CN108492345A - A kind of data block division methods based on change of scale - Google Patents
A kind of data block division methods based on change of scale Download PDFInfo
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- CN108492345A CN108492345A CN201810069242.4A CN201810069242A CN108492345A CN 108492345 A CN108492345 A CN 108492345A CN 201810069242 A CN201810069242 A CN 201810069242A CN 108492345 A CN108492345 A CN 108492345A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
- G06T11/20—Drawing from basic elements, e.g. lines or circles
- G06T11/203—Drawing of straight lines or curves
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
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- G—PHYSICS
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
Abstract
The data block division methods based on change of scale that the invention discloses a kind of, whole concept according to big data processing carries out block division to data, the method for being then based on change of scale carries out data division to it, and then data trend analysis can be carried out on this basis, it solves the problems, such as when data volume is big and excessively concern detail data point makes data be difficult to divide；The present invention also has the advantages that easy to operate, division result accurately and rapidly.
Description
Technical field
The invention belongs to data processing method technical fields, and in particular to a kind of data block division side based on change of scale
Method.
Background technology
How more when data volume is excessive along with the development of big data, technical field of data processing is also evolving,
Good carry out data block division, becomes the main problem to be solved.When data volume is excessive, if data and curves put
Big certain size will increase the difficulty of data division because of excessively detail data is paid close attention to；And if constantly reduced
Picture size, the distribution put on curve can be very intensive, and thus the easier approximate trend for finding out curve, is more favorable to
Computer fast and accurately divides data block, then can be divided to data block based on whole thought, therefore be asked for this
Topic, divides data block using a kind of method based on change of scale.
Invention content
The data block division methods based on change of scale that the object of the present invention is to provide a kind of, solve data volume mistake
When big, directly there is the problem of certain difficulty in progress data division on curve.
The technical solution adopted in the present invention is a kind of data block division methods based on change of scale, specifically according to
Lower step is implemented：
Step 1, drawing data curve, the scaled down data and curves mark the same abscissa in the data and curves
The intermediate position points of corresponding multiple pixel ordinates, and intermediate position points are sequentially connected to obtain smooth data plot；
Smooth data plot in step 1 is divided into multiple smoothed curves by step 2 using data trend analysis method
Section, is mapped to raw data plot, and raw data plot is divided into multiple curves by each smoothed curve section boundary point
Section；
Step 3 randomly selects some curved section obtained in step 2, by the curved section data volume with it is required
Data volume is compared；
If the number of the data point in the curved section is more than required data amount check, step 1, step 2 are re-executed；
If the number of the data point in the curved section is less than required data amount check, the division of data block is terminated.
Step 1 detailed process is：
Step 1.1, drawing data X=[x1,x2,...,xn] curve graph, wherein n indicates the number of data point, and removes song
The frame of line chart and all marks, and save as picture format；
The dimension of picture of the curve obtained in step 1.2, note step 1.1 is k1*k2, then the dimension of picture narrowed down into artwork
'sAnd figure figure1.jpg is saved as, note current image size is m1*m2, wherein
Step 1.2 figure figure1.jpg is first carried out gray processing, binary conversion treatment by step 1.3, finds out all pixels value
For the position of ' 0 ' (representing ' black '), new curve graph is formed；
Step 1.4, in the new curve graph that step 1.3 obtains, the pixel of multiple ordinates is corresponded under same abscissa
Point finds out ordinate pixel in an intermediate position；
The pixel in step 1.4 gained centre position is sequentially connected by step 1.5, and it is bent to obtain a new smoothed data
Line.
Step 2 specifically includes following steps：
Step 2.1, the smooth data plot obtained to step 1 using available data trend analysis are split, and are obtained
To several data blocks, and determine the boundary point of each data block；
Step 2.2, all boundary point for determining each data block obtained by step 2.1, and generate following vector：
X '=[x1′,x2′,...,xn′]T(1)；
In formula (1), the set of all boundary points of X ' expressions, xn' be each data block all boundary point；
Step 2.3 maps boundary point and initial data that step 2.2 obtains one by one, according to following position proportional relationship,
Initial data is mapped out,
In formula (2), xnFor the boundary point that initial data divides, k1For the length of primitive curve figure, m1To scheme in step 1.2
The length of figure1.jpg；
Step 2.4 divides the boundary point that inflection point is division raw data plot according to initial data required by step 2.3,
Thus initial data is divided into several data blocks, obtains data entirety division result.
A kind of data block division methods based on change of scale of the present invention have the beneficial effect that：Entirety according to big data processing
Thought carries out block division to data, and the method for being then based on change of scale carries out data division to it, and then on this basis may be used
To carry out data trend analysis, solve the problems, such as when data volume is big and excessively concern detail data point makes data be difficult to divide；
A kind of data block division methods based on change of scale of the present invention also have easy to operate, division result accurately, soon
The advantage of speed.
Description of the drawings
A kind of overview flow chart of the data block division methods based on change of scale of Fig. 1 present invention；
Fig. 2 is original data graphs in a kind of data block division methods based on change of scale of the present invention；
Fig. 3 is Fig. 2 scaled down figures；
Fig. 4 is the gray-scale map of Fig. 3；
Fig. 5 is the binary map of Fig. 4；
Fig. 6 is smooth data plot figure in a kind of data block division methods based on change of scale of the present invention；
Fig. 7 is that the larger curve graph of data volume is divided in a kind of data block division methods based on change of scale of the present invention；
Fig. 8 is that the smaller curve graph of data volume is divided in a kind of data block division methods based on change of scale of the present invention.
Specific implementation mode
Below in conjunction with the accompanying drawings and specific implementation mode the present invention is described in detail.
A kind of data block division methods based on change of scale of the present invention, as shown in Figure 1, specific real according to the following steps
It applies：
Step 1, drawing data curve, the scaled down data and curves mark the same abscissa in the data and curves
The intermediate position points of corresponding multiple pixel ordinates, and intermediate position points are sequentially connected to obtain smooth data plot；
Step 1.1, drawing data X=[x1,x2,...,xn] curve graph, as shown in Fig. 2, wherein n indicates of data point
Number, and the frame of curve graph and all marks are removed, and save as picture format；
The dimension of picture of the curve obtained in step 1.2, note step 1.1 is k1*k2, as shown in figure 3, the dimension of picture is contracted again
The small artwork of arrivingAnd figure figure1.jpg is saved as, note current image size is m1*m2, wherein
Step 1.3, as shown in Figure 4 and Figure 5, step 1.2 figure figure1.jpg is first subjected to gray processing, binary conversion treatment,
The position that all pixels value is ' 0 ' (representing ' black ') is found out, new curve graph is formed；
Step 1.4, in the new curve graph that step 1.3 obtains, the pixel of multiple ordinates is corresponded under same abscissa
Point finds out ordinate pixel in an intermediate position；
Step 1.5, as shown in fig. 6, the pixel in step 1.4 gained centre position is sequentially connected, obtain one it is new
Smooth data plot.
Smooth data plot in step 1 is divided into multiple smoothed curves by step 2 using data trend analysis method
Section, is mapped to raw data plot, and raw data plot is divided into multiple curves by each smoothed curve section boundary point
Section；
Step 2.1, the smooth data plot obtained to step 1 using available data trend analysis are split, and are obtained
To several data blocks, and determine the boundary point of each data block；
Step 2.2, all boundary point for determining each data block obtained by step 2.1, and generate following vector：
X '=[x1′,x2′,...,xn′]T(1)；
In formula (1), the set of all boundary points of X ' expressions, xn' be each data block all boundary point；
Step 2.3 maps boundary point and initial data that step 2.2 obtains one by one, according to following position proportional relationship,
Initial data is mapped out,
In formula (2), xnFor the boundary point that initial data divides, k1For the length of primitive curve figure, m1To scheme in step 1.2
The length of figure1.jpg；
Step 2.4 divides the boundary point that inflection point is division raw data plot according to initial data required by step 2.3,
Thus initial data is divided into several data blocks, obtains data entirety division result.
Step 3 randomly selects some curved section obtained in step 2, by the curved section data volume with it is required
Data volume is compared；
If as shown in fig. 7, the number of the data point in the curved section is more than required data amount check, step is re-executed
Rapid 1, step 2；
If as shown in figure 8, the number of the data point in the curved section is less than required data amount check, data block is terminated
Division.
By the above-mentioned means, a kind of data block division methods based on change of scale of the present invention, according to big data processing
Whole concept carries out block division to data, and the method for being then based on change of scale carries out it data division, and then basic herein
On can carry out data trend analysis, solve when data volume is big and excessively concern detail data point makes what data were difficult to divide to ask
Topic；The present invention also has the advantages that easy to operate, division result accurately and rapidly.
Claims (3)
1. a kind of data block division methods based on change of scale, which is characterized in that be specifically implemented according to the following steps：
Step 1, drawing data curve, the scaled down data and curves mark the same abscissa in the data and curves to correspond to
Multiple pixels ordinate intermediate position points, and intermediate position points are sequentially connected to obtain smooth data plot；
Smooth data plot in step 1 is divided into multiple smoothed curve sections by step 2 using data trend analysis method, will
Each smoothed curve section boundary point is mapped to raw data plot, and raw data plot is divided into multiple curved sections；
Step 3 randomly selects some curved section obtained in step 2, by data volume and the required data in the curved section
Amount is compared；
If the number of the data point in the curved section is more than required data amount check, step 1, step 2 are re-executed；
If the number of the data point in the curved section is less than required data amount check, the division of data block is terminated.
2. a kind of data block division methods based on change of scale as described in claim 1, which is characterized in that the specific mistake of step 1
Cheng Wei：
Step 1.1, drawing data X=[x1,x2,...,xn] curve graph, wherein n indicates the number of data point, and removes curve graph
Frame and all marks, and save as picture format；
The dimension of picture of the curve obtained in step 1.2, note step 1.1 is k1*k2, then the dimension of picture narrowed down into artworkAnd figure figure1.jpg is saved as, note current image size is m1*m2, wherein
Step 1.2 figure figure1.jpg is first carried out gray processing, binary conversion treatment by step 1.3, and it is ' 0 ' to find out all pixels value
The position of (representing ' black '), forms new curve graph；
Step 1.4, in the new curve graph that step 1.3 obtains, the pixel of multiple ordinates is corresponded under same abscissa, is looked for
Go out ordinate pixel in an intermediate position；
The pixel in step 1.4 gained centre position is sequentially connected by step 1.5, obtains a new smooth data plot.
3. a kind of data block division methods based on change of scale as described in claim 1, which is characterized in that step 2 is specifically wrapped
Include following steps：
Step 2.1, the smooth data plot obtained to step 1 using available data trend analysis are split, if obtaining
Dry data block, and determine the boundary point of each data block；
Step 2.2, all boundary point for determining each data block obtained by step 2.1, and generate following vector：
X '=[x '1,x′2,...,x′n]T(1)；
In formula (1), the set of all boundary points of X ' expressions, x 'nFor all boundary point of each data block；
Step 2.3 maps boundary point and initial data that step 2.2 obtains one by one, according to following position proportional relationship, mapping
Go out initial data,
In formula (2), xnFor the boundary point that initial data divides, k1For the length of primitive curve figure, m1To scheme in step 1.2
The length of figure1.jpg；
Step 2.4 divides the boundary point that inflection point is division raw data plot according to initial data required by step 2.3, thus
Initial data is divided into several data blocks, obtains data entirety division result.
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