Intelligent control method for crystal resonator based on adaptive frequency compensation

Abstract

The present application belongs to the technical field of crystal resonator management and control, and specifically relates to a crystal resonator intelligent control method based on adaptive frequency compensation, which comprises data monitoring and collection, feature extraction and fusion, adaptive compensation decision, dynamic execution adjustment and closed-loop calibration analysis; the present application realizes adaptive frequency compensation and automatic and accurate control of the crystal resonator by means of real-time monitoring of environmental parameters through multi-sensor fusion and dynamic generation of the optimal compensation strategy in combination with a machine learning algorithm, significantly improves the long-term stability and reliability of the crystal resonator under complex environments, is conducive to ensuring the operating performance of the crystal resonator, and is conducive to the timely adoption of corresponding improvement and optimization measures by supervisors through reasonable analysis of the abnormal performance collected and monitored for the crystal resonator and the power consumption status of the crystal resonator and timely alarm, further ensures the operating performance of the crystal resonator and significantly reduces the operating supervision difficulty.

Description

Technical Field

[0001] This invention relates to the field of crystal resonator control technology, specifically a smart control method for crystal resonators based on adaptive frequency compensation. Background Technology

[0002] A crystal resonator is an electronic component based on quartz crystals or other piezoelectric materials. Its core principle is to use the piezoelectric effect to realize the mutual conversion between mechanical vibration and electrical signals. Its high-precision and high-stability frequency output is the cornerstone of technologies such as communication, navigation, and computing.

[0003] Traditional crystal resonator frequency compensation methods generally adopt fixed compensation strategies or simple linear models, which cannot dynamically adapt to environmental changes and cope with nonlinear disturbances, resulting in insufficient accuracy. It is difficult to achieve adaptive frequency compensation and automated precise control of crystal resonators. Furthermore, it is impossible to reasonably analyze and promptly alarm on abnormal performance and power consumption of crystal resonators, making it difficult to guarantee the operating performance of crystal resonators and significantly reduce the difficulty of their operation and supervision.

[0004] To address the aforementioned technical shortcomings, a solution is proposed. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent control method for crystal resonators based on adaptive frequency compensation. This method solves the problems of existing technologies, which cannot dynamically adapt to environmental changes and cope with nonlinear disturbances, making it difficult to achieve adaptive frequency compensation and automated precise control of crystal resonators. Furthermore, it cannot reasonably analyze and promptly alarm on abnormal performance and power consumption of crystal resonators, making it difficult to guarantee the operating performance of crystal resonators and significantly reduce the difficulty of their operation and supervision.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] The intelligent control method for crystal resonators based on adaptive frequency compensation includes the following steps:

[0008] Step 1: The multi-parameter sensing and transmission module collects the operating environment parameters and output signal characteristics of the crystal resonator in real time. The raw signal is transmitted to the feature extraction and fusion module via the SPI / I2C bus.

[0009] Step 2: The feature extraction and fusion module processes the original signal to obtain a standardized feature vector, which is then transmitted to the adaptive compensation decision module via a DMA channel.

[0010] Step 3: The adaptive compensation decision module trains a lightweight neural network model based on historical datasets, learns the mapping relationship between environmental parameters and compensation amounts, inputs the current feature vector and historical data from the previous ten time steps into the model, and the model analyzes the input data and outputs a combination of compensation parameters.

[0011] Step 4: The dynamic execution adjustment module converts the compensation voltage into an analog signal via a 12-bit DAC, applies it to the voltage-controlled terminal of the resonator, synchronously adjusts the load capacitance and loop gain, and outputs a frequency signal to the phase-locked loop.

[0012] Step 5: The closed-loop calibration analysis module monitors the instantaneous deviation of the output frequency through a phase-locked loop. When the instantaneous deviation of the output frequency exceeds ±0.5ppm, recalibration is triggered.

[0013] Furthermore, in step one, the multi-parameter sensing and transmission module includes a temperature sensor, a triaxial vibration sensor, a voltage monitoring sensor, and a frequency counter; wherein, the temperature sensor is used to monitor the surface temperature of the crystal resonator and the ambient temperature, with an accuracy of ±0.05℃;

[0014] The triaxial vibration sensor is used to capture the axial vibration spectrum of the crystal resonator along the X, Y, and Z axes, with a bandwidth of 0-15kHz; the voltage monitoring sensor is used to detect the driving voltage fluctuation of the crystal resonator, with a resolution of 1mV; and the frequency counter is used to measure the instantaneous value of the output frequency, with an accuracy of ±0.1ppm.

[0015] Furthermore, in step two, the processing procedure of the feature extraction and fusion module includes:

[0016] Noise suppression: Kalman filtering and moving average processing are applied to the original signal;

[0017] Feature generation: Extract time-domain features including temperature change rate, vibration dominant frequency energy ratio and voltage ripple peak value, analyze the harmonic distribution of vibration spectrum through fast Fourier transform, and calculate aging factor;

[0018] Data fusion: Integrating multi-dimensional features into a standardized feature vector with a dimension ≤ 20.

[0019] Furthermore, in step three, the compensation parameter combination includes voltage compensation amount, dynamic PID parameters, and filter cutoff frequency; wherein, the voltage compensation amount is adjustable from 0 to 3.3V in 1mV increments; and the filter cutoff frequency is dynamically adjusted according to the vibration spectrum.

[0020] Furthermore, in step five, when recalibration is triggered, the recalibration process is as follows:

[0021] Freeze the current compensation parameters and collect frequency fluctuation data within 5ms;

[0022] The main sources of interference were identified using fast Fourier analysis;

[0023] The correction coefficients are generated and fed back to the adaptive compensation decision module to update the compensation parameters for the next cycle.

[0024] Furthermore, the adaptive compensation decision module, dynamic execution adjustment module, and closed-loop calibration analysis module are all communicatively connected to the monitoring terminal. The adaptive compensation decision module, dynamic execution adjustment module, and closed-loop calibration analysis module send compensation decision information, dynamic execution adjustment information, and closed-loop calibration analysis information to the monitoring terminal for display, and the monitoring terminal is communicatively connected to the data acquisition and monitoring module.

[0025] The data acquisition and monitoring module analyzes the data acquisition and coordination performance of all sensors in the multi-parameter sensing and transmission module, and generates a data acquisition and coordination alarm signal or a normal data acquisition and coordination signal accordingly. The alarm signal or normal data acquisition and coordination signal is then sent to the monitoring terminal for display. When the monitoring terminal receives the data acquisition and coordination alarm signal, it issues a corresponding warning.

[0026] Furthermore, the specific analysis process of the data acquisition and monitoring module is as follows:

[0027] All sensors involved in the multi-parameter sensing and transmission module are acquired, and the corresponding sensor is marked as i, where i is a natural number greater than 1; the production date of sensor i is collected and the interval between it and the current date is marked as the production time characteristic value, and the total running time of sensor i in the historical period is marked as the running time characteristic value; and the current time is taken as the end time and traced back to a traceability period with a set duration of T1, and the ratio of the number of times sensor i failed during the traceability period to the running time of sensor i during the traceability period is calculated to obtain the monitoring stability value;

[0028] The perceived hidden danger value is calculated by weighted summation of production-time characteristic value, operation-time characteristic value and monitoring stability value. The perceived hidden danger value is compared with the corresponding preset perceived hidden danger threshold. If the perceived hidden danger value exceeds the corresponding preset perceived hidden danger threshold, sensor i is marked as a resistance sensor.

[0029] If the perceived hidden danger value does not exceed the corresponding preset perceived hidden danger threshold, the number of times the sampling frequency of sensor i is not within the corresponding preset sampling frequency range within a unit time is obtained and marked as sampling frequency anomaly value. The sampling frequency anomaly value is compared with the corresponding preset sampling frequency anomaly threshold. If the sampling frequency anomaly value exceeds the corresponding preset sampling frequency anomaly threshold, sensor i is marked as a resistance sensor.

[0030] If the frequency deviation value collected does not exceed the corresponding preset frequency deviation threshold, the frequency deviation amplitude data is obtained when the collection frequency is not within the corresponding preset collection frequency range. The average value of all frequency deviation amplitude data within a unit time is calculated to obtain the frequency deviation performance value, and the frequency deviation amplitude data with the largest value within a unit time is marked as the frequency deviation amplitude value.

[0031] The collected monitoring value is obtained by weighted summation of the collected frequency variation value, frequency deviation performance value, and frequency deviation amplitude value. The collected monitoring value is then compared with the corresponding preset collection monitoring threshold. If the collected monitoring value exceeds the corresponding preset collection monitoring threshold, sensor i is marked as a resistance sensor. If the multi-parameter sensing and transmission module involves a resistance sensor, a collection coordination alarm signal is generated. If the multi-parameter sensing and transmission module does not involve a resistance sensor, a collection coordination normal signal is generated.

[0032] Furthermore, the monitoring terminal is connected to a multi-mode switching module. The multi-mode switching module calculates the environmental complexity index based on a comprehensive analysis of temperature change rate, vibration energy, and frequency deviation. Based on the environmental complexity index, it dynamically switches modes, including low-power mode, balanced mode, and high-performance mode. Among them, the low-power mode maintains only the core functions, the balanced mode maintains the baseline operating state, and the high-performance mode maintains full-load operation.

[0033] Furthermore, the monitoring terminal communicates with the power management and evaluation module. The monitoring terminal sends the collected normal signal to the power management and evaluation module. When the power management and evaluation module receives the collected normal signal, it analyzes the power consumption of the crystal resonator within a unit of time. Through analysis, it generates a power management qualified signal or a power management alarm signal and sends the power management qualified signal or power management alarm signal to the monitoring terminal for display. When the monitoring terminal receives the power management alarm signal, it issues a corresponding warning.

[0034] Furthermore, the specific analysis process of the power management evaluation module is as follows:

[0035] Several detection periods are set within a unit of time. The energy consumption of the crystal resonator in the corresponding detection period is collected and compared with the corresponding preset energy consumption standard value. If the energy consumption exceeds the preset energy consumption standard value, the corresponding detection period is marked as an abnormal energy consumption period. The number of abnormal energy consumption periods within a unit of time is obtained and the ratio with the total number of detection periods is calculated to obtain the abnormal energy consumption characteristic value. The abnormal energy consumption characteristic value is compared with the preset abnormal energy consumption characteristic threshold. If the abnormal energy consumption characteristic value exceeds the preset abnormal energy consumption characteristic threshold, a power consumption management alarm signal is generated.

[0036] If the abnormal characteristic value of the energy consumption tube does not exceed the preset abnormal characteristic threshold of the energy consumption tube, the ratio of the energy consumption of the corresponding detection period to the corresponding preset energy consumption standard value is calculated to obtain the energy consumption measurement value, the average of the energy consumption measurement values ​​of all detection periods is calculated to obtain the energy consumption analysis value, and the energy consumption measurement value with the largest value is marked as the energy consumption abnormal value.

[0037] The power management evaluation value is obtained by weighted summation of abnormal power consumption characteristic values, energy consumption analysis values, and abnormal energy consumption amplitude values. The power management evaluation value is then compared with a preset power management evaluation threshold. If the power management evaluation value exceeds the preset power management evaluation threshold, a power management alarm signal is generated; if the power management evaluation value does not exceed the preset power management evaluation threshold, a power management qualified signal is generated.

[0038] Compared with the prior art, the beneficial effects of the present invention are:

[0039] 1. In this invention, by integrating data monitoring and acquisition, feature extraction and fusion, adaptive compensation decision-making, dynamic execution adjustment and closed-loop calibration analysis, the environmental parameters of the crystal resonator can be monitored in real time and the optimal compensation strategy can be dynamically generated through analysis. This enables adaptive frequency compensation and automated precise control of the crystal resonator, significantly improving the long-term stability and reliability of the resonator in complex environments, which is beneficial to ensuring the operating performance of the crystal resonator and has a high level of intelligence.

[0040] 2. In this invention, the acquisition and coordination monitoring module analyzes the acquisition and coordination performance of all sensors in the multi-parameter sensing and transmission module. When an acquisition and coordination alarm signal is generated, the corresponding sensor is inspected, repaired, or replaced. This helps maintain the accuracy of the crystal resonator compensation analysis results and the control stability. When a normal acquisition and coordination signal is generated, the power consumption status of the crystal resonator is analyzed through the power consumption management evaluation module. When a power consumption management alarm signal is generated, reasonable improvement and optimization measures are taken to ensure the safe, stable, and energy-saving operation of the crystal resonator, further reducing the difficulty of crystal resonator operation supervision. Attached Figure Description

[0041] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0042] Figure 1 This is a flowchart of the method in Embodiment 1 of the present invention;

[0043] Figure 2 This is a system block diagram of Embodiment 1 of the present invention;

[0044] Figure 3 This is a system block diagram of Embodiments 2 and 3 of the present invention. Detailed Implementation

[0045] 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, and 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.

[0046] Example 1: As Figure 1-2 As shown, the intelligent control method for crystal resonators based on adaptive frequency compensation proposed in this invention includes the following steps:

[0047] Step 1: The multi-parameter sensing and transmission module collects the operating environment parameters and output signal characteristics of the crystal resonator in real time. The raw signal is transmitted to the feature extraction and fusion module via the SPI / I2C bus. It should be noted that the multi-parameter sensing and transmission module includes a temperature sensor, a triaxial vibration sensor, a voltage monitoring sensor, and a frequency counter. Among them, the temperature sensor is used to monitor the surface temperature of the crystal resonator and the ambient temperature, with an accuracy of ±0.05℃.

[0048] The triaxial vibration sensor is used to capture the axial vibration spectrum of the crystal resonator along the X, Y, and Z axes, with a bandwidth of 0-15kHz; the voltage monitoring sensor is used to detect the driving voltage fluctuation of the crystal resonator, with a resolution of 1mV; and the frequency counter is used to measure the instantaneous value of the output frequency, with an accuracy of ±0.1ppm.

[0049] Step 2: The feature extraction and fusion module processes the original signal to obtain a standardized feature vector. The standardized feature vector is then transmitted to the adaptive compensation decision module via a DMA channel. The specific processing procedure is as follows:

[0050] Noise suppression: Kalman filtering and moving average processing are applied to the original signal to eliminate high-frequency interference and transient noise;

[0051] Feature generation: Extract time-domain features such as temperature change rate, vibration dominant frequency energy ratio and voltage ripple peak-to-peak value, analyze the harmonic distribution of the vibration spectrum through Fast Fourier Transform (FFT), and calculate the aging factor (based on the weighted value of cumulative running time and temperature stress).

[0052] Data fusion: Integrating multi-dimensional features into standardized feature vectors with ≤20 dimensions for subsequent decision-making.

[0053] Step 3: The adaptive compensation decision module trains a lightweight neural network model based on historical datasets (including temperature, vibration, frequency deviation, etc.), learns the mapping relationship between environmental parameters and compensation amounts, and inputs the current feature vector and historical data from the previous ten time steps into the model. The model analyzes the input data and outputs a combination of compensation parameters.

[0054] The compensation parameter combination includes voltage compensation (adjustment range 0-3.3V, step 1mV), dynamic PID parameters (weight allocation of proportional, integral, and derivative terms), and filter cutoff frequency (dynamically adjusted according to the vibration spectrum).

[0055] Step 4: The dynamic execution adjustment module converts the compensation voltage into an analog signal via a 12-bit DAC and applies it to the voltage-controlled terminal of the resonator. It simultaneously adjusts the load capacitance and loop gain to ensure the stability of the resonant point and outputs the frequency signal to the phase-locked loop (PLL) to further suppress phase noise.

[0056] Furthermore, the dynamic execution adjustment module mainly involves a voltage-controlled oscillator circuit (receiving compensation voltage and adjusting the output frequency of the crystal resonator), a programmable load capacitor array (switching capacitor values ​​according to instructions, ranging from 10-30pF in 0.5pF steps), and a digital potentiometer (adjusting the feedback loop gain to match the PID parameters).

[0057] Step 5: The closed-loop calibration analysis module monitors the instantaneous deviation of the output frequency through a phase-locked loop. When the instantaneous deviation of the output frequency exceeds ±0.5ppm, recalibration is triggered. The recalibration process is as follows:

[0058] Freeze the current compensation parameters and collect frequency fluctuation data within 5ms;

[0059] The main sources of disturbance (such as temperature or vibration) were identified using fast Fourier analysis.

[0060] The correction coefficients are generated and fed back to the adaptive compensation decision module to update the compensation parameters for the next cycle; and the calibration results are written to non-volatile memory for long-term aging compensation.

[0061] It should be noted that the adaptive compensation decision module, dynamic execution adjustment module, and closed-loop calibration analysis module are all communicatively connected to the monitoring terminal. The adaptive compensation decision module, dynamic execution adjustment module, and closed-loop calibration analysis module send compensation decision information, dynamic execution adjustment information, and closed-loop calibration analysis information to the monitoring terminal for display, so that users can have a detailed understanding of the relevant information and make timely manual intervention and control, thereby further ensuring the operating effect of the crystal resonator.

[0062] Furthermore, the monitoring terminal is connected to a multi-mode switching module. This module calculates the environmental complexity index based on a comprehensive analysis of temperature change rate, vibration energy, and frequency deviation. It then dynamically switches modes based on this index, including low-power mode, balanced mode, and high-performance mode. The switching information is sent to the monitoring terminal so that monitoring personnel can have a detailed understanding of the mode information and intervene manually as needed. It should be noted that the formula for calculating the environmental complexity index is as follows:

[0063] In the formula, ECI represents the environmental complexity index, |dT / dt| represents the temperature change rate, dT represents the temperature change amount, and dt represents the duration; RMS represents the vibration energy, and |Δf| represents the frequency deviation.

[0064] Among them, the low-power mode (preferred, when ECI < 1) only maintains the core functions (e.g., only maintains the power supply for the core sensors and clock circuits); the balanced mode (preferred, when 1 ≤ ECI < 3) maintains the baseline operating state and shuts down redundant computing units; the high-performance mode (preferred, when ECI ≥ 3) maintains full-load operation; through dynamic switching management of modes, power consumption is reduced while ensuring the operating effect of the crystal resonator.

[0065] This invention achieves stable frequency deviation of crystal resonators within ±0.3ppm over a wide temperature range of -40℃ to 85℃ by real-time monitoring of environmental parameters through multi-sensor fusion and dynamic generation of optimal compensation strategies using machine learning algorithms. It also improves phase noise by more than 10dBc / Hz@1kHz and has a fast dynamic response capability of less than 10ms. This significantly enhances the long-term stability and reliability of resonators in complex environments, which is beneficial for ensuring the operating performance of crystal resonators and reducing the difficulty of operation and supervision.

[0066] Example 2: Figure 3 As shown, the difference between this embodiment and Embodiment 1 is that the monitoring terminal is connected to the acquisition and coordination monitoring module. The acquisition and coordination monitoring module analyzes the acquisition and coordination performance of all sensors in the multi-parameter sensing and transmission module, and generates an acquisition and coordination alarm signal or an acquisition and coordination normal signal accordingly. The acquisition and coordination alarm signal or acquisition and coordination normal signal is then sent to the monitoring terminal for display.

[0067] When the monitoring terminal receives an alarm signal related to data acquisition coordination, it issues a corresponding warning to remind regulatory personnel to inspect, repair, or replace the relevant sensors. This ensures the monitoring and acquisition performance of the crystal resonator, which is beneficial for maintaining the accuracy of the crystal resonator compensation analysis results and the stability of control. The specific analysis process of the data acquisition coordination monitoring module is as follows:

[0068] All sensors involved in the multi-parameter sensing and transmission module are acquired, and the corresponding sensor is marked as i, where i is a natural number greater than 1; the production date of sensor i is collected and the interval between it and the current date is marked as the production time characteristic value, and the total running time of sensor i in the historical period is marked as the running time characteristic value; and the current time is taken as the end time and traced back to a traceability period with a set duration of T1, and the ratio of the number of times sensor i failed during the traceability period to the running time of sensor i during the traceability period is calculated to obtain the monitoring stability value;

[0069] The perceived hidden danger value is obtained by weighted summation of the production-time characteristic value, the operation-time characteristic value, and the monitoring stable anomaly value. Specifically, each of the production-time characteristic value, the operation-time characteristic value, and the monitoring stable anomaly value is assigned a corresponding preset weight coefficient, and then each of the production-time characteristic value, the operation-time characteristic value, and the monitoring stable anomaly value is multiplied by the corresponding preset weight coefficient. The sum of the three sets of products is marked as the perceived hidden danger value. Furthermore, the larger the perceived hidden danger value, the worse the overall quality of sensor i is.

[0070] The perceived hazard value is compared with the corresponding preset perceived hazard threshold. If the perceived hazard value exceeds the corresponding preset perceived hazard threshold, it indicates that the overall quality of sensor i is poor, which is not conducive to ensuring its monitoring and acquisition performance. In this case, sensor i is marked as a resistance sensor.

[0071] If the perceived potential hazard value does not exceed the corresponding preset perceived potential hazard threshold, it indicates that the overall quality of sensor i is good. Then, the number of times the sampling frequency of sensor i is not within the corresponding preset sampling frequency range within a unit time is obtained and marked as sampling frequency anomaly value. The sampling frequency anomaly value is compared with the corresponding preset sampling frequency anomaly threshold. If the sampling frequency anomaly value exceeds the corresponding preset sampling frequency anomaly threshold, it indicates that the sampling frequency of sensor i is unstable initially. Then, sensor i is marked as a resistance sensor.

[0072] If the frequency deviation value collected does not exceed the corresponding preset frequency deviation threshold, the frequency deviation amplitude data is obtained when the collection frequency is not within the corresponding preset collection frequency range. The average value of all frequency deviation amplitude data within a unit time is calculated to obtain the frequency deviation performance value, and the frequency deviation amplitude data with the largest value within a unit time is marked as the frequency deviation amplitude value.

[0073] The acquired monitoring value is obtained by weighted summation of the acquired frequency anomaly value, frequency deviation performance value, and frequency deviation amplitude value. Specifically, each of the acquired frequency anomaly value, frequency deviation performance value, and frequency deviation amplitude value is assigned a corresponding preset weight coefficient, and then multiplied by the corresponding preset weight coefficient. The sum of the three products is then marked as the acquired monitoring value. Furthermore, the larger the acquired monitoring value, the worse the overall performance of sensor i's acquisition frequency execution per unit time.

[0074] The collected monitoring value is compared with the corresponding preset collection monitoring threshold. If the collected monitoring value exceeds the corresponding preset collection monitoring threshold, it indicates that the overall performance of sensor i's collection frequency is poor per unit time. Then, sensor i is marked as a resistance sensor.

[0075] If the multi-parameter sensing and transmission module involves a resistance sensor, it indicates that the acquisition and monitoring coordination of the multi-parameter sensing and transmission module is poor, and an acquisition coordination alarm signal is generated; if the multi-parameter sensing and transmission module does not involve a resistance sensor, it indicates that the acquisition and monitoring coordination of the multi-parameter sensing and transmission module is good, and a acquisition coordination normal signal is generated.

[0076] Example 3: Figure 3 As shown, the difference between this embodiment and Embodiment 1 and Embodiment 2 is that the monitoring terminal is connected to the power management evaluation module. The monitoring terminal sends the collected normal cooperation signal to the power management evaluation module. When the power management evaluation module receives the collected normal cooperation signal, it analyzes the power consumption of the crystal resonator within a unit time and generates a power management qualified signal or a power management alarm signal through the analysis.

[0077] Furthermore, the power management pass signal or power management alarm signal is sent to the monitoring terminal for display. When the monitoring terminal receives the power management alarm signal, it issues a corresponding warning to remind the monitoring personnel to promptly investigate and analyze the cause and take reasonable improvement and optimization measures to avoid the power consumption of the crystal resonator exceeding the standard, ensure the safe, stable and energy-saving operation of the crystal resonator, and further reduce the difficulty of monitoring the operation of the crystal resonator. The specific analysis process of the power management evaluation module is as follows:

[0078] Several detection periods are set within a unit of time, and all detection periods have the same duration. The energy consumption of the crystal resonator in the corresponding detection period is collected and compared with the corresponding preset energy consumption standard value. If the energy consumption exceeds the preset energy consumption standard value, it indicates that the energy consumption of the corresponding detection period is high and does not meet the power consumption management requirements. The corresponding detection period is then marked as an abnormal power consumption period.

[0079] The number of abnormal power consumption periods per unit time is obtained and the ratio of this number to the total number of detection periods is calculated to obtain the abnormal power consumption characteristic value. The abnormal power consumption characteristic value is compared with the preset abnormal power consumption characteristic threshold. If the abnormal power consumption characteristic value exceeds the preset abnormal power consumption characteristic threshold, it indicates that the power consumption management performance of the crystal resonator is poor per unit time, and a power consumption management alarm signal is generated.

[0080] If the abnormal characteristic value of the energy consumption tube does not exceed the preset abnormal characteristic threshold of the energy consumption tube, the ratio of the energy consumption of the corresponding detection period to the corresponding preset energy consumption standard value is calculated to obtain the energy consumption measurement value, the average of the energy consumption measurement values ​​of all detection periods is calculated to obtain the energy consumption analysis value, and the energy consumption measurement value with the largest value is marked as the energy consumption abnormal value.

[0081] The power management evaluation value is obtained by weighted summation of abnormal power consumption characteristic values, power consumption analysis values, and power consumption aberration values. Specifically, each of the abnormal power consumption characteristic value, power consumption analysis value, and power consumption aberration value is assigned a corresponding preset weight coefficient, and then each of these values ​​is multiplied by its respective preset weight coefficient. The sum of the three products is then marked as the power management evaluation value. Furthermore, the larger the power management evaluation value, the worse the overall power management performance of the crystal resonator per unit time.

[0082] The power management evaluation value is compared with the preset power management evaluation threshold. If the power management evaluation value exceeds the preset power management evaluation threshold, it indicates that the overall power management performance of the crystal resonator is poor per unit time, and a power management alarm signal is generated. If the power management evaluation value does not exceed the preset power management evaluation threshold, it indicates that the overall power management performance of the crystal resonator is good per unit time, and a power management qualified signal is generated.

[0083] The working principle of this invention is as follows: During use, the multi-parameter sensing and transmission module collects the operating environment parameters and output signal characteristics of the crystal resonator in real time. The feature extraction and fusion module processes the original signal to obtain a standardized feature vector. The adaptive compensation decision module analyzes the input data and outputs a combination of compensation parameters. The dynamic execution adjustment module executes corresponding compensation adjustment measures. The closed-loop calibration analysis module monitors the instantaneous deviation of the output frequency and determines whether to trigger recalibration. This achieves adaptive frequency compensation and automated precise control of the crystal resonator, improving the long-term stability and reliability of the crystal resonator in complex environments. Furthermore, by reasonably analyzing and promptly alarming the abnormal performance and power consumption of the crystal resonator, it is beneficial for supervisory personnel to take timely improvement and optimization measures, further ensuring the operating performance of the crystal resonator and significantly reducing the difficulty of its operation supervision. The invention demonstrates a high level of intelligence.

[0084] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. The determination of the threshold in the technical solution is based on the average value of data obtained through training with a large number of data dimensions. The preferred embodiments do not exhaustively describe all details, nor do they limit the invention to specific implementation methods. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A smart control method for crystal resonators based on adaptive frequency compensation, characterized in that, Includes the following steps: Step 1: The multi-parameter sensing and transmission module collects the operating environment parameters and output signal characteristics of the crystal resonator in real time. The raw signal is transmitted to the feature extraction and fusion module via the SPI / I2C bus. Step 2: The feature extraction and fusion module processes the original signal to obtain a standardized feature vector, which is then transmitted to the adaptive compensation decision module via a DMA channel. Step 3: The adaptive compensation decision module trains a lightweight neural network model based on historical datasets, learns the mapping relationship between environmental parameters and compensation amounts, inputs the current feature vector and historical data from the previous ten time steps into the model, and the model analyzes the input data and outputs a combination of compensation parameters. Step 4: The dynamic execution adjustment module converts the compensation voltage into an analog signal via a 12-bit DAC, applies it to the voltage-controlled terminal of the resonator, synchronously adjusts the load capacitance and loop gain, and outputs a frequency signal to the phase-locked loop. Step 5: The closed-loop calibration analysis module monitors the instantaneous deviation of the output frequency through a phase-locked loop. When the instantaneous deviation of the output frequency exceeds ±0.5ppm, recalibration is triggered. The adaptive compensation decision module, dynamic execution adjustment module, and closed-loop calibration analysis module are all connected to the monitoring terminal. The adaptive compensation decision module, dynamic execution adjustment module, and closed-loop calibration analysis module send compensation decision information, dynamic execution adjustment information, and closed-loop calibration analysis information to the monitoring terminal for display. The monitoring terminal is also connected to the data acquisition and monitoring module. The data acquisition and monitoring module analyzes the data acquisition and coordination performance of all sensors in the multi-parameter sensing and transmission module, and generates a data acquisition and coordination alarm signal or a normal data acquisition and coordination signal accordingly. The alarm signal or normal data acquisition and coordination signal is then sent to the monitoring terminal for display. When the monitoring terminal receives the data acquisition and coordination alarm signal, it issues a corresponding warning. The specific analysis process of the data acquisition and monitoring module is as follows: All sensors involved in the multi-parameter sensing and transmission module are acquired, and the corresponding sensor is marked as i, where i is a natural number greater than 1; the production date of sensor i is collected and the interval between it and the current date is marked as the production time feature value, and the total running time of sensor i in the historical stage is marked as the running time feature value; Furthermore, taking the current time as the end time and tracing back to a tracing period of set duration T1, the ratio of the number of times sensor i failed during the tracing period to the running time of sensor i during the tracing period is calculated to obtain the monitoring stability value. The perceived hidden danger value is calculated by weighted summation of production-time characteristic value, operation-time characteristic value and monitoring stability value. The perceived hidden danger value is compared with the corresponding preset perceived hidden danger threshold. If the perceived hidden danger value exceeds the corresponding preset perceived hidden danger threshold, sensor i is marked as a resistance sensor. If the perceived hidden danger value does not exceed the corresponding preset perceived hidden danger threshold, the number of times the sampling frequency of sensor i is not within the corresponding preset sampling frequency range within a unit time is obtained and marked as sampling frequency anomaly value. The sampling frequency anomaly value is compared with the corresponding preset sampling frequency anomaly threshold. If the sampling frequency anomaly value exceeds the corresponding preset sampling frequency anomaly threshold, sensor i is marked as a resistance sensor. If the frequency deviation value collected does not exceed the corresponding preset frequency deviation threshold, the frequency deviation amplitude data is obtained when the collection frequency is not within the corresponding preset collection frequency range. The average value of all frequency deviation amplitude data within a unit time is calculated to obtain the frequency deviation performance value, and the frequency deviation amplitude data with the largest value within a unit time is marked as the frequency deviation amplitude value. The collected monitoring value is obtained by weighted summation of the collected frequency variation value, frequency deviation performance value, and frequency deviation amplitude value. The collected monitoring value is then compared with the corresponding preset collection monitoring threshold. If the collected monitoring value exceeds the corresponding preset collection monitoring threshold, sensor i is marked as a resistance sensor. If the multi-parameter sensing and transmission module involves a resistance sensor, a collection coordination alarm signal is generated. If the multi-parameter sensing and transmission module does not involve a resistance sensor, a collection coordination normal signal is generated. The monitoring terminal communicates with the power management and evaluation module. The monitoring terminal collects and sends the normal cooperation signal to the power management and evaluation module. When the power management and evaluation module receives the normal cooperation signal, it analyzes the power consumption of the crystal resonator within a unit time. Through analysis, it generates a power management qualified signal or a power management alarm signal and sends the power management qualified signal or power management alarm signal to the monitoring terminal for display. When the monitoring terminal receives the power management alarm signal, it issues a corresponding warning. The specific analysis process of the power management evaluation module is as follows: Several detection periods are set within a unit of time. The energy consumption of the crystal resonator in the corresponding detection period is collected and compared with the corresponding preset energy consumption standard value. If the energy consumption exceeds the preset energy consumption standard value, the corresponding detection period is marked as an abnormal energy consumption period. The number of abnormal energy consumption periods within a unit of time is obtained and the ratio with the total number of detection periods is calculated to obtain the abnormal energy consumption characteristic value. The abnormal energy consumption characteristic value is compared with the preset abnormal energy consumption characteristic threshold. If the abnormal energy consumption characteristic value exceeds the preset abnormal energy consumption characteristic threshold, a power consumption management alarm signal is generated. If the abnormal characteristic value of the energy consumption tube does not exceed the preset abnormal characteristic threshold of the energy consumption tube, the ratio of the energy consumption of the corresponding detection period to the corresponding preset energy consumption standard value is calculated to obtain the energy consumption measurement value, the average of the energy consumption measurement values ​​of all detection periods is calculated to obtain the energy consumption analysis value, and the energy consumption measurement value with the largest value is marked as the energy consumption abnormal value. The power management evaluation value is obtained by weighted summation of abnormal power consumption characteristic values, energy consumption analysis values, and abnormal energy consumption amplitude values. The power management evaluation value is compared with the preset power management evaluation threshold. If the power management evaluation value exceeds the preset power management evaluation threshold, a power management alarm signal is generated. If the power management evaluation value does not exceed the preset power management evaluation threshold, a power management qualified signal is generated.

2. The intelligent control method for crystal resonators based on adaptive frequency compensation according to claim 1, characterized in that, In step one, the multi-parameter sensing and transmission module includes a temperature sensor, a triaxial vibration sensor, a voltage monitoring sensor, and a frequency counter.

3. The intelligent control method for crystal resonators based on adaptive frequency compensation according to claim 1, characterized in that, In step two, the feature extraction and fusion module's processing includes: noise suppression: performing Kalman filtering and moving average processing on the original signal; Feature generation: Extract time-domain features including temperature change rate, vibration dominant frequency energy ratio and voltage ripple peak value, analyze the harmonic distribution of vibration spectrum through fast Fourier transform, and calculate aging factor; Data fusion: Integrating multidimensional features into a standardized feature vector.

4. The intelligent control method for crystal resonators based on adaptive frequency compensation according to claim 1, characterized in that, In step three, the compensation parameter combination includes voltage compensation amount, dynamic PID parameters, and filter cutoff frequency.

5. The intelligent control method for crystal resonators based on adaptive frequency compensation according to claim 1, characterized in that, In step five, when recalibration is triggered, the recalibration process is as follows: freeze the current compensation parameters, collect frequency fluctuation data within 5ms; determine the main interference sources through fast Fourier analysis; generate correction coefficients and feed them back to the adaptive compensation decision module to update the compensation parameters for the next cycle.

6. The intelligent control method for crystal resonators based on adaptive frequency compensation according to claim 1, characterized in that, The monitoring terminal communication connection is a multi-mode switching module. The multi-mode switching module dynamically switches modes based on the environmental complexity index, including low-power mode, balanced mode and high-performance mode.

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