CN106556760A - A kind of automated test control system of the vehicle mounted multimedia based on cloud computing - Google Patents

Abstract

The invention discloses a kind of automated test control system of the vehicle mounted multimedia based on cloud computing, including singlechip controller, the outfan of singlechip controller respectively with signal generating module, gain-programmed amplifier, the input of filtration module and dynamic analog module is electrically connected with, the input of singlechip controller respectively with image information collecting processing module, signal acquisition module, fault detection module, the outfan of timing module and power module is electrically connected with, singlechip controller respectively with data processing module, RAM memory, ROM memory, data base and radio frequency transceiving module are electrically connected with, radio frequency transceiving module is connected with cloud server by GPRS network, radio frequency transceiving module is connected with external equipment by GPRS network.The invention takes full advantage of the data-handling capacity and data storage capacities of cloud computing, improves testing efficiency, and test result effectively can be stored.

Description

A kind of automated test control system of the vehicle mounted multimedia based on cloud computing

Technical field

The invention belongs to multimedia technology field, more particularly to a kind of automatization's survey of vehicle mounted multimedia based on cloud computing Examination control system.

Background technology

In-vehicle multi-media system is the embedded multimedia system used installed in automotive environment, navigation system, and which has excellent Change traffic efficiency, play the effect such as entertainment audio, and rapidly sending out to high-performance, high-quality, multi-functional, personalized direction Exhibition, consumer also important consider index using the performance of vehicle mounted multimedia as one when automobile is chosen.Therefore vehicle-mounted many matchmakers When dispatching from the factory, manufacturer has to test its performance body.

At present, mainly by manual vehicle mounted multimedia is tested, test process data and test result are also required to Artificial treatment, causes testing efficiency low, does not much catch up with the progress of enterprise's new product development and new projects' exploitation.There is part to look forward to Industry employs automated testing method test vehicle mounted multimedia, but existing automated testing method is also simply built completely On some professional virtual instrument platforms, or the automatic test of partial properties is also confined to, this also has led to these Automatic test is unsuitable for the task of mass data process, it is impossible to the essence of the signals collecting that upgraded in time according to the demand of product variations Degree；And current test control system data processing and data storage capacities are poor, test accuracy rate is not high and operation is not convenient.

The content of the invention

It is an object of the invention to provide a kind of automated test control system of the vehicle mounted multimedia based on cloud computing, purport Poor in the current test control system data processing of solution and data storage capacities, test accuracy rate is not high and operates not easily Problem.

The present invention is achieved in that a kind of automated test control system of the vehicle mounted multimedia based on cloud computing, bag Include singlechip controller, the outfan of the singlechip controller respectively with signal generating module, gain-programmed amplifier, filtering The input of module and dynamic analog module is electrically connected with, the input of the singlechip controller respectively with image information collecting The outfan of processing module, signal acquisition module, fault detection module, timing module and power module is electrically connected with, institute State singlechip controller respectively with data processing module, RAM memory, ROM memory, data base and radio frequency transceiving module It is electrically connected with, the radio frequency transceiving module is connected with cloud server by GPRS network, the radio transceiver mould Block is connected with external equipment by GPRS network.

Further, the input of described image Information Collecting ＆ Processing module is electrically connected with the outfan of photographic head.

Further, the external equipment is connected with cloud server by GPRS network.

Further, the external equipment is the electronic products with network connecting function such as computer, mobile phone.

Further, ROM memory carries out data storage using coding and storing method, specifically includes following steps：

A：The type of two-dimension codeword of the coding and storing method is C=[c_i,j]；1≤i≤m-1,1≤j≤m+m, element c_i,jTable It is shown as the i-th row, the information bit or check bit of jth row；

B：As 1≤i≤m-1,1≤j≤m-1, element c_i,jFor information bit, for depositing original data；

C：As 1≤i≤m-1, m≤j≤m+m, element c_i,jFor check bit, for depositing verification data；

D：According to more than, three steps show that first row check bit can be according to following rule constructs：

E：The redundancy check bit equation below of r row represents, makes the public regulatory factor be：

F：Show that r row check bit is according to step E：

In formula：1≤i ＜ m-1,1≤r≤m.

Further, described image Information Collecting ＆ Processing module includes that image acquisition submodule, image blur evaluate submodule Block, fuzziness adjustment submodule；Described image collection submodule is used for the image information that acquisition camera shoots；

The ambiguity evaluation submodule is used for the image for obtaining the transmission of image acquisition submodule, and schemes before and after calculating filtering As statistical information ratio；

The fuzziness adjustment submodule is connected with ambiguity evaluation submodule, is drawn most for adjusting original image fuzziness Whole image and image blur evaluation index.

Further, using ambiguity evaluation word modules, fuzziness adjustment word modules to image blur evaluation methodology it is：

Step one, image are obtained, and obtain image to be evaluated by image acquisition submodule；

Step 2, image gray processing, for convenience of the edge extracting of image, using the R of RGB image in Digital Image Processing, Coloured image is converted into gray level image by the transformational relation of the pixel value and gray level image pixel value of G, B each passage, and formula is such as Under：

Gray=R*0.3+G*0.59+B*0.11；

Step 3, Edge extraction are made using the Roberts operator edge detections technology in digital image processing method The edge of image is obtained for gray level image, different detective operators have different edge detection templates, according to concrete template The difference for intersecting pixel is calculated as current pixel value, it is as follows using template：

E (i, j)=| F (i, j)-F (i+1, j+1) |+| F (i+1, j)-F (i, j+1) |；

Step 4, image procossing are filtered process using high pass/low pass filter to be evaluated to construct to gray level image The reference picture of image, using 3*3 mean filters, using Filtering Template traversing graph as each pixel, every time by template center Current pixel is placed in, the meansigma methodss of all pixels are newly worth as current pixel using in template, and template is as follows：

Step 5, image border statistical information are calculated, and calculate respective edge half-tone information before and after image filtering, filtering respectively The image F statistical information to be evaluated of before processing is sum_orig, and the reference picture F2 statistical information after Filtering Processing is sum_ Filter, specific formula for calculation are as follows：

Wherein, w1 and w2 is according to the weights set with a distance from center pixel, w1=1, w2=1/3；

Step 6, image blur index are calculated, the image filtering front and rear edges grey-level statistics that step 5 is drawn Ratio as fuzziness index, for convenience of evaluating, take it is larger for denominator, it is less for molecule, keep the value between (0,1) Between；

Step 7, according to the DMOS scopes of the best visual effect draw a corresponding fuzziness indication range [min, max]；

Step 8, image blur adjustment, if image blur index is less than min, according to step 6, judge image filtering Change very big in front and back, original image is excessively sharpened, then adjustment is filtered using low pass filter；If being more than max, the filter of process decision chart picture Vary less after wavefront, original image is excessively obscured, then adjustment is filtered using high pass filter, to reach more preferably vision effect Really；

Step 9, draws final image and the image blur evaluation index, is transferred to singlechip controller.

Further, in step 7, corresponding fuzziness index model is drawn according to the DMOS scopes of the best visual effect [min, max] is enclosed, specially：

Fuzziness adjusting range is drawn, is evaluated in LIVE2 using the ambiguity evaluation method in step one to step 6 174 panel heights this broad images, calculates the ambiguity evaluation value of each of which, then sets up evaluation of estimate using fitting tool plot According to the corresponding DMOS scopes of the best visual effect, mapping relations between value and DMOS, show that corresponding one fuzzy is commented Value scope [min, max].

Further, the signal acquisition method of signal acquisition module includes：

Nonlinear transformation is carried out to gathering signal s (t), is carried out as follows：

WhereinA represents the amplitude of signal, and a (m) represents letter Number symbol, p (t) represent shaping function, f_cThe carrier frequency of signal is represented,The phase place of signal is represented, by this Obtain after nonlinear transformation：

Further, the signal acquisition method of signal acquisition module also includes：Obtain after obtaining nonlinear transformationSignal, and carry out processing and amplifying；

Signal carries out segment processing；Average, variance, the accumulated value of signal and peak value 4 are extracted from every segment signal Basic time domain parameter, determine whether that the situation of doubtful leakage occurs by the difference of 4 parameter values of adjacent segment signal the One layer of decision-making judges：Down execution step wavelet packet denoising, no person if having, jump to execution and obtain signal；

Wavelet packet denoising；Denoising is carried out to the signal for gathering using improvement Wavelet Packet Algorithm；

WAVELET PACKET DECOMPOSITION and reconstruct；WAVELET PACKET DECOMPOSITION is carried out to the signal for gathering with weight using improvement Wavelet Packet Algorithm Structure, obtains list band reconstruction signal；

Extract signal characteristic parameter；Extract from the list band signal of reconstruct：Time domain energy, time domain peak, frequency domain energy The parameter of amount, frequency domain peak value, coefficient of kurtosis, variance, frequency spectrum and 8 expression signal characteristics of coefficient of skewness；

Composition characteristic vector, i.e., using principal component analytical method, from above-mentioned 8 parameters selecting 3 to 8 substantially can represent The parameter composition characteristic vector of sound emission signal characteristic, and these characteristic vectors are input to into support vector machine carry out decision-making and sentence Disconnected, i.e. second layer decision-making judges, determines whether that leakage occurs according to the output of support vector machine.

Further, the wavelet packet denoising and WAVELET PACKET DECOMPOSITION are included with reconstruct：

Signals extension, enters horizontal parabola continuation to each layer signal of WAVELET PACKET DECOMPOSITION；

If signal data is x (a), x (a+1), x (a+2), then the expression formula of continuation operator E is：

Eliminate list band un-necessary frequency composition；

By the signal after continuation and decomposition low pass filter h₀Convolution, obtains low frequency coefficient, then calculates through HF-cut-IF Subprocessing, removes unnecessary frequency content, then carries out down-sampling, obtain next layer of low frequency coefficient；By the signal after continuation with Decompose high pass filter g₀Convolution, obtains high frequency coefficient, then through LF-cut-IF operators process, remove unnecessary frequency into Point, then down-sampling is carried out, and next layer of high frequency coefficient is obtained, shown in HF-cut-IF operators such as formula (2), LF-cut-IF operators such as formula (3) shown in；

In (2), (3) formula, x (n) is 2^jThe coefficient of wavelet packet, N on yardstick_jRepresent 2^jThe length of data on yardstick,K=0,1 ..., N_j-1；N=0,1 ..., N_j-1；

List band reconstruction signal is obtained in the WAVELET PACKET DECOMPOSITION and reconstruct, list band signal reconstructing method is：

The high and low frequency coefficient for obtaining is up-sampled, then respectively with high pass reconstruction filter g₁Filter with low-pass reconstruction Ripple device h₁Convolution, by the signal for obtaining respectively with the process of HF-cut-IF, LF-cut-IF operator, obtains list band reconstruction signal.

The present invention has the advantages and positive effects that：Should be based on the control of the automatic test of the vehicle mounted multimedia of cloud computing System, takes full advantage of data processing and the data storage capacities of cloud computing, can carry out image to test object by photographic head Information gathering simultaneously feeds back information to singlechip controller by image information collecting processing module, and singlechip controller can be combined Data base, and the model of discriminating test object is compared by information, and according to result of determination, test event is automatically selected, automatically Change degree is higher, and the data of collection can be transferred to cloud server, cloud server by GPRS network by singlechip controller Can be to the data processing of collection and preservation, it is to singlechip controller remotely control and outside that external equipment can pass through GPRS network Equipment can be connected with cloud server by GPRS network, direct access test data and test result, improve test ground just Victory, the parameter of various vehicle mounted multimedias that are stored with data base, enriches the multiformity of test object, improves system test Scope.

The ROM memory date storage method meeting self-organizing of the present invention forms a region and stores network, and the method will be big The big reliability for improving system data storage.Value with popularization and application.

The picture appraisal of the present invention is different from traditional evaluation methodology, and the present invention sets up special in image self structure to be evaluated On the basis of point, from the angle of relative evaluation, the reference picture of image to be evaluated is constructed using wave filter, before and after calculating change The ratio of image border statistical information is used as evaluation index；The principle of the present invention is simple, realizes the interior of image blur evaluation Hold independence and real-time, can quick and precisely compare the fuzziness between any image.So as to obtain clearly final image, The accuracy of favourable guarantee test effect, this is an innovative point of the present invention.

The signal acquisition module of the present invention can be in Real-time Collection vehicle mounted multimedia items running status audio frequency, the letter such as video Number, and by signal acquisition method accurate acquisition information, and singlechip controller is transferred in time, so as to favourable ensure that certainly The accuracy of dynamicization test data.

Description of the drawings

Fig. 1 is that the automated test control system of the vehicle mounted multimedia based on cloud computing provided in an embodiment of the present invention is illustrated Figure.

In figure：1st, singlechip controller；2nd, signal generating module；3rd, gain-programmed amplifier；4th, filtration module；5th, dynamic Analog module；6th, image information collecting processing module；7th, signal acquisition module；8th, fault detection module；9th, timing module； 10th, power module；11st, data processing module；12nd, RAM memory；13rd, ROM memory；14th, data base；15th, less radio-frequency is received Send out module；16th, GPRS network；17th, cloud server；18th, external equipment；19th, photographic head.

Specific embodiment

In order that the objects, technical solutions and advantages of the present invention become more apparent, with reference to embodiments, to the present invention It is further elaborated.It should be appreciated that specific embodiment described herein is not used to only to explain the present invention Limit the present invention.

Below in conjunction with the accompanying drawings and specific embodiment to the present invention application principle be described in detail.

As shown in figure 1, the automatic test control system of the vehicle mounted multimedia based on cloud computing provided in an embodiment of the present invention System, including singlechip controller 1, the outfan of the singlechip controller 1 is put with signal generating module 2, programme-controlled gain respectively The input of big device 3, filtration module 4 and dynamic analog module 5 is electrically connected with, the input difference of the singlechip controller 1 With image information collecting processing module 6, signal acquisition module 7, fault detection module 8, timing module 9 and power module 10 Outfan be electrically connected with, the singlechip controller 1 respectively with data processing module 11, RAM memory 12, ROM memory 13rd, data base 14 and radio frequency transceiving module 15 are electrically connected with, and the radio frequency transceiving module 15 passes through GPRS network 16 It is connected with cloud server 17, the radio frequency transceiving module 15 is connected with external equipment 18 by GPRS network 16.

Further, the input of described image Information Collecting ＆ Processing module 6 is electrically connected with the outfan of photographic head 19.

Further, the external equipment 18 is connected with cloud server 17 by GPRS network 16.

Further, the external equipment 18 is the electronic products with network connecting function such as computer, mobile phone.

Further, ROM memory carries out data storage using coding and storing method, specifically includes following steps：

A：The type of two-dimension codeword of the coding and storing method is C=[c_i,j]；1≤i≤m-1,1≤j≤m+m, element c_i,jTable It is shown as the i-th row, the information bit or check bit of jth row；

B：As 1≤i≤m-1,1≤j≤m-1, element c_i,jFor information bit, for depositing original data；

C：As 1≤i≤m-1, m≤j≤m+m, element c_i,jFor check bit, for depositing verification data；

D：According to more than, three steps show that first row check bit can be according to following rule constructs：

E：The redundancy check bit equation below of r row represents, makes the public regulatory factor be：

F：Show that r row check bit is according to step E：

In formula：1≤i ＜ m-1,1≤r≤m.

Further, described image Information Collecting ＆ Processing module includes that image acquisition submodule, image blur evaluate submodule Block, fuzziness adjustment submodule；Described image collection submodule is used for the image information that acquisition camera shoots；

The ambiguity evaluation submodule is used for the image for obtaining the transmission of image acquisition submodule, and schemes before and after calculating filtering As statistical information ratio；

The fuzziness adjustment submodule is connected with ambiguity evaluation submodule, is drawn most for adjusting original image fuzziness Whole image and image blur evaluation index.

Further, using ambiguity evaluation word modules, fuzziness adjustment word modules to image blur evaluation methodology it is：

Step one, image are obtained, and obtain image to be evaluated by image acquisition submodule；

Step 2, image gray processing, for convenience of the edge extracting of image, using the R of RGB image in Digital Image Processing, Coloured image is converted into gray level image by the transformational relation of the pixel value and gray level image pixel value of G, B each passage, and formula is such as Under：

Gray=R*0.3+G*0.59+B*0.11；

Step 3, Edge extraction are made using the Roberts operator edge detections technology in digital image processing method The edge of image is obtained for gray level image, different detective operators have different edge detection templates, according to concrete template The difference for intersecting pixel is calculated as current pixel value, it is as follows using template：

E (i, j)=| F (i, j)-F (i+1, j+1) |+| F (i+1, j)-F (i, j+1) |；

Step 4, image procossing are filtered process using high pass/low pass filter to be evaluated to construct to gray level image The reference picture of image, using 3*3 mean filters, using Filtering Template traversing graph as each pixel, every time by template center Current pixel is placed in, the meansigma methodss of all pixels are newly worth as current pixel using in template, and template is as follows：

Step 5, image border statistical information are calculated, and calculate respective edge half-tone information before and after image filtering, filtering respectively The image F statistical information to be evaluated of before processing is sum_orig, and the reference picture F2 statistical information after Filtering Processing is sum_ Filter, specific formula for calculation are as follows：

Wherein, w1 and w2 is according to the weights set with a distance from center pixel, w1=1, w2=1/3；

Step 6, image blur index are calculated, the image filtering front and rear edges grey-level statistics that step 5 is drawn Ratio as fuzziness index, for convenience of evaluating, take it is larger for denominator, it is less for molecule, keep the value between (0,1) Between；

Step 7, according to the DMOS scopes of the best visual effect draw a corresponding fuzziness indication range [min, max]；

Step 8, image blur adjustment, if image blur index is less than min, according to step 6, judge image filtering Change very big in front and back, original image is excessively sharpened, then adjustment is filtered using low pass filter；If being more than max, the filter of process decision chart picture Vary less after wavefront, original image is excessively obscured, then adjustment is filtered using high pass filter, to reach more preferably vision effect Really；

Step 9, draws final image and the image blur evaluation index, is transferred to singlechip controller.

Further, in step 7, corresponding fuzziness index model is drawn according to the DMOS scopes of the best visual effect [min, max] is enclosed, specially：

Fuzziness adjusting range is drawn, is evaluated in LIVE2 using the ambiguity evaluation method in step one to step 6 174 panel heights this broad images, calculates the ambiguity evaluation value of each of which, then sets up evaluation of estimate using fitting tool plot According to the corresponding DMOS scopes of the best visual effect, mapping relations between value and DMOS, show that corresponding one fuzzy is commented Value scope [min, max].

Further, the signal acquisition method of signal acquisition module includes：

Nonlinear transformation is carried out to gathering signal s (t), is carried out as follows：

WhereinA represents the amplitude of signal, and a (m) represents letter Number symbol, p (t) represent shaping function, f_cThe carrier frequency of signal is represented,The phase place of signal is represented, by this Obtain after nonlinear transformation：

Further, the signal acquisition method of signal acquisition module also includes：Obtain after obtaining nonlinear transformationSignal, and carry out processing and amplifying；

Signal carries out segment processing；Average, variance, the accumulated value of signal and peak value 4 are extracted from every segment signal Basic time domain parameter, determine whether that the situation of doubtful leakage occurs by the difference of 4 parameter values of adjacent segment signal the One layer of decision-making judges：Down execution step wavelet packet denoising, no person if having, jump to execution and obtain signal；

Wavelet packet denoising；Denoising is carried out to the signal for gathering using improvement Wavelet Packet Algorithm；

WAVELET PACKET DECOMPOSITION and reconstruct；WAVELET PACKET DECOMPOSITION is carried out to the signal for gathering with weight using improvement Wavelet Packet Algorithm Structure, obtains list band reconstruction signal；

Extract signal characteristic parameter；Extract from the list band signal of reconstruct：Time domain energy, time domain peak, frequency domain energy The parameter of amount, frequency domain peak value, coefficient of kurtosis, variance, frequency spectrum and 8 expression signal characteristics of coefficient of skewness；

Composition characteristic vector, i.e., using principal component analytical method, from above-mentioned 8 parameters selecting 3 to 8 substantially can represent The parameter composition characteristic vector of sound emission signal characteristic, and these characteristic vectors are input to into support vector machine carry out decision-making and sentence Disconnected, i.e. second layer decision-making judges, determines whether that leakage occurs according to the output of support vector machine.

Further, the wavelet packet denoising and WAVELET PACKET DECOMPOSITION are included with reconstruct：

Signals extension, enters horizontal parabola continuation to each layer signal of WAVELET PACKET DECOMPOSITION；

If signal data is x (a), x (a+1), x (a+2), then the expression formula of continuation operator E is：

Eliminate list band un-necessary frequency composition；

By the signal after continuation and decomposition low pass filter h₀Convolution, obtains low frequency coefficient, then calculates through HF-cut-IF Subprocessing, removes unnecessary frequency content, then carries out down-sampling, obtain next layer of low frequency coefficient；By the signal after continuation with Decompose high pass filter g₀Convolution, obtains high frequency coefficient, then through LF-cut-IF operators process, remove unnecessary frequency into Point, then down-sampling is carried out, and next layer of high frequency coefficient is obtained, shown in HF-cut-IF operators such as formula (2), LF-cut-IF operators such as formula (3) shown in；

In (2), (3) formula, x (n) is 2^jThe coefficient of wavelet packet, N on yardstick_jRepresent 2^jThe length of data on yardstick,K=0,1 ..., N_j-1；N=0,1 ..., N_j-1；

List band reconstruction signal is obtained in the WAVELET PACKET DECOMPOSITION and reconstruct, list band signal reconstructing method is：

The high and low frequency coefficient for obtaining is up-sampled, then respectively with high pass reconstruction filter g₁Filter with low-pass reconstruction Ripple device h₁Convolution, by the signal for obtaining respectively with the process of HF-cut-IF, LF-cut-IF operator, obtains list band reconstruction signal.

Application of the operation principle to the present invention below in conjunction with the accompanying drawings is further described.

The automated test control system of the vehicle mounted multimedia based on cloud computing provided in an embodiment of the present invention, signal occur Module 2 can send the characteristic signals such as audio signal, video signal and gps signal, and vehicle mounted multimedia is tested, and programme-controlled gain is put Big device 3 to the feedback signal processing and amplifying of test, can improve the scope and signal of test and receive degree of accuracy, filtration module 4 The electromagnetic wave that system itself is produced can be filtered, reduce interference of the electromagnetic wave to system, dynamic analog module 5 can be to test As a result dynamic analog, the Image Information Processing analysis that image information collecting processing module 6 can be obtained to photographic head 19, and feed back To singlechip controller 1, singlechip controller 1 can be with reference to data base 14 and the species and type of feedback information discriminating test object Number, and test mode is automatically selected, the signal such as audio frequency that signal acquisition module 7 can be during collecting test, video, and feed back To singlechip controller 1, fault detection module 8 can carry out fault detect to circuit board, and timing module 9 can be to test Time records, and power module 10 is system power supply, and data processing module 11 can be to the aggregation of data Treatment Analysis of collection, RAM The data interim storage that memorizer 12 is produced to system, ROM memory 13 can be permanently stored to the data that system is produced, data 14 internal memory of storehouse contains the detailed parameter of various vehicle mounted multimedias, and radio frequency transceiving module 15 can be received and send wireless communication Number, singlechip controller 1 is connected with cloud server 17 by GPRS network 16, is made full use of at the data of cloud server 17 Reason and data storage capacities, improve the efficiency of test, and test result effectively can be stored, and external equipment 18 passes through GPRS network 16 pairs of 1 remotely controls of singlechip controller, while external equipment 18 can be connected with cloud server 17 by GPRS network 16, directly Obtain and take test result and test data.

Presently preferred embodiments of the present invention is the foregoing is only, not to limit the present invention, all essences in the present invention Any modification, equivalent and improvement made within god and principle etc., should be included within the scope of the present invention.

Claims (9)

1. a kind of automated test control system of the vehicle mounted multimedia based on cloud computing, it is characterised in that described based on cloud meter The automated test control system of the vehicle mounted multimedia of calculation includes singlechip controller；The outfan of the singlechip controller point It is not electrically connected with the input of signal generating module, gain-programmed amplifier, filtration module and dynamic analog module；The list The input of piece machine controller respectively with image information collecting processing module, signal acquisition module, fault detection module, meter When module and power module outfan be electrically connected with；The singlechip controller is stored with data processing module, RAM respectively Device, ROM memory, data base and radio frequency transceiving module are electrically connected with；The radio frequency transceiving module passes through GPRS nets Network is connected with cloud server；The radio frequency transceiving module is connected with external equipment by GPRS network.

2. the automated test control system of the vehicle mounted multimedia based on cloud computing as claimed in claim 1, it is characterised in that The input of described image Information Collecting ＆ Processing module is electrically connected with the outfan of photographic head；

The external equipment is connected with cloud server by GPRS network；

The external equipment is the electronic product that computer, mobile phone have network connecting function.

3. the automated test control system of the vehicle mounted multimedia based on cloud computing as claimed in claim 1, it is characterised in that ROM memory carries out data storage using coding and storing method, specifically includes following steps：

A：The type of two-dimension codeword of the coding and storing method is C=[c_i,j]；1≤i≤m-1,1≤j≤m+m, element c_i,jIt is expressed as I-th row, the information bit or check bit of jth row；

B：As 1≤i≤m-1,1≤j≤m-1, element c_i,jFor information bit, for depositing original data；

C：As 1≤i≤m-1, m≤j≤m+m, element c_i,jFor check bit, for depositing verification data；

D：According to more than, three steps show that first row check bit can be according to following rule constructs：

$c_{i,m}=\underset{j=0}{\overset{m-1}{\&CirclePlus;}}{c}_{i,j},1\&le;i<m-1$

E：The redundancy check bit equation below of r row represents, makes the public regulatory factor be：

$S_r=\underset{j=1}{\overset{m-1}{\&CirclePlus;}}{c}_{<m-1+r\&CenterDot;j{>}_m,j}$

F：Show that r row check bit is according to step E：

$c_{i,m+2}=S_r\&CirclePlus;\left(\underset{<i+r\&CenterDot;j{>}_m\&NotEqual;m-1}{\overset{m-1}{\underset{j=0}{\&CirclePlus;}}}{c}_{<i+r\&CenterDot;j{>}_m,j}\right)$

In formula：1≤i ＜ m-1,1≤r≤m.

4. the automated test control system of the vehicle mounted multimedia based on cloud computing as claimed in claim 1, it is characterised in that Described image Information Collecting ＆ Processing module includes that image acquisition submodule, image blur evaluate submodule, fuzziness adjustment Module；Described image collection submodule is used for the image information that acquisition camera shoots；

The ambiguity evaluation submodule is used for the image for obtaining the transmission of image acquisition submodule, and calculates image system before and after filtering Meter information ratio；

The fuzziness adjustment submodule is connected with ambiguity evaluation submodule, draws final figure for adjusting original image fuzziness Picture and image blur evaluation index.

5. the automated test control system of the vehicle mounted multimedia based on cloud computing as claimed in claim 4, it is characterised in that Using ambiguity evaluation word modules, fuzziness adjustment word modules to image blur evaluation methodology it is：

Step one, image are obtained, and obtain image to be evaluated by image acquisition submodule；

Step 2, image gray processing, for convenience of the edge extracting of image, R, G, the B using RGB image in Digital Image Processing is each Coloured image is converted into gray level image with the transformational relation of gray level image pixel value by the pixel value of individual passage, and formula is as follows：

Gray=R*0.3+G*0.59+B*0.11；

Step 3, Edge extraction, using the Roberts operator edge detections technical role in digital image processing method in Gray level image obtains the edge of image, and different detective operators have different edge detection templates, according to concrete formwork calculation Intersect the difference of pixel as current pixel value, it is as follows using template：

E (i, j)=| F (i, j)-F (i+1, j+1) |+| F (i+1, j)-F (i, j+1) |；

Step 4, image procossing are filtered process to construct image to be evaluated using high pass/low pass filter to gray level image Reference picture, using 3*3 mean filters, using Filtering Template traversing graph as each pixel, every time template center is placed in Current pixel, using in template, the meansigma methodss of all pixels are newly worth as current pixel, and template is as follows：

$\frac{1}{9}\&times;\left[\begin{array}{ccc}1& 1& 1\\ 1& 1& 1\\ 1& 1& 1\end{array}\right];$

Step 5, image border statistical information are calculated, and calculate respective edge half-tone information before and after image filtering, Filtering Processing respectively Front image F statistical information to be evaluated is sum_orig, and the reference picture F2 statistical information after Filtering Processing is sum_filter, Specific formula for calculation is as follows：

$\begin{array}{c}sum\_orig=w1\&times;\left(\right|F\left(i,j\right)-F\left(i-1,j\right)|+|F\left(i,j\right)-F\left(i,j-1\right)|+|F\left(i,j\right)-F\left(i,j+1\right)|+|F\left(i,j\right)-F\left(i+1,j\right)\left|\right)\\ +w2\&times;\left(\right|F\left(i,j\right)-F\left(i-1,j-1\right)|+|F\left(i,j\right)-F\left(i-1,j+1\right)|+|F\left(i,j\right)-F\left(i+1,j-1\right)|+|F\left(i,j\right)-F\left(i+1,j+1\right)\left|\right)\end{array},$

$\begin{array}{c}sum\_filter=w1\&times;\left(\right|F2\left(i,j\right)-F2\left(i-1,j\right)|+|F2\left(i,j\right)-F2\left(i,j-1\right)|+|F2\left(i,j\right)-F2\left(i,j+1\right)|+|F2\left(i,j\right)-F2\left(i+1,j\right)\left|\right)\\ +w2\&times;\left(\right|F2\left(i,j\right)-F2\left(i-1,j-1\right)|+|F2\left(i,j\right)-F2\left(i-1,j+1\right)|+|F2\left(i,j\right)\left(i+1,j-1\right)|+|F2\left(i,j\right)-F2\left(i+1,j+1\right)\left|\right)\end{array},$

Wherein, w1 and w2 is according to the weights set with a distance from center pixel, w1=1, w2=1/3；

Step 6, image blur index are calculated, the ratio of the image filtering front and rear edges grey-level statistics that step 5 is drawn Value as fuzziness index, for convenience of evaluating, take it is larger for denominator, it is less for molecule, keep the value between (0,1) it Between；

Step 7, draws a corresponding fuzziness indication range [min, max] according to the DMOS scopes of the best visual effect；

Step 8, image blur adjustment, if image blur index is less than min, according to step 6, before and after judging image filtering Change is very big, and original image is excessively sharpened, then be filtered adjustment using low pass filter；If being more than max, before judging image filtering After vary less, original image is excessively obscured, then be filtered adjustment using high pass filter, to reach more preferably visual effect；

Step 9, draws final image and the image blur evaluation index, is transferred to singlechip controller.

6. the automated test control system of the vehicle mounted multimedia based on cloud computing as claimed in claim 5, it is characterised in that In step 7, a corresponding fuzziness indication range [min, max] is drawn according to the DMOS scopes of the best visual effect, specifically For：

Fuzziness adjusting range is drawn, 174 width in LIVE2 is evaluated using the ambiguity evaluation method in step one to step 6 Gaussian Blur image, calculates the ambiguity evaluation value of each of which, then sets up evaluation of estimate value using fitting tool plot Mapping relations between DMOS, draw corresponding fuzzy evaluation value model according to the corresponding DMOS scopes of the best visual effect Enclose [min, max].

7. the automated test control system of the vehicle mounted multimedia based on cloud computing as claimed in claim 1, it is characterised in that The signal acquisition method of signal acquisition module includes：

Nonlinear transformation is carried out to gathering signal s (t), is carried out as follows：

$f\&lsqb;s\left(t\right)\&rsqb;=\frac{s\left(t\right)*ln\left|s\left(t\right)\right|}{\left|s\left(t\right)\right|}=s\left(t\right)c\left(t\right);$

WhereinA represents the amplitude of signal, and a (m) represents signal Symbol, p (t) represent shaping function, f_cThe carrier frequency of signal is represented,The phase place of signal is represented, by the non-thread Property conversion after obtain：

$f\&lsqb;s\left(t\right)\&rsqb;=s\left(t\right)\frac{ln\left|Aa\left(m\right)\right|}{\left|Aa\left(m\right)\right|}.$

8. the automated test control system of the vehicle mounted multimedia based on cloud computing as claimed in claim 7, it is characterised in that The signal acquisition method of signal acquisition module also includes：Obtain after obtaining nonlinear transformationLetter Number, and carry out processing and amplifying；

Signal carries out segment processing；Average, variance, the accumulated value of signal and peak value 4 are extracted from every segment signal basic Time domain parameter, determines whether the ground floor that the situation of doubtful leakage occurs by the difference of 4 parameter values of adjacent segment signal Decision-making judges：Down execution step wavelet packet denoising, no person if having, jump to execution and obtain signal；

Wavelet packet denoising；Denoising is carried out to the signal for gathering using improvement Wavelet Packet Algorithm；

WAVELET PACKET DECOMPOSITION and reconstruct；WAVELET PACKET DECOMPOSITION and reconstruct are carried out to the signal for gathering using improvement Wavelet Packet Algorithm, is obtained To list band reconstruction signal；

Extract signal characteristic parameter；Extract from the list band signal of reconstruct：Time domain energy, time domain peak, frequency domain energy, frequency Domain peak value, coefficient of kurtosis, variance, the parameter of 8 expression signal characteristics of frequency spectrum and coefficient of skewness；

Composition characteristic vector, i.e., using principal component analytical method, select 3 to 8 substantially expression sound can send out from above-mentioned 8 parameters Penetrating the parameter composition characteristic vector of signal characteristic, and these characteristic vectors are input to support vector machine carries out decision-making judgement, i.e., Second layer decision-making judges, determines whether to leak generation according to the output of support vector machine.

9. the automated test control system of the vehicle mounted multimedia based on cloud computing as claimed in claim 8, it is characterised in that The wavelet packet denoising and WAVELET PACKET DECOMPOSITION are included with reconstruct：

Signals extension, enters horizontal parabola continuation to each layer signal of WAVELET PACKET DECOMPOSITION；

If signal data is x (a), x (a+1), x (a+2), then the expression formula of continuation operator E is：

$\left\{\begin{array}{c}x(a-1)=3x\left(a\right)-3x(a+1)+x(a+2)\\ x(a+3)=3x(a+2)-3x(a+1)+x\left(a\right)\end{array}\right.---\left(1\right);$

Eliminate list band un-necessary frequency composition；

By the signal after continuation and decomposition low pass filter h₀Convolution, obtains low frequency coefficient, then at HF-cut-IF operators Reason, removes unnecessary frequency content, then carries out down-sampling, obtain next layer of low frequency coefficient；By the signal after continuation and decomposition High pass filter g₀Convolution, obtains high frequency coefficient, then through the process of LF-cut-IF operators, removes unnecessary frequency content, then Down-sampling is carried out, next layer of high frequency coefficient is obtained, shown in HF-cut-IF operators such as formula (2), LF-cut-IF operators such as formula (3) institute Show；

$\left\{\begin{array}{c}X\left(k\right)=\underset{n=0}{\overset{N_j-1}{\&Sigma;}}x\left(n\right)W^{kn},0\&le;k\&le;\frac{N_j}{4};\frac{3N_j}{4}\&le;k\&le;N_j\\ X\left(k\right)=0,\\ x\left(n\right)=\underset{k=0}{\overset{N_j-1}{\&Sigma;}}x\left(k\right)W^{-kn},\end{array}\right.---\left(2\right)$

$\left\{\begin{array}{c}X\left(k\right)=\underset{n=0}{\overset{N_j-1}{\&Sigma;}}x\left(n\right)W^{kn},\frac{N_j}{4}\&le;k\&le;\frac{3N_j}{4}\\ X\left(k\right)=0,\\ x\left(n\right)=\underset{k=0}{\overset{N_j-1}{\&Sigma;}}x\left(k\right)W^{-kn},\end{array}\right.---\left(3\right)$

In (2), (3) formula, x (n) is 2^jThe coefficient of wavelet packet, N on yardstick_jRepresent 2^jThe length of data on yardstick, K=0,1 ..., N_j-1；N=0,1 ..., N_j-1；

List band reconstruction signal is obtained in the WAVELET PACKET DECOMPOSITION and reconstruct, list band signal reconstructing method is：

The high and low frequency coefficient for obtaining is up-sampled, then respectively with high pass reconstruction filter g₁With low-pass reconstruction filter h₁ Convolution, by the signal for obtaining respectively with the process of HF-cut-IF, LF-cut-IF operator, obtains list band reconstruction signal.