CN101794345B - Data processing method and device - Google Patents

Abstract

The invention discloses a data processing method and a device, and relates to the technical field of intelligent traffic systems. The invention aims to solve the problem that the medium filtering smoothing technology in the prior art has the detect of random prediction process, so that the prediction precision is low, thereby failing to satisfy the requirements for actual prediction. The embodiment of the invention provides a data processing method which comprises the following steps: acquiring historical data information; carrying out data preprocessing on the historical data information; carrying out data combination and filling on the preprocessed historical data information; and carrying out dynamic exponential smoothing process on the historical data information. The embodiment of the invention can enhance the prediction precision and satisfy the requirements for actual prediction.

Description

A kind of data processing method and device

Technical field

The present invention relates to the intelligent transportation system technical field, relate in particular to a kind of data processing method and device.

Background technology

Advanced transportation information service systems (Advanced Traffic Information System, ATIS) be based upon on perfect information network basis, this system can obtain all kinds of transport information by sensor or the data transmission set that is equipped in road, car, transfer stop, parking lot and forecast center, carries out overall treatment according to the described data that get.This system can provide Real-time Road traffic congestion information comprehensively and accurately to society in real time.But the data accessed by described equipment can not cover all roads fully, fill up thereby need to carry out real time data by the similar inquiry of historical data, and available historical data are predicted by analysis afterwards.

In order to improve the availability of dynamic information, the function that needs the information prediction of increase system, need to carry out independent analysis to the historical road condition data of past certain hour in the cycle, obtain every road in the variation tendency of the traffic of historical data in the cycle, the mode by interface offers transportation information service systems and uses.Yet in the prior art, usually adopt median filter smoothness of image to process historical data is processed, thereby realize the purpose of data prediction.

In realizing process of the present invention, the inventor finds that in prior art, there are the following problems at least: because the median filter smoothness of image treatment technology forecasting process that prior art adopts is comparatively random, make precision of prediction lower, can't satisfy the needs of actual prediction.

Summary of the invention

Embodiments of the invention provide a kind of data processing method and device.

For achieving the above object, embodiments of the invention adopt following technical scheme:

A kind of data processing method comprises:

Obtain historical data information;

Described historical data information is carried out the data pre-service;

Described pretreated historical data information is carried out data to be merged and fills up;

Described data are merged and fill up after historical data information carry out based Dynamic Exponential Smoothing and process.

A kind of data processing equipment comprises:

Information acquisition unit is used for obtaining historical data information;

Pretreatment unit is used for described historical data information is carried out the data pre-service;

Data merge shim, are used for that described pretreated historical data information is carried out data and merge and fill up;

The data smoothing unit is used for described data merging and the historical data information after filling up is carried out the based Dynamic Exponential Smoothing processing.

The data processing method that the embodiment of the present invention provides and device are by obtaining historical data information; Described historical data information is carried out the data pre-service; Described pretreated historical data information is carried out data to be merged and fills up; Described data are merged and fill up after historical data information carry out based Dynamic Exponential Smoothing and process.Compared with prior art, the based Dynamic Exponential Smoothing of employing of the present invention is processed, can be so that precision of prediction is higher and can satisfy the needs of actual prediction.

Description of drawings

A kind of data processing method process flow diagram that Fig. 1 provides for the embodiment of the present invention;

In a kind of data processing method that Fig. 2 provides for the embodiment of the present invention, described historical data information is carried out the implementation procedure process flow diagram of the pretreated step of data;

In a kind of data processing method that Fig. 3 provides for the embodiment of the present invention, described pretreated historical data information is carried out that data merge and the implementation procedure process flow diagram flow chart of the step filled up;

In a kind of data processing method that Fig. 4 provides for the embodiment of the present invention, described data are merged and fill up after historical data information carry out the implementation procedure process flow diagram of the step that based Dynamic Exponential Smoothing processes;

A kind of data processing equipment structural representation that Fig. 5 provides for the embodiment of the present invention.

Embodiment

Below in conjunction with accompanying drawing, embodiment of the present invention data processing method and device are described in detail.

As shown in Figure 1, be a kind of data processing method that the embodiment of the present invention provides, the method comprises:

101: obtain historical data information;

102: described historical data information is carried out the data pre-service; This step is mainly described historical data to be carried out the rejecting of abnormal data, improves the quality of historical data.

103: described pretreated historical data information is carried out data merge and fill up;

104: described data are merged and fill up after historical data information carry out based Dynamic Exponential Smoothing and process.

Exist various interference can produce a collection of abnormal data in the acquisition and processing output procedure due to described historical data, if being carried out the data analysis meeting, described abnormal data has influence on the accuracy that predicts the outcome at last, so need to carry out pre-service to described historical data, eliminate described abnormal data.Can adopt a kind of statistical method of time-based band to come the rejecting abnormalities vehicle speed value in the embodiment of the present invention.

The implementation procedure of the pretreated step of data is provided described historical data information in a kind of data processing method that provides for the embodiment of the present invention as shown in Figure 2; If the value of the time dimension of described historical data is 00:00-23:59; Every 5 minutes is a time period; The time band refers to the vehicle speed value of a day of certain road; Wherein, be with take half an hour as a time.Described historical data pre-service is rejected some abnormal datas take continuous urban history road condition data more than month as the basis, and its concrete implementation procedure comprises:

201: described historical data information is carried out the time band divide; Wherein, described historical data can be the historical vehicle speed value of reading from database; Described time band divide be to the speed of a motor vehicle of a day of particular link dividing half an hour, thereby tentatively obtain 48 times bands.

202: the time band according to dividing merges verification; The specific implementation process of this step is:

Described ready-portioned time band is successively checked to judge whether with the T of F check and two samples can merging time band; Wherein, described F check is used for judging whether the variance of two time bands to be tested equates; The T check of described pair of sample is used for judging whether the average of two time bands to be tested equates; If the T check of described F check and described pair of sample all by merge two adjacent times bands.

The formula of structure F test statistics is: $F=\frac{S_1^2}{S_2^2}~F(n_1-1,n_2-1);$

Wherein, $S_1^2=\frac{1}{n_1-1}\underset{i=1}{\overset{n_1}{\mathrm{\Σ}}}{(X_i-\stackrel{\‾}{X})}^2,S_2^2=\frac{1}{n_2-1}\underset{i=1}{\overset{n_2}{\mathrm{\Σ}}}{(Y_i-\stackrel{\‾}{Y})}^2;$

X _i, Y _iBe respectively each vehicle speed value of two normal populations;

X, Y are sample average.

The region of rejection of F check is:

$W=\{F<F_{\frac{\mathrm{\&alpha;}}{2}}(n_1-1,n_2-1)$ Or $F>F_{1-\frac{\mathrm{\&alpha;}}{2}}(n_1-1,n_2-1)\}$

α is insolation level.

The formula of structure T test statistics is: $T=\frac{\stackrel{\&OverBar;}{X}-\stackrel{\&OverBar;}{Y}}{S_W\&CenterDot;\sqrt{\frac{1}{n_1}+\frac{1}{n_2}}}~t(n_1+n_2-2);$

Wherein, n ₁, n ₂Be respectively the effective value number of two normal populations;

${s_W}^2=\frac{(n_1-1){S_1}^2+(n_2-1){S_2}^2}{n_1+n_2-2}$ (S ₁ ², S ₂ ²Be respectively the variance of two normal populations)

The region of rejection of T check is: $W=\left\{\right|T|>t_{1-\frac{\mathrm{\&alpha;}}{2}}(n_1+n_2-2)\}.$

It should be noted that if band of described time satisfies the merging condition, it is merged, the time band after then merging is processed as a time band; If described time band does not satisfy the merging condition, it is still carried out follow-up processing according to the time band of dividing after its pre-service.

203: carry out abnormal verification with described through the time band that merges verification, provide abnormal check results; The implementation procedure of this step is: traversal is through merging the time band of processing, described effective time band is carried out respectively the T check of U check or single sample, if described effective time band is not by described check, think that these data are abnormal data, are recorded to described abnormal data in abnormal check results.Wherein, described U check is applicable to fully large situation of sample size.

It should be noted that the U check is used in the very large situation of sample number, condition is that speed of a motor vehicle effective value number is greater than 30 herein.The formula of structure U test statistics is: $U=\frac{x_i-\mathrm{\&mu;}}{\mathrm{\&sigma;}/\sqrt{n}}$

Wherein, x _iRepresent the speed of a motor vehicle variable in this time band, μ represents the speed of a motor vehicle average of time band, and σ represents the speed of a motor vehicle variance of time band, and n represents the effective value number of time band;

The region of rejection of U check is: W={|U|＞μ _α/2;

Single sample T check is used in the situation of small sample amount, and condition is that speed of a motor vehicle effective value number is less than or equal to 30 herein.The formula of structure T test statistics is: $T=\frac{x_i-\stackrel{\&OverBar;}{X}}{s/\sqrt{n}};$

Wherein, x _iRepresent the speed of a motor vehicle variable in this time band, μ represents the speed of a motor vehicle average of time band, and s represents the speed of a motor vehicle variance of time band, and n represents the effective value number of time band;

The region of rejection of T check is: W={|T|＞t _α(n-1) }.

204: according to described abnormal check results, with rejecting abnormal data.

As shown in Figure 3, in a kind of data processing method that provides for the embodiment of the present invention, described pretreated historical data information is carried out that data merge and the implementation procedure of the step filled up, this process comprises:

301: receive described historical data information through rejecting abnormal data;

302: phase data in the same time in described historical data information are merged processing; Concretely, be exactly that the data on same time point merge to identical week characteristic day, after simply using the method for arithmetic mean to merge processing, obtain one group of data.

303: whether the data that detect after described merging is processed exist countless certificates on moment point;

304: if there are countless certificates on moment point, carry out data filling and process.Wherein, the described data filling that carries out is processed and can be adopted the method for least square method to fill up, and for example: before and after can getting the time point that needs fill data, each two temporal data are done sample point; Its specific implementation process is as follows:

Setting up regression model is $\hat{Y}={\widehat{\mathrm{\&beta;}}}_0+{\widehat{\mathrm{\&beta;}}}_{1}x;$

Wherein: x need to represent the time point of padding data;

Represent the data that to fill up on this time point;

$\left\{\begin{array}{c}{\widehat{\mathrm{\&beta;}}}_0=\widehat{\stackrel{\&OverBar;}{y}}-\stackrel{\&OverBar;}{x}{\widehat{\mathrm{\&beta;}}}_1\\ {\widehat{\mathrm{\&beta;}}}_1{=L}_{\mathrm{xy}}/L_{\mathrm{xx}}\end{array}\right.;$

Wherein, $L_{\mathrm{xy}}\stackrel{\mathrm{def}}{=}\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}(x_i-\stackrel{\&OverBar;}{x})(y_i-\stackrel{\&OverBar;}{y})=\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{x}_i{y}_i-n\stackrel{\&OverBar;}{x}\stackrel{\&OverBar;}{y},L_{\mathrm{xx}}\stackrel{\mathrm{def}}{=}\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{(x_i-\stackrel{\&OverBar;}{x})}^2=\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{x_i}^2-n{\stackrel{\&OverBar;}{x}}^2;$

(x _i, y _i) be each two temporal data values before and after the x time point of choosing.

As shown in Figure 4, in a kind of data processing method that provides for the embodiment of the present invention, described data are merged and fill up after historical data information carry out the implementation procedure of the step that based Dynamic Exponential Smoothing processes, the initial value that this process is established smoothing factor α is α ₀, the initial value of exponential smoothing and control accuracy ε.Wherein, described control accuracy is used for determining whether approximate optimal solution.This process specifically comprises:

401: receive the described historical data information of filling up processing through number;

402: the initial value α that obtains smoothing factor α ₀, exponential smoothing initial value and control accuracy ε;

403: according to initial value and the described exponential smoothing initial value of described smoothing factor, obtain the exponential smoothing numerical value of described next moment point of exponential smoothing initial value; Wherein, described exponential smoothing numerical value is that computing formula by exponential smoothing obtains.Described exponential smoothing is a kind of information processing method that is developed by the moving average method.The method does not need to store the time series data of n phase, and gives recent real data with larger flexible strategy, gives data at a specified future date with less flexible strategy, and the flexible strategy index of coincidence rule of each issue certificate.The exponential smoothing computing formula is:

${\hat{y}}_{t+1}=\mathrm{\&alpha;}{y}_t+(1-\mathrm{\&alpha;}){\hat{y}}_t---(1-1)$

In formula, because the data in the embodiment of the present invention are the data of every 5 minutes in 0:00-23:59, t value 1-288, expression begins every 5 minutes time points from 0:00, t=1, expression 0:00; T=288, expression 23:55;

---smooth value of 5 minutes after period t;

y _t---period t actual value;

---period t smooth value;

α----smoothing factor claims again weighting factor, and span is 0≤α≤1.

Following formula shows: Smoothing Prediction is constantly the predicted value of last issue to be revised with predicated error, and obtains the predicted value of current period. Be

And y _tThe weighted arithmetic mean number, along with the size variation of α value, determine

With yt pair

Influence degree.

Have and review character by the phase, include the impact of full issue certificate.

404: according to described exponential smoothing data and the control accuracy that gets, judge whether optimal smoothing coefficient of described smoothing factor; Concrete implementation procedure is:

Calculate $\&dtri;\mathrm{SSE}\left({\mathrm{\&alpha;}}_0\right)=(\mathrm{dSSE}/\mathrm{d\&alpha;}){|}_{\mathrm{\&alpha;}={\mathrm{\&alpha;}}_0};$ If $\left|\right|\&dtri;\mathrm{SSE}\left({\mathrm{\&alpha;}}_0\right)\left|\right|\&le;\mathrm{\&epsiv;},$ α ₀It is exactly approximate optimal solution; Otherwise not optimum solution, begin the smoothing factor search.

405: if the optimal smoothing coefficient is proceeded the exponential smoothing of next moment point and processed;

406: if not the optimal smoothing coefficient, carry out the smoothing factor search.The specific implementation process of this step is as follows:

Step 1: the Optimized model of setting up the Prediction sum squares minimum for described smoothing factor;

$\min \mathrm{SSE}=\underset{t=1}{\overset{n}{\mathrm{\&Sigma;}}}{e}_t^2=\underset{t=1}{\overset{n}{\mathrm{\&Sigma;}}}{(y_t-{\hat{y}}_t)}^2---(1-2)$

Described exponential smoothing computing formula (1-1) is given

After assignment, following formula can be expressed as

$\min \mathrm{SSE}=\underset{t=1}{\overset{n}{\mathrm{\&Sigma;}}}{[y_t-(\mathrm{\&alpha;}{y}_{t-1}+(1-\mathrm{\&alpha;})y_{t-1}^{^})]}^2---(1-3)$

Successively will

Value bring following formula into, obtain after arrangement:

$\min \mathrm{SSE}=\underset{t=1}{\overset{n}{\mathrm{\&Sigma;}}}{[y_t-\mathrm{\&alpha;}\underset{j=1}{\overset{t-1}{\mathrm{\&Sigma;}}}{(1-\mathrm{\&alpha;})}^{j-1}{y}_{t-j}-{(1-\mathrm{\&alpha;})}^{t-1}{\hat{y}}_1]}^2---(1-4)$

Due to the t value when larger,

In fact very little, can be approximated to be 0.Target equation (1-4) can be reduced to:

$\min \mathrm{SSE}=\underset{t=1}{\overset{n}{\mathrm{\&Sigma;}}}{[y_t-\mathrm{\&alpha;}\underset{j=1}{\overset{t-1}{\mathrm{\&Sigma;}}}{(1-\mathrm{\&alpha;})}^{j-1}{y}_{t-j}]}^2---(1-5)$

Find the solution above-mentioned Non-linear Optimal Model (1-5) and can obtain best α value.Ask the minimum value of following formula that a lot of methods are arranged, the steepest Descent iteration method of designed in embodiments of the present invention following fast convergence rate, easily carrying out: i.e. optimum gradient method.

Described optimum gradient method is that the negative gradient direction of application target function is as the direction of search of each step iteration.Because each step is all got the optimal step size of negative gradient direction, so be called optimum gradient method.Use optimum gradient method, its target function value is descended the fastest in former steps, therefore be called method of steepest descent.

For example: the gradient of a n dimension nonlinear function f (X) is defined as:

$\&dtri;f\left(x\right)=\frac{\&PartialD;f}{\&PartialD;X}={[\frac{\&PartialD;f}{\&PartialD;x_1},\frac{\&PartialD;f}{{\&PartialD;x}_2},...,\frac{\&PartialD;f}{\&PartialD;x_n}]}^T$

This is a n dimension local derviation vector.

The vector of unit length of gradient is: $S=\frac{\&dtri;f\left(X\right)}{\left|\right|\&dtri;f\left(X\right)\left|\right|}$

Can find out, gradient direction is the normal direction of function namely, the negative gradient direction with-S in the same way.Wherein Be called gradient

Mould, also be norm.The computing formula of mould is

$\left|\right|\&dtri;f\left(X\right)\left|\right|=\sqrt{{\left(\frac{\&PartialD;f}{\&PartialD;x_1}\right)}^2+{\left(\frac{\&PartialD;f}{\&PartialD;x_2}\right)}^2+...+{\left(\frac{\&PartialD;f}{\&PartialD;x_n}\right)}^2}$

The character of gradient direction has: descend along negative gradient directivity function value the fastest, be direction of steepest descent.The iterative formula of optimum gradient method:

Schilling X _k+1=X _k+ λ _kS _k

Wherein, vector of unit length $S_k=-\frac{\&dtri;f\left(X_k\right)}{\left|\right|\&dtri;f\left(X_k\right)\left|\right|};$

λ _kBe optimal step size.Therefore, to the specific definition of optimum gradient method be: the optimization numerical method of getting all the time the optimal step size search along the negative gradient direction.

By the iterative formula of optimum gradient method, the formula of Function Minimization can be arranged

$\underset{\mathrm{\&lambda;}}{\min }(X_k+\mathrm{\&lambda;}{S}_k)=f(X_k+{\mathrm{\&lambda;}}_k{S}_k)=f\left(X_{k+1}\right)$

With f (X) at X _kBe launched into Taylor series near point $f\left(X\right)=f\left(X_k\right)+{\&dtri;}^{T}f\left(X_k\right)\mathrm{\&Delta;X}+\frac{1}{2}\mathrm{\&Delta;}{X}^T\mathrm{A\&Delta;X};$

Wherein, $A=\left(\begin{array}{c}\frac{{\&PartialD;}^{2}f}{\&PartialD;{x_1}^2},\frac{{\&PartialD;}^{2}f}{\&PartialD;x_1\&PartialD;x_2},....\frac{{\&PartialD;}^{2}f}{\&PartialD;x_1\&PartialD;x_n}\\ \frac{{\&PartialD;}^{2}f}{\&PartialD;x_2\&PartialD;x_1},\frac{{\&PartialD;}^{2}f}{\&PartialD;{x_2}^2},....\frac{{\&PartialD;}^{2}f}{\&PartialD;x_2\&PartialD;x_n}\\ \&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\\ \frac{{\&PartialD;}^{2}f}{\&PartialD;x_n\&PartialD;x_1},\frac{{\&PartialD;}^{2}f}{\&PartialD;x_n\&PartialD;x_2},....\frac{{\&PartialD;}^{2}f}{\&PartialD;{x_n}^2}\end{array}\right);$

With Δ X=λ S _kThe substitution following formula has

$f\left(X\right)=f\left(X_k\right)+{\&dtri;}^{T}f\left(X_k\right)\mathrm{\&lambda;}{S}_k+\frac{1}{2}{\left(\mathrm{\&lambda;}{S}_k\right)}^{T}A\left(\mathrm{\&lambda;}{S}_k\right)$

And

$\frac{\&PartialD;f}{\&PartialD;\mathrm{\&lambda;}}{|}_{\mathrm{\&lambda;}={\mathrm{\&lambda;}}_k}={\&dtri;}^{T}f\left(X_k\right)S_k+\mathrm{\&lambda;}{S_k}^{T}A{S}_k=0$

So optimal step size can be expressed as

${\mathrm{\&lambda;}}_k=-\frac{{\&dtri;}^{T}f\left(X\right){\mathrm{AS}}_k}{{S_k}^{T}A{S}_k}$

Due in the embodiment of the present invention for asking $\min \mathrm{SSE}=\underset{t=1}{\overset{n}{\mathrm{\&Sigma;}}}{[y_t-\mathrm{\&alpha;}\underset{j=1}{\overset{t-1}{\mathrm{\&Sigma;}}}{(1-\mathrm{\&alpha;})}^{j-1}{y}_{t-j}]}^2$ Minimum value, this function is the one dimension function that contains variable α.So α of derivation one dimension _kValue, derivation is as follows:

α _k+1＝α _k+λS _k

Wherein $s_k=-\frac{\&dtri;f\left({\mathrm{\&alpha;}}_k\right)}{\left|\right|\&dtri;f\left({\mathrm{\&alpha;}}_k\right)\left|\right|},$ λ is step-length.

$\underset{\mathrm{\&lambda;}}{\min }f({\mathrm{\&alpha;}}_k+\mathrm{\&lambda;}{s}_k)=f({\mathrm{\&alpha;}}_k+{\mathrm{\&lambda;}}_k{s}_k)=f\left({\mathrm{\&alpha;}}_{k+1}\right)$

With f (x) at α _kBe launched into Taylor series near point $f\left(x\right)=f\left({\mathrm{\&alpha;}}_k\right)+\&dtri;f\left({\mathrm{\&alpha;}}_k\right)\mathrm{\&Delta;x}+\frac{1}{2}{f}^{\&prime;\&prime;}\left({\mathrm{\&alpha;}}_k\right)\mathrm{\&Delta;}{x}^2;$

Because of Δ x=λ s _k, bring following formula into, have

$f\left(x\right)=f\left({\mathrm{\&alpha;}}_k\right)+\&dtri;f\left({\mathrm{\&alpha;}}_k\right)\mathrm{\&lambda;}{s}_k+\frac{1}{2}{f}^{\&prime;\&prime;}\left(x\right){\left(\mathrm{\&lambda;}{s}_k\right)}^2$

And $\frac{\&PartialD;f}{\&PartialD;\mathrm{\&lambda;}}{|}_{\mathrm{\&lambda;}={\mathrm{\&lambda;}}_k}=\&dtri;f\left({\mathrm{\&alpha;}}_k\right)s_k+\mathrm{\&lambda;}{f}^{\&prime;\&prime;}\left({\mathrm{\&alpha;}}_k\right){s_k}^2=0$

So optimal step size can be expressed as

${\mathrm{\&lambda;}}_k=-\frac{\&dtri;f\left({\mathrm{\&alpha;}}_k\right)}{f^{\&prime;\&prime;}\left({\mathrm{\&alpha;}}_k\right)s_k}$

Like this, utilize the mode of optimum gradient method, from α ₀The edge of setting out

Direction is carried out linear search, asks optimal step size λ _k-1Thereby with the formula of optimum gradient method,

Get ${\mathrm{\&alpha;}}_k={\mathrm{\&alpha;}}_{k-1}-{\mathrm{\&lambda;}}_{k-1}\&dtri;\mathrm{SSE}\left({\mathrm{\&alpha;}}_{k-1}\right),k\&GreaterEqual;1;$

If $\left|\right|\&dtri;\mathrm{SSE}\left({\mathrm{\&alpha;}}_k\right)\left|\right|\&le;\mathrm{\&epsiv;},$ α _kBe exactly approximate optimal solution, output α _kAnd turn to step 2, otherwise turn to step 1.

Step 2: according to the Optimized model of described foundation, obtain described optimal smoothing coefficient.To be similar to optimum solution α exactly concretely _kBring the exponential smoothing model into, and be used for prediction.

As shown in Figure 5, be a kind of data processing equipment that the embodiment of the present invention provides, this device comprises:

Information acquisition unit 501 is used for obtaining historical data information;

Pretreatment unit 502 is used for described historical data information is carried out the data pre-service;

Data merge shim 503, are used for that described pretreated historical data information is carried out data and merge and fill up;

Data smoothing unit 504 is used for described data merging and the historical data information after filling up is carried out the based Dynamic Exponential Smoothing processing.

Wherein, described pretreatment unit comprises:

The time band is divided subelement, is used for that described historical data information is carried out the time band and divides;

Merging syndrome unit is used for merging verification according to the time band of dividing;

Abnormal syndrome unit is used for carrying out abnormal verification with described through the time band that merges verification, provides abnormal check results;

The abnormality value removing subelement is used for according to described abnormal check results, with rejecting abnormal data.

Described data merge shim, comprising:

Data message receives subelement, is used for receiving described historical data information through rejecting abnormal data;

Data merge subelement, are used for described historical data information phase data are in the same time merged processing;

The data check subelement, whether the data after processing for detection of described merging exist countless certificates on moment point;

The data filling subelement if be used for having countless certificates on moment point, carry out data filling and processes.

Described data merge shim, comprising:

Data message receives subelement, is used for receiving described historical data information through rejecting abnormal data;

Data merge subelement, are used for described historical data information phase data are in the same time merged processing;

The data check subelement, whether the data after processing for detection of described merging exist countless certificates on moment point;

The data filling subelement if be used for having countless certificates on moment point, carry out data filling and processes.

Described data smoothing unit comprises:

Data message receives subelement, is used for receiving the described historical data information of filling up processing through number;

The parameter acquiring subelement is for the initial value that obtains smoothing factor, exponential smoothing initial value and control accuracy;

The exponential smoothing value is obtained subelement, is used for initial value and described exponential smoothing initial value according to described smoothing factor, obtains the exponential smoothing numerical value of described next moment point of exponential smoothing initial value;

Optimal smoothing coefficient judgment sub-unit is used for according to described exponential smoothing data and the control accuracy that gets, and judges whether optimal smoothing coefficient of described smoothing factor;

The smoothing processing subelement is used for if the optimal smoothing coefficient is proceeded the exponential smoothing of next moment point and processed;

Smoothing factor search subelement is used for if not the optimal smoothing coefficient, carries out the smoothing factor search.

It should be noted that described smoothing factor search subelement, may further include:

Optimized model is set up subdivision, is used to described smoothing factor to set up the Optimized model of Prediction sum squares minimum;

The optimal smoothing coefficient obtains subdivision, is used for the Optimized model according to described foundation, obtains described optimal smoothing coefficient.

The data processing method that the embodiment of the present invention provides and device are by obtaining historical data information; Described historical data information is carried out the data pre-service; Described pretreated historical data information is carried out data to be merged and fills up; Described data are merged and fill up after historical data information carry out based Dynamic Exponential Smoothing and process.Compared with prior art, the based Dynamic Exponential Smoothing of employing of the present invention is processed, can be so that precision of prediction is higher and can satisfy the needs of actual prediction.

Through the above description of the embodiments, one of ordinary skill in the art will appreciate that: realize that all or part of step in above-described embodiment method is to come the relevant hardware of instruction to complete by program, described program can be stored in a computer read/write memory medium, this program is when carrying out, comprise the step as above-mentioned embodiment of the method, described storage medium, as: FLASH, ROM/RAM, magnetic disc, CD etc.

The above; be only the specific embodiment of the present invention, but protection scope of the present invention is not limited to this, anyly is familiar with those skilled in the art in the technical scope that the present invention discloses; can expect easily changing or replacing, within all should being encompassed in protection scope of the present invention.Therefore, protection scope of the present invention should be as the criterion by described protection domain with claim.

Claims (8)

1. a data processing method, is characterized in that, comprising:

Obtain historical data information; Described historical data is the historical vehicle speed value of reading from database;

Described historical data information is carried out the data pre-service, rejecting abnormalities data from described historical data;

Described pretreated historical data information is carried out data to be merged and fills up;

Described data are merged and fill up after historical data information carry out based Dynamic Exponential Smoothing and process;

Wherein, described described data are merged and fill up after historical data information carry out the step that based Dynamic Exponential Smoothing is processed, comprising:

Receive the described historical data information of filling up processing through number;

Obtain the initial value of smoothing factor, exponential smoothing initial value and control accuracy;

According to initial value and the described exponential smoothing initial value of described smoothing factor, obtain the exponential smoothing numerical value of described next moment point of exponential smoothing initial value;

According to described exponential smoothing data and the control accuracy that gets, judge whether optimal smoothing coefficient of described smoothing factor;

If the optimal smoothing coefficient is proceeded the exponential smoothing of next moment point and is processed;

If not the optimal smoothing coefficient, carry out the smoothing factor search.

2. data processing method according to claim 1, is characterized in that, described historical data information is carried out the pretreated step of data, comprising:

Described historical data information is carried out the time band divides;

Time band according to dividing merges verification;

Carry out abnormal verification with described through the time band that merges verification, provide abnormal check results;

According to described abnormal check results, with rejecting abnormal data.

3. data processing method according to claim 2, is characterized in that, described pretreated historical data information is carried out the step that data merge and fill up, and comprising:

Receive described historical data information through rejecting abnormal data;

Phase data in the same time in described historical data information are merged processing;

Whether the data that detect after described merging is processed exist countless certificates on moment point;

If there are countless certificates on moment point, carry out data filling and process.

4. data processing method according to claim 1, is characterized in that, the step of described smoothing factor search comprises:

Set up the Optimized model of Prediction sum squares minimum for described smoothing factor;

According to the Optimized model of described foundation, obtain described optimal smoothing coefficient.

5. a data processing equipment, is characterized in that,

Information acquisition unit is used for obtaining historical data information; Described historical data is the historical vehicle speed value of reading from database;

Pretreatment unit is used for described historical data information being carried out the data pre-service, rejecting abnormalities data from described historical data;

Data merge shim, are used for that described pretreated historical data information is carried out data and merge and fill up;

The data smoothing unit is used for described data merging and the historical data information after filling up is carried out the based Dynamic Exponential Smoothing processing;

Wherein, described data smoothing unit comprises:

Data message receives subelement, is used for receiving the described historical data information of filling up processing through number;

The parameter acquiring subelement is for the initial value that obtains smoothing factor, exponential smoothing initial value and control accuracy;

The exponential smoothing value is obtained subelement, is used for initial value and described exponential smoothing initial value according to described smoothing factor, obtains the exponential smoothing numerical value of described next moment point of exponential smoothing initial value;

Optimal smoothing coefficient judgment sub-unit is used for according to described exponential smoothing data and the control accuracy that gets, and judges whether optimal smoothing coefficient of described smoothing factor;

The smoothing processing subelement is used for if the optimal smoothing coefficient is proceeded the exponential smoothing of next moment point and processed;

Smoothing factor search subelement is used for if not the optimal smoothing coefficient, carries out the smoothing factor search.

6. data processing equipment according to claim 5, is characterized in that, described pretreatment unit comprises:

The time band is divided subelement, is used for that described historical data information is carried out the time band and divides;

Merging syndrome unit is used for merging verification according to the time band of dividing;

Abnormal syndrome unit is used for carrying out abnormal verification with described through the time band that merges verification, provides abnormal check results;

The abnormality value removing subelement is used for according to described abnormal check results, with rejecting abnormal data.

7. data processing equipment according to claim 6, is characterized in that, described data merge shim, comprising:

Data message receives subelement, is used for receiving described historical data information through rejecting abnormal data;

Data merge subelement, are used for described historical data information phase data are in the same time merged processing;

The data check subelement, whether the data after processing for detection of described merging exist countless certificates on moment point;

The data filling subelement if be used for having countless certificates on moment point, carry out data filling and processes.

8. data processing equipment according to claim 5, is characterized in that, described smoothing factor search subelement comprises:

Optimized model is set up subdivision, is used to described smoothing factor to set up the Optimized model of Prediction sum squares minimum;

The optimal smoothing coefficient obtains subdivision, is used for the Optimized model according to described foundation, obtains described optimal smoothing coefficient.

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