CN107025462A - A kind of unmanned plane telemetry real-time parallel compression processing method - Google Patents
A kind of unmanned plane telemetry real-time parallel compression processing method Download PDFInfo
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- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
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Abstract
The present invention relates to a kind of unmanned plane telemetry real-time parallel compression processing method, including：Unmanned plane telemetry is obtained in real time；The telemetry arrived to real-time reception carries out classification storage；Different types of data parallel is imported in different redundancy decision processors；When importing data dimension m more than M, data redudancy calculating is carried out, judges whether the data imported need compression to handle according to redundancy, is, then exported in real time after data being compressed with processing using principal component analysis method.Telemetry species can adapt to using this method various, the compression processing of data magnanimity, intuitively realize that Data Dimensionality Reduction compresses on the premise of retention data information is maximized, its variation tendency, statistical law are analyzed so as to quicklook, with important application value.
Description
Technical field
Handled the present invention relates to technical field of data processing, more particularly to a kind of unmanned plane telemetry real-time parallel compression
Method.
Background technology
, it is necessary to be acquired to multi-parameters such as the pressure of measurand unmanned plane, temperature, humidity, angles in telemetry system
And wireless communication transmission is evaluated and monitored to the state of unmanned plane by Data Analysis Services to ground.In remote measurement experiment
Need to measure reflection each several part working condition and environment multiple parameters, and meeting last longer, magnanimity will be produced
Telemetry, and telemetry system also just developing towards high-precision direction, and this will certainly also increase data volume.Assuming that remote measurement
When time is 1400s, remote measurement code check is only calculated with 320kb/s slower speed, it is contemplated that amount of redundancy during data transmission and processing,
Final data storage is about 600M.If remote measurement code check improves 10 times to million ranks, then total amount of data will up to several G even more than ten
G or more, analyzes data interpretation and proposes very big challenge.Telemetry parameter is numerous, data volume is huge, how to carry out data
Compression retains effective information, in being analyzed and processed as telemetry the problem of primary solution.
Because telemetry has Biodiversity Characteristics, in actual applications, how on the premise of data message is not lost,
The problem of most effectively progress data compression and information extraction is urgent need to resolve.During actual remote measurement, remain unchanged people's work point mostly
Analyse interpretation data, the problem of this method has inefficiency, expends substantial amounts of manpower and materials, when especially test process is longer
Become apparent.In existing data compression method, principal component analysis is the statistical analysis technique for adapting to this demand, by extracting
Go out incoherent characteristic variable, reduce the dimension and redundancy of legacy data, realize the compression and feature extraction of data.It is often
The threshold value of empirically determined component contribution rate, such as component contribution rate threshold value are typically set at 90%, carry out principal Component Extraction,
Remaining low order components is determined as that noise is blocked.Because telemetry species is various, with diversity and complexity, based on threshold
The principal component analysis of value is difficult to targetedly adjust contribution rate threshold value, it is therefore more likely that effective information can be lost, to remote measurement number
Harmful effect is produced according to compression.
The content of the invention
In view of above-mentioned analysis, the present invention is intended to provide a kind of unmanned plane telemetry real-time parallel compression processing method,
This method is for telemetry is huge, manual analysis efficiency is low, and current data compression method is difficult to effectively carry out data
The problem of batch processing is compressed, parallelly compressed processing data on the premise of retention data information is maximized, is most effectively attained sea
Measure the parallelly compressed processing of telemetry.
The purpose of the present invention is mainly achieved through the following technical solutions：
A kind of unmanned plane telemetry real-time parallel compression processing method, comprises the following steps：
Step 1, in real time acquisition unmanned plane telemetry；
The telemetry that classification processor in step 2, ground installation is arrived to real-time reception carries out classification storage；
Step 3, classification processor import different types of data parallel in different redundancy decision processors；
Step 4, for each redundancy decision processor, when importing data dimension m and being more than M, carry out data redudancy
Judge, judge whether the data imported need compression to handle according to redundancy, be, then into step 5；It is no, continue to import data；
Step 5, compression processor are exported in real time after data are compressed with processing using principal component analysis method.
Further, step 401, calculating import data x=[x1,x2,…,xm]TCovariance matrix CxAnd coefficient correlation square
Battle array Rx；
Wherein, m is data dimension, and subscript T represents transposition, each row vector xiData point comprising equivalent, i=1 ... m
Step 402, calculated according to following formula data x covariance matrix each element, constitute covariance matrix；
Cx(i, j)=E [(xi-E(xi))(xj-E(xj))], i=1 ... m, j=1 ... m；
Wherein, E () estimates for the desired value of vector.
Step 403, calculated using following formula by each element of covariance matrix and obtain data x correlation matrix Rx's
Element Rx(i, j), composition correlation matrix Rx；
Step 404, to the triangle element R in correlation matrixxThe absolute value of (i, j) is judged, when all absolute
When value is respectively less than 0.3, judged result continues to import data for that need not be compressed；Otherwise judged result is to need to be pressed
Contracting, into step 5.
Further, in the step 401, each row vector xiData are synchronization, the different parts gathered it is same
Class data；X=[x1,x2,…,xm]TThe homogeneous data collected by m collection moment.
Further, the step 5 includes：
Step 501, to covariance matrix CxEigenvalues Decomposition is carried out, characteristic value diagonal matrix D and eigenvectors matrix is obtained
V, the diagonal element of the diagonal matrix D is arranged in descending order；
Step 502, calculating principal component signal to noise ratio snr (i, i+1), i=1 ... m-1；
Principal component signal to noise ratio snr (i, the i+1)=λi/λi+1, i=1 ... m-1, is the characteristic value of adjacent two order component
Ratio；
Step 503, the critical point for obtaining according to the principal component signal to noise ratio in step 502 principal component；
Step 504, according to the critical point, take the characteristic vector before p ranks and p ranks to build condensation matrix [v1,v2,…,
vp]；
Step 505, using the condensation matrix data are compressed, the data PC after real-time output squeezingp, PCp=
[v1,v2,…,vp]Tx。
Further, in the step 501,
V,D,CxMeet following relation：
CxV=VD
Wherein：D diagonal entry λiThe characteristic value of the i-th order component of correspondence, and arrange in descending order；V each column vector vi
The corresponding characteristic vector of the i-th order component of correspondence, i=1 ... m.
Further, in the step 503, when principal component signal to noise ratio numerical value is more than 10, there is first notable peak value SNR
(p, p+1), during p≤m-1, illustrates that critical point occurs in the information content of principal component.
The present invention has the beneficial effect that：
It can adapt to that telemetry species is various using this method, the compression process problem of data magnanimity.By telemetry
Classification storage, parallelly compressed processing；The correlation matrix qualitative analysis data redudancy of data is used in processing procedure, is made
To judge whether the standard for needing to carry out data compression, then using principal component signal to noise ratio as technical indicator, when there is peak value,
Show that the information content of preceding first order component is significantly larger than the information content of rear first order component, regard preceding first order component as principal Component Extraction
Critical point, remaining low order components are determined as that noise is blocked, to carry out effective data compression.This method is to data class
Type, practical experience etc. intuitively realize that Data Dimensionality Reduction compresses without particular requirement on the premise of retention data information is maximized,
Its variation tendency, statistical law are analyzed so as to quicklook, with important application value.
Other features and advantages of the present invention will be illustrated in the following description, also, the partial change from specification
Obtain it is clear that or being understood by implementing the present invention.The purpose of the present invention and other advantages can be by the explanations write
Specifically noted structure is realized and obtained in book, claims and accompanying drawing.
Brief description of the drawings
Accompanying drawing is only used for showing the purpose of specific embodiment, and is not considered as limitation of the present invention, in whole accompanying drawing
In, identical reference symbol represents identical part.
Fig. 1 is flow chart of the embodiment of the present invention；
Fig. 2 is specific embodiment of the invention coefficient correlation schematic diagram；
Fig. 3 is specific embodiment of the invention principal component signal to noise ratio schematic diagram；
Fig. 4 is that the specific embodiment of the invention extracts each rank principal component coefficient correlation schematic diagram；
Fig. 5 is the component contribution amount schematic diagram using existing method.
Embodiment
The preferred embodiments of the present invention are specifically described below in conjunction with the accompanying drawings, wherein, accompanying drawing constitutes the application part, and
It is used for the principle for explaining the present invention together with embodiments of the present invention.
A kind of unmanned plane telemetry real-time parallel compression processing method is present embodiments provided, is comprised the following steps：
Step 1, in real time acquisition unmanned plane telemetry；
Specifically, the pressure data, temperature data, humidity at the multiple positions of unmanned plane are measured using different types of sensor
Data, angle-data, vibration data etc.；And set by wireless communication mode by obtained real-time data transmission to ground is measured
It is standby；
Classification processor in step 2, ground installation to real-time reception to telemetry classify, pressing force, temperature
The difference of the data types such as degree, humidity, angle, vibration, carries out classification storage；
Common telemetry storage format is Excel, TXT, DAT, herein by taking Excel forms as an example, carries out batch and leads
Enter operating instruction.Multiple Excel files are stored under fixed route, store different operating modes, different time in each file respectively
The initial data of point.
Step 3, classification processor import different types of data parallel in different redundancy decision processors；
Step 4, for each redundancy decision processor, when importing data dimension m and being more than M, carry out data redudancy
Judge, judge whether the data imported need compression to handle according to redundancy, be, then into step 5；It is no, continue to import data；
M can be set by those skilled in the art are self-defined in advance according to actual needs, and when data dimension is smaller, data take up space
Subsequent compression processing need not be carried out compared with I.
Specifically, including：
Step 401, calculating import data x=[x1,x2,…,xm]TCovariance matrix CxAnd correlation matrix Rx；
Wherein, m is data dimension, and subscript T represents transposition, each row vector xi(i=1 ... m) include the data of equivalent
Point, number of data points is determined by setting number of sensors；When being such as pressure data, each row vector xiData are synchronization,
The pressure data for the different parts that all pressure sensors are gathered；X=[x1,x2,…,xm]TGathered by m collection moment
Obtained pressure data；
Step 402, calculated according to following formula data x covariance matrix each element, constitute covariance matrix；
Cx(i, j)=E [(xi-E(xi))(xj-E(xj))], i=1 ... m, j=1 ... m；
Wherein, E () estimates for the desired value of vector.
Step 403, calculated using following formula by each element of covariance matrix and obtain the correlation matrix of initial data
RxElement Rx(i, j), composition correlation matrix Rx；
Step 404, to the triangle element R in correlation matrixxThe absolute value of (i, j) is judged, when all absolute
When value is respectively less than 0.3, judged result continues to import data for that need not be compressed；Otherwise judged result is to need to be pressed
Contracting, into step 5.
The absolute value of coefficient correlation represents two vectorial linear correlation degree between [0,1].It is enough in number of samples
Under conditions of many, when coefficient correlation is below 0.3, represent that the correlation of data is weaker；If coefficient correlation is higher than 0.3, table
There is redundancy in registration, it is necessary to carry out data compression process between.The selection of 0.3 value, to pass through long-term practice, is analyzed
The critical value arrived, which represent the objective indicator for being necessary progress data compression.
Step 5, compression processor are exported in real time after data are compressed with processing using principal component analysis method.
Specifically,
Step 501, to covariance matrix CxEigenvalues Decomposition is carried out, characteristic value diagonal matrix D and eigenvectors matrix is obtained
V, the diagonal element of the diagonal matrix D is arranged in descending order；
V,D,CxMeet following relation：
CxV=VD
Wherein：D diagonal entry λiThe characteristic value of the i-th order component of correspondence, and arrange in descending order；V each column vector vi
The corresponding characteristic vector of the i-th order component of correspondence, i=1 ... m.
Step 502, calculating principal component signal to noise ratio snr (i, i+1), i=1 ... m-1；
Principal component signal to noise ratio snr (i, the i+1)=λi/λi+1, i=1 ... m-1, is the characteristic value of adjacent two order component
Ratio；
Step 503, when principal component signal to noise ratio numerical value be more than 10, occur first notable peak value SNR (p, p+1), p≤m-1
When, illustrate that critical point occurs in the information content of principal component；
The variation tendency of principal component signal to noise ratio has increase to have reduction, it is understood that there may be multiple big small leaks, when its numerical value is more than
10, and during for first significant peak value, illustrate the information content of peak value and component before far above follow-up component, follow-up component
Information contained is very low, can be blocked as noise.
Step 504, according to the critical point, take the characteristic vector before p ranks and p ranks to build condensation matrix [v1,v2,…,
vp]；
Step 505, using the condensation matrix data are compressed, the data PC after real-time output squeezingp。
The compression is carried out using following formula：
PCp=[v1,v2,…,vp]Tx。
This method stores telemetry classification, parallelly compressed processing；The coefficient correlation of data is used in processing procedure
Matrix qualitative analysis data redudancy, as the standard for judging whether to need to carry out data compression, then with principal component signal to noise ratio
As technical indicator, when there is peak value, show that the information content of preceding first order component is significantly larger than the information content of rear first order component, will
Preceding first order component is as the critical point of principal Component Extraction, and remaining low order components is determined as that noise is blocked, to carry out effectively
Data compression.Principal component signal to noise ratio is introduced as object function, the parallelly compressed batch processing and storage of the data that take remote measurement are right
Data type, practical experience etc. intuitively extract principal component without particular requirement on the premise of retention data information is maximized,
Suitable for the compression and feature extraction of all kinds data.
The flow of this method can be realized using matlab script files.
1. step 4 data redudancy judges
Correlation matrix RxFor diagonal matrix, the absolute value of triangle element on matrix is judged line by line, if numerical value is equal
Less than 0.3, correlation is very weak between illustrating telemetry, exports CR_x=0；If element numerical value therein is more than 0.3,
Illustrate to there is certain redundancy between data, it is necessary to further compression processing.
The code that data redudancy judges is as follows：
2. the code that the characteristic value and characteristic vector in step 5 are solved is as follows：
3. step 505 data compression code is as follows：
PC=V (:,1:hold_IX)'*x；
xlswrite(['E:Compressed data com_', files_info (k) .name], PC)；
Hereinafter, the redundancy of multidimensional data is judged using a specific data, the critical exponent number of principal component signal to noise ratio judges, number
Numerical simulation is carried out according to compression process, the validity and robustness of this method is verified.
Data are one 6 × 3-dimensional hybrid matrix, and concrete numerical value is as follows：
[0.55,0.39,0.45；0.45,0.68,0.53；0.76,0.81,0.27；0.69,0.09,0.10；0.60,
0.28,0.55；0.12,0.04,0.94].
Calculate the covariance matrix and correlation matrix of data.Correlation matrix is as shown in Figure 2.It can be found that
Coefficient correlation absolute value is generally larger, illustrates to exist between initial data between data and there is very big relevance, and having very much must
Data compression is carried out to reduce data redudancy.
The ratio of adjacent feature value is calculated, principal component signal to noise ratio is obtained, as shown in Figure 3.Can intuitively it be found by Fig. 3,
1/2nd, 2/3 principal component noise is smaller, and the information content of third order products is basically identical before showing；3/4 principal component signal to noise ratio increases suddenly
23 are reached greatly, is first notable peak value of signal to noise ratio, shows that the information content of the 3rd order component is far above the information content of the 4th component,
4th order component information contained is very low.
Preceding 3 order component is extracted as principal component, remaining low order components is determined as that noise is blocked, realizes data compression.
The coefficient correlation of preceding 3 rank principal component is as shown in figure 4, the principal component coefficient correlation extracted is zero, the data after compression
Between it is orthogonal, reduce the redundancy of data.
In order to verify the superiority of this method, principal Component Extraction is carried out according to the component contribution rate in existing application, will be tied
Fruit is contrasted.Preceding q ranks (q ＜ m) component contribution rate is calculated as follows：The summation of preceding q ranks characteristic value sum divided by all characteristic values.
Component contribution rate is as shown in Figure 5.It can be found that the contribution rate of preceding 2 order component is made already close to 95% according to component contribution rate
Extract preceding two ranks principal component for parameter index and carry out data compression, it will lose effective information, to follow-up data analysis and
Feature extraction is impacted.
It will be understood by those skilled in the art that realizing all or part of flow of above-described embodiment method, meter can be passed through
Calculation machine program instructs the hardware of correlation to complete, and described program can be stored in computer-readable recording medium.Wherein, institute
It is disk, CD, read-only memory or random access memory etc. to state computer-readable recording medium.
The foregoing is only a preferred embodiment of the present invention, but protection scope of the present invention be not limited thereto,
Any one skilled in the art the invention discloses technical scope in, the change or replacement that can be readily occurred in,
It should all be included within the scope of the present invention.
Claims (6)
1. a kind of unmanned plane telemetry real-time parallel compression processing method, it is characterised in that comprise the following steps：
Step 1, in real time acquisition unmanned plane telemetry；
The telemetry that classification processor in step 2, ground installation is arrived to real-time reception carries out classification storage；
Step 3, classification processor import different types of data parallel in different redundancy decision processors；
Step 4, for each redundancy decision processor, when importing data dimension m and being more than M, carry out data redudancy calculating,
Judge whether the data imported need compression to handle according to redundancy, be, then into step 5；It is no, continue to import data；
Step 5, compression processor are exported in real time after data are compressed with processing using principal component analysis method.
2. according to the method described in claim 1, it is characterised in that the step 4 includes：
Step 401, calculating import data x=[x1,x2,…,xm]TCovariance matrix CxAnd correlation matrix Rx；
Wherein, m is data dimension, and subscript T represents transposition, each row vector xiData point comprising equivalent, i=1 ... m
Step 402, calculated according to following formula data x covariance matrix each element, constitute covariance matrix；
Cx(i, j)=E [(xi-E(xi))(xj-E(xj))], i=1 ... m, j=1 ... m；
Wherein, E () estimates for the desired value of vector.
Step 403, calculated using following formula by each element of covariance matrix and obtain data x correlation matrix RxElement
Rx(i, j), composition correlation matrix Rx；
Step 404, to the triangle element R in correlation matrixxThe absolute value of (i, j) is judged, when all absolute values are small
When 0.3, judged result continues to import data for that need not be compressed；Otherwise judged result is compressed for needs, enters
Step 5.
3. method according to claim 2, it is characterised in that in the step 401, each row vector xiData are same
Moment, the homogeneous data of the different parts gathered；X=[x1,x2,…,xm]TBy m collection the moment collect it is similar
Data.
4. method according to claim 2, it is characterised in that the step 5 includes：
Step 501, to covariance matrix CxEigenvalues Decomposition is carried out, characteristic value diagonal matrix D and eigenvectors matrix V is obtained, it is described
Diagonal matrix D diagonal element is arranged in descending order；
Step 502, calculating principal component signal to noise ratio snr (i, i+1), i=1 ... m-1；
Principal component signal to noise ratio snr (i, the i+1)=λi/λi+1, i=1 ... m-1, is the characteristic value ratio of adjacent two order component；
Step 503, the critical point for obtaining according to the principal component signal to noise ratio in step 502 principal component；
Step 504, according to the critical point, take the characteristic vector before p ranks and p ranks to build condensation matrix [v1,v2,…,vp],
P therein is the component exponent number of critical point；
Step 505, using the condensation matrix data are compressed, the data PC after real-time output squeezingp, PCp=[v1,
v2,…,vp]Tx。
5. method according to claim 4, it is characterised in that in the step 501,
V,D,CxMeet following relation：
CxV=VD
Wherein：D diagonal entry λiThe characteristic value of the i-th order component of correspondence, and arrange in descending order；V each column vector viCorrespondence
The corresponding characteristic vector of i-th order component, i=1 ... m.
6. the method according to claim 4 or 5, it is characterised in that：In the step 503, when principal component signal to noise ratio numerical value
More than 10, there is first notable peak value SNR (p, p+1), during p≤m-1, illustrate that critical point occurs in the information content of principal component.
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