Precision compensation method for clock source
A precision compensation and clock technology, applied in the field of precision compensation of clock sources, can solve problems such as poor consistency of compensation effects, low learning accuracy, frequency drift and temperature separation of frequency sources, and achieve the effect of ensuring compensation accuracy
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Embodiment 1
[0050] A precision compensation method for a clock source, based on a compensation system such as figure 1 , including the following steps:
[0051] Step S1: When the external reference clock is valid, the compensation system tracks the external reference clock to tame the clock in the compensation system, and records the tamed time, temperature and voltage control data in real time, establishes the self-learning data of time, temperature and voltage control data, and Record as RECORIG N ;
[0052] Step S2: Eliminate the abnormal data in the self-learning data, and obtain the corrected effective record data REC N ;
[0053] Step S3: In valid record data REC N In, sort by temperature TEMP to get a new data set RECSortByTemp n , to data group RECSortByTemp n Based on , calculate the frequency drift AgePerDay:
[0054]
[0055] AgePerDay=AgePerDayTemp÷N
[0056] Among them, TIME is time, PWM is voltage control data;
[0057] Step S4, temperature characteristic separat...
Embodiment 2
[0074] On the basis of the above-mentioned embodiment 1, the present invention eliminates abnormal data in the self-learning data in the step S2, based on abnormal changes in quality, external environment and frequency source itself. In the step S2, the abnormal data in the self-learning data is proposed, according to the temperature characteristic analysis of the frequency source, and the matching degree between the temperature of the frequency source and the voltage control.
[0075] The coefficient of determination R in the step S6 2 The maximum value is 1, and the preset threshold is 0.68.
[0076] Working principle: Goodness of Fit refers to how well the regression line fits the observed values. The statistic that measures the goodness of fit is the coefficient of determination R2, whose maximum value is 1. The closer the value of R2 is to 1, the better the fit of the regression line to the observed value; on the contrary, the smaller the value of R2, the worse the fit ...
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