Antenna two-stage optimization tuning method and device and storage medium

Abstract

This invention discloses a two-stage optimized antenna tuning method, apparatus, and storage medium. The method includes: establishing an initial mapping table between motor rotation angle and parameter values ​​of components in a tuning network; connecting a standard load to the output of the tuning network's theoretical model and traversing the initial mapping table to correct the mapping relationship between the motor rotation angle and component parameter values, resulting in a corrected mapping table; connecting the antenna feeder system to be matched to the output of the tuning network and performing a first-stage optimization on the given initial parameter values ​​of the components, with the minimum voltage standing wave ratio (VSWR) as the optimization objective, to obtain the first-stage optimization result of the component parameter values; determining the variable value range of the component parameter values ​​based on the first-stage optimization result of the component parameter values; controlling the motor to rotate to the motor angle corresponding to the variable value range based on the corrected mapping table; and performing a second-stage optimization on the component parameter values ​​corresponding to the frequency point, to obtain the second-stage optimization result of the component parameter values.

Description

Technical Field

[0001] This invention relates to the field of radio frequency transmission system technology, and specifically to an antenna dual-stage optimized tuning method, apparatus, and storage medium. Background Technology

[0002] In radio frequency (RF) transmission systems, the core function of the tuning network is to match the transmitter output impedance with the antenna feeder impedance; its performance directly affects transmission efficiency and communication quality. Traditional tuning methods mainly rely on a global search of the multidimensional solution space formed by capacitor C and inductor L. This approach suffers from problems such as large inductance measurement errors, model mismatch, and slow algorithm convergence, specifically manifested as follows:

[0003] (1) Problems in inductance measurement: The inductance value of inductance L is significantly affected by environmental factors such as eddy currents in the cavity and electromagnetic coupling of adjacent metal parts, and the direct measurement error often exceeds 20%. Moreover, due to the inaccurate mapping relationship between inductance value and motor rotation angle, the tuning parameter calculation is biased, which affects the matching accuracy.

[0004] (2) Model mismatch problem: The theoretical network model does not take into account the parasitic parameters (such as wire resistance and distributed capacitance) and dynamic changes in load impedance in the actual circuit, resulting in a deviation of 5-15% between the theoretically calculated output impedance and the actual value, which further aggravates the tuning error.

[0005] (3) Sensitivity to initial values ​​and low efficiency: The optimization method is sensitive to the initial value. Traditional optimization methods use random initial values ​​and require a large number of individuals and iterations (average > 100 generations) to converge. When used for tuning, not only is the tuning time long (usually > 20s), but frequent motor rotation will also aggravate hardware wear and reduce the service life of the equipment, making it unsuitable for antenna tuning units.

[0006] In related technologies, patent application CN118586356A describes an optimization method for RF circuit matching that automatically achieves optimization through the design and simulation search algorithm of the matching circuit of the optimized RF system; however, the benchmark in this scheme... The initial value is manually set, and the effect of the set initial value is not ideal. Moreover, the entire matching process is implemented in software without hardware intervention. However, the patent application document with publication number CN105846834A proposes that different frequency bands no longer correspond to different hardware filtering paths, but to pre-stored control parameters. When the wireless communication device receives RF signals of different frequency bands, it only needs to change the control parameters of the filtering path, and the filtering of RF signals of multiple frequency bands can be achieved through a single filtering path. However, this scheme directly uses the pre-stored control parameters to achieve the filtering of RF signals, without mentioning how the control parameters are established. The paper "Research on Automatic Tuning Design and Algorithm of Very Low Frequency Antenna, Lou Hangchuan, Master's Thesis of Huazhong University of Science and Technology" proposes an automatic tuning algorithm based on model reference, which is divided into three steps: initial value calculation, coarse tuning based on adjustment value calculation, and fine tuning based on nearest neighbor search. However, its purpose is to directly make an approximate ideal inductor, rather than to obtain an accurate inductance value.

[0007] Therefore, there is an urgent need for a fast tuning method that can accurately establish the mapping between motor position and component parameter values, correct model errors, and optimize the initial values ​​of the algorithm, in order to solve the above-mentioned technical bottlenecks. Summary of the Invention

[0008] The technical problem to be solved by this invention is how to achieve fast and accurate tuning of the antenna tuning circuit to meet the fast impedance matching requirements of radio frequency transmission systems.

[0009] The present invention solves the above-mentioned technical problems through the following technical means: A two-stage optimized antenna tuning method is proposed, the method comprising: Establish an initial mapping table between the motor rotation angle and the parameter values ​​of the components in the tuning network; The standard load is connected to the output of the tuning network theoretical model, and the initial mapping table is traversed to correct the mapping relationship between the motor rotation angle and the parameter values ​​of the components, so as to obtain the corrected mapping table. Connect the antenna feeder system to be matched to the output of the tuning network, and perform first-level optimization on the initial parameter values ​​of the given components with the minimum voltage standing wave ratio as the optimization objective, to obtain the first-level optimization results of the component parameter values. Based on the first-level optimization results of the component's parameter values, the variable value range of the component's parameter values ​​is determined. Based on the modified mapping relationship table, the motor is controlled to rotate to the motor angle corresponding to the variable value range. Then, with the voltage standing wave ratio corresponding to a certain frequency point being less than a set threshold as the optimization objective, the parameter values ​​of the component corresponding to that frequency point are optimized in a second-level manner to obtain the second-level optimization results of the component's parameter values.

[0010] Furthermore, the components in the tuning network include inductors and capacitors, and the establishment of an initial mapping table between the motor rotation angle and the parameter values ​​of the components in the tuning network includes: Control the motor connected to the capacitor to rotate to different angles, and measure the actual capacitance value of the capacitor when the motor rotates to different angles; Linear interpolation was used to fit the discrete measured capacitance values ​​to obtain a mapping table between the capacitance value and the motor rotation angle. A three-dimensional electromagnetic model of the inductor L and its surrounding cavity was established, and the three-dimensional battery model was simulated to obtain the S matrix corresponding to different motor rotation angles. The mapping relationship table between inductance value and motor rotation angle is obtained by inverting the S matrix; The initial mapping table is formed by combining the mapping tables between capacitor values ​​and motor rotation angles and the mapping tables between inductance values ​​and motor rotation angles.

[0011] Furthermore, the mapping table between inductance value and motor rotation angle obtained by inverting the S matrix includes: The input impedance of the inductor is derived by inverting the S-matrix; The inductance value is calculated based on the imaginary part of the input impedance, expressed by the following formula:

[0012] In the formula, This represents the imaginary part of the input impedance. For operating frequency, Pi Indicates product; Based on the calculated inductance value, a mapping table between the inductance value and the motor rotation angle is obtained.

[0013] Furthermore, the step of connecting the standard load to the output of the tuned network theoretical model and traversing the initial mapping table to correct the mapping relationship between the motor rotation angle and the parameter values ​​of the components, resulting in a corrected mapping table, includes: Connect different standard loads to the output of the tuned network theoretical model, traverse the parameter values ​​of different components in the initial mapping table, and calculate the theoretical output impedance corresponding to the parameter value of the currently traversed component. The impedance value of the standard load is used as the actual output impedance, and the mean absolute error between the actual output impedance and the theoretical output impedance is calculated. The parameter values ​​of the currently traversed components are corrected based on the mean absolute error, resulting in a corrected mapping table.

[0014] Furthermore, the standard load includes short-circuit load, XΩ load, YΩ load, ZΩ load and open-circuit load, where X, Y and Z represent non-zero and non-infinite values.

[0015] Furthermore, the correction of the parameter values ​​of the currently traversed elements based on the mean absolute error is expressed by the following formula:

[0016]

[0017] In the formula, This is the corrected capacitance value. This is the corrected inductance value. and These are the capacitance and inductance values ​​currently being iterated. This represents the mean absolute error.

[0018] Furthermore, the step of connecting the antenna feeder system to be matched to the output of the tuning network, and performing first-level optimization on the initial parameter values ​​of the given components with the minimum voltage standing wave ratio as the optimization objective, to obtain the first-level optimization results of the component parameter values, includes: Connect the antenna feeder system to be matched to the output of the tuning network, and calculate the initial parameter values ​​of the component, which are empirical values ​​or default values. Calculate the actual output impedance based on the known input impedance and the given initial parameter values ​​of the component; The initial parameter values ​​of a given component are optimized to minimize the voltage standing wave ratio (VSWR), resulting in the first-level optimization results.

[0019] Further, the first-level optimization result based on the component's parameter values ​​determines the variable value range of the component's parameter values. Based on the corrected mapping table, the motor is controlled to rotate to the motor angle corresponding to the variable value range. Then, with the voltage standing wave ratio (VSWR) at a certain frequency point being less than a set threshold as the optimization objective, a second-level optimization is performed on the parameter values ​​of the component at that frequency point, yielding the second-level optimization result of the component's parameter values, including: Based on the first-level optimization results of the component's parameter values, the variable range of the component's parameter values ​​is determined as follows:

[0020] in, The value range is [0,1]. For numbers greater than 1, The capacitance value is the parameter value of the component in the first-level optimization result. The inductance value is the parameter value of the component in the first-level optimization result. For all inductance values; The variable value range based on the component parameter value is used to find the corresponding motor angle from the modified mapping table, and the motor is controlled to rotate to that motor angle; With the optimization objective of the voltage standing wave ratio (VSWR) at a certain frequency point being less than a set threshold, a secondary optimization of the parameter values ​​of the component at that frequency point is performed to obtain the secondary optimization result of the component's parameter values.

[0021] Further, the optimization objective of setting the voltage standing wave ratio (VSWR) of a certain frequency point to be less than a set threshold is used to perform secondary optimization on the parameter values ​​of the component at that frequency point, resulting in the secondary optimization results of the component's parameter values, including: With the optimization objective of the voltage standing wave ratio (VSWR) at a certain frequency point being less than a set threshold, a genetic algorithm is used to perform secondary optimization on the parameter values ​​of the component at that frequency point, and the secondary optimization results of the component parameter values ​​are obtained.

[0022] Furthermore, after performing secondary optimization on the parameter values ​​of the component corresponding to a certain frequency point with the optimization objective of the voltage standing wave ratio (VSWR) being less than a set threshold, and obtaining the secondary optimization result of the component's parameter values, the method further includes: A vector network analyzer was used to read full-band standing wave data, and the full-band standing wave data was compared with historical data. If the voltage standing wave ratio at a certain frequency is less than the set threshold, the parameter value of the component corresponding to that frequency will be directly cached. If the voltage standing wave ratio at a certain frequency is close to the preset threshold, the parameter value of the component corresponding to that frequency is cached as the initial value for the next secondary optimization at that frequency.

[0023] Furthermore, after performing secondary optimization on the parameter values ​​of the component corresponding to a certain frequency point with the optimization objective of the voltage standing wave ratio (VSWR) being less than a set threshold, and obtaining the secondary optimization result of the component's parameter values, the method further includes: If the current voltage standing wave ratio (VSWR) at a certain frequency is less than the historical VSWR corresponding to that frequency in the cache, the historical VSWR will be updated to the current VSWR.

[0024] Furthermore, the present invention also proposes an antenna dual-stage optimized tuning device, the device comprising: The mapping relationship establishment module is used to establish an initial mapping relationship table between the motor rotation angle and the parameter values ​​of the components in the tuning network; The calibration module is used to connect a standard load to the output of the tuning network theoretical model and traverse the initial mapping table to correct the mapping relationship between the motor rotation angle and the parameter values ​​of the components, thus obtaining the corrected mapping table. The first-level optimization module is used to connect the antenna feeder system to be matched to the output of the tuning network, and to perform first-level optimization on the initial parameter values ​​of the given components with the minimum voltage standing wave ratio as the optimization objective, so as to obtain the first-level optimization result of the component parameter values. The secondary optimization module is used to determine the variable value range of the component's parameter values ​​based on the primary optimization results of the component's parameter values. Based on the modified mapping relationship table, it controls the motor to rotate to the motor angle corresponding to the variable value range. With the voltage standing wave ratio corresponding to a certain frequency point being less than a set threshold as the optimization objective, it performs secondary optimization on the parameter values ​​of the component corresponding to that frequency point, and obtains the secondary optimization results of the component's parameter values.

[0025] Furthermore, the present invention also proposes a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the antenna dual-stage optimized tuning method as described above.

[0026] The advantages of this invention are: (1) The present invention first establishes an initial mapping relationship between the motor rotation angle and the parameter values ​​of the components in the tuning network. However, since the theoretically calculated mapping relationship between the motor rotation angle and the component parameter values ​​will have errors, the initial mapping relationship between the motor rotation angle and the component parameter values ​​is further corrected by using various standard load test data. The corrected mapping relationship means to reduce the deviation between theory and reality, so as to provide accurate data support for subsequent online tuning, thereby improving the impedance matching accuracy. In addition, a two-level optimization architecture is adopted in the online tuning stage. The first-level optimization is to iteratively optimize the parameter values ​​of the components by using a pure optimization algorithm when the electrodes do not rotate, so as to obtain the first-level optimization result of the component parameter values. The first-level optimization result of the component parameter values ​​is used as the initial value of the second-level optimization, so as to significantly reduce the search range of the second-level optimization. The reduction of the search space reduces the number of rotations and amplitude of the motor. Combined with the accuracy of the initial value, the invalid mechanical motion is reduced. This not only meets the requirements of fast and accurate tuning, but also significantly reduces the hardware wear rate. Therefore, the present invention is particularly suitable for scenarios with strict requirements for tuning speed, matching accuracy and hardware wear.

[0027] (2) When constructing the mapping relationship between motor rotation angle and component parameter values, this invention addresses the problem that the inductance value of inductor L is difficult to measure directly due to environmental influences. It establishes an accurate mapping relationship between motor rotation angle and component parameter values ​​through "electromagnetic simulation-S-parameter inversion", solves the problem of measuring inductance value caused by environmental interference, thereby improving the accuracy of tuning parameter calculation and improving matching accuracy.

[0028] (3) The present invention uses test data of standard load to dynamically correct the mapping relationship between the established motor rotation angle and the parameter value of the component, which can reduce the error of the tuning network model and thus improve the matching accuracy.

[0029] (4) The present invention can reduce invalid motor action, improve tuning speed and extend equipment life by adopting a full-band data caching mechanism.

[0030] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0031] The accompanying drawings, which form part of this specification, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation thereof. Figure 1 This is a flowchart illustrating a dual-stage optimized tuning method for an antenna according to an embodiment of the present invention. Figure 2 This is a schematic diagram of the tuning network and components corresponding to the motor in one embodiment of the present invention; Figure 3 This is a schematic diagram of the rapid tuning principle combining the "system calibration stage" and the "online tuning stage" in one embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of an antenna bipolar optimized tuning device according to an embodiment of the present invention. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] like Figure 1 As shown, the first embodiment of the present invention proposes a two-stage optimized tuning method for an antenna, the method comprising the following steps: S10. Establish an initial mapping table between the motor rotation angle and the parameter values ​​of the components in the tuning network; S20. Connect the standard load to the output of the tuning network theoretical model and traverse the initial mapping table to correct the mapping relationship between the motor rotation angle and the parameter values ​​of the components, and obtain the corrected mapping table. It should be noted that, since the initial mapping relationship between the theoretically calculated motor rotation angle and the component parameter values ​​may contain errors, this embodiment further corrects the initial mapping relationship between the motor rotation angle and the component parameter values ​​using various standard load test data. The corrected mapping relationship is intended to minimize the deviation between theory and reality, thereby providing accurate data support for subsequent online tuning and improving impedance matching accuracy.

[0034] It should be understood that in this embodiment, various standard loads can be connected to the output of the actual circuit model of the tuning network, and the initial mapping table can be traversed to perform tests to correct the mapping relationship between the motor rotation angle and the parameter values ​​of the components.

[0035] S30. Connect the antenna feeder system to be matched to the output of the tuning network, and perform first-level optimization on the initial parameter values ​​of the given components with the minimum voltage standing wave ratio as the optimization objective, to obtain the first-level optimization results of the component parameter values. S40. Based on the first-level optimization results of the component's parameter values, determine the variable value range of the component's parameter values. Based on the modified mapping relationship table, control the motor to rotate to the motor angle corresponding to the variable value range. With the voltage standing wave ratio corresponding to a certain frequency point being less than a set threshold as the optimization objective, perform second-level optimization on the parameter values ​​of the component corresponding to that frequency point to obtain the second-level optimization results of the component's parameter values.

[0036] It should be noted that this embodiment adopts a two-level optimization architecture in the online tuning stage. The first-level optimization, without electrode rotation, uses a pure optimization algorithm to iteratively optimize the component parameter values ​​to obtain the first-level optimization result of the component parameter values. The first-level optimization result of the component parameter values ​​is used as the initial value of the second-level optimization to significantly narrow the search range of the second-level optimization. The reduced search space reduces the number and amplitude of motor rotations. Combined with the accuracy of the initial value, the second-level optimization of the component parameter values ​​is achieved to obtain a wideband tuning table, reducing invalid mechanical motion. This not only meets the requirements of fast and accurate tuning but also significantly reduces the hardware wear rate. Therefore, this invention, through a combination of first-level pure software optimization and second-level hardware optimization, is particularly suitable for scenarios with strict requirements on tuning speed, matching accuracy, and hardware wear.

[0037] As a further preferred technical solution, step S10: establishing an initial mapping relationship table between the motor rotation angle and the parameter values ​​of the components in the tuning network, specifically includes the following steps: S11. Control the motor connected to the capacitor to rotate to different angles, and measure the actual capacitance value of the capacitor when the motor rotates to different angles; S12. Linear interpolation is used to fit the discrete measured capacitance values ​​to obtain a mapping table between the capacitance value and the motor rotation angle. Specifically, the capacitance value of capacitor C can be directly measured by controlling the rotation of the motor connected to the capacitor to different angles. Then, the capacitance value corresponding to capacitor C was measured using a capacitance meter; linear interpolation was used to fit the discrete measured data to obtain the functional mapping relationship between the capacitance value and the motor rotation angle.

[0038] in, , The fitting coefficients for capacitor C are calculated using the least squares method (fitting error < 2%).

[0039] It should be noted that, as Figure 2 As shown, the tuning network can be equipped with multiple capacitors, each of which is connected to a corresponding motor, and a mapping relationship between the capacitance value and the corresponding motor rotation angle is established.

[0040] S13. Establish a three-dimensional electromagnetic model of the inductor L and its surrounding cavity, and simulate the three-dimensional battery model to obtain the S matrix corresponding to different motor rotation angles. It should be noted that this embodiment uses electromagnetic simulation software such as HFSS or CST to establish a three-dimensional electromagnetic model of the inductor L and its surrounding cavity (including factors such as the metal support, grounding structure, and inductor short-circuit bar) to simulate different motor rotation angles. The corresponding S matrix ( , , , ).

[0041] S14. Obtain the mapping relationship table between inductance value and motor rotation angle based on the S matrix inversion; S15. Combining the mapping relationship table between capacitor value and motor rotation angle and the mapping relationship table between inductance value and motor rotation angle, the initial mapping relationship table is formed.

[0042] It should be noted that the inductance value of L in the tuning network is significantly affected by environmental factors such as eddy currents in the cavity and electromagnetic coupling of adjacent metal components, and the direct measurement error often exceeds 20%. This embodiment, to improve the accuracy of inductance measurement, indirectly establishes a mapping relationship between the motor rotation angle and the inductance value through "electromagnetic simulation-S-parameter inversion," solving the measurement problem caused by environmental interference and reducing the inductance value error from >20% to <5%. This avoids inaccurate mapping between the inductance value and the motor rotation angle, which could lead to deviations in the tuning parameter calculation and affect the matching accuracy.

[0043] As a further preferred technical solution, step S14: obtaining the mapping relationship table between inductance value and motor rotation angle based on the S matrix inversion, specifically includes the following steps: S141. The input impedance of the inductor is derived from the S-matrix; Specifically, the input impedance Z of the inductor L is derived from the S matrix. 11 The formula is as follows:

[0044] In the formula, The reflection coefficient of port 1, For reverse transmission (gain or loss). Forward transmission (gain or loss). is the reflection coefficient of port 2.

[0045] S142. Calculate the inductance value based on the imaginary part of the input impedance. The formula is as follows:

[0046] In the formula, This represents the imaginary part of the input impedance. For operating frequency, Pi Indicates product; S143. Based on the calculated inductance value, obtain the relationship between the inductance value L and the motor rotation angle. A mapping table between them.

[0047] It should be noted that, as Figure 2 As shown, the tuning network can be configured with multiple inductors, each of which is connected to a corresponding motor, and a mapping relationship between the inductance value and the corresponding motor rotation angle is established.

[0048] Furthermore, the tuning network includes, but is not limited to, T-type tuning networks, π-type tuning networks, L-type tuning networks, and other types of tuning networks.

[0049] As a further preferred technical solution, step S20: connecting the standard load to the output of the tuning network theoretical model and traversing the initial mapping table to correct the mapping relationship between the motor rotation angle and the parameter values ​​of the components, thereby obtaining a corrected mapping table, specifically includes the following steps: S21. Connect different standard loads to the output of the tuned network theoretical model, traverse the parameter values ​​of different components in the initial mapping table, and calculate the theoretical output impedance corresponding to the parameter value of the currently traversed component. Specifically, in this embodiment, five standard loads are connected sequentially at the output of the tuning network: short circuit (equivalent to 0Ω impedance), XΩ, YΩ, ZΩ, and open circuit (equivalent to infinite impedance). X, Y, and Z are pre-set ohmic values, representing non-zero and non-infinite values, typically ranging from (0, 1000). The capacitance values ​​C are iterated through. Xc The capacitance range and the inductance L (type of capacitance range) and the inductance value. Xl (sensory value level) Xc × Xl ( ) combinations, using a vector network analyzer to measure the S-matrix of each combination in the target frequency band, and then based on the tuned network theoretical model, using the known input impedance Calculate the theoretical output impedance using the component parameter values ​​(C, L). :

[0050] In the formula, This represents the calculation function of the circuit model used.

[0051] S22. Use the impedance value of the standard load as the actual output impedance, and calculate the average absolute error between the actual output impedance and the theoretical output impedance. Specifically, in this embodiment, the impedance value of the standard load is used as the actual output impedance. The mean absolute error between the actual output impedance and the theoretical output impedance is calculated as follows:

[0052] In the formula, This represents the mean absolute error.

[0053] S23. Based on the mean absolute error, correct the parameter values ​​of the currently traversed elements to obtain the corrected mapping table.

[0054] Specifically, the parameter values ​​of the currently traversed components are corrected based on the mean absolute error, as expressed by the formula:

[0055]

[0056] In the formula, This is the corrected capacitance value. This is the corrected inductance value. and These are the capacitance and inductance values ​​currently being iterated.

[0057] It should be noted that, in order to reduce model mismatch error, this embodiment uses a dynamic correction mechanism based on test data of five standard loads to adjust the initial mapping relationship, thereby reducing the tuning network model error from 12.5% ​​to 3.8%, thus improving the matching accuracy.

[0058] As a further preferred technical solution, step S30: connecting the antenna feeder system to be matched to the output terminal of the tuning network, and performing first-level optimization on the initial parameter values ​​of the given components with the minimum voltage standing wave ratio as the optimization objective, to obtain the first-level optimization result of the component parameter values, specifically including the following steps: S31. Connect the antenna feeder system to be matched to the output of the tuning network, and calculate the initial parameter values ​​of the component, wherein the initial parameter values ​​of the component are empirical values ​​or default values. S32. Calculate the actual output impedance based on the known input impedance and the given initial parameter values ​​of the component; S33. Perform first-level optimization on the given initial parameter values ​​of the component with the minimum voltage standing wave ratio as the optimization objective, and obtain the first-level optimization results of the component parameter values.

[0059] Specifically, in this embodiment, the antenna feeder system to be matched is connected to the output of the tuning network, and a set of initial component values ​​(C0, L0) are given. The S-matrix is ​​measured using a vector network analyzer, combined with the known input impedance. The actual output impedance is calculated using the modified second-generation tuned network model. The calculation formulas differ for different circuit models. Then, with the goal of "impedance matching between the input and output terminals (i.e., minimizing the VSWR)," the parameter values ​​of the components are iteratively optimized using an optimization algorithm within the computer. During the first-level optimization stage, no motor rotates, and the component parameter values ​​(C1, L1) obtained after the first-level optimization are used as the initial values ​​for the second-level optimization.

[0060] It should be noted that the optimization algorithms used include, but are not limited to, genetic algorithms, ant colony algorithms, particle swarm optimization, etc. This embodiment does not specifically limit the type of optimization algorithm.

[0061] As a further preferred technical solution, step S40: Based on the first-level optimization results of the component's parameter values, the variable value range of the component's parameter values ​​is determined; based on the corrected mapping relationship table, the motor is controlled to rotate to the motor angle corresponding to the variable value range; and with the voltage standing wave ratio corresponding to a certain frequency point being less than a set threshold as the optimization objective, the parameter values ​​of the component corresponding to that frequency point are optimized in a second-level manner to obtain the second-level optimization results of the component's parameter values. Specifically, this includes the following steps: S41. Based on the first-level optimization results of the component's parameter values, the variable range of the component's parameter values ​​is determined as follows:

[0062] in, The value range is [0,1]. Numbers greater than 1; The capacitance value is the parameter value of the component in the first-level optimization result. The inductance value is the parameter value of the component in the first-level optimization result; Indicates capacitance value and inductance value , For all inductance values This is the maximum inductance value; It should be noted that in this embodiment, the parameter values ​​of the components generated by the first-level optimization are used as the benchmark to redetermine the range of variable values. The multidimensional search space of the second-level optimization algorithm is compressed from [the actual lower limit of the component and the actual upper limit of the component] to the range of variable values, thereby significantly reducing the search range. The reduction of the search space reduces the number and amplitude of motor rotations. Combined with the accuracy of the initial values, it reduces invalid mechanical motion, significantly reduces hardware wear rate, and extends equipment life to meet the requirements of rapid tuning.

[0063] S42. The variable value range based on the component parameter value is used to find the corresponding motor angle from the modified mapping table, and the motor is controlled to rotate to that motor angle; S43. With the voltage standing wave ratio corresponding to a certain frequency point being less than a set threshold as the optimization objective, perform secondary optimization on the parameter values ​​of the component corresponding to that frequency point to obtain the secondary optimization results of the component parameter values.

[0064] It should be noted that this embodiment employs an optimization algorithm for further secondary optimization. The objective function for secondary optimization is: VSWR at a specific frequency point < ( (Specific standing wave values).

[0065] It should be noted that the two-level optimization framework used in this embodiment can speed up the tuning process. The two-level optimization generates high-quality initial values ​​in the first level, which reduces the number of iterations in the second level from 50 to 5, and the number of individuals from 30 to 10.

[0066] As a further preferred technical solution, in step S43: after performing secondary optimization on the parameter values ​​of the component corresponding to a certain frequency point with the optimization objective of the voltage standing wave ratio corresponding to a certain frequency point being less than a set threshold, and obtaining the secondary optimization result of the component parameter values, the method further includes the following steps: A vector network analyzer was used to read full-band standing wave data, and the full-band standing wave data was compared with historical data. If the voltage standing wave ratio at a certain frequency is less than the set threshold, the parameter value of the component corresponding to that frequency will be directly cached. If the voltage standing wave ratio at a certain frequency is close to the preset threshold, the parameter value of the component corresponding to that frequency is cached as the initial value for the next secondary optimization at that frequency.

[0067] It should be noted that this embodiment uses a full-band data caching mechanism: The vector network analyzer reads full-band VSWR data and compares it with historical data after each optimization: If VSWR at a certain frequency point < If so, the component parameter value corresponding to that frequency point is cached, and there is no need to optimize that frequency point again in the future; If the VSWR at a certain frequency is close to (like <VSWR< +0.2), cache this value as the initial value for the next optimization at this frequency point to accelerate convergence.

[0068] It should be noted that this embodiment uses a full-band data caching mechanism to reduce invalid motor actions. The number of motor actions for full-band tuning is reduced from 120,000 to 12,000, reducing hardware wear and extending the service life of the equipment.

[0069] As a further preferred technical solution, in step S43: after performing secondary optimization on the parameter values ​​of the component corresponding to a certain frequency point with the optimization objective of the voltage standing wave ratio corresponding to a certain frequency point being less than a set threshold, and obtaining the secondary optimization result of the component parameter values, the method further includes the following steps: If the current voltage standing wave ratio (VSWR) at a certain frequency is less than the historical voltage standing wave ratio (VSWR) at that frequency in the cache, then the historical voltage standing wave ratio will be updated to the current voltage standing wave ratio (VSWR).

[0070] like Figures 2 to 3 As shown, a specific application example of the antenna dual-stage optimized tuning method proposed in this embodiment is as follows: (1) Experimental equipment: Shortwave antenna RF system: operating frequency band Fmin-Fmax; Tuning network: π-type network, including C1 (80pF~900pF, 5 levels), C2 (80pF~900pF, 5 levels), and L (0μH~3.5μH, 5 levels). Each component is driven by a corresponding stepper motor (resolution 0.1°). Vector network analyzer: The measurement frequency includes the operating frequency band, and it supports real-time measurement of S-parameter matrices; Standard load: 0Ω, 50Ω, 100Ω, 200Ω (accuracy ±1%), open circuit load (insulation resistance > 100MΩ); Simulation software: Creates an inductor cavity model; Control computer: Can run MATLAB (genetic algorithm implementation, generator rotation commands).

[0071] (2) Experimental steps: 2-1) System Calibration a. Capacitor mapping establishment: Controlling motor C1 to rotate to positions 1, 2, 3, 4, and 5 (each of the 5 positions corresponds to θ), the measured capacitance values ​​are 80pF, 280pF, 480pF, 680pF, and 900pF, respectively. The fitted value is C1 = 9.5θ_C1 - 15 (R² = 0.998); similarly, C2 = 9.2θ_C - 10 (R² = 0.997).

[0072] b. Inductor mapping establishment: An inductor cavity model was established in HFSS, and the S-parameters corresponding to θ_L = 0°, 360°, 720°, 1080°, and 1440° were simulated. The Z-parameters were then calculated in reverse. 11 And calculate L to obtain the mapping table: θ_L=0°→0.1μH, 30°→0.9μH, 60°→2μH, 90°→2.7μH, 120°→3.5μH (error <3%).

[0073] c. Model correction: Five standard loads were connected, and a total of 125 combinations of S-parameter matrices were measured. The average error delta was calculated to be 0.15. After correction, the parameter values ​​of the components were C1_adj=C1×0.97, C2_adj=C2×0.97, and L_adj=L×0.97, and the model error was reduced to 3.8%.

[0074] 2-2) Online tuning (taking 5.2MHz frequency as an example) a. First-level optimization: Initial value C 10 =400pF, C 20 =400pF, L0=2μH, Z_out=(45+j15)Ω, the genetic algorithm outputs the initial value after 50 iterations: C 11 =280pF, C 21 =220pF, L1=0.9μH.

[0075] b. Second-level optimization: The motor was controlled to rotate to the angle corresponding to the initial value. The genetic algorithm iterated for 5 generations, and the optimized parameters were: C1=300pF, C2=230pF, L=0.85μH, and VSWR=1.05 at the 5.2MHz frequency point. At the same time, parameters such as 12MHz (VSWR=1.08) and 16MHz (VSWR=1.12) were cached for subsequent tuning.

[0076] (3) Comparison of experimental results: Table 1 shows a comparison of the test results for a single frequency point: Table 1 Comparison of Experimental Results

[0077] In addition, such as Figure 4 As shown, the second embodiment of the present invention also proposes an antenna dual-stage optimized tuning device, the device comprising: The mapping relationship establishment module 10 is used to establish an initial mapping relationship table between the motor rotation angle and the parameter values ​​of the components in the tuning network; The calibration module 20 is used to connect a standard load to the output of the tuning network theoretical model and traverse the initial mapping table to correct the mapping relationship between the motor rotation angle and the parameter values ​​of the components, thereby obtaining a corrected mapping table. The first-level optimization module 30 is used to connect the antenna feeder system to be matched to the output of the tuning network, and to perform first-level optimization on the initial parameter values ​​of the given components with the minimum voltage standing wave ratio as the optimization objective, so as to obtain the first-level optimization result of the component parameter values. The secondary optimization module 40 is used to determine the variable value range of the component's parameter values ​​based on the primary optimization results of the component's parameter values, control the motor to rotate to the motor angle corresponding to the variable value range based on the modified mapping relationship table, and perform secondary optimization on the parameter values ​​of the component corresponding to a certain frequency point with the optimization objective that the voltage standing wave ratio corresponding to a certain frequency point is less than a set threshold, so as to obtain the secondary optimization results of the component's parameter values.

[0078] As a further preferred technical solution, the mapping relationship establishment module 10 specifically includes: The first mapping relationship establishment unit is used to control the motor connected to the capacitor to rotate to different angles and measure the measured value of the capacitor when the motor rotates to different angles; linear interpolation fitting is used for the discrete measured values ​​of the capacitance to obtain a mapping relationship table between the capacitance value and the motor rotation angle; The second mapping relationship establishment unit is used to establish a three-dimensional electromagnetic model of the inductor L and its surrounding cavity, and to simulate the three-dimensional battery model to obtain the S matrix corresponding to different motor rotation angles; based on the S matrix, the mapping relationship table between the inductance value and the motor rotation angle is obtained; combined with the mapping relationship table between the capacitance value and the motor rotation angle and the mapping relationship table between the inductance value and the motor rotation angle, the initial mapping relationship table is formed.

[0079] As a further preferred technical solution, the calibration module 20 specifically includes: The traversal calculation unit is used to connect different standard loads to the output of the tuned network theoretical model, traverse the parameter values ​​of different components in the initial mapping table, and calculate the theoretical output impedance corresponding to the parameter value of the currently traversed component. The error calculation unit is used to take the impedance value of the standard load as the actual output impedance and calculate the average absolute error between the actual output impedance and the theoretical output impedance. The correction unit is used to correct the parameter values ​​of the currently traversed elements based on the mean absolute error, and obtain the correction mapping table.

[0080] As a further preferred technical solution, the primary optimization module 30 specifically includes: An initial parameter value giving unit is used to connect the antenna feeder system to be matched to the output of the tuning network and to calculate the initial parameter values ​​of the component, wherein the initial parameter values ​​of the component are empirical values ​​or default values; Impedance calculation unit, used to calculate the actual output impedance based on the known input impedance and the given initial parameter values ​​of the component; The first-level optimization unit is used to perform first-level optimization on the initial parameter values ​​of a given component with the goal of minimizing the voltage standing wave ratio, and obtain the first-level optimization result of the component parameter values.

[0081] As a further preferred technical solution, the secondary optimization module 30 specifically includes: The search space determination unit is used to determine the variable range of the component's parameter values ​​based on the first-level optimization results of the component's parameter values.

[0082] in, The value range is [0,1]. For numbers greater than 1, The capacitance value is the parameter value of the component in the first-level optimization result. The inductance value is the parameter value of the component in the first-level optimization result. For all inductance values; Angle lookup unit is used to look up the corresponding motor angle from the modified mapping table based on the variable value range of the component's parameter value, and control the motor to rotate to that motor angle; The secondary optimization unit is used to perform secondary optimization on the parameter values ​​of the component at a certain frequency point with the optimization objective of the voltage standing wave ratio (VSWR) being less than a set threshold, and to obtain the secondary optimization result of the component parameter values.

[0083] As a further preferred technical solution, the device also includes a cache module, specifically used for: A vector network analyzer was used to read full-band standing wave data, and the full-band standing wave data was compared with historical data. If the voltage standing wave ratio at a certain frequency is less than the set threshold, the parameter value of the component corresponding to that frequency will be directly cached. If the voltage standing wave ratio at a certain frequency is close to the preset threshold, the parameter value of the component corresponding to that frequency is cached as the initial value for the next secondary optimization at that frequency.

[0084] As a further preferred technical solution, the third embodiment of the present invention also proposes a computer-readable storage medium storing a computer program thereon, wherein when the computer program is executed by a processor, it implements the antenna dual-stage optimization tuning method described in the first embodiment above.

[0085] It should be noted that other embodiments or specific implementation methods of the antenna dual-stage optimized tuning device and computer-readable storage medium described in this invention can be referred to the above-described method embodiments, and will not be repeated here.

[0086] It should be noted that the computer-readable medium disclosed in this embodiment may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, and portable compact disk read-only memory (CD-ROM). ROM, optical storage devices, magnetic storage devices, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0087] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform a zero-sample image anomaly detection method according to the above embodiments.

[0088] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server.

[0089] In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0090] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0091] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0092] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" or "several" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0093] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A two-stage optimized tuning method for an antenna, characterized in that, include: Establish an initial mapping table between the motor rotation angle and the parameter values ​​of the components in the tuning network; The standard load is connected to the output of the tuning network theoretical model, and the initial mapping table is traversed to correct the mapping relationship between the motor rotation angle and the parameter values ​​of the components, so as to obtain the corrected mapping table. Connect the antenna feeder system to be matched to the output of the tuning network, and perform first-level optimization on the initial parameter values ​​of the given components with the minimum voltage standing wave ratio as the optimization objective, to obtain the first-level optimization results of the component parameter values. Based on the first-level optimization results of the component's parameter values, the variable value range of the component's parameter values ​​is determined. Based on the modified mapping relationship table, the motor is controlled to rotate to the motor angle corresponding to the variable value range. Then, with the voltage standing wave ratio corresponding to a certain frequency point being less than a set threshold as the optimization objective, the parameter values ​​of the component corresponding to that frequency point are optimized in a second-level manner to obtain the second-level optimization results of the component's parameter values.

2. The antenna dual-stage optimized tuning method as described in claim 1, characterized in that, The components in the tuning network include inductors and capacitors. Establishing an initial mapping table between the motor rotation angle and the parameter values ​​of the components in the tuning network includes: Control the motor connected to the capacitor to rotate to different angles, and measure the actual capacitance value of the capacitor when the motor rotates to different angles; Linear interpolation was used to fit the discrete measured capacitance values ​​to obtain a mapping table between the capacitance value and the motor rotation angle. A three-dimensional electromagnetic model of the inductor L and its surrounding cavity was established, and the three-dimensional battery model was simulated to obtain the S matrix corresponding to different motor rotation angles. The mapping relationship table between inductance value and motor rotation angle is obtained by inverting the S matrix; The initial mapping table is formed by combining the mapping tables between capacitor values ​​and motor rotation angles and the mapping tables between inductance values ​​and motor rotation angles.

3. The antenna dual-stage optimized tuning method as described in claim 2, characterized in that, The mapping table between inductance value and motor rotation angle obtained by inverting the S matrix includes: The input impedance of the inductor is derived by inverting the S-matrix; The inductance value is calculated based on the imaginary part of the input impedance, expressed by the following formula: In the formula, This represents the imaginary part of the input impedance. For operating frequency, Pi Indicates product; Based on the calculated inductance value, a mapping table between the inductance value and the motor rotation angle is obtained.

4. The antenna dual-stage optimized tuning method as described in claim 1, characterized in that, The step involves connecting a standard load to the output of the tuned network theoretical model and traversing the initial mapping table to correct the mapping relationship between the motor rotation angle and the parameter values ​​of the components, resulting in a corrected mapping table, including: Connect different standard loads to the output of the tuned network theoretical model, traverse the parameter values ​​of different components in the initial mapping table, and calculate the theoretical output impedance corresponding to the parameter value of the currently traversed component. The impedance value of the standard load is used as the actual output impedance, and the mean absolute error between the actual output impedance and the theoretical output impedance is calculated. The parameter values ​​of the currently traversed components are corrected based on the mean absolute error, resulting in a corrected mapping table.

5. The antenna dual-stage optimized tuning method as described in claim 4, characterized in that, The standard loads include short-circuit loads, XΩ loads, YΩ loads, ZΩ loads, and open-circuit loads, where X, Y, and Z represent non-zero and non-infinite values.

6. The antenna dual-stage optimized tuning method as described in claim 1, characterized in that, The method for correcting the parameter values ​​of the currently traversed elements based on the mean absolute error is expressed by the following formula: In the formula, This is the corrected capacitance value. This is the corrected inductance value. and These are the capacitance and inductance values ​​currently being iterated. This represents the mean absolute error.

7. The antenna dual-stage optimized tuning method as described in claim 1, characterized in that, The process involves connecting the antenna feeder system to be matched to the output of the tuning network, and performing first-level optimization on the initial parameter values ​​of the given components with the goal of minimizing the voltage standing wave ratio (VSWR). The first-level optimization results for the component parameter values ​​include: Connect the antenna feeder system to be matched to the output of the tuning network, and calculate the initial parameter values ​​of the component, which are empirical values ​​or default values. Calculate the actual output impedance based on the known input impedance and the given initial parameter values ​​of the component; The initial parameter values ​​of a given component are optimized to minimize the voltage standing wave ratio (VSWR), resulting in the first-level optimization results.

8. The antenna dual-stage optimized tuning method as described in claim 1, characterized in that, The first-level optimization result based on the component's parameter values ​​determines the variable value range of the component's parameter values. Based on the corrected mapping table, the motor is controlled to rotate to the motor angle corresponding to the variable value range. Then, with the voltage standing wave ratio (VSWR) at a certain frequency point being less than a set threshold as the optimization objective, a second-level optimization is performed on the parameter values ​​of the component at that frequency point, yielding the second-level optimization result of the component's parameter values, including: Based on the first-level optimization results of the component's parameter values, the variable range of the component's parameter values ​​is determined as follows: in, The value range is [0,1]. For numbers greater than 1, The capacitance value is the parameter value of the component in the first-level optimization result. The inductance value is the parameter value of the component in the first-level optimization result. This represents all inductance values; The variable value range based on the component parameter value is used to find the corresponding motor angle from the modified mapping table, and the motor is controlled to rotate to that motor angle; With the optimization objective of the voltage standing wave ratio (VSWR) at a certain frequency point being less than a set threshold, a secondary optimization of the parameter values ​​of the component at that frequency point is performed to obtain the secondary optimization result of the component's parameter values.

9. The antenna dual-stage optimized tuning method as described in claim 8, characterized in that, The optimization objective is to achieve a voltage standing wave ratio (VSWR) less than a set threshold at a certain frequency point. This objective is then used to perform secondary optimization on the parameter values ​​of the component at that frequency point, yielding the secondary optimization results. These results include: With the optimization objective of the voltage standing wave ratio (VSWR) at a certain frequency point being less than a set threshold, a genetic algorithm is used to perform secondary optimization on the parameter values ​​of the component at that frequency point, and the secondary optimization results of the component parameter values ​​are obtained.

10. The antenna dual-stage optimized tuning method as described in claim 8, characterized in that, After performing secondary optimization on the parameter values ​​of the component at a certain frequency point with the optimization objective of the voltage standing wave ratio (VSWR) being less than a set threshold, and obtaining the secondary optimization result of the component's parameter values, the method further includes: A vector network analyzer was used to read full-band standing wave data, and the full-band standing wave data was compared with historical data. If the voltage standing wave ratio at a certain frequency is less than the set threshold, the parameter value of the component corresponding to that frequency will be directly cached. If the voltage standing wave ratio at a certain frequency is close to the preset threshold, the parameter value of the component corresponding to that frequency is cached as the initial value for the next secondary optimization at that frequency.

11. The antenna dual-stage optimized tuning method as described in claim 8, characterized in that, After performing secondary optimization on the parameter values ​​of the component at a certain frequency point with the optimization objective of the voltage standing wave ratio (VSWR) being less than a set threshold, and obtaining the secondary optimization result of the component's parameter values, the method further includes: If the current voltage standing wave ratio (VSWR) at a certain frequency is less than the historical VSWR corresponding to that frequency in the cache, the historical VSWR will be updated to the current VSWR.

12. A dual-stage optimized tuning device for an antenna, characterized in that, include: The mapping relationship establishment module is used to establish an initial mapping relationship table between the motor rotation angle and the parameter values ​​of the components in the tuning network; The calibration module is used to connect a standard load to the output of the tuning network theoretical model and traverse the initial mapping table to correct the mapping relationship between the motor rotation angle and the parameter values ​​of the components, thus obtaining the corrected mapping table. The first-level optimization module is used to connect the antenna feeder system to be matched to the output of the tuning network, and to perform first-level optimization on the initial parameter values ​​of the given components with the minimum voltage standing wave ratio as the optimization objective, so as to obtain the first-level optimization result of the component parameter values. The secondary optimization module is used to determine the variable value range of the component's parameter values ​​based on the primary optimization results of the component's parameter values. Based on the modified mapping relationship table, it controls the motor to rotate to the motor angle corresponding to the variable value range. With the voltage standing wave ratio corresponding to a certain frequency point being less than a set threshold as the optimization objective, it performs secondary optimization on the parameter values ​​of the component corresponding to that frequency point, and obtains the secondary optimization results of the component's parameter values.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the antenna dual-stage optimized tuning method as described in any one of claims 1-10.

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