Segmented adaptive gain-based short-wave band amplitude and phase control device and method

Abstract

The invention provides a segmented adaptive gain-based short-wave band amplitude and phase control device and method, and relates to the field of short-wave intelligent control. The method comprises the following steps: performing coupling detection processing on N paths of input short-wave signals to obtain coupled short-wave signals and power values of the coupled short-wave signals; based on the coupled short-wave signal and the power value of the coupled short-wave signal, calculating adjustment parameters through a preset adaptive segmented multi-target differential evolution algorithm, wherein the adjustment parameters comprise a frequency correction parameter, a phase adjustment parameter and an amplitude control voltage; adjusting the coupled short-wave signal based on the adjustment parameter to obtain a target short-wave signal; and detecting whether the target short wave signal meets a preset output condition, and if the target short wave signal meets the preset output condition, coupling the target short wave signal to output a radio frequency output signal. Through the scheme, the communication quality is improved.

Description

Technical Field

[0001] The present application relates to the field of shortwave intelligent control, and in particular to a shortwave band amplitude and phase control device and method based on segmented adaptive gain. Background Art

[0002] With the rapid development of modern communication technology, shortwave communication has been increasingly widely used in military, civilian and other fields due to its unique advantages. At the same time, the requirements for shortwave transmission equipment are also increasing, and integration and miniaturization have become the mainstream trend of design and manufacturing. In this context, multi-carrier power amplifier equipment came into being. By integrating multiple power amplifier modules, the power density and spectrum efficiency of the equipment can be greatly improved. However, while multi-carrier power amplifiers bring advantages, they also introduce new technical challenges. Since the frequency of shortwave signals is relatively low, for transmitting equipment working in a broadband range, its power amplifier circuit usually needs to cover the 2nd, 3rd or even 4th harmonics of the baseband signal. When multiple signals of different frequencies pass through the nonlinear circuit of the power amplifier at the same time, a series of new frequency components will be generated, some of which happen to fall within or near the useful signal frequency band, resulting in intermodulation distortion, especially the third-order intermodulation interference problem is very prominent. This makes it difficult for the signal-to-noise ratio and harmonic indicators of the power amplifier to meet the transmission requirements when multiple carrier signals are transmitted simultaneously in the broadband, which seriously affects the communication quality. Summary of the invention

[0003] The present application provides a short-wave band amplitude phase control device and method based on segmented adaptive gain, and the communication quality is improved through the above scheme.

[0004] In a first aspect, the present application provides a short-waveband amplitude phase control device based on segmented adaptive gain, the device comprising: an N-way input coupling detection module, an information processing module, a control module and a radio frequency coupling module; An N-channel input coupling detection module is used to perform coupling detection processing on the N-channel input shortwave signals to obtain coupled shortwave signals and power values ​​of the coupled shortwave signals; An information processing module, used for calculating adjustment parameters based on the coupled shortwave signal and the power value of the coupled shortwave signal through a preset adaptive segmented multi-objective differential evolution algorithm, the adjustment parameters including: frequency correction parameters, phase adjustment parameters and amplitude control voltage; A control module, used for adjusting the coupled shortwave signal based on the adjustment parameters to obtain a target shortwave signal; The RF coupling module is used to detect whether the target shortwave signal meets the preset output conditions. If the target shortwave signal meets the preset output conditions, the target shortwave signal is coupled to output a RF output signal.

[0005] By adopting the above technical solution, the structured data is preprocessed and the unstructured data is standardized, laying the foundation for subsequent semantic understanding. On this basis, the preprocessed structured data is semantically annotated using the preset knowledge graph model, and the standardized unstructured data is semantically understood using the preset semantic understanding model, realizing the semantic level analysis of different types of data. By introducing the preset data governance rule library, the semantically annotated structured data and the semantically annotated unstructured data are classified and graded, making the data governance process more targeted. Furthermore, the data classification results are verified and optimized based on the preset data governance knowledge base to ensure the rationality and feasibility of the governance plan. Finally, after completing the preliminary governance based on the list of data to be governed, the quality inspection is carried out through the preset quality inspection model and the problem data is repaired to form a complete governance closed loop. This multi-level governance method based on semantic understanding not only improves the accuracy of data classification and grading, but also enhances the controllability of the governance process and the reliability of the governance results, thereby realizing the unified and efficient governance of structured data and unstructured data.

[0006] In a second aspect of the present application, a short-band amplitude phase control method based on segmented adaptive gain is provided, the method comprising: Perform coupling detection processing on N-channel input shortwave signals to obtain coupled shortwave signals and power values ​​of coupled shortwave signals; Based on the coupled shortwave signal and the power value of the coupled shortwave signal, the adjustment parameters are calculated by a preset adaptive segmented multi-objective differential evolution algorithm, and the adjustment parameters include: frequency correction parameters, phase adjustment parameters and amplitude control voltage; The coupled shortwave signal is adjusted based on the adjustment parameter to obtain a target shortwave signal; It is detected whether the target shortwave signal meets the preset output condition. If the target shortwave signal meets the preset output condition, the target shortwave signal is coupled to output a radio frequency output signal.

[0007] In a third aspect of the present application, a computer storage medium is provided, wherein the computer storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the above method steps.

[0008] In the fourth aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the above method.

[0009] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. This application lays the foundation for subsequent semantic understanding by preprocessing structured data and standardizing unstructured data. On this basis, the preprocessed structured data is semantically annotated using a preset knowledge graph model, and the standardized unstructured data is semantically understood using a preset semantic understanding model, thereby achieving semantic level analysis of different types of data. By introducing a preset data governance rule library to classify and grade semantically annotated structured data and semantically annotated unstructured data, the data governance process is made more targeted.

[0010] 2. This application verifies and optimizes the data classification results based on the preset data governance knowledge base to ensure the rationality and feasibility of the governance plan. Finally, after completing the preliminary governance based on the list of data to be governed, quality inspection is performed through the preset quality inspection model and the problematic data is repaired to form a complete governance closed loop. This multi-level governance method based on semantic understanding not only improves the accuracy of data classification and grading, but also enhances the controllability of the governance process and the reliability of the governance results, thereby realizing unified and efficient governance of structured data and unstructured data. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 A schematic diagram of a short-waveband amplitude and phase control device based on segmented adaptive gain provided in an embodiment of the present application Figure 2 A schematic flow chart of a method for short-waveband amplitude and phase control based on segmented adaptive gain provided in an embodiment of the present application; Figure 3 A schematic diagram of the structure of an electronic device provided in this application. DETAILED DESCRIPTION

[0012] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0013] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0014] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0015] In order to facilitate understanding of the method and system provided by the embodiments of the present application, before introducing the embodiments of the present application, the background of the embodiments of the present application is first introduced.

[0016] With the rapid development of modern communication technology, shortwave communication has been used unprecedentedly in military command, emergency communication, civil communication and other fields due to its unique advantages such as wide coverage, long transmission distance and large channel capacity. However, along with the booming development of shortwave communication, there are higher requirements for the performance of shortwave transmitting equipment. In the context of today's information warfare and civil communications, shortwave transmitting equipment not only needs to have excellent communication quality and reliability, but also needs to meet the increasing demand for integration and miniaturization, which has become the mainstream trend of design and manufacturing.

[0017] In order to adapt to this trend, multi-carrier power amplifier equipment came into being. By integrating multiple power amplifier modules in a compact chassis, multi-carrier power amplifier equipment can greatly improve the power density of the equipment within a limited volume. At the same time, by transmitting multiple carrier signals in parallel, the spectrum utilization efficiency can be significantly improved. These advantages make it an inevitable choice for the development of shortwave communication. However, opportunities and challenges often coexist. While multi-carrier power amplifiers bring many advantages, they also introduce new technical difficulties. Since the frequency of shortwave signals is relatively low, for transmitting equipment working in a broadband range, its power amplifier circuit usually needs to cover the 2nd, 3rd or even 4th harmonics of the baseband signal to ensure signal integrity and full utilization of power. However, when multiple signals of different frequencies pass through these nonlinear power amplifier circuits at the same time, a series of new frequency components will be generated, and these intermodulation products are scattered throughout the working frequency band. What is more difficult is that some of these intermodulation products happen to fall within or near the useful signal band, making it difficult to distinguish from the desired signal, resulting in intermodulation distortion. In particular, the third-order intermodulation interference problem has a particularly significant impact on communication quality because it is close to the baseband signal and has a relatively high power.

[0018] After the background introduction of the above content, those skilled in the art can understand the problems existing in the prior art. The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments.

[0019] Based on the above background technology, please refer to Figure 1 , Figure 1 The present invention provides an architecture diagram of a short-band amplitude phase control device based on segmented adaptive gain, which can be implemented by a computer program or run as an independent tool application. Specifically, in the present invention, the method can be applied on a server, but can also be applied to electronic devices such as servers. The present invention provides an architecture diagram of a short-band amplitude phase control device based on segmented adaptive gain, which includes: an N-way input coupling detection module 1, an information processing module 2, a control module 3, and a radio frequency coupling module 4; N-channel input coupling detection module 1, used for performing coupling detection processing on N-channel input shortwave signals to obtain coupled shortwave signals and power values ​​of coupled shortwave signals; Information processing module 2, used for calculating adjustment parameters based on the coupled shortwave signal and the power value of the coupled shortwave signal through a preset adaptive segmented multi-objective differential evolution algorithm, the adjustment parameters including: frequency correction parameters, phase adjustment parameters and amplitude control voltage; A control module 3 is used to adjust the coupled shortwave signal based on the adjustment parameter to obtain a target shortwave signal; The radio frequency coupling module 4 is used to detect whether the target shortwave signal meets the preset output condition. If the target shortwave signal meets the preset output condition, the target shortwave signal is coupled to output a radio frequency output signal.

[0020] Specifically, in a typical application scenario, it is necessary to transmit multiple shortwave signals of different frequencies at the same time. First, the N input shortwave signals are sent to the N input coupling detection module 1 for processing. This module uses a coupling circuit to extract a part of the signal energy, sends it to the detection circuit for detection, and converts the RF signal into a baseband signal containing amplitude and phase information, that is, a coupled shortwave signal. At the same time, the power value of each signal is obtained through the envelope detection circuit. This step can efficiently obtain the key characteristic parameters of the signal without affecting the original signal, laying the foundation for subsequent processing.

[0021] Next, the information processing module 2 receives the coupled shortwave signal and the corresponding power value, and uses the preset adaptive segmented multi-objective differential evolution algorithm to calculate the optimal frequency correction parameters, phase adjustment parameters and amplitude control voltage. The algorithm first divides the global search space into multiple sub-intervals according to the power value, and then performs local optimization in each sub-interval to find the optimal parameter combination in the interval. This segmented search strategy can effectively reduce the optimization complexity and improve the convergence speed. At the same time, by setting multiple optimization targets such as intermodulation distortion and power amplifier efficiency, the differential evolution algorithm is used for multi-objective optimization, while suppressing intermodulation and taking into account other performance indicators. In addition, the algorithm also introduces an adaptive mechanism, which can dynamically adjust the optimization strategy and parameters according to the channel environment detected in real time, and has a strong environmental adaptability. Through this step, the optimal control parameter combination can be intelligently searched to provide a basis for signal optimization. Then, the control module 3 receives adjustment parameters such as frequency correction parameters, phase adjustment parameters and amplitude control voltage, and modulates the coupled shortwave signal. Specifically, the frequency correction parameters are used to calibrate the frequency of the signal, the phase adjustment parameters are used to adjust the phase of the signal, and the amplitude control voltage is used to control the amplitude of the signal. Through these precise parameter controls, the signal is pre-distorted at the transmitting end to compensate for the distortion caused by the nonlinearity of the power amplifier, thereby suppressing intermodulation interference, optimizing signal quality, and obtaining the target shortwave signal. This step does not require changing the original power amplifier circuit, but only requires adding a control module at the signal source end, which is simple and feasible.

[0022] Finally, the RF coupling module 4 detects the optimized target shortwave signal to determine whether it meets the preset performance indicators, such as intermodulation distortion, signal-to-noise ratio, etc. If the requirements are met, the target shortwave signal is coupled and output through the RF coupling circuit to obtain the optimized RF output signal. If the requirements are not met, the feedback mechanism is activated to return the error signal to the information processing module 2 to further optimize and adjust the parameters until the indicators are met. This adaptive feedback calibration link further improves the dynamic performance and robustness of the system, ensuring the high quality and stability of the output signal.

[0023] Based on the above embodiment, as an optional embodiment, the control module 3 includes: a frequency correction unit 31, a phase modulator 32 and an amplitude adjustment unit 33; A frequency correction unit 31, configured to perform frequency correction on the coupled shortwave signal according to a frequency correction parameter to obtain a first controlled shortwave signal; The phase modulator unit 32 is used to perform phase adjustment on the first regulated shortwave signal according to the phase adjustment parameter to obtain a second regulated shortwave signal; The amplitude adjustment unit 33 is used to adjust the amplitude of the second regulated shortwave signal according to the amplitude control voltage to obtain a target shortwave signal.

[0024] Specifically, in the segmented adaptive gain shortwave band amplitude phase control device proposed in the present invention, the control module 3 is responsible for accurately modulating the coupled shortwave signal to suppress intermodulation distortion and optimize signal quality. The module is composed of a frequency correction unit 31, a phase modulator 32 and an amplitude adjustment unit 33. By adjusting the frequency, phase, amplitude and other parameters of the signal in steps, the optimized target shortwave signal is finally obtained. The working principle and implementation process of the control module 3 are described in detail below in conjunction with a specific embodiment.

[0025] After the information processing module 2 calculates the optimal frequency correction parameters, phase adjustment parameters and amplitude control voltage, these parameters are sent to each functional unit of the control module 3 respectively.

[0026] First, the frequency correction unit 31 receives the frequency correction parameter and calibrates the frequency of the coupled shortwave signal. Specifically, the unit includes a digitally controlled oscillator and a mixer. The digitally controlled oscillator generates a correction oscillation signal according to the frequency correction parameter, and its frequency is equal to the difference between the frequency of the coupled shortwave signal and the target frequency. The mixer mixes the correction oscillation signal with the coupled shortwave signal to complete the frequency shift, and accurately calibrates the frequency of the coupled shortwave signal to the target frequency to obtain the first regulated shortwave signal. This step can compensate for the distortion caused by the frequency deviation and ensure the accuracy and stability of the signal frequency. Next, the phase modulator 32 receives the phase adjustment parameter and adjusts the phase of the first regulated shortwave signal. The unit adopts a digital orthogonal modulator architecture and contains two orthogonal modulation channels. The first regulated shortwave signal is mixed with the in-phase and orthogonal reference oscillation signals respectively, and then low-pass filtered to obtain the baseband I and Q two orthogonal signals. The digital phase adjuster shifts the phase of the I and Q signals according to the phase adjustment parameter, and then re-modulates to the radio frequency band to obtain the second regulated shortwave signal. By accurately rotating the phase of the orthogonal dual-channel signal, the phase of the RF signal can be flexibly adjusted to achieve high-precision phase control, thereby suppressing intermodulation interference caused by phase distortion.

[0027] Finally, the amplitude adjustment unit 33 receives the amplitude control voltage and adjusts the amplitude of the second regulated shortwave signal. A voltage-controlled attenuator is integrated inside the unit, which adjusts the attenuation of the RF signal by changing the control voltage, thereby controlling the signal amplitude. The amplitude control voltage is converted from the digital amplitude parameter by the digital-to-analog converter, and its size determines the gain coefficient of the attenuator. Through precise amplitude control, the signal power can be actively adjusted at the transmitting end to achieve amplitude pre-distortion compensation, suppress intermodulation distortion caused by nonlinearity of the power amplifier, optimize the dynamic range and peak-to-average power ratio of the signal, and finally obtain the optimized target shortwave signal.

[0028] It should be noted that: when the system provided in the above embodiment realizes its functions, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0029] See also Figure 2 , Figure 2 A schematic flow chart of a method for short-band amplitude and phase control based on segmented adaptive gain is provided in an embodiment of the present application. The method for short-band amplitude and phase control based on segmented adaptive gain includes: S101, performing coupling detection processing on N input shortwave signals to obtain coupled shortwave signals and power values ​​of the coupled shortwave signals; Specifically, first, a small portion of the energy of each shortwave signal is extracted using a coupling circuit to form a coupled shortwave signal. The coupling circuit is usually composed of passive devices such as directional couplers or capacitive coupling. It can obtain a coupled signal with the same frequency and phase as the original signal without affecting the transmission of the main signal, providing input for subsequent detection. Then, the coupled shortwave signal is sent to the detection circuit for detection processing. The function of the detection circuit is to convert the radio frequency signal into a baseband signal containing amplitude and phase information. Common detection methods include envelope detection, coherent detection, etc. Taking envelope detection as an example, the envelope of the coupled shortwave signal, that is, the amplitude information, can be extracted through the detection diode and the low-pass filter. Phase information can be obtained through techniques such as Arc tangent demodulation. In this way, the key characteristic parameters of the coupled shortwave signal are detected.

[0030] At the same time, in order to optimize the accuracy and efficiency of control, it is also necessary to know the power of each shortwave signal. To this end, a power detection circuit can be connected in parallel to the coupling branch to achieve the conversion of RF signal power to DC voltage through a diode, and then amplify and filter it to obtain a DC voltage value proportional to the signal power, that is, the corresponding power value.

[0031] Through the above-mentioned coupled detection processing, the original high-frequency, high-power shortwave signal is converted into an easy-to-process baseband signal form, and the key information such as amplitude, phase, and power are retained, providing the necessary input data for the subsequent adaptive optimization algorithm, which is the basis for realizing intelligent control. At the same time, this step conveniently obtains the signal characteristic parameters without affecting the normal transmission of the RF signal, making it simple and feasible to implement.

[0032] S102, calculating adjustment parameters through a preset adaptive segmented multi-objective differential evolution algorithm based on the coupled shortwave signal and the power value of the coupled shortwave signal, the adjustment parameters including: frequency correction parameters, phase adjustment parameters and amplitude control voltage; Specifically, in the segmented adaptive gain shortwave band amplitude phase control method proposed in the present invention, after obtaining the coupled shortwave signal and its power value, it is necessary to further calculate the optimal adjustment parameters to provide a basis for signal optimization. A preset adaptive segmented multi-objective differential evolution algorithm is used here to achieve precise control of shortwave signals by intelligently searching for frequency correction parameters, phase adjustment parameters and amplitude control voltage. The so-called frequency correction parameter refers to a compensation parameter that offsets the signal frequency deviation by adjusting the local oscillator frequency, which is usually calculated from factors such as frequency error and frequency stretch coefficient; the phase adjustment parameter refers to a compensation parameter that corrects phase distortion by changing the phase relationship between the IQ two-way orthogonal signals, and is usually expressed by a phase rotation angle; the amplitude control voltage refers to a DC control voltage used to adjust the amplitude of the RF signal, which is converted by DAC from the digital amplitude parameter, and its size determines the gain coefficient of the amplitude adjustment.

[0033] In multi-carrier shortwave communication systems, intermodulation distortion is easily introduced due to factors such as power amplifier nonlinearity and channel interference, resulting in a decrease in signal quality. The so-called intermodulation distortion refers to the distortion caused by mutual interference between signals of different frequencies due to the nonlinearity of the power amplifier. It is often manifested as adjacent channel leakage, parasitic modulation, etc., and is one of the main factors affecting the quality of shortwave communication. In order to suppress this distortion, it is necessary to pre-distort the signal at the transmitter, that is, to compensate for the distortion by adjusting the frequency, phase, amplitude and other parameters of the signal, thereby improving the signal quality. Predistortion is a technology that applies compensation opposite to the distortion to the signal at the transmitter to offset the influence of power amplifier nonlinearity. It can effectively suppress intermodulation distortion and improve signal linearity.

[0034] Specifically, the algorithm first divides the entire parameter search space into multiple sub-intervals according to the power value of the coupled shortwave signal. This segmentation process can decompose the global optimization problem into multiple local optimization problems, reducing the optimization complexity and improving the search efficiency. Then, in each sub-interval, the differential evolution algorithm is used for multi-objective optimization. The differential evolution algorithm is a heuristic random search algorithm based on population iterative evolution. It optimizes the objective function through operations such as mutation, crossover, and selection to find the global optimal solution. It is widely used in the field of engineering optimization.

[0035] In the iterative optimization process, the algorithm takes intermodulation distortion and power amplifier efficiency as the main optimization targets, taking into account the balance between suppressing distortion and ensuring efficiency. At the same time, an adaptive mechanism is introduced to dynamically adjust the evolution strategy and control parameters such as mutation rate and crossover rate according to the iterative results of real-time evaluation to adapt to the current convergence state and improve optimization efficiency and robustness.

[0036] After multiple rounds of iterative search, the algorithm finally obtains a set of optimal adjustment parameters, including frequency correction parameters, phase adjustment parameters and amplitude control voltage. Substituting these optimized parameters into the demodulation model of coupled shortwave signals, the ideal pre-distortion compensation value can be obtained, and the frequency, phase and amplitude of the original signal can be corrected, thereby suppressing intermodulation distortion and improving signal quality.

[0037] Based on the above embodiment, as an optional embodiment, the adjustment parameters are calculated by a preset adaptive segmented multi-objective differential evolution algorithm based on the coupled shortwave signal and the power value of the coupled shortwave signal, including: S201, using the power value of the coupled shortwave signal as a performance evaluation function, and initializing the adaptive segmented multi-objective differential evolution algorithm according to preset initial algorithm parameters to obtain an initial population, wherein the initial algorithm parameters include a mutation operator, a crossover operator, a population number, a maximum evolutionary generation, a Pareto optimal solution set size, and a number of adaptive segments; Specifically, in the segmented adaptive gain short-wave band amplitude phase control method proposed in the present invention, in order to find the optimal adjustment parameters, it is necessary to perform a computational search based on the coupled shortwave signal and its power value through a preset adaptive segmented multi-objective differential evolution algorithm. The first step of this process is to reasonably set the initial conditions and optimization goals of the algorithm and generate an initial population to lay the foundation for subsequent iterative optimization. The principle and implementation method of this step are described in detail below in conjunction with a specific embodiment.

[0038] First, the power value of the coupled shortwave signal is used as the performance evaluation function of the algorithm. The so-called performance evaluation function refers to a mathematical expression used to quantitatively evaluate the quality of each candidate solution, which is an important basis for optimization search. In this application scenario, the power characteristics of the shortwave signal are closely related to the communication quality. Therefore, using its power value as an evaluation function can better guide the algorithm to search for the optimal solution that meets actual needs. Specifically, a weighted combination function containing multiple indicators such as signal power, intermodulation distortion, and spectrum utilization can be defined. By reasonably configuring the weight coefficient, the influence of each indicator is balanced, so that the evaluation function can more comprehensively reflect the comprehensive performance of the signal. At the same time, the design of the evaluation function should take into account both computational efficiency and evaluation accuracy to avoid excessive complexity that reduces the efficiency of the algorithm search. Then, the adaptive segmented multi-objective differential evolution algorithm is initialized according to the preset initial algorithm parameters to obtain the initial population. The so-called initial population refers to a group of candidate solutions randomly generated when the algorithm starts, which is similar to the initial individual in biological evolution and is the starting point for optimization search. To generate the initial population, some key algorithm parameters need to be set in advance, including mutation operator, crossover operator, population size, maximum evolutionary generations, Pareto optimal solution set size, and number of adaptive segments. Among them, mutation operator and crossover operator are the core operations of differential evolution algorithm, corresponding to gene mutation and chromosome crossover in biological evolution respectively. By introducing random perturbations and information exchange, the population diversity is maintained and the search space is expanded. The population size determines the number of candidate solutions retained in each generation, which must be large enough to cover the solution space and appropriate to control the amount of calculation. The maximum evolutionary generations are used to limit the number of algorithm iterations and balance the convergence speed and solution accuracy. The Pareto optimal solution set is used to save the non-inferior solutions of multi-objective optimization, reflecting the trade-off between design goals. The number of adaptive segments is used to determine the dynamic partition granularity of the search space, implement the divide-and-conquer strategy, and improve the local search capability. The setting of the above parameters needs to comprehensively consider factors such as the scale, complexity, and accuracy requirements of the problem. Usually, some empirical rules and theoretical analysis can be referred to, and reasonable values ​​can be selected in combination with simulation experiments.

[0039] After determining the evaluation function and initial parameters, the algorithm can randomly generate the initial population. Specifically, for each adjustment parameter to be optimized, random sampling is performed within its definition domain according to uniform distribution or Gaussian distribution to form a chromosome encoding. The so-called chromosome encoding refers to the use of a specific data structure to represent the potential solution of the problem, such as a binary string, a real number vector, etc. Then, all chromosomes are combined into a population matrix, where each row corresponds to an individual and each column corresponds to an optimization variable. The number of rows is equal to the number of populations, and the number of columns is equal to the total number of parameters to be optimized. In this way, the initial population contains a series of randomly generated adjustment parameter combinations, which provides a diverse starting point for subsequent evolutionary search, is conducive to global optimization, and avoids premature convergence to local extremes.

[0040] Based on the above embodiment, as an optional embodiment, the adaptive segmented multi-objective differential evolution algorithm is initialized according to preset initial algorithm parameters to obtain an initial population, including: The initial population is randomly generated according to the preset search space dimension and the preset upper and lower bounds of the decision variables.

[0041] Specifically, the initial population is randomly generated according to the preset search space dimension and the upper and lower bounds of the decision variables. The search space dimension mentioned here refers to the number of parameters involved in the optimization problem, that is, the number of decision variables to be optimized. In the amplitude and phase control of shortwave signals, the search space usually includes parameters such as frequency correction values, amplitude control voltages, and phase adjustment values ​​of multiple frequency points, so its dimension is relatively high, generally three times the number of coupling frequency points. The size of the search space dimension determines the complexity of the optimization problem and the number of individuals required for the initial population. Generally speaking, the higher the dimension and the more complex the problem, the larger the size of the initial population should be to provide sufficient genetic diversity and cover as wide a solution space as possible. At the same time, the population size should not be too large to avoid wasting computing resources and slowing down the convergence speed. Therefore, it is necessary to weigh the algorithm performance and computing cost according to the characteristics of the specific problem and select an appropriate initial population size.

[0042] After determining the population size, it is also necessary to randomly generate an initial value for each dimension of each individual based on the upper and lower bounds of the decision variables. The upper and lower bounds of the decision variables here refer to the value range of each parameter to be optimized, that is, the boundary of the parameter space. In shortwave signal optimization, different types of parameters often have different value ranges, such as the frequency correction value is generally between -1 MHz and 1 MHz, the amplitude control voltage is generally between 0 V and 10 V, and the phase adjustment value is generally between 0° and 360°. These boundary conditions reflect the physical constraints and engineering limitations of the actual system. Any parameter value beyond the boundary is invalid or infeasible. Therefore, when initializing the population, the upper and lower bounds of each parameter must be strictly followed to ensure that all individuals are in the feasible solution space to avoid out-of-bounds or outliers.

[0043] S202, dividing the search space of the coupled shortwave signal into a corresponding number of sub-regions according to the number of adaptive segments, and performing optimization processing in each sub-region to obtain the optimal solution in each sub-region; Specifically, in order to further improve the efficiency and accuracy of the optimization search, after generating the initial population, it is necessary to equally divide the search space of the coupled shortwave signal according to the number of adaptive segments, and perform local optimization in each sub-region to obtain the respective optimal solutions. This approach implements a divide-and-conquer strategy by decomposing a large-scale search problem into multiple small-scale sub-problems, which can effectively overcome the computational difficulties of high-dimensional complex problems and improve the convergence speed and solution quality of the algorithm. The principle and implementation method of this step are described in detail below in conjunction with a specific embodiment.

[0044] First, according to the preset number of adaptive segments, the search space of the coupled shortwave signal is divided into a corresponding number of sub-regions. The search space mentioned here refers to the multidimensional parameter space composed of the value range of the shortwave signal adjustment parameters, such as the definition domain of the frequency correction parameter, the phase adjustment parameter, the amplitude control voltage, etc. Since these parameters often have a large span and complex changes, it is very difficult to search for the optimal solution directly in the entire domain, the amount of calculation is large, the time is long, and it is easy to fall into local extreme values. For this reason, the search space segmentation method can be used to divide the original space into several non-overlapping subspaces, each subspace corresponds to a parameter sub-region. The segmentation granularity can be controlled by the number of adaptive segments, which is pre-set according to factors such as problem complexity and computing resources, and determines the number of sub-regions. A larger number of segments means a finer-grained space division, a larger number of sub-regions, and a smaller range of each sub-region. A smaller number of segments means a coarse-grained division, a smaller number of sub-regions, and a larger range of each sub-region. Generally, for high-dimensional and strongly nonlinear problems, appropriately increasing the number of segments is conducive to simplifying the local search complexity and accelerating the convergence speed. For low-dimensional and weakly nonlinear problems, the number of segments can be reduced to avoid excessive segmentation that leads to reduced search efficiency. In addition, the selection of the number of segments must also take into account the limitations of hardware conditions, such as memory size, parallel computing capabilities, etc., to balance the time and space overhead.

[0045] After determining the number of segments, the search space can be divided equally. Taking the two-dimensional parameter space as an example, the value range of each dimension can be divided into several sub-intervals with equal distances, and then all sub-intervals are combined by Cartesian product to form a series of rectangular sub-regions, each of which corresponds to an independent local search problem. For higher-dimensional parameter spaces, similar division methods can be used to obtain sub-regions in the shape of hyperrectangles or hyperspheres. It should be noted that when dividing, the size of the sub-regions should be kept as uniform as possible to avoid the occurrence of singular regions that are too large or too small, so as not to affect the search efficiency and fairness. At the same time, for different parameter dimensions, the division granularity can be different, and can be adaptively adjusted according to its value range, change characteristics, etc., such as fine-grained division for parameters that change dramatically, and coarse-grained division for parameters that change slowly, so as to further optimize the divide-and-conquer strategy. Then, local optimization processing is performed independently in each sub-region to obtain the optimal solution in the sub-region. Specifically, the individuals belonging to the sub-region in the initial population are selected to form a sub-population as the starting point of the local search. Then, the mutation, crossover, and selection operations of the differential evolution algorithm are used to iteratively optimize the subpopulation, evaluate the fitness of each individual according to the performance evaluation function, eliminate the inferior solution, retain the superior solution, and realize the evolution of the subpopulation. During the iteration process, the algorithm parameters can be adaptively adjusted according to the characteristics of the sub-region, such as variable step length, crossover probability, etc., to balance the local search ability and the global development ability. When the termination conditions such as the maximum evolutionary generation or the performance improvement are met, the individual with the best performance in the sub-region can be output as the optimal solution for the region. Since the searches of each sub-region are independent of each other, they can be performed simultaneously in parallel computing, thereby significantly improving the efficiency of the algorithm. In addition, some local optimization strategies, such as pattern search and taboo search, can be introduced in the search process to further enhance the local solution ability and improve the accuracy of the solution.

[0046] After all sub-regions have completed local optimization, a set of local optimal solutions can be obtained, which represent the optimal parameter combinations in their respective sub-regions. These local optimal solutions usually have good local performance, but they are not necessarily global optimal, so global comprehensive evaluation and optimization are required in the next step. However, since each local optimal solution is searched within a small range, the difficulty and amount of calculation are greatly reduced, and different sub-regions can be searched in parallel, which greatly improves the overall efficiency.

[0047] Based on the above embodiment, as an optional embodiment, according to the number of adaptive segments, the search space of the coupled shortwave signal is equally divided into a corresponding number of sub-areas, including: According to the number of adaptive segments, the search space of the coupled shortwave signal is equally divided into a corresponding number of sub-areas. The search space is a 3N-dimensional hyper-rectangular area, where N is the number of frequency points of the coupled shortwave signal, and each dimension corresponds to the frequency correction value, amplitude control voltage and phase adjustment value parameters of a frequency point.

[0048] Specifically, according to the preset number of adaptive segments, the search space of the coupled shortwave signal is equally divided into a corresponding number of sub-regions. The number of adaptive segments mentioned here refers to the granularity of space division automatically determined according to factors such as the scale and complexity of the optimization problem, and its value is closely related to the size and dimension of the search space. Generally speaking, the larger the search space and the more parameter dimensions it contains, the larger the number of segments should be and the finer the granularity of division should be; conversely, the smaller the search space and the fewer parameter dimensions it contains, the smaller the number of segments should be and the coarser the granularity of division should be. This is because a larger search space usually means stronger parameter coupling and more complex nonlinear relationships, requiring more fine-grained sub-regions for key research; while a smaller search space is relatively simple, and a coarser division can meet the optimization requirements and avoid wasting computing resources. Therefore, the core of adaptive segmentation is to automatically adjust the space division strategy according to the characteristics of the specific problem to strike a balance between optimization effect and computational cost.

[0049] Specifically for the 3N-dimensional hyper-rectangular search space, a grid partitioning method can be used to divide each dimension into several intervals at equal distances according to the number of segments, and then all the intervals are arranged and combined to form a series of hyper-rectangular sub-areas. For example, if the shortwave signal contains 5 frequency points and each frequency point has 3 parameters, the search space is a 15-dimensional hyper-rectangular body; if the number of segments is set to 3, each dimension is divided into 3 intervals, and the entire space is divided into 3^15 sub-areas, each of which corresponds to an independent parameter combination. This division method is intuitive and easy to implement, and can fully cover the entire parameter space without missing important parameter combinations. At the same time, since the size of each sub-area is equal, the computational amount of local search is basically balanced, which is conducive to parallel optimization and load balancing. It should be noted that in actual division, the interval boundaries should be reasonably set according to the value range and resolution of each parameter to avoid the appearance of singular sub-areas that are too large or too small, which affects the search efficiency and optimization effect. In addition, for different types of parameters, such as continuous parameters and discrete parameters, the division method should also be different to make full use of the characteristics of the parameters and improve the performance of the algorithm.

[0050] After completing the search space division, local optimization can be performed independently in each sub-region to search for the optimal parameter combination in the region. The divide-and-conquer strategy adopted here is to decompose the large-scale global search problem into multiple small-scale local search problems, and then break them one by one. Finally, the optimization results of each sub-region are combined to obtain the global optimal solution. This method can effectively reduce the complexity of the optimization problem, reduce the amount of calculation, and improve the search efficiency. At the same time, since the search of each sub-region is relatively independent, technical means such as parallel computing can be used to give full play to the performance advantages of modern computers and further accelerate the optimization process. In each sub-region, according to the characteristics of the specific problem, appropriate optimization algorithms can be flexibly selected, such as heuristic algorithms such as differential evolution, particle swarm optimization, pattern search, or other traditional numerical optimization methods, to achieve fast and accurate local optimization. When all sub-regions have completed the search, their optimal solutions are compared and combined, the current global optimal solution is updated, and the next search strategy is adjusted accordingly until the global convergence condition is met.

[0051] Based on the above embodiment, as an optional embodiment, performing optimization processing in each sub-region to obtain the optimal solution in each sub-region includes: S301, randomly generating a sub-region initial group in each sub-region, the number of individuals in each sub-region initial group is a preset sub-region group size, the encoding of the individual is a set of real number vectors, and the encoding of the individual represents the frequency correction value, amplitude control voltage and phase adjustment value parameters of each frequency point of the coupled shortwave signal; Specifically, in order to perform efficient and accurate local optimization in each sub-region and obtain the optimal parameter combination, it is necessary to randomly generate an initial group in each sub-region as the starting point of the local search. The sub-region initial group mentioned here refers to a group of candidate solutions generated by random sampling in each parameter subspace obtained by division, which together constitute the initial search point set of the sub-region. Compared with the global search, the local search in the sub-region usually involves lower parameter dimensions and a smaller range of values, so the size of its initial group is also reduced accordingly, which is usually determined by the preset sub-region group size. Reasonable setting of the sub-region group size is of great significance for balancing the depth and breadth of the local search and improving the convergence speed and the quality of the solution. The principle and implementation method of this step are described in detail below in conjunction with specific embodiments.

[0052] First, in each sub-region, an initial group is randomly generated according to the preset sub-region group size. The sub-region group size here is a pre-set parameter used to control the number of initial solutions in each sub-region. Compared with global search, local search problems are smaller in scale and less complex, so they require fewer initial solutions. Usually, the sub-region group size is between 20 and 100, and is adjusted according to factors such as the dimension of the local problem and the complexity of the objective function. A larger group size means more initial search points, which is conducive to expanding the search range and improving the global development ability; while a smaller group size means a more concentrated initial distribution, which is conducive to accelerating local convergence and improving optimization efficiency. Therefore, it is necessary to balance exploration and utilization according to the characteristics of the specific problem and actual needs, and select an appropriate sub-region group size.

[0053] After determining the size of the population, random sampling can be performed in each sub-region to generate a corresponding number of initial individuals. The individual here refers to a candidate solution in the sub-region, which is represented by a set of real number codes, and each real number corresponds to a parameter to be optimized. In shortwave signal optimization, each individual usually contains parameters such as frequency correction values, amplitude control voltages, and phase adjustment values ​​for multiple frequency points, which together determine a complete set of signal control schemes. The individual code is in the form of a real number vector, and the value range of each component is determined by the upper and lower bounds of the corresponding parameter. Compared with other coding methods such as binary and integer, real number coding has the advantages of strong expressiveness, high precision, and easy operation, so it has been widely used in continuous parameter optimization problems.

[0054] When generating individuals, it is necessary to strictly follow the boundary constraints of each parameter to ensure that the values ​​of all components are within the corresponding sub-intervals. This can be achieved by performing uniform random sampling between the upper and lower bounds of each parameter, that is, independently generating a random number that obeys a uniform distribution in each dimension as the value of that dimension. Since the value range of each parameter is limited to the corresponding sub-interval, the parameter combination obtained by sampling must be within the corresponding hyper-rectangular sub-region, meeting the requirements of segmented search. Through multiple independent sampling, a set of initial solutions randomly distributed in the sub-region can be obtained to form the initial population of the sub-region.

[0055] S302, decoding each individual into a set of control parameters of coupled shortwave signals, substituting them into a preset shortwave communication system simulation model for evaluation, the model output includes performance indicators of coupled shortwave signals, and comparing the performance indicators with preset multi-objective requirements to obtain a fitness value; Specifically, each individual is decoded into a set of control parameters of the coupled shortwave signal. The control parameters here refer to a series of physical quantities such as frequency correction value, amplitude control voltage, phase adjustment value, etc., which together determine the actual transmission effect of the coupled shortwave signal. Since the individual is encoded with real numbers, each component directly corresponds to a continuous control variable, so the decoding process is a simple mapping relationship, and each element in the individual vector is assigned to the corresponding control parameter in sequence. For example, if the individual code is [0.2, 5.1, 1.3, ..., -0.8, 6.5, 2.7], the control parameters obtained by decoding are: frequency correction value 1 is 0.2, amplitude control voltage 1 is 5.1, phase adjustment value 1 is 1.3, ..., frequency correction value N is -0.8, amplitude control voltage N is 6.5, and phase adjustment value N is 2.7. This decoding method is intuitive and easy to implement, and ensures a one-to-one correspondence between individual codes and control parameters, avoiding information loss or deformation during the mapping process. Then, the decoded control parameters are substituted into the preset shortwave communication system simulation model for performance evaluation. The simulation model here is a computer program that abstracts and mathematically describes the actual shortwave system based on physical principles and engineering experience. It can receive a set of control parameters as input, simulate the operating state of the system under the parameter configuration, and output a series of indicators reflecting the system performance, such as signal-to-noise ratio, bit error rate, channel capacity, etc. Simulation evaluation avoids the high cost, long cycle, and difficulty in repeating of physical tests, and provides an efficient and flexible test platform for parameter optimization. At the same time, the simulation model can flexibly configure the various modules and parameters of the system according to design requirements and engineering conditions, so that the evaluation results are closer to reality, and provide reliable decision support for optimization control. When conducting simulation evaluation, it is necessary to pay attention to calibrating and verifying model parameters, controlling simulation accuracy and speed, and ensuring the credibility and real-time nature of the evaluation results.

[0056] After obtaining the performance indicators of the simulation output, it is necessary to compare them with the preset multi-objective requirements to judge the quality of the current solution. The multi-objective requirements here refer to the ideal values ​​or expected ranges set for the various performance indicators of the coupled shortwave signal, such as requiring the bit error rate to be lower than 10^-6, the signal-to-noise ratio to be higher than 20dB, and the anti-interference ability to be stronger than 80dB. These requirements often come from actual engineering needs and technical indicators, reflecting the basic demands for the quality of shortwave communications. Since the actual system often needs to take into account multiple performance goals, and there may be contradictions or constraints between different goals, it is necessary to weigh and compromise multiple indicators and give a comprehensive evaluation standard, namely the fitness function. Common fitness functions include weighted summation method, product method, transformation method, etc. They describe the degree of closeness between the solution and the target from different angles. The larger (or smaller) the value, the better the comprehensive performance of the solution. For example, the product of multiple normalized indicators can be used as the fitness value. The closer the fitness is to 1, the better the indicators meet the requirements. If the weighted sum of the deviation of the indicators is used as the penalty term, the smaller the penalty term is, the lower the degree of constraint violation of the solution is, and the higher the overall quality is. It is necessary to select the appropriate fitness function form and adjust the weights of each parameter according to the characteristics and preferences of the specific problem to balance the relative importance of different goals.

[0057] After calculating the fitness value of each solution, all candidate solutions in the group can be sorted and screened according to the fitness, and the current optimal solution can be recorded. Since fitness is a comprehensive quality measure of the solution, the higher the fitness, the better the control parameters corresponding to the solution, and the better the improvement effect on system performance. Therefore, the solution with the highest fitness is the optimal parameter combination under the current conditions. It represents the best trade-off result for multiple objectives and is an important reference for subsequent optimization searches. At the same time, since fitness is directly linked to control effect, searching for the solution with the highest fitness is equivalent to finding the parameters with the best control performance, which meets the fundamental goal of parameter optimization.

[0058] S303, based on the fitness values ​​of the individuals, the initial population of the sub-region is non-dominated and sorted to obtain a series of Pareto front layers that do not dominate each other, and the layer with the highest fitness value is selected from the Pareto front layers as the elite solution, which is merged and updated with the external archive set, and the individuals in the archive that are dominated by the new elite solution are removed, and the non-dominated solutions are retained; Specifically, first, the current sub-region population is sorted non-dominated according to the fitness value. The so-called non-domination means that under multiple optimization objectives, there is no other solution that can simultaneously improve all the objectives of one of the solutions without reducing any objective. In short, if any objective of solution A is not better than solution B, and at least one objective is better than B, then A is said to dominate B; if solutions A and B cannot dominate each other in any objective, then A and B are said to be non-dominated. Based on this criterion, the individuals in the population can be compared pairwise and divided into several levels according to the dominance relationship: the first level contains all solutions that are not dominated by any individual, which is called the current Pareto frontier; then new non-dominated solutions are found from the remaining individuals as the second level; this cycle is repeated until all individuals are assigned to a certain level. In the sorting process, individuals in the front layers often have more advantages in comprehensive performance and have better consideration and trade-offs for multiple objectives. Therefore, through non-dominated sorting, not only can the superior and inferior structures within the group be revealed, but also the distribution characteristics of the solutions in the target space can be intuitively displayed, providing an important reference for subsequent search and optimization. Then, from the obtained Pareto front layer, the layer with the highest fitness value is selected as the elite solution. Since the solutions in the first layer are not dominated by any other solutions in the group, they represent an optimal trade-off for the current multiple objectives and are the essence with the most outstanding comprehensive performance. Separating these elite individuals from the group in a timely manner can, on the one hand, avoid the destruction of genetic operators and ensure that excellent genes are not lost; on the other hand, they can continue to participate in and influence subsequent evolution through external archiving, reintroduction and other mechanisms, playing a stabilizing role as the "mainstay". It should be noted that although the solutions in the Pareto front are equivalent in the sense of non-domination, their fitness values ​​may not be exactly the same. In order to select the best from the best, it is usually necessary to compare their fitness values ​​and retain the group with the highest fitness as the elite, rather than simply treating all front solutions as the same. This can not only control the size of the elite and avoid excessive expansion of the archive, but also select the best from multiple equivalent optimal solutions to further improve the quality of the elite.

[0059] Finally, the selected elite solutions are merged and updated with the external archive set, and the old solutions that have been dominated by the new elites are eliminated, and the updated non-dominated solutions are retained as new archives. The external archive here is a global elite set independent of the evolutionary group, which is used to record all the optimal solutions found so far in real time. After each evolution, by absorbing new non-dominated elites and eliminating the original dominated solutions in the archive, the archive can always maintain the "survival of the fittest" of the current search process, so that the quality of its internal solutions is continuously improved, and the scale and distribution are continuously optimized. On the one hand, the continuous injection of fresh "blood" helps to expand the diversity of the archive, especially those beneficial genes that have been lost during the evolution process, which can be reintroduced through the archive to avoid premature convergence to the local optimum. On the other hand, timely removal of "backward molecules" in the archive helps to control the expansion of the archive, concentrate limited computing resources to search for more potential areas, and improve the efficiency of optimization. In addition, since the archive brings together the essence of different evolutionary stages, it can often more comprehensively portray the real Pareto frontier, reflect the target trade-off relationship in the design space, and provide more abundant candidate solutions for engineering decision-making. At the same time, superior archives can also be used as the initial population for secondary search, or used to guide the mutation of a new generation of populations, accelerating the convergence process of the algorithm.

[0060] S304, using a tournament selection operator based on the Pareto level and crowding distance, selecting a pair of individuals with a high Pareto level from the initial population of the sub-region as parent individuals, and executing a differential evolution operator on the parent individuals to generate new offspring individuals; Specifically, first, a number of individuals are randomly selected from the current sub-region population, and their advantages and disadvantages are compared in pairs in a binary tournament manner, and the winner becomes the candidate parent. The comparison of advantages and disadvantages here is mainly based on two indicators: Pareto level and crowding distance. Specifically, for any two individuals, their Pareto levels are compared first, and the one with the higher level wins first; if the levels are the same, their crowding distances are compared again, and the one with the larger distance wins first. The Pareto level reflects the convergence performance of the individual in the multi-objective space. The higher the level, the more likely it is that the individual is already at the current Pareto frontier, or at least closer to the real frontier than most individuals. Giving priority to high-level individuals helps to quickly guide the group to a favorable area close to the global optimum. The crowding distance mainly reflects the density of the area where the individual is located. In multi-objective optimization, the real Pareto frontier is often a continuous hypersurface. Ideally, the population should be distributed on this surface as evenly as possible, rather than over-crowding in certain areas. The larger the crowding distance of an individual, the lower the solution density of the area where it is located, and it contains more specific information. Prioritizing these "rare points" helps to expand the coverage of the population, explore new potential areas, and prevent the search from stagnating prematurely. Therefore, the selection based on the Pareto level and the crowding distance can effectively balance the algorithm's search for the optimal solution and the approximation to the entire frontier, ensuring the efficiency and robustness of the evolution process. Then, the differential evolution operator is executed on the selected pair of candidate parents to generate new offspring individuals. Differential evolution is a real-number coded mutation operator based on vector difference. Its basic idea is to use the vector difference between random individuals in the population to generate mutant individuals, thereby guiding the search to develop in the direction of continuously decreasing the value of the objective function. Specifically, for each target individual, several individuals are randomly selected from the population, and a mutation vector is constructed by linearly combining their encoding vectors. This mutation vector is then discretely recombined with the original individual to obtain a new test vector. Finally, it is determined whether to replace the original individual with the test vector according to the fitness level to complete a round of evolution. Among them, the generation strategy of the mutation vector has a greater impact on the performance of the algorithm. In order to better balance global development and local mining, the present invention adopts an adaptive weight differential evolution strategy, that is, dynamically adjusts the control parameters in the mutation formula according to the current iteration stage. In the early stage of evolution, a larger mutation weight and crossover probability are used to focus on a global rough search; in the later stage of evolution, the mutation amplitude is gradually reduced to increase the genetic opportunities of the original individuals, and the focus is shifted to the identified advantageous areas for fine development. This dynamic weight strategy helps the algorithm to adaptively adjust the evolutionary direction and step size at different stages, strike a balance between convergence speed and search breadth, and is more conducive to solving complex multimodal problems.

[0061] S305, iterate based on the new offspring individuals, and obtain the fitness improvement value of the latest offspring individuals and the corresponding parent individuals after each iteration. When the fitness improvement value remains unchanged for N generations, stop the iteration, output the optimal solution in the external archive as the optimal solution in the corresponding sub-region, and obtain the optimal solution in each sub-region.

[0062] Specifically, the present invention proposes an adaptive termination strategy based on stagnation of fitness improvement. Specifically, a stagnation generation threshold N is set. After each iteration, the fitness improvement between the current latest generation and the previous N generations is counted. If the fitness of the optimal solution of N consecutive generations remains unchanged, it is considered that the termination condition is met, and the algorithm will stop the evolution in the current sub-region, and directly output all non-dominated solutions in the external archive as the optimal parameters of the sub-region. The N value here can be adjusted according to factors such as the scale and complexity of the actual problem, and is usually between 5 and 20. The smaller the N value, the more sensitive the algorithm is to the judgment of the local optimum, and the more likely it is to stop searching prematurely; the larger the N value, the higher the tolerance of the algorithm, but unnecessary iteration waste may occur. Therefore, it is necessary to balance the accuracy of the solution and the efficiency of the algorithm, and select a suitable stagnation generation as the "sensitive factor" of evolution. If the performance of the optimal individuals of N consecutive generations has not been improved, it means that the algorithm has fully explored the optimization potential in the current subspace, and the benefits of continuing the search will be much lower than the cost. At this time, it is better to stop the loss in time and leave the limited computing resources to other sub-regions with more potential.

[0063] S203, the optimal solution in each sub-region is formed into a new global population, and the global population is used as the current initial population for a new round of adaptive segmented optimization. When the optimal solution of the current initial population meets the preset global convergence condition, the optimal solution of the current population is output as an adjustment parameter, and the algorithm is terminated; Specifically, first, the optimal solutions obtained by searching in each sub-region are combined into a new global population. The global population here refers to the population composed of the best individuals in all sub-regions, reflecting the best parameter combination in the current search stage. Compared with the initial population, the global population has been baptized by local optimization, and the individual quality is generally higher, and the diversity is also reduced. Through the recombination of local optimal solutions, the high-quality genes of each sub-region can be gathered together, providing a better starting point for subsequent global optimization. Specifically, according to the order of sub-region division, the optimal individuals of each sub-region can be copied to the corresponding positions of the global population in turn, forming a population matrix spliced ​​by local optimal solutions. This process can be automatically executed after each round of segmented search is completed without manual intervention. It should be noted that when forming a global population, it is necessary to ensure that there is no duplication between the optimal solutions of different sub-regions to avoid affecting the diversity of the population. At the same time, in order to leave room for subsequent optimization, some random perturbations can be appropriately introduced to generate several variant individuals near each local optimal solution, and join the global population together with the original optimal solution to maintain the exploration ability of the population.

[0064] Then, the global population formed is used as the current initial population for a new round of adaptive segmented optimization, and a new round of segmented search process is started. The so-called current initial population here refers to the initial population of the current iteration step, which is equivalent to taking the global population obtained in the previous round as the initial point again, and conducting a new round of local search and global reorganization on this basis. Since the initial population of this generation inherits the high-quality genes of the previous generation, the average fitness level is high, and the convergence speed will be greatly accelerated. At the same time, due to the introduction of random perturbations, the population diversity has also been maintained to a certain extent, which is conducive to jumping out of local extremes and evolving towards the global optimum. In the new round of segmented optimization, operations such as population division, local search, and global reorganization are exactly the same as before, except for the selection of the iterative starting point. It can be seen that through repeated updates of the global population and repeated refinements of local searches, the algorithm will continuously cycle between expanding the field of view and focusing on details, and gradually approach the global optimal solution. This layer-by-layer optimization strategy can effectively overcome the problems of traditional algorithms that are prone to local extremes and slow convergence, and has obvious performance advantages.

[0065] Finally, when the optimal individual of the current initial population meets the preset global convergence condition, it will be output as the final adjustment parameter and the optimization algorithm will be terminated. The global convergence condition here refers to a series of criteria to measure whether the current optimal solution is close enough to the true global optimal solution, such as optimal fitness, population diversity, number of iterations, etc. When these indicators reach the preset threshold or rate of change, it can be considered that the algorithm has basically converged, and the cost-effectiveness of continuing the search will be greatly reduced. At this time, the individual with the highest fitness in the current population can be selected, and its corresponding parameter combination can be output as the global optimal solution, and the iterative process is terminated.

[0066] S204: If the optimal solution of the current initial population does not satisfy the preset global convergence condition, the optimal solution of the current initial population is used as the current initial population of a new round of adaptive segmented optimization to start a new round of adaptive segmented optimization.

[0067] Specifically, the optimal individual of the current initial population is used as the starting point for a new round of adaptive segmented optimization, and a series of operations such as dynamic division of the search space, local optimization, and global reorganization are performed again. The dynamic division of the search space here refers to adaptively adjusting the size and distribution of each sub-region according to the position of the current optimal solution, so that it forms a finer grid near the optimal solution and a sparser grid in the area far away from the optimal solution. In this way, the algorithm can implement a fine search in the neighborhood of the optimal solution and an extensive search in the non-optimal area, while improving the convergence accuracy and taking into account the search efficiency. In each sub-region, heuristic algorithms such as differential evolution are still used for local optimization to search for new optimal solutions in their respective fields. When all sub-regions are updated, the new local optimal solution is reorganized into a new generation of global population, and based on this, the next round of segmented optimization is started.

[0068] It can be seen that this strategy of continuously transmitting the optimal solution and iterative segmented optimization is essentially a variable step-size, multi-scale optimization method. By dynamically adjusting the search granularity, we can not only continuously focus on details near the optimal solution, but also continuously expand our horizons in non-optimal areas, thereby maximizing the optimization potential. Each round of iteration is based on the optimal point of the previous round, and seeks improvements and breakthroughs on this basis, which not only makes full use of historical search information, but also provides a broad optimization space for finding new optimal solutions. As the number of iterations increases, the search focus will be increasingly concentrated on the surrounding areas of the global optimal point, and the search range and step-size will continue to shrink. The algorithm will eventually converge to the true global optimal point or its approximate point. This progressive multi-scale optimization can effectively jump out of the local extreme points that were trapped in the early stage, break through the "myopia" blind spot of the algorithm, and finally achieve optimization from coarse to fine, from local to overall.

[0069] It should be pointed out that in order to control the computational cost and avoid wasting time when the convergence condition is not met for many consecutive times, the maximum number of iterations can be set as the forced shutdown condition of the algorithm. When the number of iterations exceeds this upper limit, even if the solution is still not completely ideal, the search must be terminated and the current optimal result must be output. This measure ensures the quality of the basic solution while ensuring the computability of the algorithm within a limited time.

[0070] S103, adjusting the coupled shortwave signal based on the adjustment parameter to obtain a target shortwave signal; Specifically, in the segmented adaptive gain shortwave band amplitude phase control method proposed in the present invention, after the optimal adjustment parameters are calculated by the adaptive segmented multi-objective differential evolution algorithm, it is necessary to further adjust the coupled shortwave signal based on these parameters to obtain the optimized target shortwave signal. The so-called adjustment parameters refer to a set of optimal parameter values ​​searched for to optimize the quality of the shortwave signal, including frequency correction parameters, phase adjustment parameters, and amplitude control voltage, etc., which are used to guide signal pre-distortion. The purpose of this step is to apply pre-distortion compensation to the signal at the transmitting end to offset the distortion caused by factors such as power amplifier nonlinearity, thereby improving signal quality and communication performance. The principle and implementation process of this step are described in detail below in conjunction with specific embodiments.

[0071] First, the frequency of the coupled shortwave signal is corrected using the frequency correction parameter. The frequency correction parameter is a compensation parameter that offsets the signal frequency deviation by adjusting the local oscillator frequency, and is usually calculated from factors such as frequency error and frequency stretch coefficient. Specifically, frequency correction can be achieved through a digitally controlled oscillator and a mixer. By setting the output frequency of the digitally controlled oscillator according to the frequency correction parameter and mixing it with the coupled shortwave signal, the signal frequency can be accurately shifted to the target frequency point, completing the frequency correction and eliminating the distortion caused by the frequency deviation. Then, the phase adjustment parameter is used to adjust the phase of the frequency-corrected signal. The phase adjustment parameter is a compensation parameter that corrects the phase distortion by changing the phase relationship of the IQ two-way orthogonal signal, and is usually expressed by the phase rotation angle. In specific implementation, a digital orthogonal modulator architecture can be used. The frequency-corrected signal is mixed with the in-phase and quadrature local oscillator signals respectively to obtain the baseband IQ two-way signal, and then the phase rotation determined by the phase adjustment parameter is applied to it by the digital phase rotator, and finally re-modulated to the RF band to complete the phase correction and suppress the influence caused by the phase distortion.

[0072] Finally, the amplitude control voltage is used to adjust the amplitude of the phase-adjusted signal. The amplitude control voltage is a DC control voltage used to adjust the amplitude of the RF signal. It is converted by the DAC from the digital amplitude parameter, and its size determines the gain factor of the amplitude adjustment. In specific implementation, the signal amplitude can be adjusted by a voltage-controlled attenuator or a variable gain amplifier. The phase-adjusted signal is input to the attenuator or amplifier, and its gain is set according to the amplitude control voltage. The signal amplitude can be dynamically adjusted at the transmitter to achieve power pre-distortion compensation, suppress amplitude distortion, and optimize the dynamic range and power efficiency of the signal.

[0073] Based on the above embodiment, as an optional embodiment, adjusting the coupled shortwave signal based on the adjustment parameter to obtain the target shortwave signal includes: S301, performing frequency correction on the coupled shortwave signal according to a frequency correction parameter to obtain a first controlled shortwave signal; S302, performing phase adjustment on the first regulated shortwave signal according to the phase adjustment parameter to obtain a second regulated shortwave signal; S303, adjusting the amplitude of the second regulated shortwave signal according to the amplitude control voltage to obtain a target shortwave signal.

[0074] Specifically, in the segmented adaptive gain shortwave band amplitude phase control method proposed in the present invention, after the optimal adjustment parameters are calculated by the adaptive segmented multi-objective differential evolution algorithm, it is necessary to further fine-tune the coupled shortwave signal based on these parameters to obtain a target shortwave signal with excellent quality. This adjustment process is divided into three steps: frequency correction, phase adjustment, and amplitude adjustment. The principle and implementation method of each step are described in detail below in conjunction with a specific embodiment.

[0075] First, the frequency of the coupled shortwave signal is corrected according to the frequency correction parameters to obtain the first regulated shortwave signal. The purpose of frequency correction is to eliminate the frequency deviation in the signal so that it falls accurately on the specified frequency point, which is the basis for ensuring the communication quality. The frequency correction parameters are obtained by searching through the optimization algorithm and are used to quantitatively describe the frequency deviation of the signal, including frequency error and frequency stretch coefficient. In implementation, a digitally controlled oscillator (DCO) and a mixer can be used for frequency correction. According to the frequency correction parameters, the DCO is controlled to output a local oscillator signal with a fixed frequency difference from the coupled shortwave signal. After mixing the two, the frequency of the coupled shortwave signal can be shifted to the target frequency point, the frequency correction is completed, and the first regulated shortwave signal is output. Compared with the analog frequency synthesis scheme, the digital frequency correction based on DCO has the advantages of high control accuracy, convenient debugging, and good stability.

[0076] Then, the first regulated shortwave signal is phase-adjusted according to the phase adjustment parameter to obtain the second regulated shortwave signal. The purpose of phase adjustment is to compensate for the phase distortion introduced by the signal during transmission, improve the roundness and uniformity of the constellation diagram, and reduce the symbol error rate. The phase adjustment parameter is obtained by searching through an optimization algorithm, which is used to quantitatively describe the degree of phase distortion of the signal and is usually represented by a phase rotation angle. In implementation, an orthogonal modulator architecture can be used to perform phase adjustment using a digital phase rotator. First, the first regulated shortwave signal is mixed with two orthogonal carriers of in-phase and orthogonal phases to obtain two baseband IQ signals; then, the IQ signal is input into a digital phase rotator, and the rotation angle determined by the phase adjustment parameter is applied to complete the phase correction in the digital domain; finally, the corrected IQ signal is remodulated to the radio frequency to output the second regulated shortwave signal. Compared with the analog phase shifter solution, the digital phase rotator not only has high adjustment accuracy, but also does not introduce additional noise, and can obtain better signal quality.

[0077] Finally, the amplitude of the second regulated shortwave signal is adjusted according to the amplitude control voltage to obtain the target shortwave signal. The purpose of amplitude adjustment is to intelligently adjust the signal amplitude at the transmitting end according to the power demand of the signal, improve the power amplifier efficiency while improving the dynamic range of the signal, and achieve the effect of energy saving gain. The amplitude control voltage is a digital amplitude parameter obtained by searching the optimization algorithm, which is converted by DAC, and its size is proportional to the amplitude of the target signal. When implemented, the signal amplitude can be adjusted by a voltage controlled attenuator (VCA) or a variable gain amplifier (VGA). The second regulated shortwave signal is input into the VCA or VGA, and the amplitude control voltage is connected to its control end, so that the signal amplitude can change linearly with the control voltage. When the control voltage is equal to the value optimized by the algorithm, the target shortwave signal with ideal power is obtained. The voltage amplitude control method not only has a wide adjustment range and good continuity, but also has a fast response speed, and can realize real-time control of the signal amplitude.

[0078] S104, detecting whether the target shortwave signal meets a preset output condition. If the target shortwave signal meets the preset output condition, coupling the target shortwave signal to output a radio frequency output signal.

[0079] Specifically, in the segmented adaptive gain shortwave band amplitude phase control method proposed in the present invention, after the optimized target shortwave signal is obtained through the aforementioned steps, it is necessary to further detect whether it meets the preset output conditions to ensure the quality and safety of the output signal. The preset output conditions mentioned here refer to a set of judgment criteria pre-set according to factors such as the index requirements of the communication system and the working range of the equipment, which are used to evaluate whether the target shortwave signal can be used as a qualified RF output signal. Only when the target shortwave signal meets these conditions can it be coupled and output to form a final RF output signal for amplification by the power amplifier circuit and transmission through the antenna. The principle and implementation process of this step are described in detail below in conjunction with specific embodiments.

[0080] First, define a complete set of preset output conditions as the criteria for judging the target shortwave signal. These conditions usually include the following aspects: 1. Spectrum indicators: such as frequency accuracy, spectrum purity, spurious suppression, occupied bandwidth, etc., are used to measure whether the frequency domain characteristics of the signal meet the requirements of the communication system.

[0081] 2. Time domain indicators: such as amplitude uniformity, phase linearity, symbol error rate, etc., are used to evaluate whether the time domain waveform quality of the signal meets the standards.

[0082] 3. Power indicators: such as average output power, peak power, peak-to-average ratio, etc., are used to control the signal transmission power within a safe range to avoid harm to equipment and the environment.

[0083] 4. Dynamic range: such as input level range, output level range, etc., used to ensure that the signal is within the working range of the equipment and will not be too large or too small or distorted.

[0084] Depending on the application scenario, the preset output conditions can be a combination of the above indicators, or other specific indicators can be added. In short, the setting of these conditions should fully consider the performance requirements of the system and the working status of the equipment, ensuring both communication quality and equipment safety.

[0085] Then, the signal detection circuit is used to detect the target shortwave signal in real time, and the detection result is compared with the preset output condition. The signal detection circuit is composed of a series of special detection modules, which can accurately measure different indicators of the signal. For example, the spectrum indicator can be detected by a spectrum analyzer, the time domain indicator can be detected by an oscilloscope and a bit error meter, the power indicator can be detected by a power meter, and the dynamic range can be detected by a dynamic signal analyzer. The selection of these detection equipment should be based on the requirements of the preset output conditions, and the models with appropriate measurement accuracy and range level should be selected, and necessary calibration should be performed to ensure the accuracy and reliability of the detection results.

[0086] During the detection process, the target shortwave signal is connected to each detection module respectively to obtain its characteristic parameters such as spectrum, time domain, power, etc. in real time. These parameters are then compared with the preset thresholds to determine whether the output conditions are met. For example, whether the spurious component level of the detection signal is lower than the specified value, whether the occupied bandwidth is within the permitted range, whether the amplitude and phase errors are within the tolerance, whether the output power is appropriate, whether there is too large or too small or distortion clamping phenomenon, etc. Only when all indicators meet the standards, the target shortwave signal is considered to meet the output conditions and can be used as a qualified RF output signal.

[0087] Finally, for the target shortwave signal that meets the preset output conditions, the RF switch is connected to the coupling output circuit, and on the premise of ensuring impedance matching and signal integrity, it is coupled to the RF input end of the power amplifier circuit to finally form an RF output signal. At the same time, the qualified detection status is fed back to the control circuit for necessary status indication and recording. When the target shortwave signal does not meet the output conditions, the control circuit immediately blocks the RF switch and disconnects it from the coupling output circuit to prevent the unqualified signal from entering the power amplifier and being amplified. At the same time, an alarm is issued to prompt maintenance to ensure the safety of the equipment.

[0088] Please refer to Figure 3 The application also discloses an electronic device. Figure 3 The electronic device 300 may include: at least one processor 301, at least one network interface 304, a user interface 303, a memory 305, and at least one communication bus 302 or end-to-end wireless communication.

[0089] The communication bus 302 is used to realize the connection and communication between these components.

[0090] The user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0091] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0092] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field~Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0093] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read~Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may optionally be at least one storage system located away from the aforementioned processor 301. Refer to Figure 3 , the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module and an application based on a segmented adaptive gain short-wave band amplitude phase control method.

[0094] exist Figure 3 In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call the application program based on the segmented adaptive gain short-band amplitude phase control method stored in the memory 305. When executed by one or more processors 301, the electronic device 300 executes one or more methods in the above-mentioned embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited to the described action sequence, because according to the present application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application. In the above embodiments, the description of each embodiment has its own emphasis. For the parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0095] In the several implementations provided in this application, it should be understood that the disclosed system can be implemented in other ways. For example, the system embodiments described above are only schematic, such as the division of modules, which is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, indirect coupling or communication connection of systems or modules, which can be electrical or other forms.

[0096] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0097] The present application also provides a computer storage medium that can store multiple instructions, which are suitable for being loaded and executed by a processor as described above. Figure 1 The embodiment shown is based on the segmented adaptive gain short-waveband amplitude phase control method. The specific execution process can be found in Figure 1 The specific description of the illustrated embodiment will not be repeated here.

[0098] In addition, each functional module in each embodiment of the present application can be integrated into a processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The above integrated modules can be implemented in the form of hardware or software functional modules.

[0099] If the integrated module is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0100] The above are only exemplary embodiments of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification, those skilled in the art will easily think of other embodiments of the present disclosure.

[0101] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not described in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A short-band amplitude phase control device based on segmented adaptive gain, characterized in that: The device comprises: an N-way input coupling detection module, an information processing module, a control module and a radio frequency coupling module; The N-channel input coupling detection module is used to perform coupling detection processing on the N-channel input shortwave signals to obtain a coupled shortwave signal and a power value of the coupled shortwave signal; The information processing module is used to calculate adjustment parameters based on the coupled shortwave signal and the power value of the coupled shortwave signal through a preset adaptive segmented multi-objective differential evolution algorithm, and the adjustment parameters include: frequency correction parameters, phase adjustment parameters and amplitude control voltage; The control module is used to adjust the coupled shortwave signal based on the adjustment parameter to obtain a target shortwave signal; The radio frequency coupling module is used to detect whether the target shortwave signal meets a preset output condition, and if the target shortwave signal meets the preset output condition, couple the target shortwave signal to output a radio frequency output signal.

2. The device according to claim 1, characterized in that The control module includes: a frequency correction unit, a phase modulator and an amplitude adjustment unit; The frequency correction unit is used to perform frequency correction on the coupled shortwave signal according to the frequency correction parameter to obtain a first controlled shortwave signal; The phase modulator unit is used to perform phase adjustment on the first regulated shortwave signal according to the phase adjustment parameter to obtain a second regulated shortwave signal; The amplitude adjustment unit is used to adjust the amplitude of the second regulated shortwave signal according to the amplitude control voltage to obtain the target shortwave signal.

3. A method for short-band amplitude and phase control based on segmented adaptive gain, based on a device for short-band amplitude and phase control based on segmented adaptive gain according to any one of claims 1-2, characterized in that: The method comprises: Performing coupling detection processing on N input shortwave signals to obtain coupled shortwave signals and power values ​​of the coupled shortwave signals; Based on the coupled shortwave signal and the power value of the coupled shortwave signal, an adjustment parameter is calculated by a preset adaptive segmented multi-objective differential evolution algorithm, wherein the adjustment parameter includes: a frequency correction parameter, a phase adjustment parameter and an amplitude control voltage; Adjusting the coupled shortwave signal based on the adjustment parameter to obtain a target shortwave signal; It is detected whether the target shortwave signal meets a preset output condition. If the target shortwave signal meets the preset output condition, the target shortwave signal is coupled to output a radio frequency output signal.

4. The method according to claim 3, characterized in that The step of adjusting the coupled shortwave signal based on the adjustment parameter to obtain a target shortwave signal includes: Performing frequency correction on the coupled shortwave signal according to the frequency correction parameter to obtain a first regulated shortwave signal; Performing phase adjustment on the first regulated shortwave signal according to the phase adjustment parameter to obtain a second regulated shortwave signal; The amplitude of the second regulated shortwave signal is adjusted according to the amplitude control voltage to obtain the target shortwave signal.

5. The method according to claim 3, characterized in that: The adjusting parameters are calculated based on the coupled shortwave signal and the power value of the coupled shortwave signal by using a preset adaptive segmented multi-objective differential evolution algorithm, including: The power value of the coupled shortwave signal is used as a performance evaluation function, and the adaptive segmented multi-objective differential evolution algorithm is initialized according to preset initial algorithm parameters to obtain an initial population, wherein the initial algorithm parameters include a mutation operator, a crossover operator, a population number, a maximum evolutionary generation, a Pareto optimal solution set size, and a number of adaptive segments; According to the number of adaptive segments, the search space of the coupled shortwave signal is equally divided into a corresponding number of sub-regions, and an optimization process is performed in each of the sub-regions to obtain an optimal solution in each of the sub-regions; The optimal solution in each of the sub-regions is formed into a new global population, and the global population is used as the current initial population for a new round of adaptive segmented optimization. When the optimal solution of the current initial population meets the preset global convergence condition, the optimal solution of the current population is output as the adjustment parameter, and the algorithm is terminated; If the optimal solution of the current initial population does not meet the preset global convergence condition, the optimal solution of the current initial population is used as the current initial population of a new round of adaptive segmented optimization to start a new round of adaptive segmented optimization.

6. The method according to claim 5, characterized in that The step of equally dividing the search space of the coupled shortwave signal into a corresponding number of sub-areas according to the number of adaptive segments includes: According to the number of adaptive segments, the search space of the coupled shortwave signal is equally divided into a corresponding number of sub-areas, and the search space is a 3N-dimensional super-rectangular area, where N is the number of frequency points of the coupled shortwave signal, and each dimension corresponds to a frequency correction value, amplitude control voltage and phase adjustment value parameters of a frequency point.

7. The method according to claim 5, characterized in that The performing optimization processing in each of the sub-regions to obtain the optimal solution in each of the sub-regions includes: A sub-region initial group is randomly generated in each of the sub-regions, the number of individuals in each of the sub-region initial groups is a preset sub-region group size, the encoding of the individuals is a set of real number vectors, and the encoding of the individuals represents the frequency correction value, amplitude control voltage and phase adjustment value parameters of each frequency point of the coupled shortwave signal; Decoding each of the individuals into a set of control parameters for coupled shortwave signals, substituting them into a preset shortwave communication system simulation model for evaluation, the model output includes performance indicators of coupled shortwave signals, and comparing the performance indicators with preset multi-objective requirements to obtain a fitness value; Based on the fitness values ​​of the individuals, the initial population of the sub-region is non-dominated and sorted to obtain a series of non-dominated Pareto front layers, and the layer with the highest fitness value is selected from the Pareto front layers as the elite solution, and the layers are merged and updated with the external archive set, and the individuals dominated by the new elite solution in the archive are removed, and the non-dominated solutions are retained; A tournament selection operator based on the Pareto level and crowding distance is used to select a pair of individuals with a high Pareto level from the initial population of the sub-region as parent individuals, and a differential evolution operator is performed on the parent individuals to generate new offspring individuals; Iterate based on the new offspring individuals, and obtain the fitness improvement value of the latest offspring individuals and the corresponding parent individuals after each iteration. When the fitness improvement value remains unchanged for N generations, stop the iteration, output the optimal solution in the external archive as the optimal solution in the corresponding sub-region, and obtain the optimal solution in each of the sub-regions.

8. The method according to claim 5, characterized in that Initializing the adaptive segmented multi-objective differential evolution algorithm according to preset initial algorithm parameters to obtain an initial population includes: The initial population is randomly generated according to a preset search space dimension and preset upper and lower bounds of the decision variable.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the method according to any one of claims 3 to 8.

10. An electronic device, characterized in that: It includes a processor, a memory and a transceiver, the memory is used to store instructions, the transceiver is used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method according to any one of claims 3 to 8.

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