main principle of a linear resonator is to form a specific resonant frequency through a combination of specific geometric parameters and materials. When the frequency of the input signal matches the resonant frequency, the signal can be effectively transmitted and amplified, thereby achieving effective filtering of signals in a specific frequency band.

[0004] On the other hand, in wireless communication and optical communication systems, the Standing Wave Ratio (SWR) is one of the important indicators for measuring filter performance. The higher the standing wave ratio, the more reflected signals there are in the system, the lower the signal transmission efficiency, and the poorer the filtering effect. In the prior art, plasmon filters usually have a higher standing wave ratio, especially in the narrow band range. Due to the narrow frequency response characteristics, it is easy to cause the reflection of the input signal to increase, thereby affecting the transmission quality of the signal. In order to optimize the standing wave ratio, the existing technical solutions mainly reduce signal reflection by improving the material selection and structural design of the filter. However, this linear resonator-based filter is still subject to certain limitations, especially when dealing with complex signal environments, the optimization effect of the standing wave ratio is not ideal. In addition, with the increasing complexity of modern communication systems, the response characteristics of the filter in different frequency bands and different input powers have become more complex, and the existing linear resonator design is difficult to adapt to these complex requirements. Therefore, the prior art still has certain technical bottlenecks in the optimization of the standing wave ratio of narrowband filters. Summary of the invention

[0005] The present invention proposes a control optimization method for a plasmon-based low standing wave ratio narrowband filter. The method solves the problems of too high standing wave ratio, insufficient response speed and limited frequency control capability existing in the prior art by introducing coordinated response optimization of a nonlinear resonator and a plasmon filter.

[0006] The control optimization method of the low standing wave ratio narrowband filter based on plasmon includes the following steps:

[0007] S1. Collect the input frequency and output signal of the filter under plasmon excitation, and record the change of low standing wave ratio in a narrow band; denoise and normalize the collected standing wave ratio data under plasmon excitation to obtain basic time series data, initial response variables and corresponding standing wave ratio;

[0008] S2. By analyzing the obtained standing wave ratio curve, the dynamic behavior of the coupled system is numerically calculated according to the fourth-order Runge-Kutta method, and the coordinated response of the plasmon filter and the resonator is optimized according to the calculation results;

[0009] S3. Apply the optimized plasmon control parameters to the filter and verify the low standing wave ratio narrowband effect in a practical environment;

[0010] Wherein, the step S2 specifically includes the following sub-steps:

[0011] S201. Describing the cooperative response of the plasmon filter and the nonlinear resonator by a second-order nonlinear differential equation;

[0012] S202. Calculate the corresponding initial slope according to the basic time series data, the initial response variable and the corresponding standing wave ratio;

[0013] S203. Estimate the first midpoint slope according to the calculated initial slope, and estimate the second midpoint slope according to the calculated first midpoint slope;

[0014] S204. Calculate the final point slope according to the calculated second intermediate point slope;

[0015] S205. Calculate the displacement and velocity at the next moment using the weighted average of the four slopes;

[0016] S206. Repeat steps S203-S205 for each time step until the end time of the integration is reached; analyze the frequency response characteristics of the filter based on the numerical solution calculated for each time step.

[0017] Furthermore, in step S201, the second-order nonlinear differential equation is expressed as:

[0018]

[0019] Wherein, d represents the derivative, q represents the dynamic variable of the system, including one of the electric field intensity, displacement and charge density in the plasmon system, t represents the time variable, which is used to describe the change of the dynamic response of the plasmon over time, γ represents the damping coefficient, that is, the energy loss in the system, and ω sp represents the natural frequency term in the plasmon system, β represents the coefficient related to the nonlinearity of the plasmon system, and α represents the coefficient related to the nonlinear stiffness.

[0020] Furthermore, in step S201, after establishing the second-order nonlinear differential equation, the step further includes: converting the second-order differential equation group into two first-order equations, specifically:

[0021] Introducing the variable v, the equation transforms to:

[0022]

[0023] Wherein, v represents the dynamic response speed of the system, and F(t) represents the external excitation force, that is, the plasmon excitation at the input frequency.

[0024] Furthermore, in step S202, the calculation of the corresponding initial slope is specifically expressed as:

[0025] k1=v n ;

[0026]

[0027] Wherein, k1 represents the slope of the dynamic variable of the resonator at the nth time step, and v n represents the dynamic response speed of the resonator at the nth time step, l1 represents the slope of the dynamic response speed of the resonator at the nth time step, and t n represents the time of the initial state, the q n represents the resonator displacement at the nth time step.

[0028] Furthermore, in step S203, estimating the first intermediate point slope according to the calculated initial slope specifically includes the following sub-steps:

[0029] Compute the first midpoint:

[0030]

[0031] Calculate the slope of the first midpoint:

[0032]

[0033] Among them, the Indicates at time The resonator displacement at the moment Indicates at time The dynamic response rate of the resonator at the moment, k2 represents the time The slope of the dynamic variable of the resonator at the moment, l2 represents the time The slope of the resonator displacement of the resonator at time instant.

[0034] Furthermore, in step S203, estimating the slope of the second midpoint according to the calculated slope of the first midpoint specifically includes the following sub-steps:

[0035] Compute the second midpoint:

[0036]

[0037]

[0038] Calculate the slope of the second midpoint:

[0039]

[0040] Among them, the Indicates based on The intermediate value of the new resonator displacement calculated after further correction is Indicates based on The calculated intermediate value of the new resonator response rate, k3 represents the intermediate value of the slope of the new resonator displacement calculated after further correction, and l3 represents the intermediate value of the slope of the new resonator response rate calculated after further correction.

[0041] Furthermore, in step S204, the final point slope is calculated according to the calculated second intermediate point slope, which is specifically expressed as:

[0042] Calculate the final midpoint:

[0043]

[0044] Calculate the slope at the final point:

[0045]

[0046] Among them, the represents the predicted value of the final resonator displacement calculated in time step n+1, represents the predicted value of the final resonator response rate calculated in time step n+1, k4 represents the predicted value of the slope of the final resonator displacement calculated in time step n+1, and l4 represents the predicted value of the slope of the final resonator response rate calculated.

[0047] Furthermore, the specific process of step S205 is as follows:

[0048]

[0049] Among them, the q n+1 represents the actual value of the oscillator displacement in time step n+1, the v n+1 Represents the actual value of the oscillator response rate in time step n+1.

[0050] Furthermore, minimizing the standing wave ratio is used as the optimization objective function, and iterative optimization is performed through the optimization algorithm, and the optimal design parameters are selected based on the optimization results.

[0051] Furthermore, the coupling system is specifically a system of coupling relationship established by a nonlinear resonator and a plasmon. The beneficial effects of the invention are:

[0052] The present invention numerically solves the second-order nonlinear differential equation by the fourth-order Runge-Kutta method, which can accurately describe the dynamic coupling relationship between the plasmon filter and the nonlinear resonator, thereby achieving precise control of the frequency response. In addition, the optimization method of the present invention can effectively reduce the standing wave ratio of the narrowband filter, reduce signal reflection, and improve the transmission efficiency of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 A method flow chart of a LTCC duplexer optimization method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0054] The technical solution of the present invention is further described in detail below in conjunction with the accompanying drawings, but the protection scope of the present invention is not limited to the following.

[0055] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention is further described in detail in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention, that is, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0056] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative work are within the scope of protection of the present invention. It should be noted that relational terms such as the terms "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0057] Moreover, the terms "comprises", "includes" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or apparatus that includes a list of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or apparatus. In the absence of more restrictions, an element defined by the phrase "comprises a ..." does not exclude the presence of additional identical elements in the process, method, article or apparatus that includes the element.

[0058] The features and performance of the present invention are further described in detail below in conjunction with the embodiments.

[0059] The control optimization method of the low standing wave ratio narrowband filter based on plasmon includes the following steps:

[0060] S1. Collect the input frequency and output signal of the filter under plasmon excitation, and record the change of low standing wave ratio in a narrow band; denoise and normalize the collected standing wave ratio data under plasmon excitation to obtain basic time series data, initial response variables and corresponding standing wave ratio;

[0061] S2. By analyzing the obtained standing wave ratio curve, the dynamic behavior of the coupled system is numerically calculated according to the fourth-order Runge-Kutta method, and the coordinated response of the plasmon filter and the resonator is optimized according to the calculation results;

[0062] S3. Apply the optimized plasmon control parameters to the filter and verify the low standing wave ratio narrowband effect in a practical environment;

[0063] Wherein, the step S2 specifically includes the following sub-steps:

[0064] S201. Describing the cooperative response of the plasmon filter and the nonlinear resonator by a second-order nonlinear differential equation;

[0065] S202. Calculate the corresponding initial slope according to the basic time series data, the initial response variable and the corresponding standing wave ratio;

[0066] S203. Estimate the first midpoint slope according to the calculated initial slope, and estimate the second midpoint slope according to the calculated first midpoint slope;

[0067] S204. Calculate the final point slope according to the calculated second intermediate point slope;

[0068] S205. Calculate the displacement and velocity at the next moment using the weighted average of the four slopes;

[0069] S206. Repeat steps S203-S205 for each time step until the end time of the integration is reached; analyze the frequency response characteristics of the filter based on the numerical solution calculated for each time step.

[0070] Specifically, the process principle of the above steps S1-S3 is as follows: by exciting the plasmon effect in the filter, the output signal under different frequency inputs, especially the change of the standing wave ratio in the narrow band, is obtained. And by collecting the input frequency and output signal of the filter, the change of the standing wave ratio is recorded in detail. Since the collected data may be affected by noise and interference, the data is first denoised to ensure that it represents the true response of the filter. The denoised data is normalized so that subsequent analysis can be performed within a unified numerical range. These processed data form the basic time series data, the initial response variable, and the response value related to the standing wave ratio.

[0071] Furthermore, by conducting an in-depth analysis of the preprocessed standing wave ratio curve, the fourth-order Runge-Kutta method is used to numerically solve the coupling system between the plasmon filter and the resonator. The fourth-order Runge-Kutta method is a high-precision numerical integration method that is suitable for solving nonlinear differential equations, especially for describing the dynamic behavior of filters and resonators. In this process, the system is first described as a second-order nonlinear differential equation, and then the response of the system is calculated step by step by time stepping. The final calculation results provide an optimization basis for the coordinated response of the filter and the resonator, ensuring that the frequency response of the system has a lower standing wave ratio characteristic in a narrowband range.

[0072] The optimized plasmon filter parameters need to be applied to the actual physical filter to verify the effect under actual operating conditions. In this step, the optimal control parameters obtained by numerical analysis, including the resonant frequency of the filter, nonlinear coupling parameters, etc., are applied to the actual filter system. Through experimental measurement, observe whether the standing wave ratio and frequency response of the filter in the actual environment are consistent with expectations. The narrowband filtering effect of low standing wave ratio needs to be verified through multiple measurements and feedback under experimental conditions to ensure the stability and optimization effect of the filter performance in real applications.

[0073] Furthermore, in step S201, the second-order nonlinear differential equation is expressed as:

[0074]

[0075] Wherein, d represents the derivative, q represents the dynamic variable of the system, including one of the electric field intensity, displacement and charge density in the plasmon system, t represents the time variable, which is used to describe the change of the dynamic response of the plasmon over time, γ represents the damping coefficient, that is, the energy loss in the system, and ω sp Represents the natural frequency term in the plasmon system, the β represents the coefficient related to the nonlinearity of the plasmon system, and the α represents the coefficient related to the nonlinear stiffness. Specifically, by establishing a second-order nonlinear differential equation, the cooperative response behavior between the plasmon filter and the nonlinear resonator is accurately described. The second-order nonlinear differential equation is constructed based on the physical model of the system, and can reflect the coupling relationship between the displacement and velocity of the filter and the resonator. The establishment of the equation depends on the physical properties of the plasmon and the response behavior of the resonator. The nonlinear term in the equation can describe the complex dynamics of the system. Through the above equations, the displacement, velocity and acceleration changes of the filter at different time steps are systematically described.

[0076] Furthermore, in step S201, after establishing the second-order nonlinear differential equation, the step further includes: converting the second-order differential equation group into two first-order equations, specifically:

[0077] Introducing the variable v, the equation transforms to:

[0078]

[0079] Wherein, v represents the dynamic response speed of the system, and F(t) represents the external excitation force, that is, the plasmon excitation at the input frequency.

[0080] Furthermore, in step S202, the calculation of the corresponding initial slope is specifically expressed as:

[0081] l1=v m ;

[0082]

[0083] Wherein, k1 represents the slope of the dynamic variable of the resonator at the nth time step, and v n represents the dynamic response speed of the resonator at the nth time step, l1 represents the slope of the dynamic response speed of the resonator at the nth time step, and t n represents the time of the initial state, the q n Represents the resonator displacement at the nth time step. Specifically, based on the time series data, initial response variable and standing wave ratio obtained in step S1, the initial slope of the system is calculated. The initial slope reflects the response rate of the system at the very beginning of the time step. By analyzing the initial data, it is determined how the coupled response of the plasmon filter and the nonlinear resonator changes with time. The above steps are the starting point of the Runge-Kutta method. Solving the initial slope provides the necessary numerical basis for the subsequent intermediate point slope estimation, and provides a preliminary direction for the frequency regulation of the filter.

[0084] Furthermore, in step S203, estimating the first intermediate point slope according to the calculated initial slope specifically includes the following sub-steps:

[0085] Compute the first midpoint:

[0086]

[0087] Calculate the slope of the first midpoint:

[0088]

[0089] Among them, the Indicates at time The resonator displacement at the moment Indicates at time The dynamic response rate of the resonator at the moment, k2 represents the time The slope of the dynamic variable of the resonator at the moment, l2 represents the time The slope of the resonator displacement of the resonator at time instant.

[0090] Furthermore, in step S203, estimating the slope of the second midpoint according to the calculated slope of the first midpoint specifically includes the following sub-steps:

[0091] Compute the second midpoint:

[0092]

[0093] Calculate the slope of the second midpoint:

[0094]

[0095] Among them, the Indicates based on The intermediate value of the new resonator displacement calculated after further correction is Indicates based on The calculated intermediate value of the new resonator response rate, the k3 represents the intermediate value of the slope of the new resonator displacement calculated after further correction, and the l3 represents the intermediate value of the slope of the new resonator response rate calculated after further correction. Specifically, using the initial slope as the basis, the fourth-order Runge-Kutta method is applied to estimate the first intermediate point slope. The first intermediate point slope is a prediction based on half of the time step, representing the response rate of the system at this intermediate moment. Then, continue to use the first intermediate point slope to estimate the second intermediate point slope, which is a prediction closer to the response at the next moment. The calculation of each intermediate slope is a correction and optimization of the system state. Through this step-by-step approximation method, the accuracy of the calculation can be improved, providing a more accurate prediction for the final slope.

[0096] Furthermore, in step S204, the final point slope is calculated according to the calculated second intermediate point slope, which is specifically expressed as:

[0097] Calculate the final midpoint:

[0098]

[0099] Calculate the slope at the final point:

[0100]

[0101] Among them, the represents the predicted value of the final resonator displacement calculated in time step n+1, Represents the predicted value of the final resonator response rate calculated in time step n+1, k4 represents the predicted value of the slope of the final resonator displacement calculated in time step n+1, and l4 represents the predicted value of the slope of the calculated final resonator response rate. Specifically, after all the intermediate point slopes are calculated, the final point slope is calculated by combining the initial slope, the first intermediate slope, and the second intermediate slope. The final point slope represents the predicted response rate of the system at the end of the time step. This value is very important because it directly affects the update of the displacement and velocity of the system. Through the high-precision calculation of the Runge-Kutta method, the final point slope is an accurate estimate of the nonlinear behavior of the system, ensuring the accuracy of the numerical integration. This slope is the key to the subsequent calculation of the displacement and velocity of the next time step.

[0102] Furthermore, the specific process of step S205 is as follows:

[0103]

[0104] Among them, the q n+1 represents the actual value of the oscillator displacement in time step n+1, the v n+1 Represents the actual value of the oscillator response rate in time step n+1. Specifically, based on the previous initial slope, the slopes of the two intermediate points, and the final slope, a weighted average is used to calculate the displacement and velocity of the system at the next moment. Through the weighted combination of these slopes, the Runge-Kutta method can predict the dynamic response of the system with high accuracy. Specifically, the weighted average reflects the response contribution at different time points, ensuring the accuracy of the prediction results in nonlinear systems. The calculated displacement and velocity are used to update the state of the system as the initial value of the next time step, thereby performing continuous dynamic solution.

[0105] Further, steps S203 to S205 are repeatedly executed, and the time step is gradually advanced until the preset integration termination time is reached. Through numerical calculations at each time step, the dynamic response data of the system in the entire time range can be obtained. Combined with these numerical solutions, the frequency response characteristics of the plasmon filter are analyzed, especially the low standing wave ratio performance in the narrow band range. Through such iterative calculations, the dynamic performance of the filter can be systematically optimized, and the performance of the filter in practical applications can be further improved.

[0106] Furthermore, minimizing the standing wave ratio is used as the optimization objective function, and iterative optimization is performed through the optimization algorithm, and the optimal design parameters are selected based on the optimization results.

[0107] Furthermore, the coupling system is specifically a system of coupling relationship established by a nonlinear resonator and a plasmon.

[0108] The above is only a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the form disclosed herein, and should not be regarded as excluding other embodiments, but can be used in various other combinations, modifications and environments, and can be modified within the scope of the concept described herein through the above teachings or the technology or knowledge of the relevant field. The changes and modifications made by those skilled in the art shall not deviate from the spirit and scope of the present invention, and shall be within the scope of protection of the claims attached to the present invention.

Claims

1. A control optimization method for a low standing wave ratio narrowband filter based on plasmon, characterized in that: The following steps are involved: S1. Collect the input frequency and output signal of the filter under plasmon excitation, and record the change of low standing wave ratio in a narrow band; denoise and normalize the collected standing wave ratio data under plasmon excitation to obtain basic time series data, initial response variables and corresponding standing wave ratio; S2. By analyzing the obtained standing wave ratio curve, the dynamic behavior of the coupled system is numerically calculated according to the fourth-order Runge-Kutta method, and the coordinated response of the plasmon filter and the resonator is optimized according to the calculation results; S3. Apply the optimized plasmon control parameters to the filter and verify the low standing wave ratio narrowband effect in a practical environment; Wherein, the step S2 specifically includes the following sub-steps: S201. Describing the cooperative response of the plasmon filter and the nonlinear resonator by a second-order nonlinear differential equation; S202. Calculate the corresponding initial slope according to the basic time series data, the initial response variable and the corresponding standing wave ratio; S203. Estimate the first midpoint slope according to the calculated initial slope, and estimate the second midpoint slope according to the calculated first midpoint slope; S204. Calculate the final point slope according to the calculated second intermediate point slope; S205. Calculate the displacement and velocity at the next moment using the weighted average of the four slopes; S206. Repeat steps S203-S205 for each time step until the end time of the integration is reached; analyze the frequency response characteristics of the filter based on the numerical solution calculated for each time step.

2. The control optimization method of the low standing wave ratio narrowband filter based on plasmon as claimed in claim 1, characterized in that: In step S201, the second-order nonlinear differential equation is expressed as: Wherein, d represents the derivative, q represents the dynamic variable of the system, including one of the electric field intensity, displacement and charge density in the plasmon system, t represents the time variable, which is used to describe the change of the dynamic response of the plasmon over time, γ represents the damping coefficient, that is, the energy loss in the system, and ω sp represents the natural frequency term in the plasmon system, β represents the coefficient related to the nonlinearity of the plasmon system, and α represents the coefficient related to the nonlinear stiffness.

3. The control optimization method of the low standing wave ratio narrowband filter based on plasmon as claimed in claim 2, characterized in that: In the step S201, after establishing the second-order nonlinear differential equation, the step further includes: converting the second-order differential equation group into two first-order equations, specifically: Introducing the variable v, the equation transforms to: Wherein, v represents the dynamic response speed of the system, and F(t) represents the external excitation force, that is, the plasmon excitation at the input frequency.

4. The control optimization method of the low standing wave ratio narrowband filter based on plasmon as claimed in claim 1, characterized in that: In step S202, the calculation of the corresponding initial slope is specifically expressed as: k1=v n ; Wherein, k1 represents the slope of the dynamic variable of the resonator at the nth time step, and v n represents the dynamic response speed of the resonator at the nth time step, l1 represents the slope of the dynamic response speed of the resonator at the nth time step, and t n represents the time of the initial state, the q n represents the resonator displacement at the nth time step.

5. The control optimization method of the low standing wave ratio narrowband filter based on plasmon as claimed in claim 1, characterized in that: In step S203, estimating the first intermediate point slope according to the calculated initial slope specifically includes the following sub-steps: Compute the first midpoint: Calculate the slope of the first midpoint: Among them, the Indicates at time The resonator displacement at the moment Indicates at time The dynamic response rate of the resonator at the moment, k2 represents the time The slope of the dynamic variable of the resonator at the moment, l2 represents the time The slope of the resonator displacement of the resonator at time instant.

6. The control optimization method of the low standing wave ratio narrowband filter based on plasmon according to claim 1, characterized in that: In step S203, estimating the second midpoint slope according to the calculated first midpoint slope specifically includes the following sub-steps: Compute the second midpoint: Calculate the slope of the second midpoint: Among them, the According to The intermediate value of the new resonator displacement calculated after further correction is According to The calculated intermediate value of the new resonator response rate, k3 represents the intermediate value of the slope of the new resonator displacement calculated after further correction, and l3 represents the intermediate value of the slope of the new resonator response rate calculated after further correction.

7. The control optimization method of the low standing wave ratio narrowband filter based on plasmon as claimed in claim 1, characterized in that: In step S204, the final point slope is calculated according to the calculated second intermediate point slope as follows: Calculate the final midpoint: Calculate the slope at the final point: Among them, the represents the predicted value of the final resonator displacement calculated in time step n+1, represents the predicted value of the final resonator response rate calculated in time step n+1, k4 represents the predicted value of the slope of the final resonator displacement calculated in time step n+1, and l4 represents the predicted value of the slope of the final resonator response rate calculated.

8. The control optimization method of the low standing wave ratio narrowband filter based on plasmon as claimed in claim 1, characterized in that: The specific process of step S205 is as follows: Among them, the q n+1 represents the actual value of the oscillator displacement in time step n+1, the v n+1 Represents the actual value of the oscillator response rate in time step n+1.

9. The control optimization method of the low standing wave ratio narrowband filter based on plasmon as claimed in claim 1, characterized in that: Minimizing the standing wave ratio is used as the optimization objective function, and iterative optimization is performed through the optimization algorithm. Based on the optimization results, the optimal design parameters are selected.

10. The control optimization method of the low standing wave ratio narrowband filter based on plasmon according to claim 1, characterized in that: The coupling system is specifically a system of coupling relationship established by a nonlinear resonator and a plasmon.

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