A method, apparatus, medium, and product for radio frequency signal debugging based on metaheuristic algorithms.

Abstract

This application discloses a radio frequency (RF) signal debugging method, apparatus, medium, and product based on a metaheuristic algorithm, relating to the field of multi-coupler particle accelerator debugging. The method includes: acquiring the current phase of the RF signal output by the low-level control system and the current gain of each channel of the low-level control system; optimizing the current phase and current gain using a metaheuristic algorithm to determine the optimal phase and optimal gain; and debugging the RF signal based on the optimal phase and optimal gain. This application can achieve consistent output power on each coupler and minimize reflected power, ensuring accurate RF signal debugging and overall system stability.

Description

Technical Field

[0001] This application relates to the field of multi-coupler particle accelerator debugging, and in particular to a radio frequency signal debugging method, apparatus, medium and product based on metaheuristic algorithms. Background Technology

[0002] In the AB-BNCT (accelerator-based boron neutron capture therapy) device, the phase and gain tuning of the low-level control system (LLRF) of the multi-coupler accelerator is a complex process, especially in medical devices where the stability and accuracy requirements of the equipment are extremely high. Traditional manual tuning methods are time-consuming and difficult to guarantee optimal output, struggle to adapt to the mutual influence between multiple couplers, and are prone to excessive reflected power, affecting system stability. Summary of the Invention

[0003] The purpose of this application is to provide a radio frequency signal debugging method, device, medium and product based on metaheuristic algorithms. By introducing metaheuristic algorithms, more efficient and stable radio frequency signal debugging can be achieved.

[0004] To achieve the above objectives, this application provides the following solution:

[0005] Firstly, this application provides a radio frequency signal debugging method based on a metaheuristic algorithm, including:

[0006] Obtain the current phase of the radio frequency signal output by the low-level control system and the current gain of each channel of the low-level control system;

[0007] A metaheuristic algorithm is used to optimize the current phase and current gain to determine the optimal phase and optimal gain;

[0008] The radio frequency signal is debugged based on the optimal phase and the optimal gain.

[0009] Secondly, this application provides a radio frequency signal debugging device based on a metaheuristic algorithm, comprising:

[0010] The acquisition module is used to acquire the current phase of the radio frequency signal output by the low-level control system and the current gain of each channel of the low-level control system.

[0011] The optimization module is used to optimize the current phase and current gain using a metaheuristic algorithm to determine the optimal phase and optimal gain.

[0012] The debugging module is used to debug the radio frequency signal based on the optimal phase and the optimal gain.

[0013] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described radio frequency signal debugging method based on metaheuristic algorithms.

[0014] Fourthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the above-described radio frequency signal debugging method based on metaheuristic algorithms.

[0015] According to the specific embodiments provided in this application, this application has the following technical effects:

[0016] This application provides a radio frequency signal debugging method, device, medium, and product based on metaheuristic algorithms. The metaheuristic algorithm is used to automatically adjust the phase of the radio frequency signal output by the low-level control system and the gain of each channel of the low-level control system to achieve the consistency of output power on each coupler and minimize reflected power, thereby ensuring accurate debugging of radio frequency signals and overall system stability. It is particularly suitable for medical equipment with extremely high stability requirements. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of the AB-BNCT cavity low-level system.

[0019] Figure 2 A flowchart illustrating a radio frequency signal debugging method based on a metaheuristic algorithm provided in an embodiment of this application;

[0020] Figure 3 This is a schematic diagram illustrating the workflow of a radio frequency signal debugging method based on a meta-heuristic algorithm provided in an embodiment of this application. Detailed Implementation

[0021] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0023] The RF signal debugging method based on metaheuristic algorithms provided in this application is applied to AB-BNCT cavity low-level systems, such as... Figure 1 As shown, the AB-BNCT cavity low-level system includes a signal generator, a low-level control system, a solid-state power source, a multi-coupler, and a resonant cavity connected in sequence.

[0024] The signal generator first produces a stable radio frequency (RF) signal and inputs it to the low-level control system as a clock and RF signal reference. The low-level control system precisely controls and adjusts the amplitude and phase of the output RF signal to generate an RF signal that meets the requirements of the resonant cavity. The modulated signal is amplified by a solid-state power source to reach the required power level. The amplified signal is transmitted to the resonant cavity through a multi-coupler. The multi-coupler simultaneously collects the incident power and reflected power signals and feeds these signals back to the low-level control system for real-time monitoring to ensure stable system operation. After receiving power, the resonant cavity generates an accelerating electric field within it for particle acceleration. The signal within the cavity (cavity signal) reflects the actual operating state of the accelerating electric field and is also fed back to the low-level control system. The amplitude and phase of the RF signal are adjusted according to the debugging method provided in this application to achieve precise closed-loop control.

[0025] In one exemplary embodiment, such as Figures 2-3 As shown, a radio frequency signal debugging method based on a metaheuristic algorithm is provided. This method is executed by a computer device, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is described using a server as an example, and includes the following steps S1 to S3. Wherein:

[0026] S1: Obtain the current phase of the radio frequency signal output by the low-level control system and the current gain of each channel of the low-level control system.

[0027] S2: Use a metaheuristic algorithm to optimize the current phase and current gain to determine the optimal phase and optimal gain.

[0028] Metaheuristic algorithms are a class of algorithms that solve complex optimization problems through heuristics and heuristic search strategies. They simulate evolutionary processes, group behaviors, or physical processes in nature, performing global searches and local optimizations in a large-scale search space to find the optimal combination of parameters and ensure the best performance of the system under multi-coupler conditions.

[0029] Using metaheuristic algorithms (such as genetic algorithms) to analyze the phase (φ) i ) and gain (G i The parameters are optimized to maximize output power consistency and minimize reflected power. The specific process is as follows:

[0030] (1) Initial population generation:

[0031] Parameter representation: Each individual (solution) consists of phase and gain, i.e., Individual = {φ1, φ2, ..., φ...} n ,G1,G2,...,G n}\text{Individual}=\{\phi1,\phi2,...,\phi n ,G1,G2,...,G n \}Individual={φ1,φ2,...,φ n ,G1,G2,...,G n}, where n is the number of couplers.

[0032] Initial generation: A set of individuals satisfying the parameter range is randomly generated to form the initial population. The phase is typically in the range of 0°-360°, and the gain is within the gain range allowed by the solid-state power source.

[0033] (2) Fitness function design:

[0034] Target definition:

[0035] Maximizing output power consistency: the output power P of each coupler out_i It should be as close as possible to the average output power.

[0036] Minimize reflected power: Total reflected power It should be as small as possible.

[0037] Fitness Function:

[0038]

[0039] Where F represents the fitness function, P out_i Let i be the output power of the i-th coupler. For average output power, P ref_i Let be the reflected power of the i-th coupler, n be the number of couplers, and α and β be weighting coefficients used to balance the influence of output power consistency and reflected power on fitness. The negative sign indicates that the fitness function is a minimization problem (i.e., the smaller the better).

[0040] (3) Fitness assessment:

[0041] Parameter application: Apply the phase and gain of each individual parameter to the low-level control system.

[0042] Signal measurement: The following data are acquired through the feedback loop:

[0043] Incident power P in_i Power fed into the coupler;

[0044] Reflected power P ref_i Power reflected back from the coupler;

[0045] Output power P out_i :P out_i =P in_i -P ref_i .

[0046] Fitness calculation: Using the fitness function described above, calculate the fitness value for each individual.

[0047] (4) Genetic manipulation:

[0048] 1) Selection:

[0049] Method: Use roulette wheel selection or tournament selection to select individuals with high fitness from the current population as parents.

[0050] Objective: To ensure that individuals with better performance have a higher probability of passing on their genes to the next generation.

[0051] 2) Crossover:

[0052] Method: The phase and gain parameters of the parent individual are cross-referenced to generate the offspring individual.

[0053] Operation: For phase, analog binary crossover (SBX) can be used; for gain, single-point or multi-point crossover can be used.

[0054] Phase Crossover: λ is a random number, with a value in the range of 0 to 1.

[0055] Gain Crossover: μ is a random number, with a value in the range of 0 to 1.

[0056] 3) Mutation:

[0057] Method: Perform low-probability random mutations on the phase and gain of offspring individuals.

[0058] operate:

[0059] Phase variation: δ φThe random number that satisfies a normal distribution represents the phase fine-tuning amount.

[0060] Gain variation: δ G The random number that satisfies a normal distribution represents the gain adjustment amount.

[0061] Constraint handling: The mutated parameters must be within the allowable range; if they exceed the range, they must be corrected.

[0062] (5) Next-generation assessment:

[0063] Repeated fitness assessment: The process of repeating fitness calculation for newly generated offspring individuals.

[0064] (6) Iterative loop:

[0065] Looping execution: Repeatedly perform selection, crossover, mutation, and evaluation to form multiple generations of iteration.

[0066] Termination conditions:

[0067] 1) Fitness convergence: The algorithm is considered to have converged when the fitness no longer increases significantly over several consecutive generations.

[0068] 2) Maximum number of iterations: Reach the preset maximum number of generations (e.g., 100 generations).

[0069] 3) Performance indicators meet: Output power consistency and reflection power meet preset requirements.

[0070] (7) Application of optimal parameters:

[0071] Selecting the optimal individual: Select the individual with the highest fitness from the final population, and its phase and gain are the optimization results.

[0072] S3: Adjust the radio frequency signal based on the optimal phase and the optimal gain.

[0073] The process after step S3 also includes:

[0074] The output and reflected power of each coupler are monitored in real time, and genetic optimization is repeatedly performed until the low-level control system reaches its optimal steady state, meeting the high stability and high precision requirements of medical equipment. The collected data is stored at the ARM terminal of the FPGA for further analysis and adjustment.

[0075] Compared with the prior art, this application has the following significant advantages:

[0076] (1) High stability: In view of the high requirements for equipment stability of medical devices, the meta-heuristic algorithm optimization combined with feedback loop in this application ensures the precise adjustment of multi-coupler particle accelerator and the overall stability of the system.

[0077] (2) High efficiency: Through the global optimization capability of the meta-heuristic algorithm, the consistency of the coupler output power is achieved, the reflected power is reduced to the minimum, and the overall efficiency of the system is improved.

[0078] (3) High degree of automation: The system can automatically adapt to different operating conditions, reducing the complexity of manual debugging and ensuring stable performance output.

[0079] (4) Real-time data processing: FPGA is used for high-speed data acquisition and storage, ensuring the system's real-time response and adjustment capabilities under dynamic conditions.

[0080] Based on the same inventive concept, this application also provides an apparatus for implementing the above-mentioned radio frequency signal debugging method based on metaheuristic algorithms. The solution provided by this apparatus is similar to the implementation scheme described in the above method. Therefore, the specific limitations of one or more embodiments of the radio frequency signal debugging apparatus based on metaheuristic algorithms provided below can be found in the limitations of the radio frequency signal debugging method based on metaheuristic algorithms described above, and will not be repeated here.

[0081] In one exemplary embodiment, a radio frequency signal debugging device based on a metaheuristic algorithm is provided, comprising:

[0082] The acquisition module is used to obtain the current phase of the radio frequency signal output by the low-level control system and the current gain of each channel of the low-level control system.

[0083] The optimization module is used to optimize the current phase and current gain using a metaheuristic algorithm to determine the optimal phase and optimal gain.

[0084] The debugging module is used to debug the radio frequency signal based on the optimal phase and the optimal gain.

[0085] As an optional implementation, the debugging module includes:

[0086] A variable phase shifter is used to adjust the current phase of the radio frequency signal output by the low-level control system.

[0087] The phase of the radio frequency signal is adjusted by controlling a variable phase shifter. The specific operation is as follows:

[0088] (1) Signal reception: Receive phase adjustment instructions to optimize the module, which contain the phase angle that needs to be adjusted.

[0089] (2) Control execution: Use a digital signal processor (DSP) or field programmable gate array (FPGA) to generate precise control signals and send them to the variable phase shifter.

[0090] (3) Phase adjustment: The variable phase shifter changes its phase shift according to the control signal, thereby adjusting the phase of the radio frequency signal output by the low-level control system so that the output frequency of the power source is consistent with the resonant frequency of the resonant cavity.

[0091] (4) Feedback verification: The adjusted phase information is monitored through the feedback loop module to ensure that the phase adjustment achieves the expected effect.

[0092] The gain control unit is used to adjust the gain of each channel of the low-level control system and control the output power of each coupler.

[0093] By adjusting the gain of each channel in the low-level control system, the output power of each coupler can be precisely controlled. Specific steps include:

[0094] (1) Gain command reception: Receive the gain setting value from the optimization module.

[0095] (2) Gain adjustment: Control the gain of each channel of the low-level control system so that its output power reaches the expected value.

[0096] (3) Real-time monitoring: The output power is monitored in real time through the feedback loop module to ensure the accuracy of gain adjustment.

[0097] The radio frequency signal debugging device also includes:

[0098] The feedback loop module is used to monitor the output power and reflected power of each coupler in real time.

[0099] The feedback loop module is responsible for acquiring the incident and reflected power signals of the system in real time, providing accurate data support. Specific operations include:

[0100] (1) Signal acquisition: High-speed analog-to-digital converter (ADC) is used to acquire incident and reflected signals from the output position of the power source.

[0101] (2) Data processing: The acquired signals are digitally processed by filtering, amplification and IQ modulation and demodulation to extract effective information.

[0102] (3) Data transmission: The processed data is stored on the ARM side of the FPGA and transmitted to the optimization module and the user interface module in real time.

[0103] (4) Anomaly detection: Monitor abnormal signal conditions, such as excessive reflected power, and promptly report them to the system for adjustment.

[0104] The radio frequency signal debugging device also includes:

[0105] User interface module: Provides operators with monitoring and control functions for the system, including setting initial values ​​for optimization parameters, viewing the optimization process, and manual intervention.

[0106] Function Implementation Process: Provide a user-friendly interface for operators to monitor and control the system. Specific functions include:

[0107] (1) Parameter setting: Operators can input the initial parameters and optimization goals of the meta-heuristic algorithm through the interface.

[0108] (2) Real-time monitoring: The interface displays the output power of each coupler, the reflected power of each coupler, the phase of the radio frequency signal, and the gain of each channel of the low-level control system in real time.

[0109] (3) Manual intervention: When necessary, the operator can manually adjust the phase and gain.

[0110] (4) Log recording: Record key data and events during system operation to facilitate later analysis.

[0111] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0112] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0113] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0114] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0115] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchain. The processors involved in the embodiments provided in this application may be, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc.

[0116] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0117] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A radio frequency signal debugging method based on a metaheuristic algorithm, the debugging method being applied to an AB-BNCT cavity low-level system, the AB-BNCT cavity low-level system comprising a signal generator, a low-level control system, a solid-state power source, a multi-coupler, and a resonant cavity connected in sequence, characterized in that, The debugging method includes: Obtain the current phase of the radio frequency signal output by the low-level control system and the current gain of each channel of the low-level control system; A metaheuristic algorithm is used to optimize the current phase and current gain to determine the optimal phase and optimal gain; The radio frequency signal is adjusted based on the optimal phase and the optimal gain.

2. The radio frequency signal debugging method based on metaheuristic algorithm according to claim 1, characterized in that, A metaheuristic algorithm is used to optimize the current phase and current gain to determine the optimal phase and optimal gain, specifically including: An initial population is generated; the individuals in the initial population represent the phase and low level of the radio frequency signal, controlling the gain of each channel of the system. A fitness function is constructed with the objectives of maximizing the output power consistency of each coupler and minimizing the reflection power of each coupler. The fitness value of an individual is calculated based on the fitness function. The individual with the highest fitness value is selected as the optimal individual; the optimal individual represents the optimal phase and the optimal gain.

3. The radio frequency signal debugging method based on metaheuristic algorithm according to claim 2, characterized in that, The fitness function is: Where F represents the fitness function, P out_i Let i be the output power of the i-th coupler. P is the average output power. ref_i Let be the reflected power of the i-th coupler, n be the number of couplers, and α and β be weighting coefficients.

4. The radio frequency signal debugging method based on metaheuristic algorithm according to claim 2, characterized in that, Before calculating the fitness value of an individual based on the fitness function, the method further includes: Select, crossover, and mutation operations are performed on individuals.

5. The radio frequency signal debugging method based on metaheuristic algorithm according to claim 1, characterized in that, Radio frequency signal debugging methods also include: Real-time monitoring of the output power and reflected power of each coupler.

6. A radio frequency signal debugging device based on a metaheuristic algorithm, the debugging device being applied to an AB-BNCT cavity low-level system, the AB-BNCT cavity low-level system comprising a signal generator, a low-level control system, a solid-state power source, a multi-coupler, and a resonant cavity connected in sequence, characterized in that, The debugging device includes: The acquisition module is used to acquire the current phase of the radio frequency signal output by the low-level control system and the current gain of each channel of the low-level control system. The optimization module is used to optimize the current phase and current gain using a metaheuristic algorithm to determine the optimal phase and optimal gain. The debugging module is used to debug the radio frequency signal based on the optimal phase and the optimal gain.

7. The radio frequency signal debugging device based on metaheuristic algorithm according to claim 6, characterized in that, The debugging module includes: A variable phase shifter is used to adjust the current phase of the radio frequency signal output by the low-level control system; The gain control unit is used to adjust the gain of each channel of the low-level control system and control the output power of each coupler.

8. The radio frequency signal debugging device based on metaheuristic algorithm according to claim 6, characterized in that, The radio frequency signal debugging device also includes: The feedback loop module is used to monitor the output power and reflected power of each coupler in real time.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the radio frequency signal debugging method based on the metaheuristic algorithm as described in any one of claims 1-5.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the radio frequency signal debugging method based on the metaheuristic algorithm as described in any one of claims 1-5.

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