CN117240930A - Intelligent acquisition method and system for carrier communication data - Google Patents
Intelligent acquisition method and system for carrier communication data Download PDFInfo
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Abstract
The invention relates to the technical field of carrier communication transmission, in particular to an intelligent acquisition method and system for carrier communication data, which are used for acquiring modulation data and carrier period of a carrier signal and fitting the modulation data to obtain a signal curve; and obtaining the time difference degree and the period section of the signal curve according to the change characteristics of the signal curve. Obtaining period similarity according to the time difference, the amplitude characteristic of the period section and the corresponding carrier period; obtaining period robustness and a period interval of modulation data according to the time difference, the period similarity and the carrier period; and obtaining the period reliability and the final interval section according to the difference characteristics of the period interval. According to the invention, the final interval section obtained in a self-adaptive way is compressed through the LZW algorithm, so that the compression time is reduced, and the storage and transmission efficiency of carrier communication data is further improved.
Description
Technical Field
The invention relates to the technical field of carrier communication transmission, in particular to an intelligent acquisition method and system for carrier communication data.
Background
The carrier communication is a telephone multi-way communication system based on the frequency division multiplexing technology, and belongs to the standard of classical analog communication. The step of collecting the carrier communication data is to efficiently and accurately acquire and acquire the carrier communication data through intelligent equipment and an algorithm. When carrier communication data is stored and transmitted, the data needs to be compressed in order to improve efficiency. In the scene of carrier communication, an original signal becomes a modulated signal with a certain periodicity after modulation; the periodic data is compressed by using an LZW string table compression algorithm, so that the compression efficiency is high.
The LZW compression algorithm realizes compression by establishing a character string table and representing longer character strings by shorter codes; however, the autoregressive model in the compression algorithm has overlong reasoning speed of the period segment of the modulated signal, is unfavorable for quickly acquiring the period characteristic of the modulated signal, and reduces the character string construction speed, thereby increasing the compression time and affecting the storage and transmission efficiency of carrier communication data.
Disclosure of Invention
In order to solve the technical problem that the periodic characteristics of the modulated signals are difficult to quickly acquire through an LZW compression algorithm and the storage and transmission efficiency of carrier communication data is affected, the invention aims to provide an intelligent carrier communication data acquisition method and system, and the adopted technical scheme is as follows:
acquiring modulated data and carrier period after carrier signal modulation, and fitting the modulated data to obtain a signal curve; obtaining the time difference degree of the signal curve according to the change characteristics of the signal curve;
acquiring a period section of the signal curve according to the time difference; obtaining the period similarity according to the time difference, the carrier period corresponding to the period section and the amplitude characteristics of the data points in the period section;
obtaining period robustness according to the time difference degree, the period similarity and the carrier period; determining a period interval of the modulation data according to the period robustness; obtaining the cycle credibility of the cycle interval according to the difference characteristics of the modulation data of different cycle intervals;
determining a final interval section of the modulation data according to the period reliability; and compressing the modulation data through an LZW algorithm according to the final interval.
Further, the step of obtaining the time difference degree of the signal curve according to the change characteristic of the signal curve includes:
determining extreme points of the signal curve; calculating a data point with the absolute value of the tangential slope of the data point of the signal curve smaller than a preset slope threshold value as a characteristic data point; forming a wave band interval of the extreme point by the characteristic data points connected with the two ends of the extreme point;
calculating the time interval between the extreme point and the adjacent similar extreme point to obtain adjacent time difference; calculating the absolute value of the difference value of the adjacent time difference between the first extreme point of the signal curve and other similar extreme points to obtain a time variation difference value; calculating the absolute value of the width difference value between the first extreme point of the signal curve and the band intervals of other similar extreme points to obtain a time range difference value; and calculating and normalizing the product of the time variation difference value and the time range difference value to obtain the time difference degree of two similar extreme points of the signal curve.
Further, the step of obtaining the period segment of the signal curve according to the time difference degree includes:
stopping calculation when the time difference is smaller than a preset correlation threshold, and taking the position from a first extreme point to the extreme point at which the time difference calculation is finished as a first period segment of the signal curve; a range of signal curves of equal length adjacent to the first period segment is taken as a second period segment; and starting to continue traversing the signal curve by using the end position of the second period section to calculate the time difference degree, and obtaining all period sections of the signal curve.
Further, the step of obtaining the period similarity according to the time difference, the carrier period corresponding to the period segment and the amplitude characteristic of the data point in the period segment includes:
calculating the absolute value of the amplitude difference of the corresponding position data points of two adjacent other period sections of the period section, accumulating and summing to obtain an amplitude difference representation value, and calculating the amplitude difference representation value if the lengths of the two adjacent other period sections of the period section are different; calculating the absolute value of the length difference value of the carrier period of the corresponding position data point of two adjacent other period sections of the period section to obtain a carrier period difference characterization value; and calculating the product of the amplitude difference representation value, the time difference degree of the period section and the carrier period difference representation value and carrying out negative correlation mapping to obtain the period similarity of the period section of the signal curve.
Further, the step of obtaining the period robustness according to the time difference degree, the period similarity and the carrier period includes:
calculating the average value of the absolute values of the differences of the time lengths of the carrier periods corresponding to all adjacent period sections to obtain the average difference of the carrier periods; calculating the average value of the time difference degrees to obtain the time average difference degrees; calculating the product of the time average difference and the carrier period average difference to obtain the period dispersion; calculating the sum of the period dispersion and a preset minimum positive number to obtain a period dispersion characterization value; calculating the average value of the periodic similarity to obtain periodic average similarity; calculating and normalizing the ratio of the period average similarity to the period dispersion characteristic value to obtain the period robustness of the period section of the signal curve.
Further, the step of determining the period interval of the modulation data according to the period robustness includes:
when the period robustness is larger than a preset robustness threshold, determining a period interval of the modulation data according to the distribution characteristics of the period section; otherwise, the preset correlation threshold is reduced, and a period segment is acquired again according to the time difference degree until the period robustness is larger than the preset robustness threshold.
Further, the step of obtaining the cycle reliability of the cycle interval according to the difference characteristics of the modulation data of different cycle intervals includes:
calculating the absolute value of the amplitude difference value of the data point at the corresponding position of the adjacent period interval, and accumulating and summing to obtain the difference value of the adjacent period interval; and calculating the sum of all adjacent period interval difference values and carrying out negative correlation mapping to obtain the period reliability of the period interval of the modulation data.
Further, the step of determining a final interval of the modulated data according to the period reliability includes:
when the periodic credibility exceeds a preset credibility threshold, taking a periodic interval of the modulation data as the final interval; otherwise, the preset correlation threshold is reduced, and the period segment is acquired again according to the time difference degree until the final interval segment is acquired.
Further, the preset credibility threshold is 0.8.
The invention also provides an intelligent carrier communication data acquisition system, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program to realize the steps of any one of the intelligent carrier communication data acquisition methods.
The invention has the following beneficial effects:
in the embodiment of the invention, the acquisition of the fitting curve can facilitate the calculation of the period segment more accurately; and calculating the time difference degree can obtain different period sections based on the time characteristics of different sections of the signal curve, so that the accuracy of obtaining the same period section is improved. The period similarity can be obtained to represent the similarity degree between period segments, so that the improvement degree of the compression efficiency is analyzed; the calculation of the period robustness can represent the similarity degree between adjacent period sections of the whole signal curve, so that whether the period section of the modulated data can improve the compression efficiency or not can be determined according to the period robustness. The cycle reliability is obtained according to the difference characteristics of the cycle interval, and the degree of improvement of the compression efficiency can be analyzed based on the difference characteristics between the compressed modulation data, so that the final interval can be determined. And the character string is constructed through the LZW compression algorithm according to the final interval section to compress, so that the time for determining the period interval by the LZW algorithm is reduced, the compression time is further reduced, and finally the storage and transmission speed of carrier communication data is improved.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a method for intelligently collecting carrier communication data according to an embodiment of 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 the intelligent acquisition method and system for carrier communication data according to the invention in combination with 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 following specifically describes a specific scheme of the method and system for intelligently collecting carrier communication data provided by the invention with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of a method for intelligently collecting carrier communication data according to an embodiment of the present invention is shown, and the method includes the following steps:
step S1, obtaining modulated data and a carrier period after carrier signal modulation, and fitting the modulated data to obtain a signal curve; and obtaining the time difference degree of the signal curve according to the change characteristics of the signal curve.
In the embodiment of the invention, the implementation scene is to efficiently compress the modulated data of the carrier signal. Firstly, obtaining modulated data and a carrier period after carrier signal modulation; preferably, the original signal is modulated by an OFDM orthogonal frequency division multiplexing technology to obtain modulated data and different carrier periods, and it should be noted that, the OFDM algorithm belongs to the prior art, and the specific modulation process is not repeated.
Further, in order to improve accuracy of subsequent data analysis, a signal curve is required to be obtained by fitting discrete modulation data, preferably, a signal curve is obtained by fitting discrete modulation data through a lagrangian interpolation method, a horizontal axis of the signal curve is different time points, a vertical axis of the signal curve is an amplitude value after fitting different data points, and it is noted that the lagrangian interpolation method belongs to the prior art, and specific fitting steps are not repeated. The fitted signal curve can show different periodic characteristics, and then a self-defined dictionary in the LZW string table compression algorithm can be created through periodic information, the dictionary is a key part in the algorithm and is used for storing coded and decoded symbols, and the dictionary can contain periodic modes and corresponding coded values. The autoregressive model in the LZW string table compression algorithm has long acquisition time for the periodic segment of the long signal curve, so that the quick coding is difficult, the compression efficiency is low, and the storage and transmission instantaneity of carrier communication data is affected; therefore, a signal curve with variability in carrier period needs to be analyzed to obtain the distribution characteristics of period segments with higher certainty, so that the compression efficiency of the LZW algorithm is improved.
Because the signal curves can show periodic variation, the corresponding time periods of the signal curve sections with similar variation trend are similar, the time difference degree of the signal curves can be obtained according to the variation characteristics of the signal curves, and further different period sections can be determined according to the time difference degree.
Preferably, in one embodiment of the present invention, acquiring the time difference degree includes: determining extreme points of a signal curve, firstly determining data points with 0 in first-order derivatives in the signal curve, calculating second-order derivatives of the data points, wherein the data points are minimum value points when the second-order derivatives are larger than zero, and the data points are maximum value points when the second-order derivatives are smaller than zero. Calculating data points with absolute values of tangential slopes of the data points of the signal curve smaller than a preset slope threshold as characteristic data points; forming a wave band interval of the extreme point by the characteristic data points connected with the two ends of the extreme point; in the embodiment of the invention, the preset slope threshold is 0.5, and an implementer can determine according to implementation scenes; because the tangential slope of the extreme point is zero, the fluctuation interval formed by the characteristic data points connected with the two ends of the extreme point can reflect the wave crest or wave trough range of the extreme point, and the purpose of calculating the wave band interval is to represent the similarity of different wave band intervals, so as to determine the period section of the signal curve.
Calculating the time interval between the extreme point and the adjacent similar extreme point, wherein the adjacent direction is calculated according to the time development direction to obtain the adjacent time difference, and the similar extreme point is the same maximum point or the same minimum point; and calculating the absolute value of the difference value between the adjacent time differences of the first extreme point and other similar extreme points of the signal curve to obtain a time variation difference value, wherein the smaller the value is, the more similar the adjacent time difference between the first extreme point and other similar extreme points is, the more the change time of the first extreme point from the adjacent extreme point is, and the change time of the first extreme point from the other similar extreme points to the adjacent extreme points of the other similar extreme points is, and the more likely the first extreme point and the other similar extreme points are similar period segments. Calculating the absolute value of the width difference value of the band interval of the first extreme point and other similar extreme points of the signal curve to obtain a time range difference value; the closer the band interval widths of two similar extreme points are, the more similar the variation is, and the closer the time range difference value is, the more likely the similar period is. And calculating and normalizing the product of the time variation difference value and the time range difference value to obtain the time difference degree of two similar extreme points of the signal curve, wherein the smaller the product value of the time variation difference value and the time range difference value is, the smaller the time difference degree is, which means that the more similar the periodic variation characteristics of the first extreme point to the signal curve interval of the similar extreme point and the length of the same signal curve interval taking the similar extreme point as the starting point are. The formula for acquiring the time difference degree specifically comprises the following steps:
in the method, in the process of the invention,representing the degree of temporal differentiation>Indicate->Extreme points and->Time-varying difference values of the extreme points, +.>Indicate->Extreme points and->Time range difference values for the extreme points. />Representing the normalization function.
Step S2, acquiring a period section of a signal curve according to the time difference; and obtaining the period similarity according to the time difference, the carrier period corresponding to the period section and the amplitude characteristics of the data points in the period section.
According to the calculation process of the time difference in the step S1, the smaller the time difference, the more similar the two period segments, the more beneficial to improving the compression efficiency, so that the period segments of the signal curve can be obtained according to the time difference; preferably, in one embodiment of the present invention, the acquiring the period segment includes: stopping calculation when the time difference is smaller than a preset correlation threshold, wherein the time is a signal curve section with similar variation trend; taking the position of the extreme point from the first extreme point to the end of the time difference degree calculation of the signal curve as the first period section of the signal curve; a range of signal curves of equal length adjacent to the first period segment is taken as a second period segment; the first period and the second period have the same change characteristics and may be represented by the same characters during compression. And (5) continuously traversing the signal curve to calculate the time difference degree by starting the end position of the second period section, and obtaining all period sections of the signal curve.
After all the period segments are obtained, the change characteristics among partial period segments may be the same, the change characteristic difference among partial period segments is larger, if the change characteristic difference among most period segments is larger, the compression efficiency of the LZW algorithm is low, so that the similarity among the period segments needs to be analyzed, and the period similarity is obtained according to the time difference degree, the carrier period corresponding to the period segments and the amplitude characteristics of the data points in the period segments.
Preferably, in one embodiment of the present invention, obtaining the period similarity includes: calculating the absolute values of the amplitude differences of the data points of the corresponding positions of the left and right adjacent other period sections of the period section, accumulating and summing to obtain an amplitude difference representation value, and calculating the amplitude difference representation value if the lengths of the adjacent two other period sections of the period section are different, wherein the fact that a new change period possibly starts is that the period sections at the two ends of the signal curve do not participate in calculation is required; when the amplitude difference representing value is larger, the variation characteristic difference of the left and right adjacent other period sections of the period is larger, the probability that the period section and the two adjacent other period sections are the same period section is smaller, and the period section dividing effect is poorer. Calculating the absolute value of the length difference of the carrier periods of the data points at the corresponding positions of two adjacent other period sections of the period section to obtain a carrier period difference characterization value; the larger the difference in carrier periods when the difference in length of the corresponding two carrier periods is, the smaller the possibility that the period section and the two adjacent other period sections are the same period section, the zero value when the three period sections are in the same carrier period, and the larger the possibility that the three period sections are the same period section. Calculating the product and negative correlation mapping of the amplitude value difference representation value, the time difference degree of the period section and the carrier period difference representation value to obtain the period similarity of the period section of the signal curve; when the amplitude value, the time difference degree of the period section and the carrier period difference value are smaller, the same change characteristics of the period section and two adjacent other period sections are indicated, and the compression efficiency can be improved; the formula for obtaining the period similarity comprises the following steps:
in the method, in the process of the invention,representing the period similarity between the period segment and two adjacent other period segments, +.>Representing the time difference of the period, +.>Representing an amplitude difference characterization value, ">Representing a carrier period difference characterization value,/for>An exponential function based on a natural constant is represented.
Step S3, obtaining period robustness according to the time difference, the period similarity and the carrier period; determining a period interval of the modulation data according to the period robustness; and obtaining the cycle credibility of the cycle interval according to the difference characteristics of the modulation data of different cycle intervals.
After the cycle similarity of different cycle segments is calculated, the similarity of adjacent cycle segments of the whole signal curve needs to be analyzed, if the cycle similarity of only a part of cycle segments is high and the cycle similarity of most of cycle segments is low, the compression efficiency can not be improved, and therefore the cycle robustness can be obtained according to the time difference, the cycle similarity and the carrier cycle.
Preferably, acquiring the period robustness includes: calculating the average value of the absolute values of the differences of the time lengths of the carrier periods corresponding to all adjacent period sections to obtain the average difference of the carrier periods; in the embodiment of the invention, the carrier period average difference is carried out by the period section and the next period section, and the last period section does not participate in calculation; the larger the average difference of the carrier periods, the more adjacent period segments are allocated to different carrier periods, the smaller the possibility that the variation characteristics of the adjacent period segments are the same, and the smaller the average difference of the carrier periods, the more adjacent period segments are allocated to the same carrier period, the larger the possibility that the variation characteristics of the adjacent period segments are the same, and the larger the possibility that the adjacent period segments are the same period segments. Calculating the average value of the time difference degrees to obtain the time average difference degrees; the smaller the time-averaged degree of difference, the more similar the time length characteristics between different period segments of the signal curve, the more likely it is for the same period segment. Calculating the product of the time average difference and the carrier period average difference to obtain the period dispersion; and calculating the sum of the period dispersion and the preset minimum positive number to obtain a period dispersion characterization value, wherein the smaller the period dispersion characterization value is, the smaller the difference characteristic between any adjacent period sections of the signal curve is. Calculating the average value of the periodic similarity to obtain the periodic average similarity; the larger the value, the greater the degree of similarity between adjacent periodic segments of the signal curve as a whole. Calculating and normalizing the ratio of the period average similarity to the period dispersion characteristic value to obtain the period robustness of the period section of the signal curve; when the period average similarity is larger and the period dispersion characteristic value is smaller, the change characteristics between any adjacent period segments of the signal curve are closer, the period robustness is larger, the period segments are better in division, and the compression efficiency is improved. The equation for obtaining the period robustness includes:
in the method, in the process of the invention,period robustness of period segment representing the whole of the signal curve,/->Representing the average similarity of period>Representing the time-averaged degree of difference, +.>Representing the average difference of carrier periods, +.>Representing the period dispersion +.>Representing a preset minimum positive number, in the present embodiment 0.01,/for example>Representing a period dispersion characteristic value, +.>Representing the normalization function.
When the period robustness is larger, the more the same period sections exist in the divided period sections, the more the compression efficiency of the LZW algorithm is improved, so that the period section of the modulation data can be determined according to the period robustness, and preferably, when the period robustness is larger than a preset robustness threshold, the period section of the modulation data is determined according to the distribution characteristics of the period sections; in the embodiment of the invention, the preset robust threshold is 0.85, and an implementer can determine according to an implementation scene by himself, and corresponds the dividing point of the period section in the signal curve to the dividing period section in the modulation data; otherwise, the preset correlation threshold is reduced, the period segments are acquired again according to the time difference, which means that the period robustness is too low, and the same period segments in the divided period segments are fewer, so that the preset correlation threshold for dividing the period segments needs to be reduced, and the accuracy of dividing the period segments is improved.
After the period intervals of the modulated data are obtained, the modulated data are data composed of discrete data points before fitting the signal curve, so that the similarity between the period intervals needs to be further analyzed, and the period credibility of the period intervals is obtained according to the difference characteristics of the modulated data of different period intervals.
Preferably, in one embodiment of the present invention, obtaining the cycle reliability includes: calculating the absolute value of the amplitude difference value of the data point at the corresponding position of the adjacent period interval, and accumulating and summing to obtain the difference value of the adjacent period interval; calculating the difference value of the adjacent period intervals according to the period interval and the next period interval, wherein the last period interval does not participate in calculation; the larger the adjacent period interval difference value means the smaller the possibility that two period intervals are identical. And calculating the sum of all adjacent period interval difference values and carrying out negative correlation mapping to obtain the period reliability of the period interval of the modulation data, wherein the larger the period reliability is, the more accurate the period interval is divided, and the more the same period intervals are, so that the compression efficiency is improved.
Step S4, determining a final interval section of the modulation data according to the period reliability; and compressing the modulated data through an LZW algorithm according to the final interval.
When the period reliability is larger, the more accurate the period interval is divided, so that the final interval of the modulation data is determined according to the period reliability; preferably, when the period reliability exceeds a preset reliability threshold, taking a period interval of the modulated data as a final interval section, wherein the change characteristics among most period intervals are the same, so that the compression efficiency can be improved according to the division of the period intervals; otherwise, the preset correlation threshold is reduced, the period segments are acquired again according to the time difference, the dividing accuracy of the period segments is improved until the final interval segment is obtained, and in the embodiment of the invention, the preset reliability threshold is 0.8, and an implementer can determine according to implementation scenes.
Further, after the period interval of the modulation data is acquired, the character string can be constructed according to the LZW compression algorithm to compress, so that the time for determining the period interval by the LZW algorithm is reduced, the compression time is further reduced, and finally the storage and transmission speed of carrier communication data is improved; it should be noted that, the LZW compression algorithm belongs to the prior art, and specific compression steps are not described again.
In summary, the embodiment of the invention provides an intelligent carrier communication data acquisition method; acquiring modulation data and carrier period of a carrier signal, and fitting the modulation data to obtain a signal curve; and obtaining the time difference degree and the period section of the signal curve according to the change characteristics of the signal curve. Obtaining period similarity according to the time difference, the amplitude characteristic of the period section and the corresponding carrier period; obtaining period robustness and a period interval of modulation data according to the time difference, the period similarity and the carrier period; and obtaining the period reliability and the final interval section according to the difference characteristics of the period interval. According to the invention, the final interval section obtained in a self-adaptive way is compressed through the LZW algorithm, so that the compression time is reduced, and the storage and transmission efficiency of carrier communication data is further improved.
The invention also provides an intelligent carrier communication data acquisition system, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program to realize any one of the steps of the intelligent carrier communication data acquisition method.
It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
Claims (10)
1. The intelligent acquisition method for the carrier communication data is characterized by comprising the following steps of:
acquiring modulated data and carrier period after carrier signal modulation, and fitting the modulated data to obtain a signal curve; obtaining the time difference degree of the signal curve according to the change characteristics of the signal curve;
acquiring a period section of the signal curve according to the time difference; obtaining the period similarity according to the time difference, the carrier period corresponding to the period section and the amplitude characteristics of the data points in the period section;
obtaining period robustness according to the time difference degree, the period similarity and the carrier period; determining a period interval of the modulation data according to the period robustness; obtaining the cycle credibility of the cycle interval according to the difference characteristics of the modulation data of different cycle intervals;
determining a final interval section of the modulation data according to the period reliability; and compressing the modulation data through an LZW algorithm according to the final interval.
2. The intelligent carrier communication data collection method according to claim 1, wherein the step of obtaining the time difference degree of the signal curve according to the change characteristic of the signal curve comprises:
determining extreme points of the signal curve; calculating a data point with the absolute value of the tangential slope of the data point of the signal curve smaller than a preset slope threshold value as a characteristic data point; forming a wave band interval of the extreme point by the characteristic data points connected with the two ends of the extreme point;
calculating the time interval between the extreme point and the adjacent similar extreme point to obtain adjacent time difference; calculating the absolute value of the difference value of the adjacent time difference between the first extreme point of the signal curve and other similar extreme points to obtain a time variation difference value; calculating the absolute value of the width difference value between the first extreme point of the signal curve and the band intervals of other similar extreme points to obtain a time range difference value; and calculating and normalizing the product of the time variation difference value and the time range difference value to obtain the time difference degree of two similar extreme points of the signal curve.
3. The intelligent carrier communication data collection method according to claim 2, wherein the step of obtaining the period segment of the signal curve according to the time difference degree comprises:
stopping calculation when the time difference is smaller than a preset correlation threshold, and taking the position from a first extreme point to the extreme point at which the time difference calculation is finished as a first period segment of the signal curve; a range of signal curves of equal length adjacent to the first period segment is taken as a second period segment; and starting to continue traversing the signal curve by using the end position of the second period section to calculate the time difference degree, and obtaining all period sections of the signal curve.
4. The method for intelligently collecting carrier communication data according to claim 1, wherein the step of obtaining the period similarity according to the time difference, the carrier period corresponding to the period segment and the amplitude characteristic of the data point in the period segment comprises the steps of:
calculating the absolute value of the amplitude difference of the corresponding position data points of two adjacent other period sections of the period section, accumulating and summing to obtain an amplitude difference representation value, and calculating the amplitude difference representation value if the lengths of the two adjacent other period sections of the period section are different; calculating the absolute value of the length difference value of the carrier period of the corresponding position data point of two adjacent other period sections of the period section to obtain a carrier period difference characterization value; and calculating the product of the amplitude difference representation value, the time difference degree of the period section and the carrier period difference representation value and carrying out negative correlation mapping to obtain the period similarity of the period section of the signal curve.
5. The intelligent carrier communication data collection method according to claim 1, wherein the step of obtaining the period robustness according to the time difference, the period similarity and the carrier period comprises:
calculating the average value of the absolute values of the differences of the time lengths of the carrier periods corresponding to all adjacent period sections to obtain the average difference of the carrier periods; calculating the average value of the time difference degrees to obtain the time average difference degrees; calculating the product of the time average difference and the carrier period average difference to obtain the period dispersion; calculating the sum of the period dispersion and a preset minimum positive number to obtain a period dispersion characterization value; calculating the average value of the periodic similarity to obtain periodic average similarity; calculating and normalizing the ratio of the period average similarity to the period dispersion characteristic value to obtain the period robustness of the period section of the signal curve.
6. A method for intelligently collecting carrier communication data according to claim 3, wherein said step of determining a period interval of said modulated data according to said period robustness comprises:
when the period robustness is larger than a preset robustness threshold, determining a period interval of the modulation data according to the distribution characteristics of the period section; otherwise, the preset correlation threshold is reduced, and a period segment is acquired again according to the time difference degree until the period robustness is larger than the preset robustness threshold.
7. The intelligent carrier communication data collection method according to claim 1, wherein the step of obtaining the cycle reliability of the cycle interval according to the difference characteristics of the modulation data of different cycle intervals comprises:
calculating the absolute value of the amplitude difference value of the data point at the corresponding position of the adjacent period interval, and accumulating and summing to obtain the difference value of the adjacent period interval; and calculating the sum of all adjacent period interval difference values and carrying out negative correlation mapping to obtain the period reliability of the period interval of the modulation data.
8. A method for intelligently collecting carrier communication data according to claim 3, wherein said step of determining the final interval of modulated data according to said periodic confidence level comprises:
when the periodic credibility exceeds a preset credibility threshold, taking a periodic interval of the modulation data as the final interval; otherwise, the preset correlation threshold is reduced, and the period segment is acquired again according to the time difference degree until the final interval segment is acquired.
9. The intelligent acquisition method of carrier communication data according to claim 8, wherein the preset reliability threshold is 0.8.
10. A carrier communication data intelligent acquisition system comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to carry out the steps of the method according to any one of claims 1-9.
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| CN1117329A (en) * | 1993-11-01 | 1996-02-21 | 艾利森电话股份有限公司 | Digital control channels having logical channels for multiple access radiocommunication |
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