CN108133504A - A kind of three-dimensional flow field multivariate data method for visualizing based on polyhedron pipeline - Google Patents
A kind of three-dimensional flow field multivariate data method for visualizing based on polyhedron pipeline Download PDFInfo
- Publication number
- CN108133504A CN108133504A CN201810038185.3A CN201810038185A CN108133504A CN 108133504 A CN108133504 A CN 108133504A CN 201810038185 A CN201810038185 A CN 201810038185A CN 108133504 A CN108133504 A CN 108133504A
- Authority
- CN
- China
- Prior art keywords
- flow field
- streamline
- data
- pipeline
- polyhedron
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T13/00—Animation
- G06T13/20—3D [Three Dimensional] animation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2210/00—Indexing scheme for image generation or computer graphics
- G06T2210/24—Fluid dynamics
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Image Generation (AREA)
Abstract
The present invention relates to a kind of flow field multivariate data method for visualizing based on pipeline, belong to the flow field data visualization field in visualization in scientific computing.The method of the present invention is by constructing colored polyhedron pipeline visualization of 3 d flow field multivariate data.Its basic step is:Calculate streamline, along linear flow structure polyhedron pipeline, by pipe shape visible velocity field direction vector, multiple scalar attribute data are then visualized by color mapping in pipeline side, be achieved in multiple scalar attribute data and flow field vector data combines visualization.This not only adds the flexibilities that multivariable association analysis is carried out to three-dimensional flow field, also improve the efficiency that visual analyzing is carried out to multivariate data.
Description
Technical field
The present invention relates to a kind of three-dimensional flow field multivariate data method for visualizing, more particularly to a kind of flow field based on pipeline
Multivariate data method for visualizing belongs to the flow field data visualization field in visualization in scientific computing.
Background technology
Three-dimensional flow field is the data fields that the fields such as Fluid Mechanics Computation and meteorological numerical simulation are widely present.In order to analyze three
Flow field data are tieed up, needs to carry out visualization processing to three-dimensional flow field, be understood inside flow field by visual figure or image
Phenomenon.
Streamline (streamline) and flow tube (streamtube) are common three-dimensional flow field method for visualizing.By streamline
Or flow tube can effectively understand the flow direction in flow field, explore the mode configuration of three-dimensional flow field.However, due to itself geometric form
The visualization capability of the reason of shape, streamline and flow tube is all limited.The geometry of streamline is only suitable for showing the flowing in flow field
Direction.Traditional flow tube can also show an additional physical quantity (example other than showing flow field direction by the thickness of pipeline
Such as the size of flow velocity).If in addition color attribute, streamline and flow tube can visualize another object by color mapping
Reason amount (such as temperature).In addition to this, if there is more physical quantitys need to visualize, then streamline and flow tube are all difficult to carry.
In practical applications, three-dimensional flow field is typically all multivariate data field, other than the vector data of description flow velocity, often also has temperature
Multiple physical quantitys such as degree, pressure, density.These physical quantitys are typically all to describe the scalar data of some physical attribute.In order to divide
Analyse correlation or inner link between these physical property datas, it is often desirable that these physical property datas can be joined
Close visualization.
For the demand, the present invention provides a kind of method for visualizing for being suitable for three-dimensional flow field multivariate data.It should
Method constructs polyhedron pipeline according to the vector data of three-dimensional flow field, is then encoded in each side of pipeline by color mapping
Different scalar attribute data combine visualization so as to fulfill multiple scalar attribute data and flow field vector data.
Invention content
The object of the present invention is to provide a kind of multivariate data method for visualizing for being suitable for three-dimensional flow field, realize multiple marks
Amount attribute data combines visualization with flow field vector data, and the visual analyzing that three-dimensional flow field multivariate data is improved with this is imitated
Rate.
Three-dimensional flow field multivariate data mentioned here includes:The vector data of three-dimensional flow field flow velocity and multiple is described
The scalar attribute data of flow field physical quantity are described;If attribute data is not scalar data, multiple scalars can be split as
Data.
The purpose of the present invention is what is be achieved through the following technical solutions.
A kind of three-dimensional flow field multivariate data method for visualizing based on polyhedron pipeline, includes the following steps:
Step 1, input three-dimensional flow field multivariate data, including the description vector data of three-dimensional flow field flow velocity and more
The scalar attribute data of a description flow field physical quantity, are normalized each attribute data respectively.
Multiple visual physical quantitys of needs are chosen in step 2, dependence data, are respectively labeled as a1、a2、…、
aM, wherein M is the number for the physical quantity chosen, and M is greater than or equal to 3.
Step 3 chooses one or more streamline seed points inside three-dimensional flow field, according to streamline seed point and flow field vector
Data calculate streamline, wherein the method for calculating streamline includes but not limited to Euler algorithms and Runge-Kutta algorithms.
The part or all of streamline that step 4, selecting step 3 obtain performs step 5 for every streamline L, wherein choosing stream
The method of line includes but not limited to randomly select or be chosen at equal intervals successively according to streamline number.
Step 5, since first sample point on streamline L, take two adjacent sample point P successivelyiAnd Pi+1, perform step 6
To step 8.
Step 6 obtains sample point P from input dataiAnd Pi+1Vector data TiAnd Ti+1, respectively in PiAnd Pi+1Place is built
Vertical part orthogonal coordinate system NiBiTiAnd Ni+1Bi+1Ti+1, wherein the method for establishing local orthogonal coordinate system is:By the sample on streamline
This point connects with viewpoint and establishes unitization sight line vector V, then according to the vector T of sample point, obtains B=T × V, N=
B × T is made of the local orthogonal coordinate system NBT of sample point three mutually perpendicular vector N, B, T together.
Step 7, setting pipe radius parameter lambda, in local coordinate system NiBiTiNiBiIt is established centered on origin in plane
Circumradius is the positive M polygons G of λi, make GiA vertex in NiOn axis;In the same way in local coordinate system
Ni+1Bi+1Ti+1Ni+1Bi+1The built-in M polygons G that attentions of planei+1;By polygon GiAnd Gi+1Corresponding vertex has connected respectively
Come, obtain a bit of polyhedron pipeline Fi;In NiBiFrom N in planeiAxis starts F counterclockwiseiSide mark successively
For f1、f2、…、fM。
Step 8 obtains sample point P from input dataiPhysical quantity a1、a2、…、aMCorresponding value m1、
m2、…、mM, according to m1、m2、…、mMDetermine M kind color values C1、C2、…、CM;Using color value C1、C2、…、CMTo FiSide
f1、f2、…、fMIt colours successively.Wherein color value C1、C2、…、CMIt can be determined according to certain consistent color mapping rule,
It can be determined by searching for preset color table.
Advantageous effect
A kind of three-dimensional flow field multivariate data method for visualizing based on polyhedron pipeline of the present invention is actually logical
The shape visible velocity field direction vector of polyhedron pipeline is crossed, multiple scalars are then visualized by color mapping in pipeline side
Attribute data.Compared with traditional streamline or flow tube method for visualizing, the method have the advantages that:Realize multiple scalars
The visualization of combining of attribute data and flow field vector data, and the upper limit of scalar attribute number is unrestricted in theory
's.This not only adds the flexibilities that multivariable association analysis is carried out to three-dimensional flow field, also improve and multivariate data is carried out
The efficiency of visual analyzing.
Description of the drawings
The example streamline of Fig. 1 embodiments;
Fig. 2 constructs the schematic diagram of polyhedron pipeline;
The front view of a bit of colored polyhedron pipelines of Fig. 3;
The prism view of a bit of colored polyhedron pipelines of Fig. 4;
The front view of the colored polyhedron pipeline of Fig. 5 example streamlines;
The rear view of the colored polyhedron pipeline of Fig. 6 example streamlines.
Specific embodiment
It elaborates with reference to the accompanying drawings and examples to the present invention.
A kind of three-dimensional flow field multivariate data method for visualizing based on polyhedron pipeline, includes the following steps:
Step 1, input three-dimensional flow field multivariate data, including the description vector data of three-dimensional flow field flow velocity and more
The scalar attribute data of a description flow field physical quantity, are normalized each attribute data respectively.
The present invention is using a disclosed hurricane field stimulation data set as embodiment.The data set is National Weather
Research center is for the three-dimensional multivariable flow field data of the Isabel hurricanes simulation generation of 2003.Data fields size for 500 ×
500×100.Other than the vector data of description flow velocity, the attribute data of 10 physical quantitys is further included in data set.Present invention choosing
The data at T=30 moment in the data set are taken as the input data of embodiment, and to each attribute data respectively into professional etiquette one
Change is handled.
Multiple visual physical quantitys of needs are chosen in step 2, dependence data, are respectively labeled as a1、a2、…、
aM, wherein M is the number for the physical quantity chosen, and M is greater than or equal to 3.
The present embodiment enables M=6, and 6 are chosen from the attribute data of hurricane field needs visual physical quantity:P
(Pressure)、TC(Temperature)、QVAPOR(Water Vapor)、QGRAUP(Graupel)、QRAIN(Rain)、
These physical quantitys are respectively labeled as a by QSNOW (Snow)1、a2、…、a6。
Step 3 chooses one or more streamline seed points inside three-dimensional flow field, according to streamline seed point and flow field vector
Data calculate streamline, wherein the method for calculating streamline includes but not limited to Euler algorithms and Runge-Kutta algorithms.
Without loss of generality, the present embodiment has chosen a streamline seed point inside hurricane field, using quadravalence Runge-
Kutta algorithms, are calculated a streamline, and the streamline is as shown in Figure 1.
The part or all of streamline that step 4, selecting step 3 obtain performs step 5 for every streamline L, wherein choosing stream
The method of line includes but not limited to randomly select or be chosen at equal intervals successively according to streamline number.
The present embodiment chooses streamline successively according to streamline number, choose number be 1 streamline as example streamline, enable it to flow
Line L.Since the present embodiment step 3 has to a streamline, so what is chosen here is whole streamlines of step 3.
Step 5, since first sample point on streamline L, take two adjacent sample point P successivelyiAnd Pi+1, perform step 6
To step 8.
Step 6 obtains sample point P from input dataiAnd Pi+1Vector data TiAnd Ti+1, respectively in PiAnd Pi+1Place is built
Vertical part orthogonal coordinate system NiBiTiAnd Ni+1Bi+1Ti+1, wherein the method for establishing local orthogonal coordinate system is:By the sample on streamline
This point connects with viewpoint and establishes unitization sight line vector V, then according to the vector T of sample point, obtains B=T × V, N=
B × T is made of the local orthogonal coordinate system NBT of sample point three mutually perpendicular vector N, B, T together.
Property without loss of generality enables two adjacent sample point PiAnd Pi+1As illustrated in fig. 2, it is assumed that the position of viewpoint is E, even
Meet viewpoint E and sample point PiObtain unitization sight line vector
In the case, it then enables
Bi=Ti×Vi
Ni=Bi×Ti
Thus three mutually perpendicular vector N are obtainedi、Bi、Ti, they form sample point P togetheriLocal orthogonal coordinates
It is NiBiTi.Sample point Pi+1Local coordinate system Ni+1Bi+1Ti+1Building process it is similar, the results are shown in Figure 2.
Step 7, setting pipe radius parameter lambda, in local coordinate system NiBiTiNiBiIt is established centered on origin in plane
Circumradius is the positive M polygons G of λi, make GiA vertex in NiOn axis;In the same way in local coordinate system
Ni+1Bi+1Ti+1Ni+1Bi+1The built-in M polygons G that attentions of planei+1;By polygon GiAnd Gi+1Corresponding vertex has connected respectively
Come, obtain a bit of polyhedron pipeline Fi;In NiBiFrom N in planeiAxis starts F counterclockwiseiSide mark successively
For f1、f2、…、fM。
Without loss of generality, radius parameter λ=1 of the present embodiment setting pipeline, in local coordinate system NiBiTiNiBiPlane
It is interior that the positive 6 side shape G that circumradius is 1 is established centered on origini, make GiA vertex in NiOn axis;According to same side
Formula is in local coordinate system Ni+1Bi+1Ti+1Ni+1Bi+1The built-in 6 side shape G that attention of planei+1;By polygon GiAnd Gi+1Corresponding vertex
It connects respectively, obtains a bit of polyhedron pipeline F as shown in Figure 2i;In NiBiFrom N in planeiAxis starts by counterclockwise
Sequentially by FiSide successively be labeled as f1、f2、…、f6, the results are shown in Figure 2.
Step 8 obtains sample point P from input dataiPhysical quantity a1、a2、…、aMCorresponding value m1、
m2、…、mM, according to m1、m2、…、mMDetermine M kind color values C1、C2、…、CM;Using color value C1、C2、…、CMTo FiSide
f1、f2、…、fMIt colours successively.Wherein color value C1、C2、…、CMIt can be determined according to certain consistent color mapping rule,
It can be determined by searching for preset color table.
Originally it is that the color of embodiment determines that method is:According to hsv color model, i.e. " hue-saturation-brightness " color mould
All colours are set as fully saturated by type, and intensity value is fixed as 1.0, are then physical quantity a1、a2、…、aMIt respectively refers to
Fixed maximum equally spaced tone value h1、h2、…、hM, and by the value m of each physical quantity1、m2、…、mMBrightness as color
Value, thus obtains M kind colors C1、C2、…、CM。
The present embodiment has chosen 6 physical quantitys:P、TC、QVAPOR、QGRAUP、QRAIN、QSNOW(a1、a2、…、a6), it presses
Method is determined according to above-mentioned color, and the maximum tone at equal intervals specified for these physical quantitys is respectively red (h1=0), yellow (h2=
60), green (h3=120), cyan (h4=180), blue (h5=240) and purple (h6=300), it is assumed that sample point PiPhysics
Measure a1、a2、…、aMRespective value m1、m2、…、m6All it is 1.0, then this makes it possible to obtain 6 kinds of determining color C1、C2、…、C6。
Using C1、C2、…、C6To FiSide f1、f2、…、f6As shown in Figure 3 and Figure 4, wherein Fig. 3 is F to the result of coloringiAfter coloring
Front view, Fig. 4 are FiPrism view after coloring.
To numbering the example streamline for being 1 in this present embodiment, (wherein step 8) is arrived comprising step 6 in execution of step 5
Afterwards, Fig. 5 and colored polyhedron pipeline shown in fig. 6 will be obtained, wherein Fig. 5 is front view, and Fig. 6 is that the back side after rotation regards
Figure.Comparison diagram 1 and Fig. 5 can see:The direction of polyhedron pipeline is consistent with streamline, has reacted the direction of flow field flow;Often
A pipeline side is all changed by the light and shade of color, illustrates value condition of the corresponding physical quantity in different location.For example, in pipe
The yellow face of road tail portion (Fig. 5 upper right corner) is brighter, this shows that the TC (temperature) corresponding to yellow compares in the region value
It is high;Thus it is seen along pipeline toward spiral part, understands that TC (temperature) value continuously decreases from the light and shade variation of color, then arrived spiral shell
Gone up again on the top for revolving region.In addition, the green face of coil region top shown in Fig. 5 is there are apparent light and shade striped, this
Show that the QVAPOR values corresponding to green in the region are unstable.
Due to spatial occlusion, all pipe surfaces can not be viewed from a visual angle.For this purpose, to the pipe established
Road carries out the operations such as rotation translation, can obtain rear view shown in fig. 6, therefrom it can be seen that purple, cyan and blue etc.
Sightless pipe surface in Figure 5.But, the blue face overwhelming majority of Fig. 6 is all black, this shows corresponding to blue
Value of the QRAIN values in these regions is all very low (being equal or close to 0.0).QGRAUP corresponding to the cyan face of Fig. 6 also has
Similar feature.With reference to Fig. 5 and Fig. 6, it is further seen that some correlations between different physical attributes, such as:QSNOW values are high
Region (violet region of Fig. 6), QRAIN values are very low (it is black that corresponding blue is all dimmed).
These above-mentioned intuitive visualization results, disclose the changing pattern between three-dimensional flow field multivariate data and association
The embodiment of relationship, the phenomenon that helping efficiently to explore inside three-dimensional flow field or rule and advantageous effect of the present invention.
Above-mentioned steps illustrate that a kind of three-dimensional flow field multivariate data based on polyhedron pipeline of the present invention is visual
The all processes of change method.
It should be understood that present embodiment is the specific example that the present invention is implemented, should not be present invention protection model
The limitation enclosed.In the case where not departing from spirit and scope of the invention, equivalent modification is carried out to the above or change is equal
It should include within scope of the present invention.
Claims (5)
1. a kind of three-dimensional flow field multivariate data method for visualizing based on polyhedron pipeline, it is characterised in that including following step
Suddenly:
Step 1: input three-dimensional flow field multivariate data, including the description vector data of three-dimensional flow field flow velocity and multiple
The scalar attribute data of flow field physical quantity are described, each attribute data is normalized respectively;
Step 2: choosing multiple visual physical quantitys of needs in dependence data, it is respectively labeled as a1、a2、…、aM,
Middle M is the number for the physical quantity chosen, and M is greater than or equal to 3;
Step 3: one or more streamline seed points are chosen inside three-dimensional flow field, according to streamline seed point and flow field vector number
According to calculating streamline;
Step 4: the part or all of streamline that selecting step three obtains, step 5 is performed for every streamline L;
Step 5: since first sample point on streamline L, two adjacent sample point P are taken successivelyiAnd Pi+1, perform step 6 and arrive
Step 8;
Step 6: sample point P is obtained from input dataiAnd Pi+1Vector data TiAnd Ti+1, respectively in PiAnd Pi+1Place establishes
Local orthogonal coordinate system NiBiTiAnd Ni+1Bi+1Ti+1, wherein the method for establishing local orthogonal coordinate system is:By the sample on streamline
Point is connected with viewpoint establishes unitization sight line vector V, then according to the vector T of sample point, obtains B=T × V, N=B
× T is made of the local orthogonal coordinate system NBT of sample point three mutually perpendicular vector N, B, T together;
Step 7: setting pipe radius parameter lambda, in local coordinate system NiBiTiNiBiIt is established centered on origin in plane external
Radius of circle is the positive M polygons G of λi, make GiA vertex in NiOn axis;In the same way in local coordinate system Ni+1Bi+ 1Ti+1Ni+1Bi+1The built-in M polygons G that attentions of planei+1;By polygon GiAnd Gi+1Corresponding vertex connects respectively, obtains
A bit of polyhedron pipeline Fi;In NiBiFrom N in planeiAxis starts F counterclockwiseiSide successively be labeled as f1、
f2、…、fM;
Step 8: sample point P is obtained from input dataiPhysical quantity a1、a2、…、aMCorresponding value m1、m2、…、mM,
According to m1、m2、…、mMDetermine M kind color values C1、C2、…、CM;Using color value C1、C2、…、CMTo FiSide f1、f2、…、
fMIt colours successively.
2. a kind of three-dimensional flow field multivariate data method for visualizing based on polyhedron pipeline according to claim 1,
It is characterized in that:The method that streamline is calculated in the step 3 includes but not limited to Euler algorithms and Runge-Kutta algorithms.
3. a kind of three-dimensional flow field multivariate data method for visualizing based on polyhedron pipeline according to claim 1,
It is characterized in that:The method that streamline is chosen in the step 4 include but not limited to randomly select or according to streamline number successively etc.
It chooses at interval.
4. a kind of three-dimensional flow field multivariate data method for visualizing based on polyhedron pipeline according to claim 1,
It is characterized in that:Color value C in the step 81、C2、…、CMIt can be determined according to certain consistent color mapping rule,
It can be determined by searching for preset color table.
5. a kind of three-dimensional flow field multivariate data method for visualizing based on polyhedron pipeline according to claim 1 and 4,
It is characterized in that, the color in the step 8 determines that scheme includes:According to hsv color model, i.e. " tone-saturation degree-bright
All colours are set as fully saturated, intensity value is fixed as 1.0, is then physical quantity a by degree " color model1、a2、…、
aMRespectively specify that maximum equally spaced tone value h1、h2、…、hM, and by the value m of each physical quantity1、m2、…、mMAs color
Brightness value, thus obtain M kind colors C1、C2、…、CM。
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201810038185.3A CN108133504B (en) | 2018-01-16 | 2018-01-16 | Three-dimensional flow field multivariable data visualization method based on polyhedral pipeline |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201810038185.3A CN108133504B (en) | 2018-01-16 | 2018-01-16 | Three-dimensional flow field multivariable data visualization method based on polyhedral pipeline |
Publications (2)
Publication Number | Publication Date |
---|---|
CN108133504A true CN108133504A (en) | 2018-06-08 |
CN108133504B CN108133504B (en) | 2021-03-23 |
Family
ID=62399835
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201810038185.3A Active CN108133504B (en) | 2018-01-16 | 2018-01-16 | Three-dimensional flow field multivariable data visualization method based on polyhedral pipeline |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN108133504B (en) |
Citations (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20090252436A1 (en) * | 2008-04-02 | 2009-10-08 | Novospark Corporation | Method for visualization of multidimensional data |
US20130038605A1 (en) * | 2011-08-11 | 2013-02-14 | Siemens Corporation | Selective flow visualization of traced particles |
CN102999936A (en) * | 2012-11-19 | 2013-03-27 | 北京中海新图科技有限公司 | Three-dimensional streamline volume rendering algorithm based on ocean flow field data |
CN103279977A (en) * | 2013-06-19 | 2013-09-04 | 北京理工大学 | Three-dimensional flow line illumination method for enhancing spatial awareness |
US20150097836A1 (en) * | 2013-10-08 | 2015-04-09 | Siemens Aktiengesellschaft | Web based fast query visualization of time-varying multi-variate vessel flow field by using uniform partition strategy |
CN104658027A (en) * | 2015-02-11 | 2015-05-27 | 中国海洋大学 | Three-dimensional streamline dynamic visualization algorithm facing irregular ocean flow field data |
CN104867186A (en) * | 2015-04-29 | 2015-08-26 | 中国海洋大学 | GPU-based interactive ocean three-dimensional flow field dynamic visual algorithm |
-
2018
- 2018-01-16 CN CN201810038185.3A patent/CN108133504B/en active Active
Patent Citations (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20090252436A1 (en) * | 2008-04-02 | 2009-10-08 | Novospark Corporation | Method for visualization of multidimensional data |
US20130038605A1 (en) * | 2011-08-11 | 2013-02-14 | Siemens Corporation | Selective flow visualization of traced particles |
CN102999936A (en) * | 2012-11-19 | 2013-03-27 | 北京中海新图科技有限公司 | Three-dimensional streamline volume rendering algorithm based on ocean flow field data |
CN103279977A (en) * | 2013-06-19 | 2013-09-04 | 北京理工大学 | Three-dimensional flow line illumination method for enhancing spatial awareness |
US20150097836A1 (en) * | 2013-10-08 | 2015-04-09 | Siemens Aktiengesellschaft | Web based fast query visualization of time-varying multi-variate vessel flow field by using uniform partition strategy |
CN104658027A (en) * | 2015-02-11 | 2015-05-27 | 中国海洋大学 | Three-dimensional streamline dynamic visualization algorithm facing irregular ocean flow field data |
CN104867186A (en) * | 2015-04-29 | 2015-08-26 | 中国海洋大学 | GPU-based interactive ocean three-dimensional flow field dynamic visual algorithm |
Non-Patent Citations (2)
Title |
---|
宋汉戈 等: "三维流场可视化综述", 《系统仿真学报》 * |
张文耀 等: "基于HSV颜色模型的二维流场可视化", 《北京理工大学学报》 * |
Also Published As
Publication number | Publication date |
---|---|
CN108133504B (en) | 2021-03-23 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN108363797B (en) | Transformation-based association graph visual analysis method and system | |
Geng et al. | Angular histograms: Frequency-based visualizations for large, high dimensional data | |
CN102903128B (en) | The video image content editor's transmission method kept based on Similarity of Local Characteristic Structure | |
Oliveira et al. | NPCirc: An R package for nonparametric circular methods | |
CN104899288B (en) | Dimension hierarchical method for visualizing based on radial direction layout | |
CN102521863B (en) | Three-dimensional fluid scalar vector uniform dynamic showing method based on particle system | |
CN107564087A (en) | A kind of Three-D linear symbol rendering intent based on screen | |
CN108615229B (en) | Collision detection optimization method based on curvature point clustering and decision tree | |
CN107871337B (en) | Visualization method of supersonic two-dimensional flow field data | |
Blaas et al. | Smooth graphs for visual exploration of higher-order state transitions | |
CN107291918A (en) | A kind of visual mining methods of point of interest POI with bit pattern | |
CN108171781A (en) | A kind of three-dimensional multivariable vector field data method for visualizing based on icon | |
CN108133504A (en) | A kind of three-dimensional flow field multivariate data method for visualizing based on polyhedron pipeline | |
CN113345052B (en) | Classification data multi-view visualization coloring method and system based on similarity significance | |
CN114386295A (en) | Textile computer simulation method based on color separation and color change of colored spun yarns | |
CN103258061B (en) | A kind of region enclosed hypergraph method for visualizing based on interpolation algorithm | |
CN104778308B (en) | The recognition methods of aircaft configuration section bar and device | |
CN107330209B (en) | Modeling wall intelligent template implementation method based on parametric design | |
CN109993695A (en) | A kind of the images fragment joining method and system of irregular figure mark | |
CN107767458A (en) | TIN surface geometry topological coherence analysis method and system | |
Li et al. | Online temperature‐monitoring technology for grain storage: a three‐dimensional visualization method based on an adaptive neighborhood clustering algorithm | |
CN102855624A (en) | Image segmentation method based on generalized data field and Normalized cut (Ncut) algorithm | |
CN113673137A (en) | Three-dimensional explosion field visualization method based on field line processing technology | |
CN110211207A (en) | A kind of three-dimensional flow field method for visualizing to be added up based on streamline length | |
Pal | Pattern recognition in soft computing paradigm |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
CB03 | Change of inventor or designer information |
Inventor after: Zhang Wenyao Inventor after: Zhao Wen Inventor after: Wang Cheng Inventor before: Zhang Wenyao Inventor before: Zhao Wen |
|
CB03 | Change of inventor or designer information | ||
GR01 | Patent grant | ||
GR01 | Patent grant |