CN108492345B - Data block dividing method based on scale transformation - Google Patents

Data block dividing method based on scale transformation Download PDF

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CN108492345B
CN108492345B CN201810069242.4A CN201810069242A CN108492345B CN 108492345 B CN108492345 B CN 108492345B CN 201810069242 A CN201810069242 A CN 201810069242A CN 108492345 B CN108492345 B CN 108492345B
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curve
points
original
dividing
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CN108492345A (en
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谢国
李锦妮
冯楠
王文卿
王晓帆
赵金伟
赵钦
黑新宏
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GUANGZHOU SINOBEST SOFTWARE TECHNOLOGY Co.,Ltd.
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Abstract

The invention discloses a data block dividing method based on scale transformation, which is characterized in that data is divided into blocks according to the integral idea of big data processing, then the data is divided based on the scale transformation method, and further data trend analysis can be carried out on the basis, so that the problem that when the data volume is large, and the data is difficult to divide due to the fact that detailed data points are too much concerned is solved; the invention also has the advantages of simple operation, accurate and rapid division result.

Description

Data block dividing method based on scale transformation
Technical Field
The invention belongs to the technical field of data processing methods, and particularly relates to a data block dividing method based on scale transformation.
Background
Along with the development of big data, the technical field of data processing is also continuously developed, and how to better divide data blocks when the data volume is too large becomes a main problem to be solved. When the data volume is too large, if the data curve is enlarged to a certain size, the difficulty of data division is increased because the detail data is over-concerned; if the image size is continuously reduced, the distribution of points on the curve is very dense, so that the rough trend of the curve can be easily seen, the data block can be divided more conveniently and accurately by a computer, the data block can be divided based on the whole idea, and therefore, for the problem, the data block is divided by using a method based on scale transformation.
Disclosure of Invention
The invention aims to provide a data block dividing method based on scale transformation, which solves the problem that when the data volume is too large, the data division is directly performed on a curve with certain difficulty.
The technical scheme adopted by the invention is that the data block dividing method based on scale transformation is implemented according to the following steps:
step 1, drawing a data curve, reducing the data curve in equal proportion, marking middle position points of a plurality of pixel point vertical coordinates corresponding to the same horizontal coordinate on the data curve, and sequentially connecting the middle position points to obtain a smooth data curve;
step 2, dividing the smooth data curve in the step 1 into a plurality of smooth curve segments by using a data trend analysis method, mapping the boundary point of each smooth curve segment to the original data curve, and dividing the original data curve into a plurality of curve segments;
step 3, randomly selecting a certain curve segment obtained in the step 2, and comparing the data volume in the curve segment with the required data volume;
if the number of the data points in the curve segment is larger than the required data number, re-executing the step 1 and the step 2;
and if the number of the data points in the curve segment is less than the required number of the data, terminating the division of the data block.
The specific process of the step 1 is as follows:
step 1.1, drawing data X ═ X1,x2,...,xn]A graph, wherein n represents the number of data points, and the border and all labels of the graph are removed and stored in a picture format;
step 1.2, recording the picture size k of the curve obtained in step 1.11*k2Then the picture size is reduced to the original picture
Figure BDA0001557672240000021
And saved as figure1.jpg, noting that the current picture size is m1*m2Wherein, in the step (A),
Figure BDA0001557672240000022
step 1.3, carrying out graying and binarization processing on the figure1.jpg in the step 1.2, finding out the positions of all pixel values of '0' (representing 'black'), and forming a new curve graph;
step 1.4, in the new curve chart obtained in the step 1.3, corresponding to a plurality of pixel points of the ordinate under the same abscissa, finding out the pixel point of which the ordinate is in the middle position;
and step 1.5, sequentially connecting the pixel points at the middle position obtained in the step 1.4 to obtain a new smooth data curve.
The step 2 specifically comprises the following steps:
step 2.1, the smooth data curve obtained in the step 1 is segmented by adopting the existing data trend analysis method to obtain a plurality of data blocks, and the boundary point of each data block is determined;
step 2.2, determining all boundary points of each data block obtained in step 2.1, and generating the following vectors:
X′＝[x1′,x2′,...,xn′]T (1)；
in the formula (1), X' represents a set of all boundary points, and Xn' is the entire boundary point of each data block;
step 2.3, mapping the boundary points obtained in the step 2.2 with the original data one by one, mapping the original data according to the following position proportion relation,
Figure BDA0001557672240000031
in the formula (2), xnBoundary points, k, divided for the original data1Length of the original graph, m1Is the length of figure1.jpg in step 1.2;
and 2.4, dividing inflection points of the original data, namely boundary points for dividing an original data curve, according to the original data obtained in the step 2.3, so that the original data are divided into a plurality of data blocks to obtain the integral data dividing result.
The data block dividing method based on scale transformation has the beneficial effects that: the data are divided into blocks according to the overall idea of big data processing, and then are divided based on a scale transformation method, so that data trend analysis can be performed on the basis, and the problem that when the data volume is large and the data are difficult to divide due to the fact that detailed data points are too much concerned is solved;
the data block dividing method based on scale transformation also has the advantages of simple operation, accurate and quick dividing result.
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FIG. 1 is a general flow chart of a method for data block partitioning based on scale transformation according to the present invention;
FIG. 2 is a graph of raw data in a data block division method based on scale transformation according to the present invention;
FIG. 3 is an isometric view of FIG. 2;
FIG. 4 is a grayscale diagram of FIG. 3;
FIG. 5 is a binary diagram of FIG. 4;
FIG. 6 is a graph of a smoothed data graph in a data block division method based on scale transformation according to the present invention;
FIG. 7 is a graph showing a large amount of data to be divided in a data block division method based on scale transformation according to the present invention;
fig. 8 is a graph showing a small amount of divided data in a data block division method based on scale transformation according to the present invention.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
The invention relates to a data block dividing method based on scale transformation, which is implemented according to the following steps as shown in figure 1:
step 1, drawing a data curve, reducing the data curve in equal proportion, marking middle position points of a plurality of pixel point vertical coordinates corresponding to the same horizontal coordinate on the data curve, and sequentially connecting the middle position points to obtain a smooth data curve;
step 1.1, drawing data X ═ X1,x2,...,xn]A graph, as shown in fig. 2, wherein n represents the number of data points, and the border and all labels of the graph are removed and stored in a picture format;
step 1.2, recording the picture size k of the curve obtained in step 1.11*k2Then, as shown in FIG. 3, the picture is reduced to the original picture size
Figure BDA0001557672240000041
And saved as figure1.jpg, noting that the current picture size is m1*m2Wherein, in the step (A),
Figure BDA0001557672240000042
step 1.3, as shown in fig. 4 and 5, graying and binarizing the fig. 1.jpg of step 1.2, to find out the positions of all pixel values of '0' (representing 'black'), and form a new graph;
step 1.4, in the new curve chart obtained in the step 1.3, corresponding to a plurality of pixel points of the ordinate under the same abscissa, finding out the pixel point of which the ordinate is in the middle position;
step 1.5, as shown in fig. 6, sequentially connecting the pixel points at the intermediate position obtained in step 1.4 to obtain a new smooth data curve.
Step 2, dividing the smooth data curve in the step 1 into a plurality of smooth curve segments by using a data trend analysis method, mapping the boundary point of each smooth curve segment to the original data curve, and dividing the original data curve into a plurality of curve segments;
step 2.1, the smooth data curve obtained in the step 1 is segmented by adopting the existing data trend analysis method to obtain a plurality of data blocks, and the boundary point of each data block is determined;
step 2.2, determining all boundary points of each data block obtained in step 2.1, and generating the following vectors:
X′＝[x1′,x2′,...,xn′]T (1)；
in the formula (1), X' represents a set of all boundary points, and Xn' is the entire boundary point of each data block;
step 2.3, mapping the boundary points obtained in the step 2.2 with the original data one by one, mapping the original data according to the following position proportion relation,
Figure BDA0001557672240000051
in the formula (2), xnBoundary points, k, divided for the original data1Length of the original graph, m1Is the length of figure1.jpg in step 1.2;
and 2.4, dividing inflection points of the original data, namely boundary points for dividing an original data curve, according to the original data obtained in the step 2.3, so that the original data are divided into a plurality of data blocks to obtain the integral data dividing result.
Step 3, randomly selecting a certain curve segment obtained in the step 2, and comparing the data volume in the curve segment with the required data volume;
as shown in fig. 7, if the number of data points in the curve segment is greater than the required number of data points, step 1 and step 2 are executed again;
as shown in fig. 8, if the number of data points in the curve segment is less than the required number of data points, the division of the data block is terminated.
Through the mode, the data block division method based on the scale transformation, disclosed by the invention, is used for carrying out block division on data according to the overall idea of big data processing, then carrying out data division on the data based on the scale transformation method, further carrying out data trend analysis on the basis, and solving the problem that when the data volume is large, and the data is difficult to divide due to the fact that the data points are too much concerned; the invention also has the advantages of simple operation, accurate and rapid division result.

Claims (2)

1. A data block dividing method based on scale transformation is characterized by comprising the following steps:
step 1, drawing a data curve, reducing the data curve in equal proportion, marking middle position points of vertical coordinates of a plurality of pixel points corresponding to the same horizontal coordinate on the data curve, and sequentially connecting the middle position points to obtain a smooth data curve;
the specific process of the step 1 is as follows:
step 1.1, drawing data X ═ X1,x2,...,xn]A graph, wherein n represents the number of data points, and the border and all labels of the graph are removed and stored in a picture format;
step 1.2, recording the picture size k of the curve obtained in step 1.11*k2Then the picture size is reduced to the original picture
Figure FDA0003221117060000011
And saved as figure1.jpg, noting that the current picture size is m1*m2Wherein, in the step (A),
Figure FDA0003221117060000012
step 1.3, carrying out graying and binarization processing on the figure1.jpg in the step 1.2, finding out the positions of all pixel values of '0' (representing 'black'), and forming a new curve graph;
step 1.4, in the new curve chart obtained in the step 1.3, corresponding to a plurality of pixel points of the ordinate under the same abscissa, finding out the pixel point of which the ordinate is in the middle position;
step 1.5, sequentially connecting the pixel points at the middle position obtained in the step 1.4 to obtain a new smooth data curve;
step 2, dividing the smooth data curve in the step 1 into a plurality of smooth curve segments by using a data trend analysis method, mapping the boundary point of each smooth curve segment to the original data curve, and dividing the original data curve into a plurality of curve segments;
step 3, randomly selecting a certain curve segment obtained in the step 2, and comparing the data volume in the curve segment with the required data volume;
if the number of the data points in the curve segment is larger than the required data number, re-executing the step 1 and the step 2;
and if the number of the data points in the curve segment is less than the required number of the data, terminating the division of the data block.
2. The method for dividing data blocks based on scale transformation as claimed in claim 1, wherein the step 2 specifically comprises the following steps:
step 2.1, the smooth data curve obtained in the step 1 is segmented by adopting the existing data trend analysis method to obtain a plurality of data blocks, and the boundary point of each data block is determined;
step 2.2, determining all boundary points of each data block obtained in step 2.1, and generating the following vectors:
X′＝[x1′,x2′,...,xn′]T (1)；
in the formula (1), X' represents a set of all boundary points, and Xn' is the entire boundary point of each data block;
step 2.3, mapping the boundary points obtained in the step 2.2 with the original data one by one, mapping the original data according to the following position proportion relation,
Figure FDA0003221117060000021
in the formula (2), xnBoundary points, k, divided for the original data1Length of the original graph, m1Is the length of figure1.jpg in step 1.2;
and 2.4, dividing inflection points of the original data, namely boundary points for dividing an original data curve, according to the original data obtained in the step 2.3, so that the original data are divided into a plurality of data blocks to obtain the integral data dividing result.
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