A calibration system for radio frequency and communication circuits

Abstract

The application discloses a kind of based on radio frequency and communication circuit calibration system, the present application relates to wireless communication transmission technical field, calibration system includes following module: environment perception module, intelligent algorithm module, adaptive hardware module and interactive verification module, the advantages of the present application are: reinforcement learning agent and cross-domain transfer learning model collaborative optimization calibration parameter, combine multi-modal environment perception data dynamic adjustment radio frequency circuit center frequency and impedance matching value, programmable hypersurface array is based on electromagnetic property reconstruction and realizes millisecond level beamforming, FPGA real-time loading optimization parameter, calibration cycle is greatly shortened, AR interactive interface is simplified artificial operation by high-precision gesture mapping and high-frequency calibration technology, overall calibration efficiency is improved by more than 40%, bit error rate is reduced to extremely low level, meet the real-time demand of 5G / 6G high-frequency communication, break through the efficiency bottleneck of traditional artificial debugging.

Description

Technical Field

[0001] This invention relates to the field of wireless communication transmission technology, specifically to a calibration system based on radio frequency and communication circuits. Background Technology

[0002] Existing RF calibration technologies largely rely on manual operation and static models, which have the following limitations:

[0003] Low calibration efficiency: Traditional methods require repeated adjustments to parameters such as the Smith chart and impedance matching, which is time-consuming and easily affected by human error;

[0004] Poor environmental adaptability: Existing systems mostly use a single sensor (such as a standing wave ratio detector), which cannot coordinate the sensing of multi-dimensional environmental changes such as temperature and humidity, resulting in significant deviations in calibration results under extreme temperature ranges or high humidity scenarios;

[0005] Insufficient intelligence: Most solutions lack dynamic optimization capabilities and cannot predict interference trends by combining real-time data;

[0006] To address the aforementioned issues, this invention proposes a calibration system based on radio frequency and communication circuits. It constructs an environmental compensation model through the collaborative use of a terahertz wave scanner and a temperature and humidity sensor, dynamically optimizes the calibration strategy using a PPO reinforcement learning agent, and achieves hardware-level adaptive beamforming using a programmable metasurface array. Furthermore, it integrates an AR visualization interface and blockchain evidence storage technology, significantly improving operational efficiency and data credibility, and breaking through the bottlenecks of traditional calibration technologies in terms of efficiency, accuracy, and cross-scenario adaptability. Summary of the Invention

[0007] The purpose of this invention is to provide a calibration system based on radio frequency and communication circuits.

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] A calibration system for radio frequency and communication circuits, the calibration system comprising the following modules:

[0010] An environmental sensing module, consisting of a terahertz wave scanner, an infrared thermal imaging sensor, and a humidity sensor, is used to collect impedance distortion parameters, temperature distribution, and environmental humidity data of the radio frequency circuit in real time.

[0011] The intelligent algorithm module includes a reinforcement learning agent and a cross-domain transfer learning model. The reinforcement learning agent dynamically optimizes calibration parameters based on environmental perception data. The transfer learning model supports parameter adaptation for different communication scenarios. The reinforcement learning agent defines the action space as the adjustment operation of the center frequency and impedance matching value of the radio frequency circuit.

[0012] An adaptive hardware module includes a programmable metasurface array and a biomimetic heat dissipation structure. The programmable metasurface array achieves beamforming based on dynamic reconstruction of electromagnetic properties. The biomimetic heat dissipation structure ensures calibration stability under extreme temperatures and also has electromagnetic shielding function.

[0013] The interactive verification module integrates an augmented reality (AR) interface and a blockchain-based evidence storage unit, which is used to visualize the calibration process and store tamper-proof calibration records.

[0014] As a further aspect of the present invention: in the environmental sensing module, the terahertz wave scanner detects the microscopic impedance distortion of the radio frequency circuit through multi-band scanning, and its detection accuracy meets the following requirements:

[0015] ;

[0016] in, This is the impedance offset. The characteristic impedance is used to detect frequencies from Sub-6GHz to terahertz.

[0017] The infrared thermal imaging sensor and humidity sensor work together to construct an environmental temperature and humidity compensation model. The compensation formula is as follows:

[0018] ;

[0019] in, This is the impedance correction value after temperature and humidity compensation. The impedance value is measured in real time. These are the temperature compensation coefficient and the humidity compensation coefficient, respectively. , These represent the changes in temperature and humidity, respectively.

[0020] As a further aspect of the present invention: the optimization objective of the reinforcement learning agent is to minimize the weighted sum of the bit error rate and power consumption, and its reward function is defined as:

[0021] ;

[0022] in, These are dynamic weighting coefficients that are automatically adjusted based on the priority of the communication scenario. The initial bit error rate before calibration. The bit error rate after calibration. The initial power consumption of the system before calibration. The system power consumption after calibration. Characteristic impedance, The reflection coefficient is provided by the environmental perception module, and the action space includes the center frequency. and impedance matching value ,in The calculation is performed in real time using the Smith chart, and the formula is as follows:

[0023] ;

[0024] The reflection coefficient is provided by the environment perception module.

[0025] As a further aspect of the present invention: the operating frequency band of the programmable metasurface array covers the Sub-6GHz to terahertz frequency band, and the modulation coefficient of its surface units... Optimize using the following constraints:

[0026] ;

[0027] in, The transmission coefficients are used, and the optimization results are loaded in real time via FPGA.

[0028] As a further aspect of the present invention: the biomimetic heat dissipation structure also has an electromagnetic shielding function, and its equivalent heat transfer coefficient satisfies:

[0029] ;

[0030] Furthermore, within the temperature range of -40℃ to 85℃, the heat dissipation efficiency formula is:

[0031] ;

[0032] in, For heat dissipation power, The equivalent heat transfer coefficient, Effective heat dissipation area The operating temperature of the chip. The system's ambient temperature is used to keep the calibration accuracy error fluctuation within ±0.5dB;

[0033] The honeycomb structure also has electromagnetic shielding capabilities, and the shielding effectiveness meets the following requirements:

[0034] ;

[0035] in, For shielding effectiveness, The frequency range covers Sub-6GHz and some millimeter-wave communication bands, enabling the system to meet the electromagnetic compatibility requirements of 5G / 6G scenarios.

[0036] As a further aspect of the present invention: the AR interface is mapped to matching points on a Smith chart via gesture operations, and the gesture capture accuracy is achieved by a depth camera, the coordinate mapping error of which satisfies:

[0037] ;

[0038] The calibration frequency of the depth camera is no less than 30Hz, and the attitude offset is corrected in real time using the following calibration formula:

[0039] ;

[0040] in, Here is the pose transformation matrix of the depth camera. For the camera intrinsic parameter matrix, For rotation matrix, It is a translation matrix.

[0041] As a further aspect of the present invention: the blockchain evidence storage unit generates a hash value for the calibration certificate. It meets the following conditions:

[0042] ;

[0043] in, It is a hash algorithm used to convert input data into a fixed-length cryptographic hash value to ensure data integrity and traceability.

[0044] As a further aspect of the present invention: the adaptation process of the cross-domain transfer learning model is achieved through the transfer regularization coefficient. To balance the new scene data with the pre-trained model parameters, the loss function is:

[0045] ;

[0046] in, For loss function, For the first The actual output value of each sample For the model to the first The predicted output value for each sample. The total number of samples in the training dataset. , For the total number of parameters, This is the set of parameters for optimizing the transfer learning model in a new scenario. The model is a set of parameters that has been trained in the source scenario. It is dynamically adjusted according to the sample size of the target scenario. The model supports parameter migration from 5G base stations to satellite communication scenarios. After adaptation, the bit error rate decreases by no less than 25%.

[0047] As a further aspect of the present invention: the calibration efficiency of the system in a 5G base station scenario is improved by at least 40%, and the bit error rate after calibration meets the following requirements:

[0048] ;

[0049] Furthermore, the calibrated bit error rate exhibits a standing wave ratio fluctuation range of less than 1.5 and a stability error of less than 0.02dB under dense electromagnetic interference conditions.

[0050] Compared with the prior art, the beneficial effects of the present invention by adopting the above technical solution are as follows:

[0051] 1. This invention optimizes calibration parameters through reinforcement learning agents and cross-domain transfer learning models, dynamically adjusts the center frequency and impedance matching value of the RF circuit by combining multimodal environmental perception data, achieves millisecond-level beamforming based on electromagnetic property reconstruction of programmable metasurface arrays, loads optimized parameters in real time by FPGA, significantly shortens the calibration cycle, and simplifies manual operation through high-precision gesture mapping and high-frequency calibration technology of AR interactive interface. The overall calibration efficiency is improved by more than 40%, the bit error rate is reduced to an extremely low level, meets the real-time requirements of 5G / 6G high-frequency communication, and breaks through the efficiency bottleneck of traditional manual debugging.

[0052] 2. This invention integrates a terahertz wave scanner, infrared thermal imaging, and humidity sensor through a multimodal sensing module to construct an environmental temperature and humidity compensation model, dynamically correcting impedance shifts, achieving a detection accuracy within 10% of the characteristic impedance. The biomimetic heat dissipation structure adopts a honeycomb porous design, maintaining efficient heat dissipation within an extreme temperature range of -40℃ to 85℃, with an equivalent heat transfer coefficient exceeding 150. It also has an electromagnetic shielding efficiency of no less than 30dB in the 1GHz to 10GHz frequency band, effectively suppressing external interference. The system maintains a standing wave ratio fluctuation of less than 1.5 and a stability error of less than 0.02dB in dense electromagnetic environments, ensuring reliable calibration in extreme environments.

[0053] 3. This invention supports seamless adaptation from 5G base stations to satellite communication by dynamically balancing parameters in new and old scenarios, with an error rate optimization of over 25%. Blockchain evidence storage technology combined with encrypted hash algorithms ensures the immutability and complete traceability of calibration records, meeting the needs of high-security scenarios. The AR visualization interface lowers the operating threshold, supports multi-user collaboration, promotes the transformation of RF calibration to an open platform, and is compatible with various communication devices. It can be extended to the fields of industrial IoT and autonomous driving, significantly reducing cross-scenario deployment costs and improving the level of intelligence. Attached Figure Description

[0054] Figure 1 This is a flowchart illustrating the implementation of the radio frequency calibration system in a 5G base station scenario according to an embodiment of the present invention.

[0055] Figure 2 This is a flowchart illustrating the implementation of cross-domain migration calibration in a satellite communication scenario according to an embodiment of the present invention.

[0056] Figure 3This is a flowchart illustrating the implementation process of multi-device collaborative calibration in an industrial IoT scenario, as described in this invention.

[0057] Figure 4 This is a flowchart illustrating the implementation process of multi-device collaborative calibration in an industrial IoT scenario according to an embodiment of the present invention. Detailed Implementation

[0058] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings. It should be noted that the description of these embodiments is for the purpose of helping to understand the present invention, but does not constitute a limitation of the present invention.

[0059] Furthermore, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0060] Please see the appendix Figure 1 - Appendix Figure 4 The present invention discloses a calibration system for radio frequency and communication circuits, the calibration system comprising the following modules:

[0061] The environmental sensing module consists of a terahertz wave scanner, an infrared thermal imaging sensor, and a humidity sensor, and is used to collect impedance distortion parameters, temperature distribution, and environmental humidity data of the radio frequency circuit in real time.

[0062] The intelligent algorithm module includes a reinforcement learning agent and a cross-domain transfer learning model. The reinforcement learning agent dynamically optimizes calibration parameters based on environmental perception data, and the transfer learning model supports parameter adaptation for different communication scenarios. The reinforcement learning agent defines the action space as the adjustment operation of the center frequency and impedance matching value of the radio frequency circuit.

[0063] The adaptive hardware module includes a programmable metasurface array and a biomimetic heat dissipation structure. The programmable metasurface array achieves beamforming based on dynamic reconstruction of electromagnetic properties, and the biomimetic heat dissipation structure ensures calibration stability under extreme temperatures. The biomimetic heat dissipation structure also has electromagnetic shielding function.

[0064] The interactive verification module integrates an augmented reality (AR) interface and a blockchain-based evidence storage unit to visualize the calibration process and store tamper-proof calibration records.

[0065] In one embodiment of the present invention: in the environmental sensing module, the terahertz wave scanner detects the microscopic impedance distortion of the radio frequency circuit through multi-band scanning, and its detection accuracy meets the following requirements:

[0066] ;

[0067] in, This is the impedance offset. The characteristic impedance is used to detect frequencies from Sub-6GHz to terahertz.

[0068] The infrared thermal imaging sensor and humidity sensor work together to construct an environmental temperature and humidity compensation model. The compensation formula is as follows:

[0069] ;

[0070] in, This is the impedance correction value after temperature and humidity compensation. The impedance value is measured in real time. These are the temperature compensation coefficient and the humidity compensation coefficient, respectively. , These represent the changes in temperature and humidity, respectively.

[0071] In one embodiment of the present invention: the optimization objective of the reinforcement learning agent is to minimize the weighted sum of the bit error rate and power consumption, and its reward function is defined as:

[0072] ;

[0073] in, These are dynamic weighting coefficients that are automatically adjusted based on the priority of the communication scenario. The action space includes the center frequency. and impedance matching value ,in The calculation is performed in real time using the Smith chart, and the formula is as follows:

[0074] ;

[0075] The reflection coefficient is provided by the environment perception module.

[0076] In one embodiment of the present invention: the programmable metasurface array operates in the Sub-6GHz to terahertz frequency band, and the modulation coefficients of its surface units... Optimize using the following constraints:

[0077] ;

[0078] in, The transmission coefficients are used, and the optimization results are loaded in real time via FPGA.

[0079] In one embodiment of the present invention: the biomimetic heat dissipation structure also has an electromagnetic shielding function, and its equivalent heat transfer coefficient satisfies:

[0080] ;

[0081] Furthermore, within the temperature range of -40℃ to 85℃, the heat dissipation efficiency formula is:

[0082] ;

[0083] in, For heat dissipation power, The equivalent heat transfer coefficient, Effective heat dissipation area ensures that calibration accuracy error fluctuation is less than ±0.5dB;

[0084] The honeycomb structure also provides electromagnetic shielding, and its shielding effectiveness meets the following requirements:

[0085] ;

[0086] in, For shielding effectiveness, The frequency range covers Sub-6GHz and some millimeter-wave communication bands, enabling the system to meet the electromagnetic compatibility requirements of 5G / 6G scenarios.

[0087] In one embodiment of the present invention: the AR interface is mapped to matching points on a Smith chart via gesture operations, and the gesture capture accuracy is achieved by a depth camera, whose coordinate mapping error satisfies:

[0088] ;

[0089] The depth camera's calibration frequency is no less than 30Hz, and attitude offset is corrected in real time using the following calibration formula:

[0090] ;

[0091] in, For the camera intrinsic parameter matrix, For rotation matrix, It is a translation matrix.

[0092] In one embodiment of the present invention: the blockchain evidence storage unit generates the hash value of the calibration certificate. It meets the following conditions:

[0093]

[0094] in, It is a hash algorithm used to convert input data into a fixed-length cryptographic hash value to ensure data integrity and traceability.

[0095] In one embodiment of the present invention: the adaptation process of the cross-domain transfer learning model is achieved through the transfer regularization coefficient. To balance the new scene data with the pre-trained model parameters, the loss function is:

[0096] ;

[0097] in, For loss function, For the first The actual output value of each sample For the model to the first The predicted output value for each sample. The total number of samples in the training dataset. , For the total number of parameters, This is the set of parameters for optimizing the transfer learning model in a new scenario. The model uses a set of parameters that have been trained in the source scenario and is dynamically adjusted according to the sample size of the target scenario. The model supports parameter migration from 5G base stations to satellite communication scenarios, and the bit error rate decreases by no less than 25% after adaptation.

[0098] In one embodiment of the present invention: the calibration efficiency of the system in a 5G base station scenario is improved by at least 40%, and the bit error rate after calibration meets the following requirements:

[0099] ;

[0100] Furthermore, after calibration, the bit error rate under dense electromagnetic interference environment has a standing wave ratio fluctuation range of less than 1.5 and a stability error of less than 0.02dB.

[0101] Please see the appendix Figure 1 Example 1: Implementation of an RF calibration system in a 5G base station scenario:

[0102] Implementation environment:

[0103] The deployment environment of a 5G base station in a densely populated urban area of ​​a city is characterized by the base station operating at 3.5 GHz (Sub-6 GHz), an ambient temperature range of -10℃ to 50℃, and humidity fluctuations ranging from 30% to 80%. The base station's radio frequency circuitry faces challenges from intense electromagnetic interference (such as signals from nearby base stations and radiation from industrial equipment) and impedance distortion caused by temperature and humidity variations.

[0104] Implementation process:

[0105] 1. Environmental perception module starts:

[0106] Terahertz wave scanners perform microscopic scanning of radio frequency circuits across multiple frequency bands (covering 3.5 GHz to 300 GHz) to detect impedance shifts in real time. The test results showed .

[0107] The infrared thermal imaging sensor captures the temperature distribution of the radio frequency chip, with the highest temperature reaching 65℃. The humidity sensor measures the ambient humidity at 45%. The temperature and humidity compensation model is based on the formula:

[0108] ;

[0109] in, =0.002 / ℃ =0.001 / %RH =15℃, dynamically correct impedance measurement value.

[0110] 2. Optimization of the intelligent algorithm module:

[0111] The reinforcement learning agent (based on the PPO algorithm) aims to minimize the bit error rate (BER) and power consumption by adjusting its center frequency according to environmental data. and impedance matching value

[0112] The reward function is calculated as follows:

[0113] ;

[0114] After optimization The frequency was slightly adjusted from 3.5GHz to 3.502GHz. Calculated using the Smith chart Reflectance coefficient Provided by the environmental perception module.

[0115] 3. Adaptive hardware module execution:

[0116] Programmable metasurface arrays utilize FPGA to load and optimize parameters in real time, allowing for control of surface unit coefficients. Constrained optimization of transmission coefficients This allows the beam to approach the target value, achieving beamforming and suppressing interference from nearby base stations.

[0117] The biomimetic heat dissipation structure, with its honeycomb porous design, effectively dissipates heat at a chip temperature of 65°C. ( , =0.08㎡), ensuring calibration accuracy error fluctuation <±0.3dB.

[0118] 4. Interactive verification module record:

[0119] The AR interface captures hand gestures using a depth camera (calibrated at 30Hz), maps matching points to the Smith chart, and achieves a coordinate error of <0.8mm, thus completing the visual calibration.

[0120] Blockchain notarization unit generates hash value The data is stored in a distributed ledger to ensure that it cannot be tampered with.

[0121] Implementation results:

[0122] The calibration efficiency is improved by 45%, and the time taken is reduced from 30 minutes in the traditional method to 16 minutes.

[0123] Bit error rate after calibration The VSWR fluctuation range is <1.3, and the stability error is <0.015dB.

[0124] The system maintains electromagnetic shielding effectiveness under dense interference. This meets the high reliability requirements of 5G base stations.

[0125] Please see the appendix Figure 2 Example 2: Cross-domain migration calibration implementation in satellite communication scenarios:

[0126] Implementation environment:

[0127] Low-Earth orbit satellite communication terminal, operating frequency band 28GHz (millimeter wave), ambient temperature -40℃ to 70℃, humidity <10%, needs to be migrated from 5G base station scenario to satellite communication scenario, and is adapted to high dynamic channel and extreme temperature range.

[0128] Implementation process:

[0129] 1. Cross-domain transfer learning adaptation:

[0130] The pre-trained model (source scenario: 5G base station) is loaded into the transfer learning framework and regularized by coefficients. To balance the data from the old and new scenarios, the loss function is:

[0131] ;

[0132] Using a small number of satellite scene samples ( (=500) Optimized model parameters, resulting in a 28% reduction in bit error rate after adaptation.

[0133] 2. Environmental perception and hardware collaboration:

[0134] Terahertz wave scanner detects impedance distortion in the millimeter-wave band ( The infrared sensor monitors the chip temperature at -30℃, triggering the biomimetic heat dissipation structure. ( , =110℃), to maintain calibration stability.

[0135] Programmable metasurface arrays dynamically reconstruct beam direction and optimize The transmission coefficient is brought to the target value to suppress the effects of atmospheric attenuation.

[0136] 3. Extreme Environment Validation:

[0137] Even at -40℃, the heat dissipation structure still ensures 200W, calibration error fluctuation < ±0.4dB.

[0138] Reinforcement learning agent dynamic adjustment up to 28.005GHz 48 Bit error rate Superior to satellite communication standards ( ).

[0139] 4. Blockchain and AR Collaboration:

[0140] The AR interface allows for precise positioning on the Smith chart in the millimeter-wave band via gestures, with a mapping error of <0.7mm.

[0141] Calibration record hash value Evidence storage supports cross-institutional audits.

[0142] Implementation results:

[0143] After the transfer learning model was adapted, the bit error rate decreased by 28% and the calibration efficiency improved by 38%.

[0144] The system maintains a VSWR fluctuation of <1.4 within a temperature range of -40℃ to 70℃, demonstrating excellent shielding effectiveness. .

[0145] It supports seamless switching between satellite and ground base stations, providing hardware-level calibration assurance for 6G integrated air-space-ground communication.

[0146] Please see the appendix Figure 3 Example 3: Multi-device collaborative calibration in an Industrial Internet of Things (IIoT) scenario:

[0147] Implementation environment:

[0148] The wireless sensor network in a smart manufacturing plant operates at a frequency of 2.4 GHz (ISM band), with the ambient temperature consistently between 25°C and 55°C and humidity fluctuating between 40% and 90%. Mechanical vibrations (frequency 10 Hz - 200 Hz, amplitude ≤ 2 mm) and radio frequency interference from multiple devices (such as AGV navigation signals and PLC control signals) within the plant cause impedance mismatch and signal attenuation in the radio frequency circuits.

[0149] Implementation process:

[0150] 1. Integration of vibration and environmental perception:

[0151] Terahertz wave scanners detect impedance distortion in radio frequency circuits and discover periodicity caused by mechanical vibration. Fluctuation (maximum) ).

[0152] The vibration compensation formula is expanded to include collaborative modeling using a new triaxial accelerometer and humidity sensor:

[0153] ;

[0154] in, / (m / s²) is the vibration compensation coefficient. =1.5m / s² is the vibration acceleration, and the impedance value is dynamically corrected.

[0155] 2. Multi-device interference suppression:

[0156] The reinforcement learning agent aims at multi-objective optimization (bit error rate, power consumption, channel capacity), and adds interference channel avoidance frequencies to the action space. .

[0157] The reward function is adjusted as follows:

[0158] ;

[0159] After optimization, Switching from 2.4GHz to 2.412GHz avoids AGV interference bands, increasing channel capacity by 18%.

[0160] 3. Vibration-resistant hardware design:

[0161] Programmable metasurface array surface unit control coefficient By introducing vibration frequency feedback, the phase is adjusted in real time via FPGA to counteract beam offset caused by vibration.

[0162] The biomimetic heat dissipation structure has been upgraded to a double-layer honeycomb design, improving heat dissipation power at 55℃. =250W ( =170 It integrates a shock-absorbing base to reduce the vibration transmission rate to below 5%.

[0163] 4. Multi-device collaborative calibration:

[0164] The AR interface supports multi-user collaborative operation. Users can select multiple RF nodes in the factory by gesture and load optimized parameters in batches, reducing calibration time from 5 minutes for a single device to 15 minutes for 10 devices.

[0165] The blockchain-based evidence storage unit uses sharding storage technology to store the hash values ​​of calibration records from 100 sensor nodes. to Aggregates into Merkle tree roots It is written to both the public and private blockchains.

[0166] Implementation results:

[0167] The calibration accuracy error fluctuation under vibration environment is <±0.4dB, which is 50% higher than the traditional method.

[0168] Bit error rate after multi-device interference suppression The channel capacity has been increased to 12Mbps.

[0169] The system supports concurrent calibration of 200+ devices within the factory, improving efficiency by 60% and reducing data storage and query latency to <50ms.

[0170] Please see the appendix Figure 4 Example 4: Dynamic Channel Prediction Calibration in Autonomous Driving V2X Scenarios:

[0171] Implementation environment:

[0172] The V2X communication network for urban highways operates at a frequency of 5.9GHz (DSRC band), with a vehicle speed range of 0-120km / h. The environment includes rainy and foggy weather (humidity ≥95%) and Doppler frequency shift (maximum ±500Hz). The radio frequency circuit needs to maintain low-latency communication (≤10ms) under high-speed movement and weather interference.

[0173] Implementation process:

[0174] 1. Weather perception and Doppler compensation:

[0175] Terahertz wave scanner combined with weather radar to detect rain and fog particle density and humidity compensation coefficient in real time. The value was dynamically adjusted from 0.001 / %RH to 0.002 / %RH.

[0176] The Doppler frequency shift prediction model estimates vehicle speed and direction using a Kalman filter, and the compensation formula is as follows:

[0177] ;

[0178] in, =30m / s (108km / h) =30°, after compensation, the frequency deviation error is <10Hz.

[0179] 2. Real-time channel reinforcement learning:

[0180] The reinforcement learning agent is introduced into the LSTM network to predict the channel state in the next 5ms, and the action space is expanded to the transmit power. With modulation method (QPSK / 16-QAM).

[0181] Add time delay constraints to the reward function:

[0182] ;

[0183] After optimization, latency Reduced from 12ms to 8ms under 16-QAM modulation .

[0184] 3. Waterproof and dynamic beam tracking:

[0185] The programmable metasurface array is coated with a nano-waterproof coating, which improves the dielectric constant stability by 30% in rain and fog environments.

[0186] The metasurface beam direction dynamically tracks the vehicle's position based on GPS data, with an azimuth adjustment rate of up to 50° / s and signal strength fluctuation of <1dB.

[0187] Implementation results:

[0188] Bit error rate in rain and fog This represents a 40% improvement compared to the uncompensated scenario.

[0189] After Doppler frequency shift compensation, the signal bit error rate remained stable at a vehicle speed of 120 km / h (fluctuation <0.5dB).

[0190] The system supports dense communication scenarios with 1000+ vehicles / km², with calibration parameters updated at a frequency of 100Hz and a latency compliance rate of 99.9%.

[0191] While the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the invention. Any variations and modifications can be made by those skilled in the art without departing from the spirit and scope of the invention. Therefore, any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention, without departing from the scope of the invention, fall within the protection scope defined by the claims of the present invention.

Claims

1. A calibration system based on radio frequency and communication circuits, characterized in that: The calibration system includes the following modules: An environmental sensing module, consisting of a terahertz wave scanner, an infrared thermal imaging sensor, and a humidity sensor, is used to collect impedance distortion parameters, temperature distribution, and environmental humidity data of the radio frequency circuit in real time. The intelligent algorithm module includes a reinforcement learning agent and a cross-domain transfer learning model. The reinforcement learning agent dynamically optimizes calibration parameters based on environmental perception data. The transfer learning model supports parameter adaptation for different communication scenarios. The reinforcement learning agent defines the action space as the adjustment operation of the center frequency and impedance matching value of the radio frequency circuit. An adaptive hardware module includes a programmable metasurface array and a biomimetic heat dissipation structure. The programmable metasurface array achieves beamforming based on dynamic reconstruction of electromagnetic properties. The biomimetic heat dissipation structure ensures calibration stability under extreme temperatures and also has electromagnetic shielding function. The interactive verification module integrates an augmented reality (AR) interface and a blockchain-based evidence storage unit, which is used to visualize the calibration process and store tamper-proof calibration records.

2. The calibration system based on radio frequency and communication circuits according to claim 1, characterized in that: In the environmental sensing module, the terahertz wave scanner detects the microscopic impedance distortion of the radio frequency circuit through multi-band scanning, and its detection accuracy meets the following requirements: ； in, This is the impedance offset. The characteristic impedance is used to detect frequencies from Sub-6GHz to terahertz. The infrared thermal imaging sensor and humidity sensor work together to construct an environmental temperature and humidity compensation model. The compensation formula is as follows: ； in, This is the impedance correction value after temperature and humidity compensation. The impedance value is measured in real time. These are the temperature compensation coefficient and the humidity compensation coefficient, respectively. , These represent the changes in temperature and humidity, respectively.

3. The calibration system based on radio frequency and communication circuits according to claim 2, characterized in that: The optimization objective of the reinforcement learning agent is to minimize the weighted sum of the bit error rate and power consumption, and its reward function is defined as: ； in, These are dynamic weighting coefficients that are automatically adjusted based on the priority of the communication scenario. The initial bit error rate before calibration. The bit error rate after calibration. The initial power consumption of the system before calibration. The system power consumption after calibration. Characteristic impedance, The reflection coefficient is provided by the environmental perception module, and the action space includes the center frequency. impedance matching value ,in The calculation is performed in real time using the Smith chart, and the formula is as follows: ； The reflection coefficient is provided by the environment perception module.

4. The calibration system based on radio frequency and communication circuits according to claim 3, characterized in that: The programmable metasurface array operates in the Sub-6GHz to terahertz frequency band, and the modulation coefficients of its surface units... Optimize using the following constraints: ； in, The transmission coefficients are used, and the optimization results are loaded in real time via FPGA.

5. A calibration system based on radio frequency and communication circuits according to claim 4, characterized in that: The biomimetic heat dissipation structure also has electromagnetic shielding function, and its equivalent heat transfer coefficient satisfies: ； Furthermore, within the temperature range of -40℃ to 85℃, the heat dissipation efficiency formula is: ； in, For heat dissipation power, The equivalent heat transfer coefficient, Effective heat dissipation area The operating temperature of the chip. The system's ambient temperature is used to keep the calibration accuracy error fluctuation within ±0.5dB; The honeycomb structure also provides electromagnetic shielding, and its shielding effectiveness meets the following requirements: ； in, For shielding effectiveness, The frequency range covers Sub-6GHz and some millimeter-wave communication bands, enabling the system to meet the electromagnetic compatibility requirements of 5G / 6G scenarios.

6. A calibration system based on radio frequency and communication circuits according to claim 5, characterized in that: The AR interface maps gesture operations to matching points on a Smith chart. The gesture capture accuracy is achieved by a depth camera, and its coordinate mapping error satisfies the following: ； The calibration frequency of the depth camera is no less than 30Hz, and the attitude offset is corrected in real time using the following calibration formula: ； in, Here is the pose transformation matrix of the depth camera. For the camera intrinsic parameter matrix, Let be a rotation matrix. It is a translation matrix.

7. A calibration system based on radio frequency and communication circuits according to claim 6, characterized in that: The blockchain evidence storage unit generates the hash value of the calibration certificate. It meets the following conditions: ； in, It is a hash algorithm used to convert input data into a fixed-length cryptographic hash value to ensure data integrity and traceability.

8. A calibration system based on radio frequency and communication circuits according to claim 7, characterized in that: The adaptation process of the cross-domain transfer learning model is achieved through the transfer regularization coefficient. To balance the new scene data with the pre-trained model parameters, the loss function is: ； in, For loss function, For the first The actual output value of each sample For the model to the first The predicted output value for each sample. The total number of samples in the training dataset. , For the total number of parameters, This is the set of parameters for optimizing the transfer learning model in a new scenario. The model is a set of parameters that has been trained in the source scenario. It is dynamically adjusted according to the sample size of the target scenario. The model supports parameter migration from 5G base stations to satellite communication scenarios. After adaptation, the bit error rate decreases by no less than 25%.

9. A calibration system for radio frequency and communication circuits according to claim 8, characterized in that: The system improves calibration efficiency by at least 40% in 5G base station scenarios, and the bit error rate after calibration meets the following requirements: ； Furthermore, the calibrated bit error rate exhibits a standing wave ratio fluctuation range of less than 1.5 and a stability error of less than 0.02dB under dense electromagnetic interference conditions.

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