CN109299343A - A kind of dynamic and visual method and system towards multi-source global ocean big data - Google Patents
A kind of dynamic and visual method and system towards multi-source global ocean big data Download PDFInfo
- Publication number
- CN109299343A CN109299343A CN201811030763.5A CN201811030763A CN109299343A CN 109299343 A CN109299343 A CN 109299343A CN 201811030763 A CN201811030763 A CN 201811030763A CN 109299343 A CN109299343 A CN 109299343A
- Authority
- CN
- China
- Prior art keywords
- data
- dynamic
- source global
- global ocean
- ocean big
- 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.)
- Pending
Links
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The present invention relates to a kind of dynamic and visual method and system towards multi-source global ocean big data, 1) method for visualizing is the following steps are included: obtain multi-source global ocean big data, and pre-processed, the multi-source global ocean big data includes directionless static data and with direction dynamic data；2) one simulation terrestrial network of building, pretreated data are mapped in the simulation terrestrial network, realize automatic visual.Compared with prior art, the present invention can automatically realize effective visualization of ocean big data, the data that quick obtaining needs, solve the existing display data to hydrological data visualizing without analysis data, visual effect is not intuitive the problems such as.
Description
Technical field
The invention belongs to big datas to visualize field, more particularly, to a kind of dynamic towards multi-source global ocean big data
Method for visualizing and system.
Background technique
Not only self structure is complicated for oceanographic data, and data volume is big but also these data also have higher-dimension, dynamic, multi-source
Property, isomerism the features such as, therefore, it is difficult to realize to its quick access analysis and intuitive present.Data visualization can be by mixed and disorderly nothing
The data of chapter are converted to the graph image that intuitively people are easy to understand, and therefrom find their rule, and not only interactivity is good
Practicability is high but also has high efficiency.Therefore the visualization towards oceanographic data has to pass the research of Yu Haiyang big data
Important role is the important component of scientific management oceanographic data, substantially increases researcher and grind to oceanographic data
Study carefully Utilization ability.But it is existing to hydrological data visualizing there is also only display datas without analysis data, result show it is not intuitive etc.
Problem.
Usually all there is functional relations between data same type of in the research of big data, however, it is typically very difficult to
There is direct analytical expression to indicate this relationship, because these data are some discrete numerical value, it is not easy to be counted
It calculates and analyzes.
Summary of the invention
It is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and provide one kind towards the multi-source whole world
The dynamic and visual method and system of ocean big data.
The purpose of the present invention can be achieved through the following technical solutions:
A kind of dynamic and visual method towards multi-source global ocean big data, comprising the following steps:
1) multi-source global ocean big data is obtained, and is pre-processed, the multi-source global ocean big data includes not properly
To static data and with direction dynamic data；
2) one simulation terrestrial network of building, pretreated data are mapped in the simulation terrestrial network, are realized automatic
Visualization.
Further, described pre-process includes:
Data normalization；
Each data are divided into the portion header and the portion data, the portion header storing data category in a manner of key-value pair
Property information, the portion data stores the numerical value of each data point, wherein directionless static data by one group of portion header and
The portion data indicates that band direction dynamic data is indicated by the portion multiple groups header and the portion data, and the portion multiple groups data constitutes a vector；
Clustering processing is carried out to data.
Further, the simulation terrestrial network includes the latitude and longitude from west to east by north orientation south, the data
Attribute information is data longitude and latitude.
Further, when pretreated data being mapped in the simulation terrestrial network, for directionless static data,
With color mapping method come indicate numerical values recited change；For band direction dynamic data, numerical values recited is indicated with color mapping method
Variation, and dynamic characteristic is simulated with fluid form.
Further, the color mapping method includes: to establish a color mapping table, the color mapping table be continuous table or from
Table is dissipated, the mapping relations between data and color are realized based on the color mapping table.
Further, when the simulation dynamic characteristic with fluid form, the display of each Motion Particles has a display life
Period only visualizes the Motion Particles being located in the display life cycle.
It is further, described to map to pretreated data in the simulation terrestrial network further include:
Realize that data continuously display in simulation terrestrial network using interpolation algorithm.
Further, the visualization further includes the display of the statistical chart, radar map and rose figure of data.
The present invention also provides a kind of dynamic and visual systems towards multi-source global ocean big data, comprising:
Data acquisition module, for obtaining multi-source global ocean big data, including directionless static data and dynamic with direction
State data；
Preprocessing module, for being pre-processed to the multi-source global ocean big data；
Visualization model, for constructing a simulation terrestrial network, by pretreated data with mapping to simulation net
In network, automatic visual is realized.
Further, the preprocessing module includes:
Data normalization unit, for being standardized to multi-source global ocean big data；
Data dividing unit, for each data to be divided into the portion header and the portion data, the portion header is with key assignments
Pair mode storing data attribute information, the portion data stores the numerical value of each data point, wherein directionless static data is logical
Crossing one group of portion header and the portion data indicates, band direction dynamic data is indicated by the portion multiple groups header and the portion data, multiple groups
The portion data constitutes a vector
Cluster cell, for carrying out clustering processing to data.
Further, the visualization system further include:
Locating module is retrieved, response external retrieves information, matches the external retrieval information by interpolation algorithm, and in mould
The positioning and visualization of Access Points are realized in quasi- terrestrial network.
Compared with prior art, the invention has the following advantages:
1) existing hydrological data visualizing mainly biases toward bandwagon effect, and the image sometimes shown is excessively complicated to be taken out
As, it is not easy to the understanding of people, and the present invention has fully considered the key property of data analysis, illustrates the pass between data
System, and with the association between novel visual chart mode display data.
2) present invention, which provides, may be implemented oceanographic data acquisition, pre-processes, the system of visual full automatic treatment, use
It is convenient, be conducive to the visual experience and the interaction effect that improve user.
3) preprocessing process of the present invention includes data clusters processing, is gathered when showing the data of multiple periods
Class not only reduces the redundancy between data, keeps effect of visualization more targeted, also facilitates researcher's observed number
It handles accordingly and to specific data.
4) in order to make data realize the display of consecutive variations trend, present invention uses interpolation algorithms, according to point of proximity
The approximation of current point is calculated in value, improves display effect.Meanwhile interpolation calculation can also be used when inquiring data in the present invention
The data for obtaining query point, not only ensure that the accuracy of data, also improve search efficiency,
5) present invention is for statistical analysis using different statistical charts for different data, and researcher can be made quick
The accurately variation relation between discovery different data.
In short, the application present invention can image, the motion conditions and physics of lively simulation shows Global Ocean Data
Characteristic, while it is not straight without analysis data, result displaying to be also able to solve the existing display data to hydrological data visualizing
The problems such as sight, compare more other hydrological data visualizing methods, the data volume that the present invention is capable of handling is bigger, speed faster,
Effect of visualization is more intuitive, and analyzing data to oceanaut has certain practical value.
Detailed description of the invention
Fig. 1 is the flow diagram of the method for the present invention；
Fig. 2 is the warm effect of visualization figure in sea of the invention；
Fig. 3 is salinity effect of visualization figure of the invention；
Fig. 4 is sea wind effect of visualization figure of the invention；
Fig. 5 is ocean current effect of visualization figure of the invention；
Fig. 6 is wave effect of visualization figure of the invention；
Fig. 7 is query effect figure of the invention；
Fig. 8 is search of the invention and statistical data functional block；
Fig. 9 is numerical value change statistical chart of the invention；
Figure 10 is that direction of the invention shows radar map and data analysis statistical rose figure.
Specific embodiment
The present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings.The present embodiment is with technical solution of the present invention
Premised on implemented, the detailed implementation method and specific operation process are given, but protection scope of the present invention is not limited to
Following embodiments.
As shown in Figure 1, the present invention provides a kind of dynamic and visual method towards multi-source global ocean big data, including with
Lower step:
1) multi-source global ocean big data is obtained, and is pre-processed, the multi-source global ocean big data includes not properly
To static data and with direction dynamic data.
Multi-source global ocean big data refers to the different types of oceanographic data obtained from separate sources.The pretreatment packet
It includes:
101) data normalization.Since original data volume is unfavorable for greatly very much subsequent use, so the number that this method will acquire
According to being reorganized, because inevitably having some exceptional values in data, data normalization, data are defined
Maximum value and minimum value indicate the data greater than maximum value with maximum value, the data of minimum value are represented less than with minimum value.
102) each data are divided into the portion header and the portion data, the portion header stores number in a manner of key-value pair
According to attribute information, the portion data stores the numerical value of each data point, wherein directionless static data passes through one group of portion header
It is indicated with the portion data, band direction dynamic data is indicated by the portion multiple groups header and the portion data, and the portion multiple groups data constitutes a vector.
103) clustering processing is carried out to data: according to characteristic (some data band directions, some data of different types of data
It is directionless) and the longitude and latitude characteristic of the earth data are reorganized, to reduce the redundancy of data file；To mutually similar
The set of metadata of similar data of type different time clusters, and keeps effect of visualization apparent.
Since user sometimes wants the not instead of one group of data checked, the multi-group data of same type different time,
And it is often closely similar between data similar in the time, so this method has carried out clustering processing to data, by similar number
According to cluster is classified as, thus the mixed and disorderly of effect of visualization will not be caused because of much like between data, user also can be clearer
The variation of data is observed, concentration is further analyzed processing to the cluster for wanting to continue analysis.
K-means clustering algorithm is classical one of the clustering algorithm of a comparison, high-efficient due to it, realize it is simple,
So being widely used in big data cluster analysis.Its main thought is exactly the cluster that a given data set and needs divide,
Then nearest cluster is divided into data according to distance function, this process constantly repeats, and until criterion function convergence, that is, meets
Some termination condition.The present embodiment is by k-means clustering algorithm, the otherness that can be more easily visualized between data, from
And it can be handled for specific data, while can also effect of visualization be made to be more clear.
2) one simulation terrestrial network of building, pretreated data are mapped in the simulation terrestrial network, are realized automatic
Visualization.The simulation terrestrial network includes the latitude and longitude from west to east by north orientation south, and the data attribute information is
Data longitude and latitude.
For directionless static data, the attribute information of the portion header storing data in a manner of key-value pair, such as la1,
Lo1 respectively represents the starting longitude and latitude of data, and la2, lo2 respectively represent the termination longitude and latitude of data, and dx, dy respectively represent longitude and latitude
The fractionation unit of degree, even dx, which is 0.25, indicates that every 0.25 longitude has a data, according to la1, lo1, la2, lo2, dx, dy
It can calculate in the data bulk numberPoints, data on warp and weft in the quantity and data of data point in total
Data respectively correspond the numerical value of each data point.The mapping process of directionless static data to simulation terrestrial network can be with
Are as follows: starting point is (la1, lo1), and longitude lo1 is successively increased as unit of dx, and latitude la1 is successively reduced as unit of dy, until
Longitude is added to lo2, and latitude reduces to la2.As shown in table 1, which show the passes between the position and longitude and latitude of data in " data "
System, it is 90 that (90,0) expression longitude, which is 0 latitude, the sequence of data in side digital representation " data " field.
Table 1
For band direction static data, there is a portion multiple groups header and the portion data, for example, sea wind and ocean current only have direction and
Two attributes of numerical values recited, so sea wind and ocean current have two groups of header and data, two groups of data constitute a vector, this vector
Direction just represent sea wind blow to or the flow direction of ocean current, the value of each point on the numerical value representative simulation grid of this vector；
And wave has wave high, average period and three, direction attribute can pass through average period so there is three groups of header and data
The flow velocity of wave is indicated with direction.
Realize that automatic visual specifically includes:
I) visualization of static multi-source global ocean big data；
Because human eye is more intuitive to the perception of color, numerical value usually is indicated with color in visualization application
Variation, i.e., establish a kind of mapping relations between data and color, different data be mapped in different colors.Ocean
The physical characteristic datas such as temperature, Hai Wen, density and salinity are all one-dimensional, so the present invention is using Color Mapping Approach to these
Data carry out simulative display.Here color mapping is divided into two steps, initially sets up a color mapping table, defines the transition of color
Range, for example indicate from yellow to red the variation of temperature, color here, which can be, to be continuously also possible to discrete, is used
Family can could be adjusted to generate color mapping table according to actual needs, then map the data into corresponding color, if
Color table be it is continuous, then can directly specify the variation range [min, max] of data, if color table be it is discrete, can
To specify the color value of representational data, thus the variation of realization color changed to indicate data.
Because the position of each mesh point be it is discrete, i.e., only just have data on specific mesh point, and an ocean
It should be continuous on earth that these physical characteristics, which are shown, it is possible that there is no data in certain positions, in order to obtain these
Data, the present invention devise a kind of interpolation algorithm, i.e., the approximation of current point are calculated according to the value of point of proximity, uses here
Be linear interpolation, the value of current point: g here is calculated by the value of four points around00、g10、g01、g11It respectively represents and works as
Four points around preceding point, gx、gyRespectively represent the longitude error when longitude and latitude of current point is rounded downwards, rx、ryIt respectively represents
The trueness error when longitude and latitude of current point rounds up.
F (x)=g00×rx×ry+g10×gx×ry+g01×rx×gy+g11×gx×gy (1)
Ii) the visualization of dynamic multi-source global ocean big data:
Flow Visualization is mainly used for visualization in scientific computing, its essence is using computer graphics and image
Science data are carried out transformation calculations and drafting and are ultimately converted to image and are displayed on the screen by processing technique, and researcher can be with
The analog result of these science data is intuitively observed, and carries out interaction process, so flow-field visualized is the calculating that studies science
A visual important component part, especially in the simulation of various dynamic datas.Sea wind in oceanographic data, ocean
Stream and wave all have flow behavior, therefore present invention uses flow-field visualized methods to simulate dynamic multi-source whole world sea
The motion conditions of foreign big data.
It is all multidimensional that sea wind, ocean current, wave etc., which have directive data, so this method uses and static data is visual
Change the same color mapping method to indicate the numerical values recited situation of change of dynamic data, uses the method for visualizing pair of fluid form
The dynamic characteristic of these data is simulated.If sea wind and ocean current data are two-dimensional, only direction and size, vector can be used
Field takes out its model, and each data point has a speed and direction, can be decomposed into u component and the side y on a direction x
Upward v component, u, v respectively indicate the speed in the x and y direction, can be with particle come the variation of analogue data, it is transported in this way
Dynamic to be expressed as, tail is to next direction and speed in tow under current direction and speed for particle, thus visually
Has the effect of movement.
Thxe present method defines a kind of methods of Particles Moving, first progress interpolation calculation, because of wind and ocean current and wave
It is all continuous, and the data of mesh point are discrete so needing to carry out interpolation calculation to obtain the position between mesh point
Data approximation calculates the u of current point, v component: here by four points around here using bilinear interpolation
g00、g10、g01、g11Respectively represent the vector of four points around current point, gx、gyThe longitude and latitude for respectively representing current point is downward
Longitude error when rounding, rx、ryRespectively represent the trueness error when longitude and latitude of current point rounds up.
U=g00[0]×rx×ry+g10[0]×gx×ry+g01[0]×rx×gy+g11[0]×gx×gy (2)
V=g00[1]×rx×ry+g10[1]×gx×ry+g01[1]×rx×gy+g11[1]×gx×gy (3)
In order to improve visual accuracy, thxe present method defines a function judge this particle whether definition range
It is interior, because off-limits particle can not be shown on the screen, if particle in indication range, carries out it
Display operation.The place that particle has the place of beginning just to have end, it is impossible to some particle is displayed on the screen always, with
The passage of time has new particle and adds will also disappear come so old particle, so thxe present method defines the life of particle
Period determines the display time of particle on the screen, its age of particle one frame of every movement can add 1 to reach definition until age
Particle annihilation when life cycle.Thxe present method defines initial number of particles, i.e., simultaneously how many bar wind line or ocean current line or wave
Wave is shown on the screen, if it is the movement of this linear flowing of wind and ocean current, is then added newly according to the position of current particle
U, v component of particle update particle position, if it is this movement with ripple effect of wave, then according to the speed * time
(frame)=distance updates wave particle position, while detecting the age of particle, if age is more than or equal to the life cycle of definition
It then re-creates a new particle and replaces original particle.In order to keep movement effects more true to nature, also gradual change can be added to particle
Effect, i.e., the color of old particle can be gradually faded away until disappearing, here by with RGB color similar in background colour and transparency come
It indicates, the old particle of one frame of every movement all can be thin out primary, can thus achieve the effect that one kind is true to nature, while also can be good
Show the alternating of the old and new's particle.
The visualization further includes the display of the statistical chart, radar map and rose figure of data.Traditional ocean visualization is only
Focus on the feature of display data there is no the connection between specific statistical data, cannot intuitively find the variation of data, and this hair
It is bright by statistical chart, radar map and rose figure come comparative analysis data, can more intuitively find the variation of data, be more advantageous to
Ocean research person analyzes data.
It can also be achieved a kind of dynamic and visual system towards multi-source global ocean big data based on above-mentioned method for visualizing,
It can be realized by client and server-side on the system hardware.In certain embodiments, javascript can be used to carry out for client
Exploitation, node.js can be selected in server-side, and data volume is huge, data are very intensive to meet, fast response time and real-time it is good
Requirement.So that javascript is may operate in server-side in this way but also do not need using database, so that system can be made
It is lighter.In order to realize without using database while can quickly obtain mass data, which can be by way of index
Request data and by obtained data in the form of array asynchronous transmission to client.Statistical chart, radar map and rose figure are in institute
The display stated in system is specific as follows:
Statistical chart can intuitively show the variation tendency of numerical value in data, it is possible to be shown with statistical chart one day
The variation of data, and the library d3 has been used, when user requests statistical data, position and the time of current point can be judged automatically, so
After pass to node.js server-side, other data of current point current time are obtained, in order to preferably show the variation between data
Relationship dynamically defines the domain and codomain of statistical chart y-axis, then dynamically by corresponding data according to the range of data
It is mapped on statistical graph.
Radar map can graphically simulate direction, the judgement data that user can be clear and intuitive on radar map
Direction, so showing the direction of the data of current time current location with radar map.Since direction is the data of a point,
So can be directly obtained by above-mentioned interpolation computing method without request data file, it is marked on radar map each
A direction, according to the tissue of data, the data that angle is 180 ° in southern corresponding data, north corresponds to the number that angle is 0 ° in data
According to the corresponding 90 ° of east in west is 270 ° corresponding, can define the direction that a small sector carrys out display data, when user requests other data points
Direction when, system can be quickly obtained the direction of the point, the sector of definition then be rotated by this direction, thus new side
Original direction is removed to being shown on radar map, and using the remove function of d3, realizes the direction of current time current point
Statistics.
Rose figure can indicate the much information of data in one drawing, so analyzing data, Yong Huke with rose figure
Voluntarily to select the data of one week one day or one month for statistical analysis, rose figure has direction sign as radar map
Note, by the agency of is crossed and is just repeated no more here in radar map for mapping relations between direction and data.It is obtained by node.js
After the data of request, the direction of data is represented with the method as radar map, is referred to if data are sea wind from outside
The direction of regional center (RC) is blowed to, the ratio of the wind direction of all directions, the line drawn in all directions by statistic are then counted
The length of section indicates the size of this direction wind frequency, and line segment is longer, and the number for indicating that the wind direction occurs is more.According to the model of wind speed
It encloses and defines a color-ratio ruler, different wind speed is represented with different colors, the line of wind frequency will be indicated in all directions
Section at the separated time section of different colours, that is, represents the mean wind speed of each wind direction by wind speed numerical value per cents.
On the basis of above-mentioned visual, the present invention can also be achieved the positioning of data quick-searching, and response external retrieves information,
The external retrieval information is matched by interpolation algorithm, and realizes the positioning and visualization of Access Points in simulation terrestrial network.
The not request data file when user only searches the data value of a point, but the adjoining of the point inquired is needed by calculating
Four points greatly improve the speed of inquiry come the value needed in this way.Oneself institute's query point is observed for the ease of user
Specific location, the present invention can mark the position of user query data by a label on map.In certain embodiments,
A projection function can be constructed and carry out mapping calculation between tellurian latitude and longitude coordinates and vector field.
If Fig. 2-Figure 10 is the experimental example figure obtained based on above-mentioned method for visualizing and system.
Fig. 2 and Fig. 3 shows respectively the effect of visualization of the mild salinity in sea in static data, other static datas and figure
2, the similar only color of Fig. 3 is different, just no longer shows here.The colorbar of Fig. 2 illustrate data it is ascending when color
Variation, it can be seen that the color closer to equator temperature is redder, and the color closer to the temperature of south poles is more blue, this is also reflected
Temperature closer to equator seawater is higher, and the temperature closer to south poles seawater is lower.Other static datas are visually analyzed
Similar with Fig. 2, which is not described herein again.
Fig. 4 and Fig. 5 shows respectively the effect of visualization of sea wind and ocean current in dynamic data, and respective colorbar is aobvious
The variation range for having shown its numerical value can be seen that blowing to for wind from the flow direction of Fig. 4 particle, from color it can be seen that there is vortex
Local wind speed is larger.Equally, from Fig. 5 particle flow direction it is also seen that ocean current flow direction, can be seen that from the variation of color
The variation of the flow velocity of ocean current.Fig. 6 shows the effect of visualization of wave, can be seen that the stream of wave from the fluctuation direction of wave
To, while it is also seen that old wave particle is being gradually faded away until disappearing, and new wave is had over time
Unrestrained particle generation is displayed on the screen.
Fig. 7 illustrate user by Fig. 5 search box input longitude and latitude inquired as a result, user can also pass through click
Certain point on interface is inquired, the position for the point that the yellow circles mark of Fig. 7 center 2 user is inquired, the text in frame 1
This information illustrates the numerical information for the data that user query arrive.
User can select oneself to want the quantity of the data of statistics by the frame 3 of Fig. 8, and what Fig. 9 was shown is exactly user institute
The variation of wind speed in one day of query point, it can be seen that wind speed is maximum when morning 3, and over time
Wind speed is gradually reduced.Yellow sector in radar map on the right of Figure 10 illustrates the wind direction of current time current location, can see
Wind direction at this time is probably on 40 ° of south by west of direction out.The rose figure on the left side Figure 10 then show in one day wind speed, wind direction with
And the situation of change of wind frequency, the mark in the upper right corner illustrates that different colours in the rose figure of the left side correspond to the numerical value of wind speed in Fig. 9
Information, percentage illustrates that the wind that this side up accounts for the ratio of wind direction total in data, as can be seen from the figure direction northwest
Wind is more, while most of wind speed concentrate on 10-40m/s, can be simpler intuitive by this display mode researcher
Understanding data.
The preferred embodiment of the present invention has been described in detail above.It should be appreciated that those skilled in the art without
It needs creative work according to the present invention can conceive and makes many modifications and variations.Therefore, all technologies in the art
Personnel are available by logical analysis, reasoning, or a limited experiment on the basis of existing technology under this invention's idea
Technical solution, all should be within the scope of protection determined by the claims.
Claims (10)
1. a kind of dynamic and visual method towards multi-source global ocean big data, which is characterized in that this method includes following step
It is rapid:
1) multi-source global ocean big data is obtained, and is pre-processed, the multi-source global ocean big data includes directionless quiet
State data and with direction dynamic data；
2) one simulation terrestrial network of building, pretreated data are mapped in the simulation terrestrial network, realize automatic visual
Change.
2. the dynamic and visual method according to claim 1 towards multi-source global ocean big data, which is characterized in that institute
Stating pretreatment includes:
Data normalization；
Each data are divided into the portion header and the portion data, the portion header storing data attribute in a manner of key-value pair is believed
Breath, the portion data store the numerical value of each data point, wherein directionless static data passes through one group of portion header and the portion data
It indicates, band direction dynamic data is indicated by the portion multiple groups header and the portion data, and the portion multiple groups data constitutes a vector；
Clustering processing is carried out to data.
3. the dynamic and visual method according to claim 1 towards multi-source global ocean big data, which is characterized in that will
When pretreated data are mapped in the simulation terrestrial network, for directionless static data, indicated with color mapping method
Numerical values recited variation；For band direction dynamic data, indicate that numerical values recited changes with color mapping method, and with fluid form mould
Quasi- dynamic characteristic.
4. the dynamic and visual method according to claim 3 towards multi-source global ocean big data, which is characterized in that institute
Stating color mapping method includes: to establish a color mapping table, which is continuous table or discrete sheet, is based on the color mapping
Table realizes the mapping relations between data and color.
5. the dynamic and visual method according to claim 3 towards multi-source global ocean big data, which is characterized in that institute
When stating with fluid form simulation dynamic characteristic, the display of each Motion Particles has a display life cycle, only to positioned at the display
Motion Particles in life cycle are visualized.
6. the dynamic and visual method according to claim 1 towards multi-source global ocean big data, which is characterized in that institute
It states and maps to pretreated data in the simulation terrestrial network further include:
Realize that data continuously display in simulation terrestrial network using interpolation algorithm.
7. the dynamic and visual method according to claim 1 towards multi-source global ocean big data, which is characterized in that institute
State the display that visualization further includes the statistical chart, radar map and rose figure of data.
8. a kind of dynamic and visual system towards multi-source global ocean big data characterized by comprising
Data acquisition module, for obtaining multi-source global ocean big data, including directionless static data and with direction dynamic number
According to；
Preprocessing module, for being pre-processed to the multi-source global ocean big data；
Visualization model simulates terrestrial network for constructing one, pretreated data is mapped in the simulation terrestrial network,
Realize automatic visual.
9. the dynamic and visual system according to claim 8 towards multi-source global ocean big data, which is characterized in that institute
Stating preprocessing module includes:
Data normalization unit, for being standardized to multi-source global ocean big data；
Data dividing unit, for each data to be divided into the portion header and the portion data, the portion header is with key-value pair
Mode storing data attribute information, the portion data store the numerical value of each data point, wherein directionless static data passes through one
The group portion header and the portion data indicate that band direction dynamic data is indicated by the portion multiple groups header and the portion data, the portion multiple groups data
Constitute a vector
Cluster cell, for carrying out clustering processing to data.
10. the dynamic and visual system according to claim 8 towards multi-source global ocean big data, which is characterized in that
Further include:
Locating module is retrieved, response external retrieves information, matches the external retrieval information by interpolation algorithm, and on simulation ground
The positioning and visualization of Access Points are realized in net network.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811030763.5A CN109299343A (en) | 2018-09-05 | 2018-09-05 | A kind of dynamic and visual method and system towards multi-source global ocean big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811030763.5A CN109299343A (en) | 2018-09-05 | 2018-09-05 | A kind of dynamic and visual method and system towards multi-source global ocean big data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109299343A true CN109299343A (en) | 2019-02-01 |
Family
ID=65166102
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811030763.5A Pending CN109299343A (en) | 2018-09-05 | 2018-09-05 | A kind of dynamic and visual method and system towards multi-source global ocean big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109299343A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111639149A (en) * | 2020-05-29 | 2020-09-08 | 山东浪潮通软信息科技有限公司 | Ocean data visualization method and device |
| CN111859222A (en) * | 2020-07-28 | 2020-10-30 | 自然资源部第一海洋研究所 | Three-dimensional sea wave display method and device |
| CN114581043A (en) * | 2022-02-21 | 2022-06-03 | 国家海洋环境预报中心 | Cross-network modular multi-source multi-factor wave-condition analysis system |
| CN114742854A (en) * | 2022-04-02 | 2022-07-12 | 西安电子科技大学 | SAR image sea-land segmentation method based on scene prior knowledge and region combination |
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 |
| CN102521863A (en) * | 2011-12-01 | 2012-06-27 | 武汉大学 | Three-dimensional fluid scalar vector uniform dynamic showing method based on particle system |
| CN102831644A (en) * | 2012-07-09 | 2012-12-19 | 哈尔滨工程大学 | Marine environment information three-dimensional visualization method |
| CN102855662A (en) * | 2012-07-25 | 2013-01-02 | 中国科学院对地观测与数字地球科学中心 | Ocean environment visualization method |
| 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 |
| CN104732582A (en) * | 2015-02-07 | 2015-06-24 | 中国海洋大学 | Three-dimensional ocean vector field dynamic pulse surface visualization algorithm based on GPU |
| CN105261063A (en) * | 2015-09-29 | 2016-01-20 | 北京三维易达科技有限公司 | Three-dimensional particle system large scale sea climate simulation method based on octree |
-
2018
- 2018-09-05 CN CN201811030763.5A patent/CN109299343A/en active Pending
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 |
| CN102521863A (en) * | 2011-12-01 | 2012-06-27 | 武汉大学 | Three-dimensional fluid scalar vector uniform dynamic showing method based on particle system |
| CN102831644A (en) * | 2012-07-09 | 2012-12-19 | 哈尔滨工程大学 | Marine environment information three-dimensional visualization method |
| CN102855662A (en) * | 2012-07-25 | 2013-01-02 | 中国科学院对地观测与数字地球科学中心 | Ocean environment visualization method |
| CN104732582A (en) * | 2015-02-07 | 2015-06-24 | 中国海洋大学 | Three-dimensional ocean vector field dynamic pulse surface visualization algorithm based on GPU |
| 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 |
| CN105261063A (en) * | 2015-09-29 | 2016-01-20 | 北京三维易达科技有限公司 | Three-dimensional particle system large scale sea climate simulation method based on octree |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111639149A (en) * | 2020-05-29 | 2020-09-08 | 山东浪潮通软信息科技有限公司 | Ocean data visualization method and device |
| CN111639149B (en) * | 2020-05-29 | 2023-07-25 | 浪潮通用软件有限公司 | Ocean data visualization method and device |
| CN111859222A (en) * | 2020-07-28 | 2020-10-30 | 自然资源部第一海洋研究所 | Three-dimensional sea wave display method and device |
| CN111859222B (en) * | 2020-07-28 | 2021-09-14 | 自然资源部第一海洋研究所 | Three-dimensional sea wave display method and device |
| CN114581043A (en) * | 2022-02-21 | 2022-06-03 | 国家海洋环境预报中心 | Cross-network modular multi-source multi-factor wave-condition analysis system |
| CN114581043B (en) * | 2022-02-21 | 2023-01-31 | 国家海洋环境预报中心 | Cross-network modular multi-source multi-factor wave-condition analysis system |
| CN114742854A (en) * | 2022-04-02 | 2022-07-12 | 西安电子科技大学 | SAR image sea-land segmentation method based on scene prior knowledge and region combination |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109299343A (en) | A kind of dynamic and visual method and system towards multi-source global ocean big data | |
| Heinrich et al. | State of the Art of Parallel Coordinates. | |
| McLoughlin et al. | Similarity measures for enhancing interactive streamline seeding | |
| Kreuseler et al. | A scalable framework for information visualization | |
| Zhou et al. | Edge bundling in information visualization | |
| CN104899288B (en) | Dimension hierarchical method for visualizing based on radial direction layout | |
| EP3561688A1 (en) | Hierarchical tree data structures and uses thereof | |
| Gallas et al. | Periodicity versus chaos in the dynamics of cobweb models | |
| Delort | Hierarchical cluster visualization in web mapping systems | |
| Small | A general framework for creating large-scale maps of science in two or three dimensions: The SciViz system | |
| Rodrigues et al. | Nonlinear dot plots | |
| Atkin et al. | An analysis of urban structure using concepts of algebraic topology | |
| Yan et al. | Research and application of the test data visualization | |
| Li et al. | Construct boundaries and place labels for multi-class scatterplots | |
| Timpf et al. | A multi-scale data structure for cartographic objects | |
| Ge | Generalization of LOD2 buildings with different roof structures | |
| Richer et al. | Corfish: Coordinating emphasis across multiple views using spatial distortion | |
| Nocke et al. | Images for data analysis: The role of visualization in climate research processes | |
| KR101295063B1 (en) | Realistic and Real-Time Modeling System and Method thereof | |
| Noik | Dynamic Fisheye Views: Combining Dynamic Queries and Mapping with Database View Definition | |
| Teng et al. | Design and Implementation of Flow Visualization Software based on MATLAB | |
| Du et al. | Interactive details on demand visual analysis on large attributed networks | |
| Neuhauser et al. | Visual analysis of model parameter sensitivities along warm conveyor belt trajectories using Met. 3D (1.6. 0-multivar0) | |
| Schatz et al. | Abstracted Visualization of Halo Topologies in Dark Matter Simulations | |
| Tan | Data warehousing and its potential using in weather forecast |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190201 |