CN106447743A - Construction method and device of trend analysis graph - Google Patents
Construction method and device of trend analysis graph Download PDFInfo
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- G—PHYSICS
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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Abstract
The application provides a construction method and device of a trend analysis graph. The construction method of the trend analysis graph comprises the following steps of acquiring vector input parameters as an input matrix, wherein the vector input parameters are a set of a series of input scattered points; interpolating the input matrix, so as to obtain a dense matrix; simplifying the dense matrix into a sparse matrix; generating the trend analysis graph by a Marching Square algorithm and the sparse matrix obtained by simplification. In the application, a vector trend analysis graph can be generated by adopting the manner.
Description
Technical field
The application is related to software field, particularly to a kind of trend analysiss figure construction method and device.
Background technology
Electronic chart is numerical map, is to utilize computer technology, the map storing in a digital manner and consulting.Electronically
Figure offers convenience to user's trip because of it, is extensively applied by popular.
In electronic map technique, geodata needs to show analysis in real time with vector trend analysiss figure, but traditional
Marching Square method due to calculating speed slow, and the vector graphics cannot being applied in electronic map technique is drawn.
Content of the invention
For solving above-mentioned technical problem, the embodiment of the present application provides a kind of trend analysiss figure construction method and device, to reach
To the purpose that can generate vector trend analysiss figure, technical scheme is as follows：
A kind of trend analysiss figure construction method, including：
Obtain vector |input paramete as input matrix, described vector |input paramete is a series of set of input scatterplot；
Row interpolation is entered to described input matrix, obtains dense matrix；
Described dense matrix abbreviation is sparse matrix；
The sparse matrix being obtained using Marching Square algorithm and abbreviation, generates trend analysiss figure.
Preferably, row interpolation is entered to described input matrix, obtain dense matrix and include：
A dense matrix is set up on the basis of described input matrix；
Using formulaRow interpolation is entered to the dense matrix set up；
Matrix (x, y) denotation coordination is the element value of (x, y), and value [i] represents the property value of i-th input scatterplot,
Dist [i] represents i-th input scatterplot and the distance of coordinate (x, y), and ∑ is summing function.
Preferably, described dense matrix abbreviation is included for sparse matrix：
Begin stepping through each unit rectangles matrix from the upper left corner of described dense matrix, determine each described unit
The rectangle of the bigger layer of area that rectangle and surrounding neighbors unit rectangles are formed；
Calculate the gradient between four summits of the rectangle of the bigger layer of area that each is determined, obtain six gradients
Value；
If six Grad are respectively less than predetermined threshold value, retain four summits of the determined rectangle of the bigger layer of area
Value；
The matrix of the rectangle composition of the bigger layer of area that each is determined is as dense matrix, and returns execution from institute
State the step that the upper left corner of dense matrix begins stepping through each unit rectangles in matrix, until each described list cannot be determined
The rectangle of the bigger layer of area that position rectangle is formed with surrounding neighbors unit rectangles.
Preferably, the sparse matrix being obtained using Marching Square algorithm and abbreviation, generates trend analysiss figure bag
Include：
The property value inputting scatterplot to each in described vector |input paramete carries out division from low to high, obtains n
Segment；
Distribute a kind of color for each segment, obtain the corresponding relation of each segment and color；
Four tops by each unit rectangles in the corresponding relation of each described segment and color and described sparse matrix
The value of point, as the input value of described Marching Square algorithm, generates described trend analysiss figure.
A kind of trend analysiss figure construction device, including：
Acquisition module, for obtaining vector |input paramete as input matrix, described vector |input paramete is a series of defeated
Enter the set of scatterplot；
Interpolating module, for entering row interpolation to described input matrix, obtains dense matrix；
Abbreviation module, for being sparse matrix by described dense matrix abbreviation；
Generation module, for the sparse matrix obtaining using Marching Square algorithm and abbreviation, generates trend analysiss
Figure.
Preferably, described interpolating module includes：
Set up unit, for setting up a dense matrix on the basis of described input matrix；
Interpolating unit, for utilizing formulaTo the dense matrix set up
Enter row interpolation；
Matrix (x, y) denotation coordination is the element value of (x, y), and value [i] represents the property value of i-th input scatterplot,
Dist [i] represents i-th input scatterplot and the distance of coordinate (x, y), and ∑ is summing function.
Preferably, described abbreviation module includes：
First determining unit, begins stepping through each unit rectangles in matrix for the upper left corner from described dense matrix,
Determine the rectangle of the bigger layer of area that each described unit rectangles and surrounding neighbors unit rectangles are formed；
Computing unit, for the gradient between four summits of the rectangle calculating the bigger layer of area that each is determined,
Obtain six Grad；
Stick unit, if being respectively less than predetermined threshold value for six Grad, retains the determined bigger layer of area
The value on four summits of rectangle；
Second determining unit, the matrix of the rectangle composition of the bigger layer of the area for being determined each is as dense square
Battle array, and return described first determining unit of execution, until each described unit rectangles and surrounding neighbors unit square cannot be determined
The rectangle of the bigger layer of area that shape is formed.
Preferably, described generation module includes：
Division unit, the property value for each in described vector |input paramete is inputted with scatterplot is carried out from low to high
Divide, obtain n segment；
Allocation unit, for distributing a kind of color for each segment, obtains the corresponding relation of each segment and color；
Signal generating unit, for by each unit in the corresponding relation of each described segment and color and described sparse matrix
The value on four summits of rectangle, as the input value of described Marching Square algorithm, generates described trend analysiss figure.
Compared with prior art, the having the beneficial effect that of the application：
In this application, obtain vector |input paramete first as input matrix, secondly described input matrix is inserted
Value, obtain dense matrix, then by described dense matrix abbreviation be sparse matrix；Wherein, by dense matrix abbreviation be sparse square
The processing mode of battle array is on the basis of ensureing that image definition is constant, it is possible to reduce image rendering number of times, lifts Marching
The computational efficiency of Square algorithm, makes Marching Square algorithm can be applicable to vector graphics and draws, thus utilizing
The sparse matrix that Marching Square algorithm and abbreviation obtain, can generate vector trend analysiss figure.
Brief description
For the technical scheme being illustrated more clearly that in the embodiment of the present application, will make to required in embodiment description below
Accompanying drawing be briefly described it should be apparent that, drawings in the following description are only some embodiments of the present application, for
For those of ordinary skill in the art, without having to pay creative labor, it can also be obtained according to these accompanying drawings
His accompanying drawing.
Fig. 1 is a kind of flow chart of the trend analysiss figure construction method that the application provides；
Fig. 2-1 is a kind of labelling schematic diagram of the input scatterplot that the application provides；
Fig. 2-2 is a kind of schematic diagram of the dense matrix that the application provides；
Fig. 2-3 is a kind of schematic diagram of the sparse matrix that the application provides；
Fig. 3 is a kind of sub-process figure of the trend analysiss figure construction method that the application provides；
Fig. 4 is another kind of sub-process figure of the trend analysiss figure construction method that the application provides；
Fig. 5 is another sub-process figure of the trend analysiss figure construction method that the application provides；
Fig. 6 is a kind of logical construction schematic diagram of the trend analysiss figure construction device that the application provides；
Fig. 7 is a kind of logical construction schematic diagram of the interpolating module that the application provides；
Fig. 8 is a kind of logical construction schematic diagram of the abbreviation module that the application provides；
Fig. 9 is a kind of logical construction schematic diagram of the generation module that the application provides.
Specific embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present application, the technical scheme in the embodiment of the present application is carried out clear, complete
Site preparation describes it is clear that described embodiment is only some embodiments of the present application, rather than whole embodiments.It is based on
Embodiment in the application, it is every other that those of ordinary skill in the art are obtained under the premise of not making creative work
Embodiment, broadly falls into the scope of the application protection.
Embodiment one
Refer to Fig. 1, a kind of flow chart of the trend analysiss figure construction method of the application offer is provided, can include
Following steps：
Step S11：Obtain vector |input paramete as input matrix, described vector |input paramete is a series of input scatterplot
Set.
In the present embodiment, vector |input paramete is the data having size and Orientation, and vector |input paramete with need structure
The type of the trend analysiss figure built is related, and the type of the trend analysiss figure that such as need to build is temperature trend analysiss figure, then vector is defeated
Entering parameter is then temperature vector |input paramete.
Wherein, each input scatterplot includes coordinate position and property value.As if the input of vector |input paramete temperature vector
Parameter, then a series of set of input scatterplot is a series of set that temperature input scatterplot, and the property value that input scatterplot includes is
Temperature property value.
The coordinate position of each input scatterplot with stain formal notation, such as schemed with numeral mark on the right side of stain by property value
Shown in 2-1.
Step S12：Row interpolation is entered to described input matrix, obtains dense matrix.
Row interpolation is entered to described input matrix, the dense matrix obtaining refers to Fig. 2-2, Fig. 2-2 is that the local of Fig. 2-1 is put
Big figure, wherein, each cross point represents one of matrix element.
Step S13：Described dense matrix abbreviation is sparse matrix.
The schematic diagram of the sparse matrix that abbreviation obtains refers to Fig. 2-3, and wherein Fig. 2-3 is by the dense square shown in Fig. 2-2
Battle array carries out the sparse matrix that abbreviation obtains.
The element number of sparse matrix is less than the element number of dense matrix.
Step S14：The sparse matrix being obtained using Marching Square algorithm and abbreviation, generates trend analysiss figure.
Certainly, in the present embodiment, vector |input paramete be not limited to a series of input scatterplot set or
Sparse matrix or dense matrix.
Above-mentioned trend analysiss figure construction method is applied to the structure of various types of trend analysiss figures, such as temperature trend, pin
Amount trend, total output value trend etc..
In this application, obtain vector |input paramete first as input matrix, secondly described input matrix is inserted
Value, obtain dense matrix, then by described dense matrix abbreviation be sparse matrix；Wherein, by dense matrix abbreviation be sparse square
The processing mode of battle array is on the basis of ensureing that image definition is constant, it is possible to reduce image rendering number of times, lifts Marching
The computational efficiency of Square algorithm, makes Marching Square algorithm can be applicable to vector graphics and draws, thus utilizing
The sparse matrix that Marching Square algorithm and abbreviation obtain, can generate vector trend analysiss figure.
In the present embodiment, above-mentioned row interpolation is entered to described input matrix, the detailed process obtaining dense matrix can be joined
See Fig. 3, may comprise steps of：
Step S31：A dense matrix is set up on the basis of described input matrix.
The dense matrix set up can cover all data in input matrix.
Step S32：Using formulaThe dense matrix set up is inserted
Value.
Matrix (x, y) denotation coordination is the element value of (x, y), and value [i] represents the property value of i-th input scatterplot,
Dist [i] represents i-th input scatterplot and the distance of coordinate (x, y), and ∑ is summing function.
Wherein,Summation operation is carried out to the analog value of all input scatterplot,I.e.
Summation operation is carried out to the analog value of all input scatterplot.
In the present embodiment, the process described dense matrix abbreviation being sparse matrix specifically may refer to Fig. 4, can wrap
Include following steps：
Step S41：Begin stepping through each unit rectangles matrix from the upper left corner of described dense matrix, determine each
The rectangle of the bigger layer of area that described unit rectangles and surrounding neighbors unit rectangles are formed.
The rectangle of described unit rectangles and the bigger layer of the area that surrounding neighbors unit rectangles are formed can but be not limited to
Rectangle (rectangle being made up of four described unit rectangles) for 2 × 2 or 3 × 3 rectangle (i.e. by 9 described unit rectangles
The rectangle of composition).
Step S42：Calculate the gradient between four summits of the rectangle of the bigger layer of area that each is determined, obtain six
Individual Grad.
Four summits of the rectangle of the bigger layer of area going out as defined are respectively a, b, c, d, then calculate between a, b, c, d
Gradient calculate gradient between gradient between gradient between gradient between a and b, a and c, a and d, b and c, b and d
Between gradient and c and d between gradient, obtain six Grad.
Step S43：If six Grad are respectively less than predetermined threshold value, retain the determined rectangle of the bigger layer of area
The value on four summits.
If six Grad are respectively less than predetermined threshold value, retain four summits of the determined rectangle of the bigger layer of area
Value, even six Grad are respectively less than predetermined threshold value, then only retain four tops of the determined rectangle of the bigger layer of area
The value of point, deletes other data in the determined rectangle of the bigger layer of area.
The setting principle of predetermined threshold value may refer to procedure below：If input data is set and the equivalent zone of input scatterplot
Set it is assumed that input scatterplot collection be combined into { (x1, y1, val1), (x2, y2, val2) ... (xn, yn, valn) }, wherein
X, y represent input scatterplot coordinate, val represent input scatterplot property value, can be any business implication, as temperature, height above sea level,
Turnover etc..If the collection of equivalent zone be combined into (val1, val2, color1), (val2, val3, color2) ... (val n,
Val n+1, color n) }, two val represent lower limit and the higher limit of this equivalent zone, and color represents positioned at this bound area
The color of interior point, the bound scope between two val is " predetermined threshold value ".
Step S44：The matrix of the rectangle composition of the bigger layer of area that each is determined is as dense matrix, and returns
The step that execution begins stepping through each unit rectangles matrix from the upper left corner of described dense matrix, until cannot determine each
The rectangle of the bigger layer of area that individual described unit rectangles and surrounding neighbors unit rectangles are formed.
Wherein, until cannot determine that each described unit rectangles is bigger with the area that surrounding neighbors unit rectangles are formed
The rectangle of layer is i.e. until all elements in dense matrix no longer change.
In the present embodiment, the above-mentioned sparse matrix being obtained using Marching Square algorithm and abbreviation, generates trend
The detailed process of analysis chart refers to Fig. 5, may comprise steps of：
Step S51：The property value inputting scatterplot to each in described vector |input paramete carries out division from low to high,
Obtain n segment.
Step S52：Distribute a kind of color for each segment, obtain the corresponding relation of each segment and color.
Step S53：By each unit rectangles in the corresponding relation of each described segment and color and described sparse matrix
Four summits value as described Marching Square algorithm input value, generate described trend analysiss figure.
Four tops by each unit rectangles in the corresponding relation of each described segment and color and described sparse matrix
Point value as described Marching Square algorithm input value, afterwards the calculating process of Marching Square algorithm with
Traditional calculating process is identical, will not be described here.
Embodiment two
Corresponding with said method embodiment, present embodiments provide a kind of trend analysiss figure construction device, refer to figure
6, trend analysiss figure construction device includes：Acquisition module 61, interpolating module 62, abbreviation module 63 and generation module 64.
Acquisition module 61, for obtaining vector |input paramete as input matrix, described vector |input paramete is a series of
The set of input scatterplot.
Interpolating module 62, for entering row interpolation to described input matrix, obtains dense matrix.
Abbreviation module 63, for being sparse matrix by described dense matrix abbreviation.
Generation module 64, for the sparse matrix being obtained using Marching Square algorithm and abbreviation, generation trend is divided
Analysis figure.
Interpolating module 62 specifically can include：Set up unit 621 and interpolating unit 622, as shown in Figure 7.
Set up unit 621, for setting up a dense matrix on the basis of described input matrix.
Interpolating unit 622, for utilizing formulaTo the dense square set up
Battle array enters row interpolation.
Matrix (x, y) denotation coordination is the element value of (x, y), and value [i] represents the property value of i-th input scatterplot,
Dist [i] represents i-th input scatterplot and the distance of coordinate (x, y), and ∑ is summing function.
Abbreviation module 63 specifically can include：First determining unit 631, computing unit 632, stick unit 633 and second
Determining unit 634, as shown in Figure 8.
First determining unit 631, begins stepping through each the unit square in matrix for the upper left corner from described dense matrix
Shape, determines the rectangle of the bigger layer of area that each described unit rectangles and surrounding neighbors unit rectangles are formed.
Computing unit 632, for the ladder between four summits of the rectangle calculating the bigger layer of area that each is determined
Degree, obtains six Grad.
Stick unit 633, if being respectively less than predetermined threshold value for six Grad, retains the determined bigger layer of area
Four summits of rectangle value.
Second determining unit 634, the matrix of the rectangle composition of the bigger layer of the area for being determined each is as thick
Close matrix, and return described first determining unit 631 of execution, until each described unit rectangles and surrounding neighbors cannot be determined
The rectangle of the bigger layer of area that unit rectangles are formed.
In the present embodiment, generation module 64 specifically can include：Division unit 641, allocation unit 642 and signal generating unit
643, as shown in Figure 9.
Division unit 641, in described vector |input paramete each input scatterplot property value carry out from low to
High division, obtains n segment.
Single 642 yuan of distribution, for distributing a kind of color for each segment, obtains each segment pass corresponding with color
System.
Signal generating unit 643, for by the corresponding relation of each described segment and color and described sparse matrix each
The value on four summits of unit rectangles, as the input value of described Marching Square algorithm, generates described trend analysiss figure.
It should be noted that each embodiment in this specification is all described by the way of going forward one by one, each embodiment weight
Point explanation is all difference with other embodiment, between each embodiment identical similar partly mutually referring to.
For device class embodiment, due to itself and embodiment of the method basic simlarity, so description is fairly simple, related part ginseng
See that the part of embodiment of the method illustrates.
Last in addition it is also necessary to explanation, herein, such as first and second or the like relational terms be used merely to by
One entity or operation are made a distinction with another entity or operation, and not necessarily require or imply these entities or operation
Between there is any this actual relation or order.And, term " inclusion ", "comprising" or its any other variant meaning
Covering comprising of nonexcludability, so that including a series of process of key elements, method, article or equipment not only include that
A little key elements, but also include other key elements being not expressly set out, or also include for this process, method, article or
The intrinsic key element of equipment.In the absence of more restrictions, the key element being limited by sentence "including a ...", does not arrange
Remove and also there is other identical element in the process including described key element, method, article or equipment.
Above a kind of trend analysiss figure construction method provided herein and device are described in detail, herein
Apply specific case the principle of the application and embodiment are set forth, the explanation of above example is only intended to help
Understand the present processes and its core concept；Simultaneously for one of ordinary skill in the art, according to the thought of the application,
All will change in specific embodiments and applications, in sum, this specification content should not be construed as to this
The restriction of application.
Claims (8)
1. a kind of trend analysiss figure construction method is it is characterised in that include：
Obtain vector |input paramete as input matrix, described vector |input paramete is a series of set of input scatterplot；
Row interpolation is entered to described input matrix, obtains dense matrix；
Described dense matrix abbreviation is sparse matrix；
The sparse matrix being obtained using Marching Square algorithm and abbreviation, generates trend analysiss figure.
2. method according to claim 1, it is characterised in that entering row interpolation to described input matrix, obtains dense matrix
Including：
A dense matrix is set up on the basis of described input matrix；
Using formulaRow interpolation is entered to the dense matrix set up；
Matrix (x, y) denotation coordination is the element value of (x, y), and value [i] represents the property value of i-th input scatterplot, dist
[i] represents i-th input scatterplot and the distance of coordinate (x, y), and ∑ is summing function.
3. method according to claim 2 it is characterised in that include described dense matrix abbreviation for sparse matrix：
Begin stepping through each unit rectangles matrix from the upper left corner of described dense matrix, determine each described unit rectangles
Rectangle with the bigger layer of the area that surrounding neighbors unit rectangles are formed；
Calculate the gradient between four summits of the rectangle of the bigger layer of area that each is determined, obtain six Grad；
If six Grad are respectively less than predetermined threshold value, retain determined four summits of the rectangle of the bigger layer of area
Value；
The matrix of the rectangle composition of the bigger layer of area that each is determined is as dense matrix, and returns execution from described thick
The step that the upper left corner of close matrix begins stepping through each unit rectangles in matrix, until each described unit square cannot be determined
The rectangle of the bigger layer of area that shape and surrounding neighbors unit rectangles are formed.
4. method according to claim 3 is it is characterised in that obtained using Marching Square algorithm and abbreviation
Sparse matrix, generates trend analysiss figure and includes：
The property value inputting scatterplot to each in described vector |input paramete carries out division from low to high, obtains n interval
Section；
Distribute a kind of color for each segment, obtain the corresponding relation of each segment and color；
By four summits of each unit rectangles in the corresponding relation of each described segment and color and described sparse matrix
Value, as the input value of described Marching Square algorithm, generates described trend analysiss figure.
5. a kind of trend analysiss figure construction device is it is characterised in that include：
Acquisition module, for obtaining vector |input paramete as input matrix, described vector |input paramete is that a series of inputs dissipate
The set of point；
Interpolating module, for entering row interpolation to described input matrix, obtains dense matrix；
Abbreviation module, for being sparse matrix by described dense matrix abbreviation；
Generation module, for the sparse matrix obtaining using Marching Square algorithm and abbreviation, generates trend analysiss figure.
6. device according to claim 5 is it is characterised in that described interpolating module includes：
Set up unit, for setting up a dense matrix on the basis of described input matrix；
Interpolating unit, for utilizing formulaThe dense matrix set up is carried out
Interpolation；
Matrix (x, y) denotation coordination is the element value of (x, y), and value [i] represents the property value of i-th input scatterplot, dist
[i] represents i-th input scatterplot and the distance of coordinate (x, y), and ∑ is summing function.
7. device according to claim 6 is it is characterised in that described abbreviation module includes：
First determining unit, begins stepping through each unit rectangles in matrix for the upper left corner from described dense matrix, determines
Go out the rectangle of the bigger layer of area that each described unit rectangles is formed with surrounding neighbors unit rectangles；
Computing unit, for the gradient between four summits of the rectangle calculating the bigger layer of area that each is determined, obtains
Six Grad；
Stick unit, if being respectively less than predetermined threshold value for six Grad, retains the rectangle of the determined bigger layer of area
Four summits value；
Second determining unit, the bigger layer of the area for being determined each rectangle composition matrix as dense matrix,
And return described first determining unit of execution, until each described unit rectangles and surrounding neighbors unit rectangles institute cannot be determined
The rectangle of the bigger layer of area being formed.
8. device according to claim 7 is it is characterised in that described generation module includes：
Division unit, in described vector |input paramete each input scatterplot property value carry out from low to high draw
Point, obtain n segment；
Allocation unit, for distributing a kind of color for each segment, obtains the corresponding relation of each segment and color；
Signal generating unit, for by each unit rectangles in the corresponding relation of each described segment and color and described sparse matrix
Four summits value as described Marching Square algorithm input value, generate described trend analysiss figure.
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