Radio frequency signal standing wave intelligent protection method and system based on adaptive algorithm

Abstract

The invention belongs to the technical field of radio frequency signal standing wave protection, and particularly provides a radio frequency signal standing wave intelligent protection method and system based on an adaptive algorithm, and the method mainly comprises the steps: obtaining the original data of a temperature sensor and the instantaneous impedance of an antenna port; performing environmental parameter fusion on the original data of the temperature sensor and the instantaneous impedance of the antenna port to obtain a stress calibration impedance vector; obtaining a forward power instantaneous value, a reflection power instantaneous value and a radio frequency signal sample; performing signal quality analysis on the stress calibration impedance vector, the forward power instantaneous value, the reflection power instantaneous value and the radio frequency signal sample to obtain a risk level code; and performing decision threshold generation processing on the risk level code to obtain a physical security permission. According to the invention, accurate adaptation of the protection decision to the dynamic environment is realized, the timeliness and accuracy of equipment protection are improved, misoperation is reduced, and the stability and reliability of a radio frequency system under complex working conditions are enhanced.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of standing wave protection of radio frequency signals, and particularly relates to a standing wave intelligent protection method and system for radio frequency signals based on an adaptive algorithm. BACKGROUND

[0002] In a radio frequency system, standing wave protection technology is a key link to ensure the safe operation of equipment. Currently, the standing wave protection method usually calculates the standing wave ratio by monitoring the forward power and the reflected power, and sets a fixed threshold as the protection trigger condition. Some technologies combine a single environmental parameter for simple compensation, and use linear adjustment or step decision logic to execute protection actions to avoid damage to transmitters, antennas and other components caused by excessively high reflected power.

[0003] However, under the combined conditions of wide temperature range, wide frequency band, multiple modulation modes and random load mutations, it is difficult to comprehensively perceive the real-time coupling effects of multiple parameters such as temperature, frequency, modulation mode and impedance, which leads to difficulties in dynamically adapting the protection decision to environmental changes, and further causes device protection lag or misoperation, affecting system stability and reliability. SUMMARY

[0004] The present application provides a standing wave intelligent protection method and system for radio frequency signals based on an adaptive algorithm, which effectively solves the problem in the prior art that under the combined conditions of wide temperature range, wide frequency band, etc., it is difficult to comprehensively perceive the real-time coupling effects of multiple parameters, leading to a mismatch between protection decisions and dynamic environment, causing device protection lag or misoperation, and realizes precise adaptation of protection decisions to dynamic environment.

[0005] In order to achieve the above-mentioned purpose, the application adopts the following technical solutions: In a first aspect, the application provides a standing wave intelligent protection method for radio frequency signals based on an adaptive algorithm, comprising: Obtaining temperature sensor raw data and antenna port instantaneous impedance; performing environmental parameter fusion on the temperature sensor raw data and the antenna port instantaneous impedance to obtain a stress calibrated impedance vector.

[0006] Obtaining forward power instantaneous value, reflected power instantaneous value and radio frequency signal sample; performing signal quality analysis on the stress calibrated impedance vector, the forward power instantaneous value, the reflected power instantaneous value and the radio frequency signal sample to obtain a risk level code.

[0007] Performing decision threshold generation processing on the risk level code to obtain a physical safety permission.

[0008] Performing execution control processing based on the physical safety permission to obtain a simulation control voltage.

[0009] Acquire the attenuator position feedback signal and the reflected power update sample value; perform feedback calibration on the analog control voltage, attenuator position feedback signal and reflected power update sample value to obtain the optimization strategy flag.

[0010] The optimization strategy flag is processed in a closed-loop system to obtain a closed-loop reset command.

[0011] In some embodiments, environmental parameters are fused from the raw data of the temperature sensor and the instantaneous impedance of the antenna port to obtain a stress-calibrated impedance vector, including: The raw data from the temperature sensor is processed to eliminate step-change interference, resulting in a filtered temperature value.

[0012] The filtered temperature value is superimposed and corrected according to the preset frequency-temperature characteristic curve to obtain the frequency compensation temperature; the instantaneous impedance of the antenna port is decomposed into capacitive components to obtain the impedance characteristic label.

[0013] By integrating the frequency compensation temperature and impedance characteristic identifiers, a composite environmental feature code is generated, resulting in an environmental coupling index.

[0014] The dielectric stress parameters are obtained by analyzing the environmental coupling index based on the temperature drift model of the dielectric material.

[0015] The instantaneous value of the antenna impedance is calibrated using the dielectric stress parameter to obtain the stress-calibrated impedance vector.

[0016] In some embodiments, signal quality analysis is performed on the stress calibration impedance vector, the instantaneous forward power value, the instantaneous reflected power value, and the radio frequency signal sample to obtain a risk level code, including: Based on the stress-calibrated impedance vector and the instantaneous values ​​of forward power and reflected power, the standing wave ratio is derived, and the original standing wave ratio is generated.

[0017] The original standing wave ratio is offset by using environmental coupling indices to obtain the calibrated standing wave value; modulation depth analysis is performed on the radio frequency signal sample to output the modulation robustness index.

[0018] Based on the calibrated VSWR value and modulation robustness index, a risk probability mapping is constructed to obtain the risk level code.

[0019] In some embodiments, modulation depth analysis is performed on the radio frequency signal samples to output a modulation robustness index, including: The modulation scheme of the radio frequency signal sample is identified to obtain a modulation type identifier; the modulation depth fluctuation variance of the radio frequency signal sample is quantified to obtain a depth fluctuation parameter.

[0020] The anti-interference coefficient is calculated based on the modulation type identifier and the depth fluctuation parameter, and the modulation robustness index is output.

[0021] In some embodiments, the risk level code is subjected to a decision threshold generation process to obtain a physical security permit, including: Based on the analysis of the equipment damage history database, the risk level code is obtained to obtain the empirical protection threshold.

[0022] By merging the maximum safe VSWR of the fusion device with the relaxed boundary of the empirical protection threshold, a dynamic protection threshold is obtained.

[0023] The dynamic protection threshold is dynamically scaled to adapt to the working conditions, thereby obtaining the real-time action threshold.

[0024] The out-of-bounds amount is calculated by comparing the calibrated standing wave value with the real-time action threshold, and the emergency response coefficient is obtained.

[0025] Based on the emergency response coefficient, the radiator temperature change trend is analyzed, and a thermal state safety code is generated.

[0026] Verify the redundancy of the actuator based on the hot state security code, and output physical security permission.

[0027] In some embodiments, the thermal status safety code is generated by analyzing the radiator temperature change trend based on the emergency response coefficient, including: Obtain multi-node temperature time-series data of the heat sink and construct a temperature distribution matrix.

[0028] Calculate the gradient rate of change of the temperature distribution matrix to obtain the temperature change feature vector.

[0029] The thermal state safety code is obtained by weighting the temperature change feature vector using the emergency response coefficient.

[0030] In some embodiments, performing control processing based on the physical security permission to obtain an analog control voltage includes: Map the physical security permission to the attenuator gear table to obtain the gear control queue.

[0031] The timing of the mechanism response in the gear control queue is optimized to obtain the timing optimization sequence.

[0032] By constraining the power tolerance of the timing optimization sequence, a power limit instruction is obtained.

[0033] The digital-to-analog converter is driven to execute a power limiting command to obtain an analog control voltage.

[0034] In some embodiments, feedback calibration is performed on the analog control voltage, attenuator position feedback signal, and reflected power update sample value to obtain an optimization strategy flag, including: By comparing the analog control voltage with the attenuator position feedback signal, the position difference correction value is output.

[0035] The position difference correction value is used to compensate for the analog control voltage offset and generate a closed-loop control signal.

[0036] The suppression efficiency of the reflected power update sample value is evaluated, and the actual protection performance is output.

[0037] Cross-validate the closed-loop control signal against the actual protection performance to generate an optimization strategy flag.

[0038] In some embodiments, the optimization strategy flag is subjected to system closed-loop processing to obtain a closed-loop reset instruction, including: The device damage database is refreshed based on the optimization strategy flag to obtain the database version identifier.

[0039] The parameters of the frequency-temperature compensation curve are reconstructed based on the database version identifier to generate a new compensation curve.

[0040] The environmental coupling index is reset based on the new compensation curve to obtain a new environmental characteristic scale.

[0041] The system initialization is triggered based on the new environmental characteristic scale, and a closed-loop reset command is obtained.

[0042] Secondly, this application provides a radio frequency signal standing wave intelligent protection system based on an adaptive algorithm, comprising: Environmental parameter fusion module: acquires raw data from the temperature sensor and instantaneous impedance of the antenna port; performs environmental parameter fusion on the raw data from the temperature sensor and instantaneous impedance of the antenna port to obtain a stress calibration impedance vector.

[0043] Signal quality analysis module: acquires instantaneous forward power value, instantaneous reflected power value, and RF signal sample; performs signal quality analysis on the stress calibration impedance vector, instantaneous forward power value, instantaneous reflected power value, and RF signal sample to obtain risk level code.

[0044] Decision threshold generation module: Performs decision threshold generation processing on the risk level code to obtain physical security permission.

[0045] Execution control processing module: Based on the physical security permission, it performs execution control processing to obtain the analog control voltage.

[0046] Feedback calibration module: acquires the attenuator position feedback signal and the reflected power update sample value; performs feedback calibration on the analog control voltage, attenuator position feedback signal and reflected power update sample value to obtain the optimization strategy flag.

[0047] System closed-loop processing module: Performs system closed-loop processing on the optimization strategy flag to obtain a closed-loop reset command.

[0048] Thirdly, this application provides a radio frequency signal standing wave intelligent protection device based on an adaptive algorithm, which includes a memory and a processor; the memory is used to store a computer program; the processor is used to execute the computer program to implement the steps of the radio frequency signal standing wave intelligent protection method based on the adaptive algorithm as described in the first aspect.

[0049] Fourthly, this application provides a storage medium storing computer program instructions, which are read and executed by a processor to perform the steps of the radio frequency signal standing wave intelligent protection method based on adaptive algorithm as described in the first aspect.

[0050] The beneficial effects of this invention are: This application effectively solves the problem in existing technologies where, under complex operating conditions such as wide temperature range and wide frequency band, the difficulty in comprehensively sensing the real-time coupling effect of multiple parameters leads to a mismatch between protection decisions and the dynamic environment, resulting in equipment protection delays or malfunctions. This is achieved by acquiring temperature and impedance data and fusing them to obtain a stress-calibrated impedance vector, combining power and signal sample analysis to generate a risk level code, processing a decision threshold to obtain a safety permit and generate a control voltage, and then outputting a reset command through feedback calibration and closed-loop processing.

[0051] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description and the drawings. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 A flowchart illustrating the intelligent protection method for radio frequency signal standing waves based on an adaptive algorithm according to the present invention is shown. Figure 2 A schematic diagram of the module of the intelligent protection system for radio frequency signal standing waves based on an adaptive algorithm according to the present invention is shown. Detailed Implementation

[0054] To address the problems raised in the background technology, a scheme is proposed that acquires temperature and impedance data and fuses them to obtain a stress-calibrated impedance vector. This vector is then combined with power and signal sample analysis to generate a risk level code. After decision threshold processing, a safety permit is obtained and a control voltage is generated. Finally, a reset command is output through feedback calibration and closed-loop processing. This approach achieves precise adaptation of protection decisions to dynamic environments.

[0055] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] In some embodiments, such as Figure 1 As shown, this application provides a method for intelligent protection of radio frequency signal standing waves based on an adaptive algorithm, including: S1. Obtain the raw data of the temperature sensor and the instantaneous impedance of the antenna port; fuse the raw data of the temperature sensor and the instantaneous impedance of the antenna port with environmental parameters to obtain the stress calibration impedance vector.

[0057] S2. Obtain instantaneous values ​​of forward power, reflected power, and RF signal samples; perform signal quality analysis on the stress calibration impedance vector, instantaneous values ​​of forward power, reflected power, and RF signal samples to obtain risk level codes.

[0058] S3. Perform decision threshold generation processing on the risk level code to obtain physical security permission.

[0059] S4. Perform control processing based on physical security permission to obtain analog control voltage.

[0060] S5. Obtain the attenuator position feedback signal and the reflected power update sample value; perform feedback calibration on the analog control voltage, attenuator position feedback signal and reflected power update sample value to obtain the optimization strategy flag.

[0061] S6. Perform system closed-loop processing on the optimization strategy flag to obtain the closed-loop reset command.

[0062] In some embodiments, the raw data from the temperature sensor in S1 represents the ambient temperature of the device's operating environment.

[0063] The instantaneous impedance at the antenna port represents the complex impedance value of the antenna feed point under high-frequency operating conditions, including the real part R and the imaginary part X.

[0064] By fusing environmental parameters from the raw data of the temperature sensor and the instantaneous impedance of the antenna port, a stress-calibrated impedance vector is obtained, including: S11. Perform step-change interference cancellation processing on the raw data of the temperature sensor to obtain the filtered temperature value.

[0065] Specifically, the raw data from the temperature sensor is processed using a moving window filtering technique, and the output is a filtered temperature value that eliminates jump interference.

[0066] S12. The filtered temperature value is superimposed and corrected according to the preset frequency-temperature characteristic curve to obtain the frequency compensation temperature; the instantaneous impedance of the antenna port is decomposed into capacitive components to obtain the impedance characteristic label.

[0067] Specifically, the frequency-temperature characteristic curve is corrected after filtering the temperature value, and linear compensation is performed by calling the device's pre-stored calibration table, thus obtaining the frequency-compensated temperature.

[0068] Perform admittance circle analysis on the instantaneous impedance of the antenna port, calculate the magnitude and phase angle, and classify them with a 45° threshold to obtain impedance characteristic labels.

[0069] S13. By integrating the frequency compensation temperature and impedance characteristic identifiers, a composite environmental feature code is generated to obtain the environmental coupling index.

[0070] Extract the impedance magnitude |Z| from the impedance characteristic label, and denot the frequency compensation temperature as... The environmental coupling value is calculated using a linear weighted formula. ;in, Represents the temperature weighting coefficient. The impedance weighting coefficient can be obtained by optimizing it using the least squares method through orthogonal experiments with the goal of minimizing the environmental feature resolution error.

[0071] Environmental coupling value Mapping to the [0,100] interval generates environmental coupling indicators. .

[0072] S14 analyzes the environmental coupling index based on the temperature drift model of the dielectric material to obtain the dielectric stress parameters.

[0073] Dielectric stress parameters are calculated using the S-type function. ;in, The slope representing the material's response can be determined through temperature change experiments and is used to measure the degree to which a material responds to stress changes. The central reference value corresponds to the glass transition temperature of the material and is a key reference parameter reflecting the material's properties.

[0074] S15. The instantaneous value of the antenna impedance is calibrated using dielectric stress parameters to obtain the stress-calibrated impedance vector.

[0075] Stress compensation is applied to the original impedance components: ;in, The stress gain coefficient is represented by R, which can be determined through VSWR optimization experiments. R and X represent the real and imaginary parts of the instantaneous impedance at the antenna port, respectively. These represent the real and imaginary parts after calibration, respectively.

[0076] The real and imaginary parts after calibration are combined into a two-dimensional vector to obtain the stress calibration impedance vector.

[0077] In some embodiments, the forward power instantaneous value in S2 represents the instantaneous value of the radio frequency signal power transmitted in the forward direction at the antenna port, which can be obtained by real-time sampling through a directional coupler.

[0078] The instantaneous reflected power value represents the instantaneous RF signal power of the reflected echo from the antenna port, and can be obtained by sampling through the reverse coupling port of the same directional coupler.

[0079] Radio frequency (RF) signal samples represent time-domain segments of real-time RF signals from the antenna transmission channel, which can be acquired using a high-speed ADC.

[0080] Signal quality analysis was performed on the stress calibration impedance vector, instantaneous forward power, instantaneous reflected power, and RF signal samples to obtain risk level codes, including: S21. Based on the stress-calibrated impedance vector and the instantaneous values ​​of forward power and reflected power, derive the standing wave ratio and generate the original standing wave ratio.

[0081] The original standing wave ratio can be calculated using the voltage standing wave ratio formula.

[0082] S22. Use environmental coupling index to cancel the error of the original standing wave ratio to obtain the calibrated standing wave value; perform modulation depth analysis on the radio frequency signal sample and output the modulation robustness index.

[0083] The original standing wave ratio is dynamically compensated using environmental coupling parameters to obtain the calibrated standing wave value: ;in, Represents the original standing wave ratio, Represents an environmental coupling index, ranging from 0 to 100. The environmental coupling compensation coefficient can be obtained by minimizing the temperature-induced deviation in the standing wave ratio measurement through temperature-varying environmental experiments. This represents the VSWR value after calibration.

[0084] S23. Construct a risk probability mapping based on the calibrated standing wave value and the modulation robustness index to obtain the risk level code.

[0085] Using the calibrated standing wave value Modulation robustness index Calculate the risk probability using the S-shaped function ;in, and These represent the calibrated standing wave ratios. Modulation robustness index The weighting coefficients can be obtained by fitting historical equipment damage data using logistic regression and minimizing the cross-entropy loss function.

[0086] Risk level code (RC) is: ;in, represent Round down to the nearest integer.

[0087] In some embodiments, S22 performs modulation depth analysis on the radio frequency signal samples and outputs a modulation robustness index, including: S221. Identify the modulation scheme of the radio frequency signal sample to obtain the modulation type identifier; quantify the modulation depth fluctuation variance of the radio frequency signal sample to obtain the depth fluctuation parameter.

[0088] By acquiring the constellation diagram of the radio frequency signal, clustering algorithms can be used to classify and identify the distribution patterns of constellation points, thereby obtaining modulation type identifiers. This is used to specify the modulation scheme used in the signal.

[0089] The fluctuation variance of the modulation depth is calculated and quantized to obtain the depth fluctuation parameters.

[0090] S222. Calculate the anti-interference coefficient based on the modulation type identifier and depth fluctuation parameter, and output the modulation robustness index.

[0091] The anti-interference performance of the signal is quantified using a mathematical model, and the formula is as follows: ;in, Represents the modulation robustness index. This represents the normalization coefficient, ensuring that the output value is within a typical range, such as 0-100. Represents the variance of modulation depth fluctuation. This represents the base value for interference suppression based on the modulation type. Different modulation systems correspond to fixed base values.

[0092] Specifically, the signal-to-noise ratio difference required at the bit error rate inflection point for different modulation types can be tested using an AWGN channel, with 16QAM as the benchmark. The base values ​​for QPSK and 64QAM are determined by scaling proportionally, for example: QPSK modulation. 16QAM modulation, 64QAM modulation, .

[0093] In some embodiments, the decision threshold generation process for the risk level encoding in S3 to obtain physical security permission includes: S31. Based on the equipment damage history database, analyze the risk level code to obtain the empirical protection threshold.

[0094] Based on historical fault data, the fault data are grouped according to risk level coding, and the safety boundary of each group is calculated. The minimum safety margin in the same group is taken as the empirical protection threshold.

[0095] S32. By merging the maximum safe VSWR of the fusion device with the relaxed boundary of the empirical protection threshold, a dynamic protection threshold is obtained.

[0096] Construct a relaxed boundary fusion model, the formula is: DT represents the dynamic protection threshold. The relaxation weighting coefficients, determined through fault tree analysis, aim to minimize the false negative rate. ET represents the maximum safe standing wave ratio of the device, and ET represents the empirical protection threshold.

[0097] S33. Dynamically scale the dynamic protection threshold to adapt to the working conditions and obtain the real-time action threshold.

[0098] Dynamic protection thresholds are scaled based on operating parameters, such as temperature and humidity. Where RTT represents the real-time action threshold, The scaling sensitivity coefficient is calibrated through accelerated aging tests. Represents the environmental offset factor. Temp represents ambient temperature, and Humidity represents ambient humidity.

[0099] S34. Compare the calibrated standing wave value with the real-time action threshold to calculate the out-of-bounds amount and obtain the emergency response coefficient.

[0100] Calculate the VSWR overshoot and convert it into an emergency response coefficient: Where EC represents the emergency response coefficient, This represents the calibrated standing wave ratio. represent The round-up operation, when At that time, EC=0.

[0101] S35. Analyze the radiator temperature change trend based on the emergency response coefficient and generate a thermal state safety code.

[0102] S36. Verify the redundancy of the actuator based on the hot state security code and output physical security permission.

[0103] When the thermal safety code (HSC) is greater than the safety clearance threshold, the physical safety clearance is 1, indicating permission; otherwise, it is 0, indicating disallowance. The safety clearance threshold is determined as a critical safety value through failure mode and effects analysis.

[0104] In some embodiments, S35, based on the analysis of the radiator temperature change trend using the emergency response coefficient, generates a thermal state safety code, including: S351. Obtain multi-node temperature time-series data of the radiator and construct a temperature distribution matrix.

[0105] S352. Calculate the gradient rate of change of the temperature distribution matrix to obtain the temperature change eigenvector.

[0106] For the temperature sequence of each node, perform the first derivative operation to calculate the instantaneous rate of temperature change.

[0107] Then, calculate the absolute value of the average rate of change based on all instantaneous temperature change rates at that node, and combine the absolute values ​​of the average rate of change of all nodes into a temperature change feature vector.

[0108] S353. The thermal state safety code is obtained by weighting the temperature change feature vector using the emergency response coefficient.

[0109] A coupled model of the emergency response coefficient and the rate of temperature change is established, with the following formula: Where HSC represents the thermal safety code and EC represents the emergency response coefficient, The second norm of the eigenvector representing temperature variation. represent Round down to the nearest integer.

[0110] In some embodiments, S4 performs control processing based on physical security permissions to obtain an analog control voltage, including: S41. Map the physical security permission to the attenuator gear table to obtain the gear control queue.

[0111] The risk level code and attenuator gear response surface are obtained by fitting historical fault data. When the physical safety permission is 1, the gear value is matched according to the risk level code.

[0112] For example, when the risk level is coded as [0,30), it represents low risk. When the setting is set to 3, the attenuation is -10dB, which can suppress slight reflection fluctuations. When the risk level is coded as [30,70), it represents medium risk. When the setting is set to 5, the attenuation is -20dB, which controls moderate reflections. When the risk level is coded as [70,100], it represents high risk. When the setting is set to 8, the attenuation is -30dB, which blocks severe reflections.

[0113] S42. Optimize the mechanism response timing of the gear control queue to obtain the timing optimization sequence.

[0114] By compensating for mechanical delays and avoiding overshoot during switching, the timing calculation is performed using the following formula: Where TS[i] represents the timing optimization sequence, and i represents the gear number in the queue. This represents the user-preset base switching time for the i-th gear. Represents the mechanical delay constant. represents the exponential decay coefficient, and n represents the number of gears.

[0115] Among these measures, step response tests are conducted on stepper motors, such as obtaining the median of the 90% settling time from multiple sets of test data, to determine the mechanism's delay constant. .

[0116] With the goal of reducing gear shift overshoot, regression analysis was conducted on the measured data to ultimately determine the attenuation coefficient. .

[0117] S43. Constrain the power tolerance of the timing optimization sequence to obtain the power limit instruction.

[0118] Dynamically limiting the power ramp-up rate to avoid sudden power surges, the formula is defined as: ; where PL represents the power limiting instruction, Represents the maximum power of the power supply. This represents the base power of the gear. Represents the time interval between adjacent gear positions. The power gradient coefficient is determined by testing the power ramp-up rate when the power supply is under maximum load. The value corresponding to a safety margin of 80% is taken as the power gradient coefficient.

[0119] S44. Drive the digital-to-analog converter to execute the power limiting command and obtain the analog control voltage.

[0120] By calibrating the equipment or system, the power-to-voltage conversion factor is determined, and the input power limit is converted into the target voltage value to obtain the analog control voltage. .

[0121] In some embodiments, the feedback calibration of the analog control voltage, attenuator position feedback signal, and reflected power update sample value in S5 to obtain the optimization strategy flag includes: S51. Compare the analog control voltage with the attenuator position feedback signal and output the position difference correction value.

[0122] The difference between the analog control voltage and the attenuator position feedback signal is the error, which is directly used as the position difference correction value.

[0123] S52. The position difference correction value is used to compensate for the analog control voltage offset and generate a closed-loop control signal.

[0124] Based on the proportional gain specified by the equipment, and combined with the level difference correction value, the analog control voltage is compensated using the following formula: ;in, This represents the closed-loop control signal. Represents analog control voltage. Represents the proportional gain, a fixed value calibrated at the factory and used to adjust the compensation strength; PEr represents the position difference correction value.

[0125] S53. Evaluate the suppression efficiency of the reflected power update sample value and output the actual protection performance.

[0126] By quantifying the reflection power suppression effect, the actual effectiveness of the current protection mechanism is calculated, and the formula is defined as: ; where PEy represents the actual protective efficacy, with a value ranging from 0 to 100. A higher value indicates a better inhibitory effect. This represents the updated sampled value of the reflected power, which is the current reflected power collected in real time. Represents the initial reflected power, and represents the instantaneous reflected power value "before the protection action", serving as a comparison benchmark.

[0127] S54. Cross-validate the closed-loop control signal and actual protection performance to generate an optimization strategy flag.

[0128] Specifically, two key thresholds are set: a control voltage safety threshold, determined by the equipment's maximum withstand voltage; and a protection performance qualification threshold, derived through regression analysis of historical data.

[0129] When the closed-loop control signal When the voltage is less than the control voltage safety threshold and the actual protection performance PEy is greater than or equal to the protection performance qualification threshold, the optimization strategy flag is 1, indicating that the current strategy is effective; otherwise, it is 0, meaning that the strategy needs to be optimized.

[0130] In some embodiments, the optimization strategy flag in S6 undergoes system closed-loop processing to obtain a closed-loop reset instruction, including: S61. Refresh the device damage database according to the optimization strategy flag to obtain the database version identifier.

[0131] When the optimization strategy flag is 1, an SQL update operation is executed, and a unique database version identifier is generated after the update. S62. Reconstruct the frequency-temperature compensation curve parameters based on the database version identifier to generate a new compensation curve.

[0132] Based on the database version identifier, the formula is used. Reconstruct the frequency-temperature curve; among which, This represents the new compensation curve function, characterizing the frequency shift corresponding to temperature T, where T represents the temperature independent variable and k represents the polynomial term index. Represents the coefficients of a polynomial.

[0133] The least squares method is used for fitting, and the solution is found to make Curve coefficients that hold true ;in, Represents the total number of data points. Represents temperature Below is the measured frequency offset value. This represents the actual temperature of the i-th data point. The constant term coefficient represents the reference offset for temperature compensation. The coefficients of the linear term characterize the linear sensitivity of the frequency to temperature changes. Represents the quadratic coefficient, characterizing the nonlinear correction coefficient for frequency-temperature drift.

[0134] Finally, output the new compensation curve. .

[0135] S63. Reset the environmental coupling index according to the new compensation curve to obtain a new environmental characteristic scale.

[0136] To reset the indicators, refer to the following formula: ;in, This represents a new environmental characteristic scale, i.e., a reset environmental coupling index. This represents the normalization constant, determined by statistically analyzing the maximum integral value from historical data. These represent the upper and lower limits of the temperature range, respectively.

[0137] S64. Trigger system initialization based on the new environmental characteristic scale to obtain a closed-loop reset command.

[0138] At the same time, the new environmental characteristic benchmark will be used. The overload is an environmental coupling indicator, and the final output is a closed-loop reset command.

[0139] In some embodiments, such as Figure 2 As shown, this application provides an intelligent protection system for radio frequency signal standing waves based on an adaptive algorithm, comprising: Environmental parameter fusion module: acquires raw data from the temperature sensor and instantaneous impedance at the antenna port; performs environmental parameter fusion on the raw data from the temperature sensor and instantaneous impedance at the antenna port to obtain the stress calibration impedance vector.

[0140] Signal quality analysis module: acquires instantaneous forward power, instantaneous reflected power, and RF signal samples; performs signal quality analysis on stress calibration impedance vector, instantaneous forward power, instantaneous reflected power, and RF signal samples to obtain risk level coding.

[0141] Decision threshold generation module: Generates decision thresholds for risk level codes to obtain physical security permission.

[0142] Execution control processing module: Performs execution control processing based on physical security permissions to obtain analog control voltage.

[0143] Feedback calibration module: acquires the attenuator position feedback signal and the updated sample value of reflected power; performs feedback calibration on the analog control voltage, attenuator position feedback signal and the updated sample value of reflected power to obtain the optimization strategy flag.

[0144] System closed-loop processing module: Performs system closed-loop processing on the optimization strategy flag to obtain the closed-loop reset command.

[0145] In some embodiments, this application provides an intelligent protection device for radio frequency signal standing waves based on an adaptive algorithm, which includes a memory and a processor; the memory is used to store a computer program; the processor is used to implement the steps of the intelligent protection method for radio frequency signal standing waves based on the adaptive algorithm when executing the computer program.

[0146] In some embodiments, this application provides a storage medium storing computer program instructions, which are read and executed by a processor to perform the steps of a radio frequency signal standing wave intelligent protection method based on an adaptive algorithm.

[0147] Any references to memory, storage, database, or other media used in the embodiments provided in this invention may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory.

[0148] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0149] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for intelligent protection of radio frequency signal standing waves based on an adaptive algorithm, characterized in that, include: Acquire raw data from the temperature sensor and instantaneous impedance at the antenna port; The raw data from the temperature sensor and the instantaneous impedance of the antenna port are fused with environmental parameters to obtain a stress-calibrated impedance vector. Acquire instantaneous values ​​of forward power, reflected power, and radio frequency signal samples; perform signal quality analysis on the stress calibration impedance vector, instantaneous values ​​of forward power, instantaneous values ​​of reflected power, and radio frequency signal samples to obtain a risk level code; The risk level code is processed to generate a decision threshold, thereby obtaining a physical security permit; Based on the aforementioned physical security permission, control processing is performed to obtain a simulated control voltage; Acquire the attenuator position feedback signal and the reflected power update sample value; perform feedback calibration on the analog control voltage, attenuator position feedback signal and reflected power update sample value to obtain the optimization strategy flag; The optimization strategy flag is processed in a closed-loop system to obtain a closed-loop reset command.

2. The intelligent protection method for radio frequency signal standing waves based on adaptive algorithms according to claim 1, characterized in that, The raw data from the temperature sensor and the instantaneous impedance of the antenna port are fused using environmental parameters to obtain a stress-calibrated impedance vector, including: The raw data from the temperature sensor is subjected to step-change interference cancellation processing to obtain the filtered temperature value; The filtered temperature value is superimposed and corrected according to the preset frequency-temperature characteristic curve to obtain the frequency compensation temperature; the instantaneous impedance of the antenna port is decomposed into capacitive components to obtain the impedance characteristic label. By integrating the frequency compensation temperature and impedance characteristic identifiers, a composite environmental feature code is generated to obtain the environmental coupling index; The dielectric stress parameters are obtained by analyzing the environmental coupling index based on the temperature drift model of the dielectric material. The instantaneous value of the antenna impedance is calibrated using the dielectric stress parameter to obtain the stress-calibrated impedance vector.

3. The intelligent protection method for radio frequency signal standing waves based on adaptive algorithms according to claim 2, characterized in that, Signal quality analysis is performed on the stress calibration impedance vector, instantaneous forward power, instantaneous reflected power, and RF signal samples to obtain a risk level code, including: Based on the stress-calibrated impedance vector and the instantaneous values ​​of forward power and reflected power, the standing wave ratio is derived to generate the original standing wave ratio. The original standing wave ratio is offset by using environmental coupling indices to obtain the calibrated standing wave value; modulation depth analysis is performed on the radio frequency signal sample to output the modulation robustness index; Based on the calibrated VSWR value and modulation robustness index, a risk probability mapping is constructed to obtain the risk level code.

4. The intelligent protection method for radio frequency signal standing waves based on adaptive algorithms according to claim 3, characterized in that, Perform modulation depth analysis on the radio frequency signal samples and output a modulation robustness index, including: The modulation scheme of the radio frequency signal sample is identified to obtain the modulation type identifier; the modulation depth fluctuation variance of the radio frequency signal sample is quantified to obtain the depth fluctuation parameter. The anti-interference coefficient is calculated based on the modulation type identifier and the depth fluctuation parameter, and the modulation robustness index is output.

5. The intelligent protection method for radio frequency signal standing waves based on an adaptive algorithm according to claim 3, characterized in that, The risk level code is processed to generate a decision threshold to obtain physical security permission, including: Based on the analysis of the equipment damage history database, the risk level code is obtained to determine the empirical protection threshold. By merging the maximum safe VSWR of the fusion device with the relaxed boundary of the empirical protection threshold, a dynamic protection threshold is obtained; The dynamic protection threshold is dynamically scaled to adapt to the working conditions, and the real-time action threshold is obtained. The out-of-bounds amount is calculated by comparing the calibrated standing wave value with the real-time action threshold, and the emergency response coefficient is obtained. Based on the emergency response coefficient, the radiator temperature change trend is analyzed, and a thermal state safety code is generated. Verify the redundancy of the actuator based on the hot state security code, and output physical security permission.

6. The intelligent protection method for radio frequency signal standing waves based on an adaptive algorithm according to claim 5, characterized in that, Based on the emergency response coefficient, the radiator temperature change trend is analyzed, and a thermal state safety code is generated, including: Acquire time-series temperature data of multiple nodes of the heat sink and construct a temperature distribution matrix; Calculate the gradient rate of change of the temperature distribution matrix to obtain the temperature change feature vector; The thermal state safety code is obtained by weighting the temperature change feature vector using the emergency response coefficient.

7. The intelligent protection method for radio frequency signal standing waves based on an adaptive algorithm according to claim 1, characterized in that, Based on the aforementioned physical security permission, control processing is performed to obtain an analog control voltage, including: Map physical security permissions to attenuator gear table to obtain gear control queue; Optimize the mechanism response timing of the gear control queue to obtain the timing optimization sequence; Constrain the power tolerance of the timing optimization sequence to obtain the power limiting command; The digital-to-analog converter is driven to execute a power limiting command to obtain an analog control voltage.

8. The intelligent protection method for radio frequency signal standing waves based on an adaptive algorithm according to claim 1, characterized in that, Feedback calibration is performed on the analog control voltage, attenuator position feedback signal, and reflected power update sample value to obtain the optimization strategy flag, including: By comparing the analog control voltage with the attenuator position feedback signal, the position difference correction value is output. The position difference correction value is used to compensate for the analog control voltage offset and generate a closed-loop control signal; Evaluate the suppression efficiency of the reflected power update sample value and output the actual protection performance; Cross-validate the closed-loop control signal against the actual protection performance to generate an optimization strategy flag.

9. The intelligent protection method for radio frequency signal standing waves based on an adaptive algorithm according to claim 1, characterized in that, The optimization strategy flag is processed in a system closed-loop manner to obtain a closed-loop reset instruction, including: The equipment damage database is refreshed based on the optimization strategy flag to obtain the database version identifier; Reconstruct the frequency-temperature compensation curve parameters based on the database version identifier to generate a new compensation curve; The environmental coupling index is reset based on the new compensation curve to obtain a new environmental characteristic scale; The system initialization is triggered based on the new environmental characteristic scale, and a closed-loop reset command is obtained.

10. A radio frequency signal standing wave intelligent protection system based on an adaptive algorithm, characterized in that, include: Environmental parameter fusion module: acquires raw data from the temperature sensor and instantaneous impedance at the antenna port; The raw data from the temperature sensor and the instantaneous impedance of the antenna port are fused with environmental parameters to obtain a stress-calibrated impedance vector. Signal quality analysis module: acquires instantaneous forward power value, instantaneous reflected power value, and RF signal sample; performs signal quality analysis on the stress calibration impedance vector, instantaneous forward power value, instantaneous reflected power value, and RF signal sample to obtain risk level code; Decision threshold generation module: Performs decision threshold generation processing on the risk level code to obtain physical security permission; Execution control processing module: Based on the physical security permission, it performs execution control processing to obtain the analog control voltage; Feedback calibration module: acquires attenuator position feedback signal and reflected power update sample value; performs feedback calibration on the analog control voltage, attenuator position feedback signal and reflected power update sample value to obtain optimization strategy flag; System closed-loop processing module: Performs system closed-loop processing on the optimization strategy flag to obtain a closed-loop reset command.

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