CN107480819A - A kind of method and device of data analysis - Google Patents
A kind of method and device of data analysis Download PDFInfo
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- CN107480819A CN107480819A CN201710677405.2A CN201710677405A CN107480819A CN 107480819 A CN107480819 A CN 107480819A CN 201710677405 A CN201710677405 A CN 201710677405A CN 107480819 A CN107480819 A CN 107480819A
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Abstract
The invention discloses a kind of method and device of data analysis, and normalized is done by the K lines data set and history data set that are inputted to user, and calculates the two daily amount of increase and amount of decrease distance, and most optimal distance collection is presented to user at last.Both K line morphologies the most similar in history can be looked for from the form of current stock, can also be from any K lines, find also has the stock of similar form recently, there is provided a kind of method that can predict short-term stock form tendency, the chance of selection stock is added for user.Consumer's Experience can not be improved to K line numbers according to the forecasting problem for providing objective analysis in the prior art by solving.
Description
Technical field
The invention belongs to data analysis field, in particular it relates to a kind of method and device of data analysis.
Background technology
Data analysis is more and more applied in all kinds of financial analysis instruments, by taking securities market as an example, more and more
Stock market's trend analysis instrument and short run analysis judgement, early warning instrument, but instrument as majority is professional too strong.
In current financial analysis instrument, the K line charts of user are especially presented to, have correlation predictive figure and analysis text
Chapter, but the prediction that artificial experience provides is all based on, lack visual in image data analysis, and drawn after data analysis
A series of prediction conclusions, i.e. lack the data that objective data are planned as a whole and analyzed so that user can not make conjunction to related data
The judgement of reason.Consumer's Experience is low, and usage experience is low.
The content of the invention
The invention provides a kind of method of data analysis, passes through the K lines data set and history data set inputted to user
Normalized is done, and calculates the two daily amount of increase and amount of decrease distance, most optimal distance collection is presented to user at last.Both can be from current
The form of stock looks for K line morphologies the most similar in history, can also be from any K lines, and searching also has similar recently
The stock of form, there is provided a kind of method that can predict short-term stock form tendency, the machine of selection stock is added for user
Meeting.Consumer's Experience can not be improved to K line numbers according to the forecasting problem for providing objective analysis in the prior art by solving.
To achieve these goals, the invention provides a kind of method of data analysis, including：
Server obtains raw sample data collection and corresponding multiple weight factor；
According to the raw sample data collection and the multiple weight factor, determine multiple corresponding to the raw sample data collection
Data set is weighed, the data set of weighing again is as the first data set；
User input instruction is received, according to the input instruction, gets data set to be analyzed, the data set to be analyzed
As the second data set；
Normalized is made to first data set and the second data set respectively, determines that first data set is corresponding
Daily amount of increase and amount of decrease data set and the second data set corresponding to daily amount of increase and amount of decrease data set；
Described two groups daily amount of increase and amount of decrease data sets are each subtracted each other, calculate after two groups of amount of increase and amount of decrease data are subtracted each other away from
From data set；
The minimum result of the distance value is obtained, the result is sent to client, so that the client is by described in
Result presentation gives the user.
In one embodiment of the present of invention, methods described also includes：
By described two groups daily amount of increase and amount of decrease data sets according to different time sections, multiple daily amount of increase and amount of decrease data are respectively divided into
Subset；
Two groups of the same period daily amount of increase and amount of decrease data subsets are subtracted each other, determine of the range data after subtracting each other
Collection；
Previous step is repeated, determines the range data subset in each period after subtracting each other；
Obtain the result that the distance value is minimum in each period respectively, using the minimum result of the distance value as
Optimal result collection is sent to the client, so that institute's optimal result collection is presented to the user by the client.
It is described to determine to weigh data set again corresponding to the raw sample data collection in one embodiment of the present of invention, tool
Body is：
Obtain the sample data and concentrate each sample data, each sample data is corresponding with the sample data multiple
Weight factor is multiplied, and the result after multiplication is multiple flexible strategy evidence corresponding to the sample data；Weighed again corresponding to the sample data
The factor is first multiple weight factor before the sample data corresponds to the date.
It is described that first data set and the second data set are made at normalization respectively in one embodiment of the present of invention
Reason, it is specially：
The first data in first data set are arranged to reference data；
By each data in first data set respectively compared with the reference data, its ratio is converged to 1
On the basis of curve on；
The first data in second data set are arranged to reference data；
By each data in second data set respectively compared with the reference data, its ratio is converged to 1
On the basis of curve on.
In one embodiment of the present of invention, the client gives the result presentation to the user, including：
The result is shown according to K line charts and is presented to the user.
The embodiment of the present invention additionally provides a kind of device of data analysis, including：
Acquiring unit, for obtaining raw sample data collection and corresponding multiple weight factor；
Determining unit, for according to the raw sample data collection and the multiple weight factor, determining the original sample
Data set is weighed corresponding to data set again, the data set of weighing again is as the first data set；
The acquiring unit, it is additionally operable to receive user input instruction, according to the input instruction, gets data to be analyzed
Collection, the data set to be analyzed is as the second data set；
Normalized unit, for making normalized to first data set and the second data set respectively, it is determined that
Go out daily amount of increase and amount of decrease data set corresponding to daily amount of increase and amount of decrease data set and the second data set corresponding to first data set；
Computing unit, for each subtracting each other to described two groups daily amount of increase and amount of decrease data sets, calculate two groups of amounts of increase and amount of decrease
Data subtract each other after range data collection；
Transmitting element, the result minimum for obtaining the distance value, the result is sent to client, so that described
Client gives the result presentation to the user.
In one embodiment of the present of invention, described device also includes：
Division unit, for described two groups daily amount of increase and amount of decrease data sets according to different time sections, to be respectively divided into multiple
Daily amount of increase and amount of decrease data subset；
The determining unit is additionally operable to, and two groups of the same period daily amount of increase and amount of decrease data subsets are subtracted each other, it is determined that
The range data subset gone out after subtracting each other, and repeat the above steps, determine range data in each period after subtracting each other
Collection；
The transmitting element, it is additionally operable to obtain the result that the distance value is minimum in each period respectively, by described in
The minimum result of distance value is sent to the client as optimal result collection, so that the client is by institute's optimal result collection exhibition
Now give the user.
In one embodiment of the present of invention, the determining unit determines multiple flexible strategy corresponding to the raw sample data collection
According to collection, it is specially：
The determining unit obtains the sample data and concentrates each sample data, by each sample data and the sample
Multiple weight factor is multiplied corresponding to data, and the result after multiplication is multiple flexible strategy evidence corresponding to the sample data；The sample number
It is first multiple weight factor before the sample data corresponds to the date according to corresponding multiple weight factor.
In one embodiment of the present of invention, the normalized unit is respectively to first data set and the second data
Collection makees normalized, is specially：
The first data in first data set are arranged to reference data by the normalized unit；
By each data in first data set respectively compared with the reference data, its ratio is converged to 1
On the basis of curve on；
The first data in second data set are arranged to reference data；
By each data in second data set respectively compared with the reference data, its ratio is converged to 1
On the basis of curve on.
In one embodiment of the present of invention, the client gives the result presentation to the user, is specially：
The result is shown according to K line charts and is presented to the user.
The data analysing method and device of the embodiment of the present invention have following advantages：
Normalized is done by the K lines data set and history data set that are inputted to user, and calculates the two daily ups and downs
Range is from most optimal distance collection is presented to user at last.Both can have been looked for from the form of current stock the most similar in history
K line morphologies, can also be from any K lines, find also has the stock of similar form recently, there is provided one kind can be predicted short
The method of option share ticket form tendency, the chance of selection stock is added for user, improves Consumer's Experience.
Brief description of the drawings
Fig. 1 is the schematic flow sheet of data analysing method in the embodiment of the present invention 1；
Fig. 2 a are 30 days trend analysis schematic diagrames of K lines of data analysing method in the embodiment of the present invention 1；
Fig. 2 b are 30 days trend analysis schematic diagrames of another K lines of data analysing method in the embodiment of the present invention 1；
Fig. 2 c are 60 days trend analysis schematic diagrames of K lines of data analysing method in the embodiment of the present invention 1；
Fig. 2 d are 60 days trend analysis schematic diagrames of another K lines of data analysing method in the embodiment of the present invention 1；
Fig. 3 is data analysis set-up structure chart in the embodiment of the present invention 3.
Embodiment
In order to make the purpose , technical scheme and advantage of the present invention be clearer, it is right below in conjunction with drawings and Examples
The present invention is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, and
It is not used in the restriction present invention.As long as in addition, technical characteristic involved in each embodiment of invention described below
Conflict can is not formed each other to be mutually combined.
Embodiment 1
To achieve the above objectives, as shown in figure 1, the invention provides a kind of method of data analysis, including：
S101. server obtains raw sample data collection and corresponding multiple weight factor；
In the embodiment of the present invention, raw sample data collection can be some stocks historical data of some days in history,
Can also be a certain stock historical data of some days, or all historical datas of whole stocks, the present invention are implemented
In example, the historical data can be share price, exchange hand and/or K line number evidences.
Raw sample data collection is obtained, its object is to：Can by some in history or the K line tendencies of all stocks,
The tendency for the stock for needing to analyze with user is compared, and can be to use so as to be quickly found out K line charts most like in history
Family provides the reference of following short-term trend.
Server obtains raw sample data collection and corresponding multiple weight factor, can directly be obtained from the database of itself
Arrive, the data can also be asked from remaining Core server, by Core server come indirect gain.
S102. according to the raw sample data collection and the multiple weight factor, the raw sample data set pair is determined
The multiple power data set answered, the data set of weighing again is as the first data set；
Multiple power is exactly to carry out power breath to share price and exchange hand to repair, and price movement of stocks is drawn according to the actual ups and downs of stock,
And exchange hand is adjusted to identical capital stock bore.For stock except power, after ex dividend, share price generates change therewith, but actually into
This is not changed.
Stock raw sample data and the multiple weight factor information meeting record storage of stock history are got off, and multiple flexible strategy evidence is from history
Market and history weigh what information was calculated again, and first before formula is one day multiple flexible strategy market evidence=one day * one days is multiple
Weight factor.I.e.：Obtain the sample data and concentrate each sample data, each sample data is corresponding with the sample data
Multiple weight factor is multiplied, and the result after multiplication is multiple flexible strategy evidence corresponding to the sample data；It is multiple corresponding to the sample data
Weight factor is first multiple weight factor before the sample data corresponds to the date.
S103. user input instruction is received, according to the input instruction, gets data set to be analyzed, it is described to be analyzed
Data set is as the second data set；
Server receives user input instruction, and the input instruction can be 6 stock codes of stock exchange defined,
It can also be user-defined K lines curve.After server receives the input instruction, it can be obtained by way of database retrieval
The K line number evidences of a certain stock of inquiry required for getting user, can also be by interacting, from other with other Core servers
The data are got at server.
S104. normalized is made to first data set and the second data set respectively, determines first data
Daily amount of increase and amount of decrease data set corresponding to daily amount of increase and amount of decrease data set and the second data set corresponding to collection；
The one the second data sets are normalized, that is, needs to shield the price factor of K lines, the data of K lines is advised
It is scheduled in same scale range.
Alternatively, it is described that normalized is made to first data set and the second data set respectively, be specially：
The first data in first data set are arranged to reference data 1；
By each data in first data set respectively compared with the reference data, its ratio is converged to 1
On the basis of curve on；That is, the closing price of every day is arranged to same day closing price/reference data, and the value of such first day is exactly
1, the value of follow-up the 3rd day second day is exactly the ups and downs situation on 1.After converging to the curve on the basis of 1, convenient follow-up meter
The form for only focusing on curve is calculated, and without being concerned about specific share price value；
, can be by second data set according to the processing method same with the first data set for the second data set
First data are arranged to reference data；And by each data in second data set respectively compared with the reference data,
Its ratio is converged on the curve on the basis of 1.
S105. described two groups daily amount of increase and amount of decrease data sets are each subtracted each other, calculates two groups of amount of increase and amount of decrease data and subtract each other
Range data collection afterwards；
S106. the minimum result of the distance value is obtained, the result is sent to client, so that the client will
The result presentation gives the user.
Alternatively, handle for convenience, the first data set and/or the second data set can only intercept certain a period of time or a few
The data in section period, its data processing amount is few, and analysis digit rate is fast.Then, methods described can be：
By described two groups daily amount of increase and amount of decrease data sets according to different time sections, multiple daily amount of increase and amount of decrease data are respectively divided into
Subset, for example, each subset name that the first data set includes is followed successively by A1-An (wherein n is positive integer)；Second data set includes
Each subset name be followed successively by B1-Bn.
Two groups of daily amount of increase and amount of decrease data subsets of the same period (such as one month) are subtracted each other, determined after subtracting each other
Range data subset；Such as：The range data subset can be { A1-B1, A2-B2, A3-B3......An-Bn }.
Previous step is repeated, determines in each period after subtracting each other the range data subset of (such as several moons)；
Obtain the result that the distance value is minimum in each period respectively, using the minimum result of the distance value as
Optimal result collection is sent to the client, so that institute's optimal result collection is presented to the user by the client.It is presented
As a result as shown in Figure 2 a-2d.It can be seen that from Fig. 2 a-2d, be compared by the personal share most like to tendency, personal share can be analyzed
K line Short Term prospects, provide the user graphical reference.Fig. 2 a are 30 days trend analysis figures, it can be seen that defeated with user
" Haikang prestige regards " stock K lines for entering personal share the most similar is " Shandong medicine glass ", its similarity degree 94%.Fig. 2 b are then except providing
Outside the most like personal share of tendency, most like history tendency is additionally provided, the Short Term for follow-up " Haikang prestige depending on " provides
One objective reference.Fig. 2 c are similar with Fig. 2 b respectively at Fig. 2 a with Fig. 2 d, and difference is that the former is 30 days, and the latter is to walk for 60 days
Potential analysis.
The invention provides a kind of method of data analysis, passes through the K lines data set and history data set inputted to user
Normalized is done, and calculates the two daily amount of increase and amount of decrease distance, most optimal distance collection is presented to user at last.Both can be from current
The form of stock looks for K line morphologies the most similar in history, can also be from any K lines, and searching also has similar recently
The stock of form, there is provided a kind of method that can predict short-term stock form tendency, the machine of selection stock is added for user
Meeting.Consumer's Experience can not be improved to K line numbers according to the forecasting problem for providing objective analysis in the prior art by solving.
Embodiment 2
The embodiment of the present invention 2 provides a kind of device of data analysis, and the device can be server or client
End, as shown in figure 3, the present apparatus is included with lower unit：
Acquiring unit 201, for obtaining raw sample data collection and corresponding multiple weight factor；
In the embodiment of the present invention, raw sample data collection can be some stocks historical data of some days in history,
Can also be a certain stock historical data of some days, or all historical datas of whole stocks, the present invention are implemented
In example, the historical data can be share price, exchange hand and/or K line number evidences.
Acquiring unit 201 obtains raw sample data collection, its object is to：Some in history or all stocks can be passed through
K line tendencies, the tendency of stock for needing to analyze with user is compared, so as to be quickly found out K lines most like in history
Figure, can provide the user the reference of following short-term trend.
Acquiring unit 201 obtains raw sample data collection and corresponding multiple weight factor, can be straight from the database of itself
Obtain and get, the data can also be asked from remaining Core server, by Core server come indirect gain.
Determining unit 202, for according to the raw sample data collection and the multiple weight factor, determining the original sample
Data set is weighed corresponding to notebook data collection again, the data set of weighing again is as the first data set；
Multiple power is exactly to carry out power breath to share price and exchange hand to repair, and price movement of stocks is drawn according to the actual ups and downs of stock,
And exchange hand is adjusted to identical capital stock bore.For stock except power, after ex dividend, share price generates change therewith, but actually into
This is not changed.
Stock raw sample data and the multiple weight factor information meeting record storage of stock history are got off, and multiple flexible strategy evidence is from history
Market and history weigh what information was calculated again, and first before formula is one day multiple flexible strategy market evidence=one day * one days is multiple
Weight factor.I.e.：Acquiring unit 201 obtains the sample data and concentrates each sample data, and determining unit 202 is by each sample
Data multiple weight factor corresponding with the sample data is multiplied, and the result after multiplication is multiple flexible strategy corresponding to the sample data
According to；Multiple weight factor corresponding to the sample data is first multiple weight factor before the sample data corresponds to the date.
The acquiring unit 201, it is additionally operable to receive user input instruction, according to the input instruction, gets to be analyzed
Data set, the data set to be analyzed is as the second data set；
Acquiring unit 201 receives user input instruction, and the input instruction can be 6 stocks of stock exchange defined
Code or user-defined K lines curve.After server receives the input instruction, database retrieval can be passed through
Mode gets the K line number evidences of a certain stock of inquiry required for user, can also by being interacted with other Core servers,
The data are got at other servers.
Normalized unit 203, for making normalized to first data set and the second data set respectively, really
Make daily amount of increase and amount of decrease data set corresponding to daily amount of increase and amount of decrease data set and the second data set corresponding to first data set；
The one the second data sets are normalized normalized unit 203, that is, need to shield the prices of K lines because
Element, the data of K lines are provided in same scale range.
Alternatively, normalized unit 203 makees normalized to first data set and the second data set respectively,
Specially：
The first data in first data set are arranged to reference data 1；
By each data in first data set respectively compared with the reference data, its ratio is converged to 1
On the basis of curve on；That is, the closing price of every day is arranged to same day closing price/reference data, and the value of such first day is exactly
1, the value of follow-up the 3rd day second day is exactly the ups and downs situation on 1.After converging to the curve on the basis of 1, convenient follow-up meter
The form for only focusing on curve is calculated, and without being concerned about specific share price value；
, can be by second data set according to the processing method same with the first data set for the second data set
First data are arranged to reference data；And by each data in second data set respectively compared with the reference data,
Its ratio is converged on the curve on the basis of 1.
Computing unit 204, for each subtracting each other to described two groups daily amount of increase and amount of decrease data sets, calculate two groups of ups and downs
Width data subtract each other after range data collection；
Transmitting element 205, the result minimum for obtaining the distance value, the result is sent to client, so that
The client gives the result presentation to the user.
Alternatively, handle for convenience, the first data set and/or the second data set can only intercept certain a period of time or a few
The data in section period, its data processing amount is few, and analysis digit rate is fast.Then, methods described can be：
By described two groups daily amount of increase and amount of decrease data sets according to different time sections, multiple daily amount of increase and amount of decrease data are respectively divided into
Subset, for example, each subset name that the first data set includes is followed successively by A1-An (wherein n is positive integer)；Second data set includes
Each subset name be followed successively by B1-Bn.
Two groups of daily amount of increase and amount of decrease data subsets of the same period (such as one month) are subtracted each other, determined after subtracting each other
Range data subset；Such as：The range data subset can be { A1-B1, A2-B2, A3-B3......An-Bn }.
And repeat the above steps, the range data subset of (such as several moons) was determined in each period after subtracting each other；
Obtain the result that the distance value is minimum in each period respectively, using the minimum result of the distance value as
Optimal result collection is sent to the client, so that institute's optimal result collection is presented to the user by the client.
The invention provides a kind of device of data analysis, the device obtains normalized unit by being inputted to user
K lines data set and history data set do normalized, and calculate the two daily amount of increase and amount of decrease distance, and most optimal distance collection is at last
Now give user.Both K line morphologies the most similar in history can be looked for from the form of current stock, can also be from any K lines
Set out, find the stock for also having similar form recently, the chance of selection stock is added for user.Solve in the prior art without
Method, according to the forecasting problem for providing objective analysis, improves Consumer's Experience to K line numbers.
As it will be easily appreciated by one skilled in the art that the foregoing is merely illustrative of the preferred embodiments of the present invention, not to
The limitation present invention, all any modification, equivalent and improvement made within the spirit and principles of the invention etc., all should be included
Within protection scope of the present invention.
Claims (10)
- A kind of 1. method of data analysis, it is characterised in that including：Server obtains raw sample data collection and corresponding multiple weight factor；According to the raw sample data collection and the multiple weight factor, multiple flexible strategy corresponding to the raw sample data collection are determined According to collection, the data set of weighing again is as the first data set；User input instruction is received, according to the input instruction, gets data set to be analyzed, the data set conduct to be analyzed Second data set；Normalized is made to first data set and the second data set respectively, determined every corresponding to first data set Daily amount of increase and amount of decrease data set corresponding to day amount of increase and amount of decrease data set and the second data set；Described two groups daily amount of increase and amount of decrease data sets are each subtracted each other, calculate the distance number after two groups of amount of increase and amount of decrease data are subtracted each other According to collection；The minimum result of the distance value is obtained, the result is sent to client, so that the client is by the result It is presented to the user.
- 2. according to the method for claim 1, it is characterised in that methods described also includes：By described two groups daily amount of increase and amount of decrease data sets according to different time sections, multiple daily amount of increase and amount of decrease data are respectively divided into Collection；Two groups of the same period daily amount of increase and amount of decrease data subsets are subtracted each other, determine the range data subset after subtracting each other；Previous step is repeated, determines the range data subset in each period after subtracting each other；The result that the distance value is minimum in each period is obtained respectively, using the minimum result of the distance value as optimal Result set is sent to the client, so that institute's optimal result collection is presented to the user by the client.
- 3. according to the method for claim 1, it is characterised in that described to determine corresponding to the raw sample data collection again Data set is weighed, including：Obtain the sample data and concentrate each sample data, by each sample data it is corresponding with the sample data weigh again because Son is multiplied, and the result after multiplication is multiple flexible strategy evidence corresponding to the sample data；Multiple weight factor corresponding to the sample data First multiple weight factor before the date is corresponded to for the sample data.
- 4. according to the method for claim 1, it is characterised in that described respectively to first data set and the second data set Make normalized, including：The first data in first data set are arranged to reference data；By each data in first data set respectively compared with the reference data, its ratio is converged to 1 as base On accurate curve；The first data in second data set are arranged to reference data；By each data in second data set respectively compared with the reference data, its ratio is converged to 1 as base On accurate curve.
- 5. according to the method described in claim any one of 1-4, it is characterised in that the client is by the result presentation to institute User is stated, including：The result is shown according to K line charts and is presented to the user.
- 6. a kind of device of data analysis, it is characterised in that described device includes：Acquiring unit, for obtaining raw sample data collection and corresponding multiple weight factor；Determining unit, for according to the raw sample data collection and the multiple weight factor, determining the raw sample data Data set is weighed corresponding to collection again, the data set of weighing again is as the first data set；The acquiring unit, it is additionally operable to receive user input instruction, according to the input instruction, gets data set to be analyzed, The data set to be analyzed is as the second data set；Normalized unit, for making normalized to first data set and the second data set respectively, determine institute State daily amount of increase and amount of decrease data set corresponding to daily amount of increase and amount of decrease data set and the second data set corresponding to the first data set；Computing unit, for each subtracting each other to described two groups daily amount of increase and amount of decrease data sets, calculate two groups of amount of increase and amount of decrease data Range data collection after subtracting each other；Transmitting element, the result minimum for obtaining the distance value, the result is sent to client, so that the client The user is given the result presentation in end.
- 7. device according to claim 6, it is characterised in that described device also includes：Division unit, for described two groups daily amount of increase and amount of decrease data sets according to different time sections, to be respectively divided into multiple daily Amount of increase and amount of decrease data subset；The determining unit is additionally operable to, and two groups of the same period daily amount of increase and amount of decrease data subsets are subtracted each other, determine phase Range data subset after subtracting, and repeat the above steps, determine the range data subset in each period after subtracting each other；The transmitting element, it is additionally operable to obtain the result that the distance value is minimum in each period respectively, by the distance The minimum result of value is sent to the client as optimal result collection, so that institute's optimal result collection is presented to by the client The user.
- 8. device according to claim 6, it is characterised in that the determining unit determines the raw sample data collection It is corresponding to weigh data set again, including：The determining unit obtains the sample data and concentrates each sample data, by each sample data and the sample data Corresponding multiple weight factor is multiplied, and the result after multiplication is multiple flexible strategy evidence corresponding to the sample data；The sample data pair The multiple weight factor answered is first multiple weight factor before the sample data corresponds to the date.
- 9. device according to claim 6, it is characterised in that the normalized unit is respectively to first data Collection and the second data set make normalized, including：The first data in first data set are arranged to reference data by the normalized unit；By each data in first data set respectively compared with the reference data, its ratio is converged to 1 as base On accurate curve；The first data in second data set are arranged to reference data；By each data in second data set respectively compared with the reference data, its ratio is converged to 1 as base On accurate curve.
- 10. according to the device described in claim any one of 6-9, it is characterised in that the client gives the result presentation The user, including：The result is shown according to K line charts and is presented to the user.
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710677405.2A CN107480819A (en) | 2017-08-09 | 2017-08-09 | A kind of method and device of data analysis |
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