CN103235892B - The fast quantization method of parameter value distribution character in plasma panel manufacture process - Google Patents

Abstract

The invention discloses the fast quantization method of parameter value distribution character in a kind of plasma panel manufacture process, by computing machine, the data source of input and statistical indicator are judged, according to operation and time, data source is divided; Obtain maximal value, minimum value, average, variance, the degree of bias, kurtosis by operation and time, and make data profile accordingly; To needing two data acquisitions contrasted to make classification annotation, then merging the data of two classifications, obtaining the data acquisition that has two type marks; Key character extraction etc. is carried out again on data acquisition.Good effect of the present invention is: can compare in the quantification of the enterprising line parameter observed reading of a large amount of production data fast, fundamentally solve when in the face of complexity and mass data, it is difficult to understand, enter slow with one's hands and problem that is long processing period, effective information feedback can be carried out in time to production status, thus for producers' fast finding with deal with problems and provide shortcut.

Description

The fast quantization method of parameter value distribution character in plasma panel manufacture process

Technical field

The present invention relates to a kind of data mining technology of plasma panel manufacture process characteristic, especially relate to the fast quantization method of parameter value distribution character in plasma panel manufacture process.

Background technology

Article on plasma display screen data complexity so and data volume face to face, apparatus volume data analytical approach (such as feature extraction, decision tree classification analysis, frequent rule digging etc.) often needs to expend a few minutes at least, and the time of several hours carries out analytical calculation at most.When needs carry out a large amount of replica test, such time loss can become and be difficult to bear.

Summary of the invention

In order to overcome the above-mentioned shortcoming of prior art, the invention provides the fast quantization method of parameter value distribution character in a kind of plasma panel manufacture process, a series of statistical indicator can be represented to user, allow user can have the understanding of quantification fast to the distribution character of data.

The technical solution adopted for the present invention to solve the technical problems is: the fast quantization method of parameter value distribution character in a kind of plasma panel manufacture process, comprises the steps:

Step one, input data source and statistical indicator;

Step 2, computing machine judge the data source of input and statistical indicator:

(1) when data source is individual data collection, then step 3 is entered;

(2) when data source is two data sets, then step 5 is entered;

Step 3, according to operation and time, data source to be divided; Obtain maximal value, minimum value, average, variance, the degree of bias, kurtosis by operation and time, and make data profile accordingly; Then continue to judge whether that there is statistical indicator: if not, then return step 2; If so, then step 4 is entered;

Step 4, each result to be sorted by statistical indicator, and K attribute before sequence gained is carried out showing and preserving; Then step 2 is returned;

Step 5, to needing two data acquisitions contrasting to make classification annotation, then merging the data of two classifications, obtaining the data acquisition that has two types marks; Key character extraction is carried out again on data acquisition;

Step 6, according to operation and time, data source to be divided; In important parameter set, calculate maximal value, minimum value, average, variance, the degree of bias, kurtosis by operation and time, and make data profile accordingly; Then continue to judge whether that there is statistical indicator: if not, then data profile is carried out showing and preserving in contrast list, then return step 2; If so, then step 7 is entered;

Step 7, each result to be sorted by statistical indicator, and K attribute before sequence gained is carried out showing and preserving contrasting in list, then return step 2.

Compared with prior art, good effect of the present invention is: the present invention can compare in the quantification of the enterprising line parameter observed reading of a large amount of production data fast, fundamentally solve when in the face of complexity and mass data, it is difficult to understand, enter slow with one's hands and problem that is long processing period, effective information feedback can be carried out in time to production status, thus for producers' fast finding with deal with problems and provide shortcut.

Embodiment

In plasma panel manufacture process, a fast quantization method for parameter value distribution character, comprises the steps:

Step one, input data source and statistical indicator:

Step 2, computing machine judge the data source of input and statistical indicator:

(1) when data source is individual data collection, then step 3 is entered;

(2) when data source is two data sets, then step 5 is entered;

Step 3, according to operation and time (comprise daily and monthly), data source to be divided; Obtain maximal value, minimum value, average, variance, the degree of bias, kurtosis by operation and time, and make data profile accordingly; Then continue to judge whether that there is statistical indicator: if not, then return step 2; If so, then step 4 is entered;

Step 4, each result to be sorted by statistical indicator, and K attribute before sequence gained is carried out showing and preserving; Then step 2 is returned;

Step 5, to needing two data acquisitions contrasting to make classification annotation, being considered as (being treated to) classification by a data acquisition, and another data acquisition being considered as (being treated to) another classification; Then the data merging these two classifications obtain the data acquisition that has two type marks; Then on this data acquisition, carry out key character extraction, concrete key character abstracting method is as follows:

(1) missing values process and normalized are carried out to input data set conjunction:

Described missing values process refers to: for missing values, fills according to the mean value of the observed reading of the identical parameters of other PDP display, and target is that the missing values of this filling is not had an impact to analytical framework afterwards;

After missing values process, observed reading in all parameters is normalized, by the observed reading unification of this parameter in different PDP display to 0 average and unit variance this interval in and ensure that initial different relative size of shielding this observed reading is constant simultaneously, illustrate as follows:

If α _i=(α _i1, α _i2..., α _in) representing a group observations of n PDP display i-th parameter, after normalization, the adjustment mode of new argument observed reading is:

in formula, μ _ifor α _iaverage, σ _ifor α _ivariance.

(2) adopt feature extracting method to carry out feature extraction to the product of different grouping, filter out the important parameter list that can maximize the important evaluation index comprising information gain, the ratio of gains, minimizing redundant degree and maximize the degree of correlation, concrete grammar is as follows:

Definition of T is a production data set, comprises (x, y)=(x ₁, x ₂..., x _n, y), wherein x _i∈ vals (t) represents the value of i-th parameter in production PDP display data, and y represents the grade of this PDP display current or bad code.

1) computing information gain (Information Gain):

$\mathrm{IG}(T,a)=H\left(T\right)-{\mathrm{\Σ}}_{v\∈\mathrm{vals}\left(t\right)}\frac{\left|\right\{x\∈T|x_i=v\}|}{\left|T\right|}\·H\left(\right\{x\∈T|x_i=v\}),$ Wherein

for information entropy, p (x _i) representative observe x _iprobability, x can be there is by historical data _inumber of times calculate.

2) calculated gains ratio (Gain Ratio):

IGR (T, a)=IG (T, a)/IV (T, a) wherein, IG (T, a) is information gain, IV (T, a) account form is as follows:

$\mathrm{IV}(T,a)=-{\mathrm{\Σ}}_{v\∈\mathrm{vals}\left(t\right)}\frac{\left|\right\{x\∈T|x_i=v\}|}{\left|T\right|}\·{\log }_2\left(\frac{\left|\right\{x\∈T|x_i=v\}|}{\left|T\right|}\right);$

3) maximum relation degree and minimum redundancy (minimum Redundancy and Maximum Relevancy, mRMR) is calculated

A) computational minimization redundance (mR):

$\min W_I{,W}_I=\frac{1}{{\left|T\right|}^2}\underset{a,b\∈T}{\mathrm{\Σ}}I(a,b)$

Wherein, $I(a,b)={\mathrm{\Σ}}_{i,j}p(a_i,b_j)\·\log \frac{p(a_i,b_j)}{p\left(a_j\right)p\left(b_j\right)},$ Be defined as the interactive information between Two Variables a and b;

B) the maximization degree of correlation (MR) is calculated:

$\max V_I,V_I=\frac{1}{\left|T\right|}\underset{a\∈T}{\mathrm{\Σ}}I(y,a)$

C) calculating the mode that can meet mR and MR is to the full extent found the feature (parameter) of satisfied following condition by the method for searching of incremental:

maxΦ(W _I,V _I),Φ=V _I-W _I

Suppose that existing key character (parameter) set is for S _m-1, wherein contain m-1 key character (parameter), target is to find m key character (parameter) to make it to maximize Φ from residue character (parameter) set.

The computing method of each feature selecting can export one group of key character (parameter) above, namely by IG, three stack features (parameter) that IGR, mRMR export respectively characterize has the feature (parameter) of discrimination to combine most in current PDP display grouping.

(3) three groups of important parameter lists that (2) obtain are integrated, obtain out one group of unified important parameter list, which includes the parameter appeared in three groups of key character lists at least two groups.

Step 6, according to operation and time (comprise daily and monthly), data source to be divided; In important parameter set, calculate maximal value, minimum value, average, variance, the degree of bias, kurtosis by operation and time, and make data profile accordingly; Then continue to judge whether that there is statistical indicator: if not, then data profile is carried out showing and preserving in contrast list, then return step 2; If so, then step 7 is entered;

Step 7, each result to be sorted by statistical indicator, and K attribute before sequence gained is carried out showing and preserving contrasting in list, then return step 2.

Claims (4)

1. the fast quantization method of parameter value distribution character in plasma panel manufacture process, is characterized in that: comprise the steps:

Step one, input data source and statistical indicator;

Step 2, computing machine judge the data source of input and statistical indicator:

(1) when data source is individual data collection, then step 3 is entered;

(2) when data source is two data sets, then step 5 is entered;

Step 3, according to operation and time, data source to be divided; Obtain maximal value, minimum value, average, variance, the degree of bias, kurtosis by operation and time, and make data profile accordingly; Then continue to judge whether that there is statistical indicator: if not, then return step 2; If so, then step 4 is entered;

Step 4, each result to be sorted by statistical indicator, and K attribute before sequence gained is carried out showing and preserving; Then step 2 is returned;

Step 5, to needing two data acquisitions contrasting to make classification annotation, then merging the data of two classifications, obtaining the data acquisition that has two types marks; Key character extraction is carried out again on data acquisition;

Step 6, according to operation and time, data source to be divided; In important parameter set, calculate maximal value, minimum value, average, variance, the degree of bias, kurtosis by operation and time, and make data profile accordingly; Then continue to judge whether that there is statistical indicator: if not, then data profile is carried out showing and preserving in contrast list, then return step 2; If so, then step 7 is entered;

Step 7, each result to be sorted by statistical indicator, and K attribute before sequence gained is carried out showing and preserving contrasting in list, then return step 2.

2. the fast quantization method of parameter value distribution character in plasma panel manufacture process according to claim 1, is characterized in that: described key character abstracting method is:

(1) missing values process and normalized are carried out to input data set conjunction;

(2) parameter list that can maximize the three groups of key characters comprising information gain, the ratio of gains, minimizing redundant degree and maximize the degree of correlation is filtered out;

(3) parameter list of three groups of key characters is integrated, obtain one group of unified important parameter list, which includes the parameter appeared in three groups of key character lists at least two group key characters.

3. the fast quantization method of parameter value distribution character in plasma panel manufacture process according to claim 2, it is characterized in that: described missing values process refers to: for missing values, fills according to the mean value of the observed reading of the identical parameters of other PDP display.

4. the fast quantization method of parameter value distribution character in plasma panel manufacture process according to claim 2, is characterized in that: described normalized refers to: by the observed reading unification of each parameter in different PDP display to 0 average and unit variance this interval in and ensure that initial different relative size of shielding this observed reading is constant simultaneously.