CN-116400578-B - Constant-temperature crystal oscillator time keeping system and method based on BP neural network

CN116400578BCN 116400578 BCN116400578 BCN 116400578BCN-116400578-B

Abstract

The invention relates to the technical field of constant-temperature crystal oscillator time keeping algorithms and time synchronization, in particular to a constant-temperature crystal oscillator time keeping system and method based on a BP neural network, wherein the constant-temperature crystal oscillator time keeping system comprises a GPS receiver, a constant-temperature crystal oscillator, a time interval measuring module, a processor, an algorithm processing module, a frequency dividing module, a D/A module, a temperature sensor, a liquid crystal display module and a serial port communication module, and the GPS receiver outputs 1PPS signals; the constant-temperature crystal oscillator is used as an external input clock of the system, the frequency of the 10MHz crystal oscillator is input to a processor, a time interval measuring module measures the phase difference between a local second pulse signal and a 1PPS signal, the processor generates the local second pulse signal, the constant-temperature crystal oscillator is tamed through the phase difference to obtain the standard 10MHz crystal oscillator frequency, a temperature sensor collects the ambient temperature of the constant-temperature crystal oscillator, an algorithm processing module predicts and compensates the output frequency of the constant-temperature crystal oscillator by utilizing a BP neural network algorithm based on effective historical data and the ambient temperature to obtain the standard 10MHz crystal oscillator frequency.

Inventors

JI YUANFA
LI LONG
ZHAO SONGKE
YAN SUQING
HUANG SHENGRONG
SUN XIYAN
FU QIANG
FU WENTAO
LIANG WEIBIN
BAI YANG
Jia Xizi
LI JINGJING

Assignees

桂林电子科技大学
南宁桂电电子科技研究院有限公司

Dates

Publication Date: 20260505
Application Date: 20230413

Claims (2)

1. A constant temperature crystal oscillator time keeping system based on BP neural network is characterized in that, The device comprises a GPS receiver, a constant temperature crystal oscillator, a time interval measurement module, a processor, an algorithm processing module, a frequency division module, a D/A module, a temperature sensor, a liquid crystal display module and a serial port communication module, wherein the GPS receiver, the constant temperature crystal oscillator, the time interval measurement module, the frequency division module, the D/A module, the temperature sensor and the serial port communication module are respectively connected with the processor, the algorithm processing module is connected with the serial port communication module, and the liquid crystal display module is connected with the algorithm processing module; the GPS receiver is used for receiving GPS satellite signals, sequentially carrying out filtering, amplifying, frequency conversion, capturing and tracking processing on the GPS satellite signals to obtain time information broadcast by GPS satellites, and outputting 1PPS signals to the time interval measuring module and the processor based on the time information; The constant-temperature crystal oscillator is used as an external input clock of the system and is used for inputting the frequency of the 10MHz crystal oscillator to the processor; The time interval measuring module is used for measuring the phase difference between the local second pulse signal and the 1PPS signal; The processor is used for realizing real-time detection and effective judgment of the 1PPS signal in a tame mode, generating the local second pulse signal based on the 10MHz crystal oscillator frequency, filtering the phase difference, and synchronizing the filtered local second pulse signal with the 1PPS signal by utilizing a digital PID algorithm to tame the constant-temperature crystal oscillator; The serial port communication module is used for realizing communication between the processor and the algorithm processing module; The temperature sensor is used for collecting the ambient temperature of the constant-temperature crystal oscillator; The algorithm processing module is used for predicting and compensating the output frequency of the constant-temperature crystal oscillator by utilizing a BP neural network algorithm based on effective historical data and the ambient temperature in a hold mode to obtain a standard 10MHz crystal oscillator frequency; the D/A module is used for converting the data processed by the PID algorithm into corresponding analog voltage values and adjusting the constant-temperature crystal oscillator frequency output; the frequency division module multiplies the standard 10MHz crystal oscillator frequency to a system clock by using a PLL (phase locked loop) technology; the liquid crystal display module is used for displaying the time interval measurement value and the working state of the whole system; The time keeping method applied to the constant-temperature crystal oscillator time keeping system based on the BP neural network comprises the following steps of: Detecting the validity of a 1PPS signal in real time, and determining whether the system is currently in a tame mode or a hold mode; In the holding mode, the system detects the 1PPS signal, if the 1PPS signal is invalid, the 1PPS signal is output after being filtered by Savitzky-Golay in a preset time period, if the 1PPS signal is valid, the effective historical data in the taming process is recorded, and when the recorded data volume of the effective historical data meets the BP neural network training data volume, the BP neural network is trained; When the BP neural network training is finished, if the 1PPS signal is detected to be invalid, the system predicts the output frequency of the constant-temperature crystal oscillator through the BP neural network after the training is finished to obtain the standard 10MHz crystal oscillator frequency, and if the 1PPS signal is detected to be valid, the system judges the updating of the BP neural network after the training is finished; the historical data in the preset time period is the latest 50 times of historical data; In the training process of the BP neural network, if 1PPS signals are detected to be invalid, savitzky-Golay filtering is still carried out on the latest 50 times of historical data and then the latest 50 times of historical data are output; The system judges the update of the BP neural network, and comprises the following steps: the BP neural network is updated with network training every 2 hours.
2. The constant temperature crystal oscillator time keeping system based on BP neural network as set forth in claim 1, wherein, The GPS receiver is ublox receiver; The time interval measuring module is a TDC-GPX2 time interval measuring module; the processor is an FPGA processor; the algorithm processing module is an STM32 algorithm processing module; The liquid crystal display module is an LCD liquid crystal display module.

Description

Constant-temperature crystal oscillator time keeping system and method based on BP neural network Technical Field The invention relates to the technical field of constant-temperature crystal oscillator time keeping algorithms and time synchronization, in particular to a constant-temperature crystal oscillator time keeping system and method based on a BP neural network. Background As a core component of each large electronic device, a high stability and accuracy of the frequency thereof have been a constantly pursued goal. In order to meet the requirements of technological development, the construction of time frequency and time frequency service systems is very important in all countries of the world, and particularly, the improvement of time frequency precision is in an exponentially developed situation in nearly 20 years, and is improved by an order of magnitude every 5-10 years. Among various high-precision frequency sources, cesium atomic clocks and hydrogen atomic clocks which are used as primary frequency sources have very high long-term stability and accuracy, but are very expensive, have strict requirements on the external use environment, are generally only suitable for national time service laboratories, and are difficult to apply to civil fields with high requirements on cost. For the high-stability constant-temperature crystal oscillator and the rubidium clock, the high-stability constant-temperature crystal oscillator and the rubidium clock belong to secondary frequency sources, are low in cost, but are relatively poor in long-term stability and accuracy, and are difficult to apply to the field with high requirements on time synchronization precision. Therefore, improvement of the secondary frequency source is highly desired, so that the secondary frequency source has low cost and long-term stability and accuracy. The constant temperature crystal oscillator (OCXO, oven Controlled Crystal Oscillator) is used as a secondary frequency standard source, and is widely applied to the fields of scientific research metering, industrial equipment and the like due to the advantages of high short-term stability, low price, small volume and the like. However, the constant-temperature crystal oscillator output frequency is easily affected by the environmental temperature and aging factors, so that the crystal oscillator frequency gradually drifts, and the frequency stability and accuracy of the constant-temperature crystal oscillator are reduced. Therefore, many scholars propose to use the advantage of high long-term stability of the GPS pulse-second signal (1Pulse Per Second,1PPS) to tame the local constant-temperature crystal oscillator, so that the long-term stability and accuracy of the local crystal oscillator are effectively improved. However, the satellite signals are affected by factors such as an ionosphere and a troposphere in the transmission process, so that the short-term stability of the GPS 1PPS signals is poor, and the 1PPS signals are easy to lose effectiveness when the interference is serious, so that the stable output of the crystal oscillator frequency cannot be ensured. Disclosure of Invention The invention aims to provide a constant-temperature crystal oscillator time keeping system and a constant-temperature crystal oscillator time keeping method based on a BP neural network, and aims to solve the problem of poor stability of crystal oscillator frequency output of a constant-temperature crystal oscillator. In order to achieve the above objective, in a first aspect, the present invention provides a constant-temperature crystal oscillator time keeping system based on a BP neural network, which includes a GPS receiver, a constant-temperature crystal oscillator, a time interval measurement module, a processor, an algorithm processing module, a frequency division module, a D/a module, a temperature sensor, a liquid crystal display module, and a serial port communication module, where the GPS receiver, the constant-temperature crystal oscillator, the time interval measurement module, the frequency division module, the D/a module, the temperature sensor, and the serial port communication module are respectively connected with the processor, the algorithm processing module is connected with the serial port communication module, and the liquid crystal display module is connected with the algorithm processing module; the GPS receiver is used for receiving GPS satellite signals, sequentially carrying out filtering, amplifying, frequency conversion, capturing and tracking processing on the GPS satellite signals to obtain time information broadcast by GPS satellites, and outputting 1PPS signals to the time interval measuring module and the processor based on the time information; The constant-temperature crystal oscillator is used as an external input clock of the system and is used for inputting the frequency of the 10MHz crystal oscillator to the processor; The time interval measuring module is u