CN116776094A - Crystal oscillator temperature test data intelligent analysis storage system - Google Patents
Crystal oscillator temperature test data intelligent analysis storage system Download PDFInfo
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Abstract
The invention relates to the field of data processing, in particular to an intelligent analysis and storage system for crystal oscillator temperature test data, which comprises the following components: the data acquisition module acquires actual temperature data; the data processing module is used for obtaining data after the first smoothing according to the actual temperature data; obtaining a first characteristic according to the actual temperature data and the data after the first smoothing, and grouping the original data according to the first characteristic to obtain a grouping result; calculating errors of each group by using the actual temperature data and the data after the first smoothing, and obtaining optimal smoothing factors of each group; the data storage module is used for obtaining data after the second smoothing according to each group of optimal smoothing factors; then storing the same; and the data analysis module is used for analyzing the data after the second smoothing to obtain frequency characteristics at different temperatures, and judging the quality of the crystal oscillator according to the change of the frequency. The invention uses the data processing mode to group the data, obtains the smoothing factors of each group, and improves the accuracy of the predicted data.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to an intelligent analysis and storage system for crystal oscillator temperature test data.
Background
With the development and popularization of electronic equipment, the crystal oscillator is widely applied to the electronic equipment, and the requirements of people on the quality and stability of the crystal oscillator are also higher. The crystal oscillator is an electronic component, and the circuit works in a stable frequency range by generating a stable high-frequency signal. Therefore, the stability and accuracy of the crystal oscillator have an important influence on the performance and reliability of the device.
The crystal oscillator can generate frequency change at different temperatures, and when the temperature test is performed, the acquired actual temperature data often has certain fluctuation and noise due to the influence of various factors. If these raw actual temperature data are used directly for analysis and storage, this may lead to increased instability and errors in the results. Therefore, before analyzing and storing the temperature test data of the crystal oscillator, the actual temperature data needs to be subjected to smoothing treatment, so that the noise and fluctuation of the data are effectively reduced, and the accuracy and reliability of the data are improved.
Exponential smoothing is a commonly used method of time series prediction, the core of which is the choice of smoothing factors. In the traditional exponential smoothing method, the smoothing factor is considered to be set, and the too small smoothing factor can lead to insufficient data smoothing degree and does not eliminate the influence caused by noise points; too large a smoothing factor can lead to too smooth data, loss of original temperature change characteristics and inaccurate analysis results.
Disclosure of Invention
The invention provides an intelligent analysis and storage system for crystal oscillator temperature test data, which aims to solve the existing problems.
The intelligent analysis and storage system for the crystal oscillator temperature test data adopts the following technical scheme:
the embodiment of the invention provides a crystal oscillator temperature test data intelligent analysis and storage system, which comprises the following modules:
the data acquisition module acquires actual temperature data;
the data processing module is used for obtaining data after the first smoothing according to the actual temperature data and a preset initial smoothing factor; recording actual temperature data and data after the first smoothing as original data;
obtaining a first characteristic according to the actual temperature data and the data after the first smoothing, and grouping the original data according to the first characteristic to obtain a grouping result, wherein the grouping result comprises a plurality of groups;
obtaining errors of each group under the initial smoothing factors according to the actual temperature data in the grouping result and the data after the first smoothing, and obtaining each group of optimal smoothing factors according to the errors of each group under the initial smoothing factors;
the data storage module is used for carrying out smoothing on the actual temperature data in each group again according to the optimal smoothing factors of each group to obtain data after the second smoothing of each group; splicing the data subjected to the second smoothing of each group to obtain data subjected to the second smoothing of the actual temperature data, and storing the data subjected to the second smoothing of the actual temperature data;
and the data analysis module is used for obtaining frequencies at different temperatures by using a temperature-frequency characteristic curve for the data after the second smoothing, and detecting the quality of the crystal oscillator by using a frequency deviation measuring method according to the change of the frequencies.
Further, the specific acquisition steps of the data after the first smoothing are as follows:
and taking the initial smoothing factor as a smoothing factor in an exponential smoothing formula, and calculating smoothing prediction data of each actual temperature data according to the exponential smoothing formula to serve as data after first smoothing.
Further, the specific acquiring steps of the first feature are as follows:
formula of the first feature:
in the method, in the process of the invention,represent the firstSmoothing the predicted data;represent the firstThe data of the actual temperature of the water,the first feature at time t is indicated.
Further, the grouping of the original data according to the first feature to obtain a grouping result includes the following specific steps:
the method comprises the following steps of: setting an accumulator g, wherein the initial value of the accumulator g is 0; acquiring a first characteristic of a first moment；
Then calculateCompared with the variation threshold A, whenWhen the accumulator g is added 1, whenWhen the accumulator g is unchanged;
then calculate again to obtainFirst characteristic of second momentThe method comprises the steps of carrying out a first treatment on the surface of the Then calculateCompared with the variation threshold A, whenWhen the accumulator g is added 1, whenWhen the accumulator g is unchanged;
then calculate and acquire the first characteristic of the third momentThe method comprises the steps of carrying out a first treatment on the surface of the Then calculateCompared with the variation threshold A, whenWhen the accumulator g is added 1, whenWhen the accumulator g is unchanged;
and so on until the nth time is reachedCalculated byCompared with the variation threshold A, whenAt this time, the accumulator g is added with 1, and if the accumulator g=10 at this time, n data are put into the first packet, so as to obtain the first group;
removing the data in the first group from the original data to obtain new original data, and similarly, obtaining a second group according to the new original data;
and so on until all groups meeting the conditions are acquired, taking the rest of original data as a new group, and acquiring all groups.
Further, the error of each group under the initial smoothing factor is obtained according to the actual temperature data in the grouping result and the data after the first smoothing, and the method comprises the following specific steps:
and obtaining the error of each group under the initial smoothing factor according to the average value of the square difference between the actual temperature data and the smooth prediction data at each moment in each group.
Further, the specific acquisition method of each group of optimal smoothing factors comprises the following steps:
marking any group as a target group, and when the error of the target group under the initial smoothing factor is less than or equal to a preset threshold value, the smoothing factor of the target group is not changed; when the error of the target group under the initial smoothing factor is larger than a preset threshold, correcting the initial smoothing factor of the target group through the adjustment value of the smoothing factor, and taking the sum of the initial smoothing factor of the target group and the adjustment value of the smoothing factor as a new smoothing factor of the target group;
and similarly, correcting the new smoothing factors of the target group until the error of the new smoothing factors of the target group is smaller than or equal to a preset threshold value, and taking the smoothing factors at the moment as the optimal smoothing factors of the grouping results.
Further, the specific method for obtaining the adjustment value of the smoothing factor comprises the following steps:
the calculation formula of the adjustment value of the smoothing factor is:
in the method, in the process of the invention,representing the adjusted value of the smoothing factor,the smoothing factor representing the set of objects is,representing the smoothing factor asError at that time, B represents the error threshold.
Further, the specific obtaining steps of the exponential smoothing formula are as follows:
in the method, in the process of the invention,represent the firstSmoothing the predicted data;representing a smoothing factor;represent the firstActual temperature data;represent the firstSmoothing the predicted data;represented in [1, t-1 ]]The first of the intervalsA plurality of positions.
Further, the specific acquisition steps of the data after the second smoothing of each group are as follows:
and taking the optimal smoothing factor of the grouping result as a smoothing factor in an exponential smoothing formula, and calculating smoothing prediction data of each actual temperature data in the grouping result according to the exponential smoothing formula to obtain data after the second smoothing of the grouping result.
Further, the second smoothing data of the actual temperature data is obtained by splicing the second smoothing data of each group, which comprises the following specific steps:
and splicing the data subjected to the second smoothing of each group according to the original position sequence to obtain the data subjected to the second smoothing of the actual temperature data.
The technical scheme of the invention has the beneficial effects that: the exponential smoothing method is one of methods for smoothing data, and smoothing processing is performed on the data by setting a smoothing factor. The traditional exponential smoothing method sets the global as the same smoothing factor to carry out smoothing treatment. In the crystal oscillator data, along with the change of temperature, the frequency data of the crystal oscillator also changes differently, and a global single smoothing factor is adopted to smooth the data, so that larger errors are caused. Therefore, the invention adaptively segments the data by setting the difference between the data after the initial smoothing factor and the actual temperature data, so that the data difference in each segment is smaller. Then, according to the data in each segment, calculating the optimal smoothing factor in each segment, so that the error when the data in the same segment is smoothed by the same smoothing factor is smaller. The error of the data is reduced when the data is smoothed, so that the accuracy of the analysis of the actual temperature data of the crystal is improved.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a block flow diagram of a system for intelligent analysis and storage of crystal oscillator temperature test data according to the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following is a detailed description of specific implementation, structure, characteristics and effects of a crystal oscillator temperature test data intelligent analysis and storage system according to the invention with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The specific scheme of the intelligent analysis storage system for the crystal oscillator temperature test data is specifically described below with reference to the accompanying drawings.
Referring to fig. 1, a block flow diagram of a crystal oscillator temperature test data intelligent analysis storage system according to an embodiment of the invention is shown, and the method includes the following blocks:
the data acquisition module 101:
the crystal oscillator temperature test data refers to data obtained when the crystal oscillator device is subjected to temperature test in the production process of the crystal oscillator device. In a crystal oscillator device, the precise structure and material characteristics of a crystal can change along with the change of temperature, so that the performances of frequency, stability, accuracy and the like of the crystal oscillator are affected. Therefore, the crystal oscillator needs to be subjected to temperature test to evaluate the performance and reliability of the crystal oscillator.
In general, the crystal oscillator temperature test data comprises two indexes of temperature and frequency. The temperature index represents the ambient temperature of the crystal oscillator device during testing, and is usually in the unit of degrees centigrade; the frequency index represents the frequency of the crystal oscillator signal measured at that temperature, typically in hertz. And acquiring and analyzing the temperature test data of the crystal oscillator to obtain the performance of the crystal oscillator at different temperatures, and judging the quality and reliability of the crystal oscillator.
Specifically, a crystal oscillator temperature detector was used to collect temperature data at 30 second intervals over two hours, and the temperature data was recorded as actual temperature data.
So far, the actual temperature data acquisition is completed.
The data processing module 102:
it should be noted that the smoothing factor is an important parameter in exponential smoothing, and is used to balance weights of the historical data and the current data, so as to achieve smoothness and sensitivity of the smoothed predicted data. In the traditional exponential smoothing model, the smoothing factor is a constant and is a fixed value, but as the temperature changes, the frequency characteristic of the crystal oscillator changes along with the temperature changes, and the prediction error is possibly larger due to the adoption of the fixed smoothing factor, so that the smoothing factor needs to be converged through calculation errors, and the smoothing factor is selected in a self-adaptive mode according to the frequency changes of the data at different temperatures, so that the data error smoothed by the smoothing factor is minimized.
(1) Setting an initial smoothing factor, and acquiring smooth prediction data according to the initial smoothing factor.
It should be further noted that, when data smoothing is performed, various errors exist in the process of collecting and processing data, such as measurement errors, sampling errors, calculation errors, and the like, so that errors may also exist in data smoothing. These errors may cause some deviation of the smoothed result from the actual situation.
Specifically, the initial smoothing factor is preset asWherein the present embodiment usesThe present embodiment is not specifically limited, and will be described by way of exampleDepending on the particular implementation. And taking the initial smoothing factor as a smoothing factor in an exponential smoothing formula, and calculating smoothing prediction data of each actual temperature data according to the exponential smoothing formula to serve as data after first smoothing.
The exponential smoothing formula is as follows:
in the method, in the process of the invention,represent the firstSmoothing the predicted data;representing a smoothing factor;represent the firstActual temperature data;represent the firstSmoothing the predicted data;represented in [1, t-1 ]]The first of the intervalsA plurality of positions.
Thus, smooth predicted data is obtained and recorded as data after the first smoothing.
(2) Grouping is performed according to the difference between the actual temperature data and the smoothed prediction data.
It should be noted that, when smoothing data, along with the continuous change of temperature, the frequency corresponding to the crystal oscillator is continuously changed, that is, when the data is smoothed by using a single smoothing factor, the difference between the predicted value and the actual value after smoothing is continuously changed, and certain regularity exists in the changes, that is, local differences are similar, when smoothing, the data are grouped according to the local regularity, and different smoothing factors are assigned to different groups, so that the smoothing result is more accurate. Therefore, in this step, the data needs to be grouped according to the difference between the data before and after the smoothing, so that the same smoothing value can be used in each group.
Specifically, the grouping rule is as follows:
a difference change threshold a is preset, where the embodiment is described by taking a=2 as an example, and the embodiment is not specifically limited, where a may be determined according to the specific implementation situation. The data is grouped by the difference of each data point before and after the data is smoothed, so that the difference before and after the data is smoothed in each group is similar, and the error of smoothing the data in the group by using the same smoothing factor is reduced to the minimum.
Recording any time as a target time, and calculating the difference between the actual temperature data and the smooth predicted data corresponding to the target time as a first difference, wherein the difference is the absolute value of the difference; calculating the average value of the differences between the actual temperature data and the smooth predicted data corresponding to all moments, and recording the average value as a first average value; subtracting the first average value from the first difference to obtain a second difference, and obtaining a first characteristic according to the average value of the squares of the second difference at all moments, wherein the first characteristic is represented by G.
When (when)In this case, the degree is grouped by accumulating successive times to obtain the length of one time, that is, the length of data. And marking the data subjected to grouping as original data, deleting the group of data in the original data after the continuous data meet the conditions and are grouped, obtaining deleted original data, and the like, and deleting all the data lengths meeting the conditions in the original data. Wherein the number of data meeting the condition is denoted as t, and the grouping process can be performed by one cycle traversal.
The first characteristic calculation formula of the actual data at the t-th moment is as follows:
in the method, in the process of the invention,representing the first t original data numbers;represent the firstSmoothing the predicted data;represent the firstThe data of the actual temperature of the water,the first feature at time t is indicated.
The method comprises the following steps of:
setting an accumulator g, wherein the initial value of the accumulator g is 0;
acquiring a first characteristic of a first moment；
Then calculateCompared with the variation threshold A, whenWhen the condition of accumulating g is considered to be met, the accumulator g is added with 1 at the moment, whenIf the condition of the accumulation g is not satisfied, the accumulator g is unchanged.
Then, the first characteristic of the second moment is obtained through calculationThe method comprises the steps of carrying out a first treatment on the surface of the Then calculateCompared with the variation threshold A, whenWhen the condition of accumulating g is considered to be met, the accumulator g is added with 1 at the moment, whenIf the condition of the accumulation g is not satisfied, the accumulator g is unchanged.
Then, the first characteristic of the third moment is obtained through calculationThe method comprises the steps of carrying out a first treatment on the surface of the Then calculateCompared with the variation threshold A, whenWhen the condition of accumulating g is considered to be met, the accumulator g is added with 1 at the moment, whenIf the condition of the accumulation g is not satisfied, the accumulator g is unchanged.
And so on until the nth time is reachedCalculated byCompared with the variation threshold A, whenAt this time, the accumulator g is incremented by 1, and if at this time the accumulator g=10, n data are put into the first packet, so far as the first group is obtained.
And removing the data in the first group from the original data to obtain new original data, and similarly, obtaining a second group according to the new original data.
And so on until all groups meeting the conditions are acquired, taking the rest of original data as a new group, and acquiring all groups.
To this end, the grouping of the original data is completed.
(3) An optimal smoothing factor within each packet is calculated.
The actual temperature data is grouped by the difference change of the data before and after the smoothing in the steps, so that the error when the data in each group is smoothed by the same smoothing factor is reduced to the minimum. When smoothing data, the selection of a smoothing factor is very important, the smoothing factor is a key parameter for controlling the weight of historical data and current data, the accuracy of a prediction result is directly affected, and the characteristics of frequency data of the crystal oscillator are continuously changed along with the change of temperature. Therefore, it is necessary to adaptively adjust the smoothing factor according to the fluctuation characteristics of the data, so that the data error after smoothing the data in each packet is minimized. When calculating the optimal smoothing factor, the value of the smoothing factor needs to be adjusted according to the error magnitude of the smoothing prediction data and the actual temperature data. In the above step, it has been calculated that all data are smoothed by the initial smoothing factorAll the smoothed data in the process, the step needs to calculate the optimal smoothing factor according to the data difference before and after smoothing.
It should be further noted that, the larger the smoothing factor, the smaller the influence on the history data, and the larger the influence on the latest data, so that the trend of the data change can be reflected more quickly. However, if the smoothing factor is too large, the prediction result is too sensitive, and the situation of over fitting is easy to occur, so that the error is larger. Conversely, if the value of the smoothing factor is too small, the prediction result is too stable, the change trend of the data cannot be reflected in time, and larger errors are easy to occur. Although too large or too small a smoothing factor will result in a large error, the two errors are not identical. When the value of the smoothing factor is too large, the exponential smoothing is more sensitive to the latest observed value, so that the smooth curve is too violent in change and easy to be over-fitted, and the predicted result is far away from the true value; when the value of the smoothing factor is too small, the exponential smoothing is smoother, the dependence on the latest observed value is reduced, but the reaction capacity to the latest observed value is reduced, the smooth curve is slow to change, the under-fitting condition is easy to occur, and therefore the error between the predicted result and the true value is larger. Therefore, whether the smoothing factor is too large or too small can be judged according to the smoothed data fluctuation condition, and if the smoothed data fluctuation is large, the smoothing factor is too large; if the smoothed data fluctuation is too small, the smoothing factor is too small.
Specifically, the process of calculating the optimal smoothing factor in a packet based on the data characteristics in the packet is as follows:
and marking any group as a target group, and calculating the optimal smoothing factor of the target group as follows:
first, according to the initial smoothing factorAnd carrying out data errors in the target group result, namely solving the errors under the smoothing factors according to the data before and after smoothing, wherein the formula for calculating the errors is as follows:
in the method, in the process of the invention,representing the smoothing factor asAn error of the time target group;representing the total number of data in the target group;representing the first of the target groupsA smooth predictive prediction;representing the first of the target groupsAnd actual temperature data.
Wherein if the difference between the data before and after smoothing is larger, the smoothing factor is utilizedThe larger the error in smoothing, the poorer the smoothing effect at the value of the smoothing factor.
Presetting an error coefficient x, wherein the embodiment is described by taking x=0.1 as an example, the embodiment is not particularly limited, and x can be determined according to the specific implementation situation; the error threshold B is obtained from a preset error coefficient x,，representing the first of the target groupsAnd actual temperature data. If you getThen the error under the value of the smoothing factor is considered to be within an acceptable range, and the value of the smoothing factor is used to be better; if it isIt is considered that the smoothing effect is poor at the value of the smoothing factor, and the smoothing factor needs to be updated.
And secondly, adjusting the smoothing factor with the error not in the acceptable range so that the data smoothed by the smoothing factor is in the acceptable range.
When (when)When the smoothing factor is not adjusted, whenWhen the smoothing factor needs to be adjusted, the formula for calculating the adjustment value is as follows:
in the method, in the process of the invention,representing the adjusted value of the smoothing factor,the smoothing factor representing the set of objects is,representing the smoothing factor asError at that time, B represents the error threshold.
If the error is out of the acceptable range, that is, the larger the error between the smoothed predicted data and the actual temperature data is, the larger the value of the smoothing factor to be adjusted is; conversely, the smaller the error between the smoothed prediction data and the actual temperature data, the smaller the value of the smoothing factor that needs to be adjusted.
And taking the sum of the smoothing factors of the target group and the adjustment values of the smoothing factors as new smoothing factors of the target group.
And thirdly, after calculating a new smoothing factor, repeating the first step and the second step, calculating an error under the new smoothing factor by using the new smoothing factor, judging whether the new smoothing factor needs to be updated according to the error, and repeating the steps until the error is cut off within an acceptable range if the new smoothing factor needs to be updated.
So far, the smoothing factor corresponding to each grouping result is obtained.
Data storage module 103:
specifically, the optimal smoothing factor of each grouping result is utilized to smooth the respective grouping result, smoothed data are obtained, and the smoothed data are stored, so that the subsequent analysis of the data result is facilitated.
And smoothing the actual temperature data by using the existing exponential smoothing method according to the grouping result and the optimal smoothing factor corresponding to each grouping result, taking the optimal smoothing factor of the grouping result as the smoothing factor in the exponential smoothing formula, and calculating the smoothing prediction data of each actual temperature data in the grouping result according to the exponential smoothing formula to obtain the data after the second smoothing of the grouping result.
Thus, the smoothed data of the target group is obtained.
And sequentially obtaining all data with smooth grouping results.
And then rearranging all grouping results to the original positions according to the positions deleted before, so as to obtain the data after the second smoothing.
And finally, storing the data after the second smoothing.
The data analysis module 104:
and acquiring crystal oscillator temperature data and frequency data at the previous moment, and establishing a temperature-frequency characteristic curve according to the temperature data and the frequency data.
And analyzing the stored data, obtaining frequencies at different temperatures from the smoothed data by using a temperature-frequency characteristic curve, and detecting the quality of the crystal oscillator by using a measuring frequency deviation method according to the change of the frequencies.
This embodiment is completed.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (10)
1. The intelligent analysis and storage system for the crystal oscillator temperature test data is characterized by comprising the following modules:
the data acquisition module acquires actual temperature data;
the data processing module is used for obtaining data after the first smoothing according to the actual temperature data and a preset initial smoothing factor; recording actual temperature data and data after the first smoothing as original data;
obtaining a first characteristic according to the actual temperature data and the data after the first smoothing, and grouping the original data according to the first characteristic to obtain a grouping result, wherein the grouping result comprises a plurality of groups;
obtaining errors of each group under the initial smoothing factors according to the actual temperature data in the grouping result and the data after the first smoothing, and obtaining each group of optimal smoothing factors according to the errors of each group under the initial smoothing factors;
the data storage module is used for carrying out smoothing on the actual temperature data in each group again according to the optimal smoothing factors of each group to obtain data after the second smoothing of each group; splicing the data subjected to the second smoothing of each group to obtain data subjected to the second smoothing of the actual temperature data, and storing the data subjected to the second smoothing of the actual temperature data;
and the data analysis module is used for obtaining frequencies at different temperatures by using a temperature-frequency characteristic curve for the data after the second smoothing, and detecting the quality of the crystal oscillator by using a frequency deviation measuring method according to the change of the frequencies.
2. The intelligent analysis and storage system for crystal oscillator temperature test data according to claim 1, wherein the specific acquisition steps of the first smoothed data are as follows:
and taking the initial smoothing factor as a smoothing factor in an exponential smoothing formula, and calculating smoothing prediction data of each actual temperature data according to the exponential smoothing formula to serve as data after first smoothing.
3. The intelligent analysis and storage system for crystal oscillator temperature test data according to claim 1, wherein the specific acquisition steps of the first characteristic are as follows:
formula of the first feature:
in the method, in the process of the invention,indicate->Smoothing the predicted data; />Indicate->Actual temperature data, ">The first feature at time t is indicated.
4. The intelligent analysis and storage system for crystal oscillator temperature test data according to claim 1, wherein the grouping of the raw data according to the first characteristic to obtain the grouping result comprises the following specific steps:
the method comprises the following steps of: setting an accumulator g, wherein the initial value of the accumulator g is 0; acquiring a first characteristic of a first moment；
Then calculateCompared with the difference change threshold A, when +.>The accumulator g is then incremented by 1 when +.>When the accumulator g is unchanged;
then calculate and acquire the first characteristic of the second momentThe method comprises the steps of carrying out a first treatment on the surface of the Then calculate +.>Compared with the difference change threshold A, when +.>The accumulator g is then incremented by 1 when +.>When the accumulator g is unchanged;
then calculate and acquire the first characteristic of the third momentThe method comprises the steps of carrying out a first treatment on the surface of the Then calculate +.>Compared with the difference change threshold A, when +.>The accumulator g is then incremented by 1 when +.>When the accumulator g is unchanged;
and so on until the nth time is reachedCalculated->Compared with the difference change threshold A, when +.>At this time, the accumulator g is incremented by 1 if accumulated at this timeG=10, placing n data into the first packet, so as to obtain a first group;
removing the data in the first group from the original data to obtain new original data, and similarly, obtaining a second group according to the new original data;
and so on until all groups meeting the conditions are acquired, taking the rest of original data as a new group, and acquiring all groups.
5. The intelligent analysis and storage system for crystal oscillator temperature test data according to claim 1, wherein the error of each group under the initial smoothing factor is obtained according to the actual temperature data in the grouping result and the data after the first smoothing, comprising the following specific steps:
and obtaining the error of each group under the initial smoothing factor according to the average value of the square difference between the actual temperature data and the smooth prediction data at each moment in each group.
6. The intelligent analysis and storage system for crystal oscillator temperature test data according to claim 1, wherein the specific acquisition method of each group of optimal smoothing factors is as follows:
marking any group as a target group, and when the error of the target group under the initial smoothing factor is less than or equal to a preset threshold value, the smoothing factor of the target group is not changed; when the error of the target group under the initial smoothing factor is larger than a preset threshold, correcting the initial smoothing factor of the target group through the adjustment value of the smoothing factor, and taking the sum of the initial smoothing factor of the target group and the adjustment value of the smoothing factor as a new smoothing factor of the target group;
and similarly, correcting the new smoothing factors of the target group until the error of the new smoothing factors of the target group is smaller than or equal to a preset threshold value, and taking the smoothing factors at the moment as the optimal smoothing factors of the grouping results.
7. The intelligent analysis and storage system for crystal oscillator temperature test data according to claim 6, wherein the specific acquisition method of the adjustment value of the smoothing factor is as follows:
the calculation formula of the adjustment value of the smoothing factor is:
in the method, in the process of the invention,representing the adjustment value of the smoothing factor +.>Smoothing factor representing target group, +.>Representing a smoothing factor of +.>Error at that time, B represents the error threshold.
8. The intelligent analysis and storage system for crystal oscillator temperature test data according to claim 2, wherein the specific acquisition steps of the exponential smoothing formula are as follows:
in the method, in the process of the invention,indicate->Smoothing the predicted data; />Representing a smoothing factor; />Indicate->Actual temperature data; />Indicate->Smoothing the predicted data; />Represented in [1, t-1 ]]First->A plurality of positions.
9. The intelligent analysis and storage system for crystal oscillator temperature test data according to claim 8, wherein the specific acquisition steps of the second smoothed data of each group are as follows:
and taking the optimal smoothing factor of the grouping result as a smoothing factor in an exponential smoothing formula, and calculating smoothing prediction data of each actual temperature data in the grouping result according to the exponential smoothing formula to obtain data after the second smoothing of the grouping result.
10. The intelligent analysis and storage system for crystal oscillator temperature test data according to claim 1, wherein the step of splicing the sets of data after the second smoothing to obtain the data after the second smoothing of the actual temperature data comprises the following specific steps:
and splicing the data subjected to the second smoothing of each group according to the original position sequence to obtain the data subjected to the second smoothing of the actual temperature data.
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