CN116579854A

CN116579854A - Visual data processing method of financial risk control data

Info

Publication number: CN116579854A
Application number: CN202211737682.5A
Authority: CN
Inventors: 王作全; 王庆华; 侯思思; 马巾祺; 刘颖源
Original assignee: Bank Of Changan Ltd By Share Ltd
Current assignee: Bank Of Changan Ltd By Share Ltd
Priority date: 2022-12-30
Filing date: 2022-12-30
Publication date: 2023-08-11
Anticipated expiration: 2042-12-30
Also published as: CN116579854B

Abstract

The invention discloses a visual data processing method of financial risk control data, wherein in the visual data processing method of the financial risk control data, a data set B is assembled based on a data set A, and a data set C is generated by combining a region mapping table T; loading a wind control display rule R1, taking a data set C as input, taking the wind control display rule R1 as a mapping relation, generating a color configuration data set E1, and loading map lattice data D1 to generate map fusion data F1 related to the data set C; visual data interaction is performed when the mouse is moved or even clicked on the map fusion data F1 on the data set C.

Description

Visual data processing method of financial risk control data

Technical Field

The invention belongs to the technical field of financial risk control, and particularly relates to a visual data processing method of financial risk control data.

Background

In the risk control of banking financial services, risk monitoring needs to be performed on a block map for different indexes of nationwide, provinces and cities. Such as national (province, city) bond holding distribution, regional annual population growth rate, regional land financial dependence, regional liability rate, regional government liability rate, etc. Because operations such as positioning, navigation and route planning are not needed, and the system only needs to use a regional schematic map, the cost, localization and simplicity and easiness are considered. Therefore, it is required to create a reusable block map, and to integrate and use the block map with different banking and financial management and wind control indexes.

The above information disclosed in the background section is only for enhancement of understanding of the background of the invention and therefore may contain information that does not form the prior art that is already known to a person of ordinary skill in the art.

Disclosure of Invention

Aiming at the problems in the prior art, the invention provides a visual data processing method of financial risk control data.

The invention aims at realizing the technical proposal that the visualized data processing method of financial risk control data comprises the following steps,

the first step, importing a financial data set A, and processing and assembling the financial data set A into a data set B, wherein the financial data set A is a financial wind control initial data set; the method comprises the steps of,

taking the data set B as input and taking the region mapping table T as a mapping relation to generate a data set C, wherein the region mapping table is used for reflecting the region matching mapping relation;

the second step, loading a wind control display rule R1, taking a data set C as input, and taking the wind control display rule R1 as a mapping relation to generate a color configuration data set E1; the method comprises the steps of,

loading map lattice data D1, taking a color configuration data set E1 and the map lattice data D1 as inputs, and carrying out data fusion processing on the color configuration data set E1 and the map lattice data D1 to generate map fusion data F1 related to a data set C, wherein the map fusion data F1 is visualized as an interactive graph;

A third step of obtaining map data Q1 corresponding to the position of the mouse according to the map fusion data F1 and the position (x, y) of the current mouse when the mouse moves on the interactable map of the map fusion data F1 related to the data set C, wherein the map data Q1 corresponding to the position of the current mouse belongs to the data in the map lattice data D1 and is related to the position (x, y) of the current mouse; the method comprises the steps of,

and inquiring the map data Q1 value corresponding to the data item in the data set C in the map fusion data F1 to obtain the data item in the data set C corresponding to the position of the mouse and visually displaying the data item.

The visual data processing method of financial risk control data further comprises the following steps:

a fourth step of obtaining a region code Q2 corresponding to a mouse click position according to the map fusion data F1 and the mouse click position (x, y) when the mouse clicks on the interactable map of the map fusion data F1 related to the data set C, wherein the region code Q2 corresponding to the current mouse click position belongs to the data in the map dot matrix data D1 and is related to the current mouse click position (x, y); the method comprises the steps of,

inquiring the region code Q2 in the map fusion data F1, loading region map lattice data D2 corresponding to the region code of the geographic region corresponding to the region code in the map lattice data D1 according to the Q2 value, and extracting and generating the region map fusion data F2 from the map fusion data F1 by taking the region map lattice data D2 as an extraction condition; wherein,

The regional map lattice data D2 corresponding to the current mouse click position belongs to the data in the map lattice data D1 and is related to the current mouse click position (x, y);

the map dot matrix data D1 is reusable map dot matrix data.

The visual data processing method of financial risk control data, wherein,

the data set A is nationwide data and comprises provincial and municipal name two-stage area information, debt subject names and holding amount;

the data set B is nationwide data and comprises provincial and municipal name two-stage area information and the holding amount of the provincial and municipal name two-stage area information and area codes;

the data set C is nationwide data and comprises provincial and municipal name two-stage regional information and holding amount and regional codes.

According to the visual data processing method of financial risk control data, map lattice data D1 or regional map lattice data D2 are based on a shaped two-dimensional array matrix under a computer screen coordinate system, and each point in the lattice data is a pixel on a computer screen under the condition that an image is not zoomed.

The visual data processing method of financial risk control data, wherein map lattice data D1 is calculated by the following steps:

Loading a configuration region mapping table T, and acquiring boundary gps data and contour point gps longitude and latitude data G of a region for any determined region;

taking the lower left corner as an origin, establishing a right-hand coordinate system CR1 taking a computer pixel as a unit in the Y-axis direction to the right, and mapping longitude and latitude data G of the contour point gps into the coordinate system CR1 to obtain a data set H1;

transforming the coordinate system, namely transforming the data set H1 into a data set H2 under a screen coordinate system CR2 of a left-hand coordinate system with an upper left corner as an origin, an X-axis to the right and a Y-axis to the down;

creating lattice data K1 with matrix size [ Wi, H i ] and initializing each data in the K1 to be 0, wherein Wi represents width, H i represents height, wi is the maximum abscissa of the coordinate system CR2, and H i is the maximum ordinate of the coordinate system CR 2;

processing each coordinate Point of the data set H2 according to the region coding in the region mapping table T corresponding to the current region _i (x, y) such that K1 of each point in the same regionThe values are all region codes of the region to which the corresponding point belongs;

generating a lattice K2 based on the point sealing processing on the non-sealing contour line in the lattice data K1;

map lattice data D1 is generated based on the lattice K2 and the map of the current area.

The visual data processing method of financial risk control data, wherein,

A certain contour point of the break in the lattice data K1;

firstly, finding broken contour points adjacent to the contour points;

and then filling the contour points between the contour point and the broken contour point.

The visual data processing method of financial risk control data specifically comprises the following sub-steps,

substep 2.1: for the lattice data K1, a contour point list L1[ P1, P2, P3, P4, … … ] is obtained in a scanning mode, wherein P1, P2, P3, P4 are contour point data;

let P (x, y) be the point of all outline points of the cyclic dot matrix data K1, and K1[ P (x, y) ] be the region code of the region to which the point belongs;

whether the point P (x, y) is a continuous contour point, a broken contour point or an isolated point is judged according to the following steps:

the reference matrix is established as follows:

the variable t=0 is set and,

the coordinates (x, y) of P (x, y) are added to the reference matrix for each element to obtain the following addition matrix:

for each element [ xx, yy ] except the center point (x, y) in the addition matrix, if [ xx, yy ] corresponds to K1[ xx, yy ] in the lattice data K1, the area code of the area where the point is located is given, namely the value t=t+1;

if t > =2, it means that the point P (x, y) is a continuous contour point;

if t=1, it means that point P (x, y) is a broken contour point;

If t=0, then it means that point P (x, y) is an isolated point, let K1[ xx, yy ] =0 directly, and remove this point directly;

substep 2.2, when the first broken profile point P1 (x, y) is found, removing P1 (x, y) from L1;

sub-step 2.3, for L1 with P1 removed, reading a broken contour point again according to sub-step 2.1;

substep 2.4, determining whether the first broken contour point P1 is connected to the contour point read out in substep 2.3, if so, taking the next contour point in the rotor jumping step 2.3, otherwise, executing downwards, wherein,

the communication judging flow is as follows:

sub-step 2.4.1, establish a reference matrix:

setting an array variable S in the sub-step 2.4.2, wherein the number of elements of the array is equal to the number of points in the contour point list L1 in the sub-step 2.1, each element is an x and y coordinate, adding the coordinate of P1 (x and y) into the array S and taking the coordinate as a first element PP1 (x and y) after zeroing each element in the array variable S;

sub-step 2.4.3, take the first element PP1 (x, y) in array S and remove the first element from S for marking;

the coordinates (x, y) of PP1 (x, y) are added to each element of the reference matrix to obtain the following sum matrix:

for each element [ xx, yy ] in the above sum matrix except for the center point (x, y):

If [ xx, yy ] is the coordinates of the contour point extracted in the substep 2.3, then P1 is communicated with the contour point, and the algorithm is ended;

otherwise, further judging K1[ xx, yy ]: if K1[ xx, yy ] is the region code of the region to which the K1[ xx, yy ] belongs, adding PP (xx, yy) to the array S; jump again to substep 2.4.3 until array S is empty, P1 and the contour point are not connected.

The visualized data processing method of financial risk control data further comprises the following substeps after the substep 2.4,

and calculating the distance from P1 to other contour points, wherein the other contour points are as follows: removing all remaining contour points in L1 of P1;

marking the point corresponding to the shortest distance as P3, marking the sitting point as P3 (x 3, y 3), and renumbering other contour points except P1 and P3 so that the number of each contour point is unique and not repeated; wherein,

d represents the shortest distance between P1 and other contour points, (x 1, y 1) is the point P1 coordinate, (xn, yn) is the other contour point coordinate, n is from 1 to the number of L1 elements-1;

when x1 is not equal to x3, the cycle x is a cycle of x-axis coordinates from the x-axis coordinates of P1 to the x-axis coordinates of P3, with x stepping from x1 to xmin, each step by 1, i.e., x=x+1; wherein x1 is the x-axis coordinate of P1 and xmin is the x-axis coordinate of P3;

Calculating a corresponding y value through slope y= (y 3-y 1)/(x 3-x 1), and after the y value is obtained, enabling K1[ x, y ] to be the region code of the region where the y value is located, and filling contour points;

x3≠x1，x1≤x≤x3

when the slope isAbove 1, missing point coordinates may be generated for the Y-axis, specifically: y=2x, there is x=1, y=2; when x isStep 1, x=2, y=4, where y [2,4 ]]3 coordinate points are left in between, so that the contour is still not closed;

cycle y: y is the y-axis coordinate cycle from the y-axis coordinate of P1 to P3, y steps from y1 to y3, each step by 1, i.e., y=y+1; wherein y1 is the y-axis coordinate of P1 and y3 is the y-axis coordinate of P3;

calculating an x value through slope x= (x 3-x 1)/(y 3-y 1), enabling K1[ x, y ] to be the region code of the region, and filling contour points;

y3≠y1，y1≤y≤y3，

when x1 is equal to x3, and belongs to special cases, a vertical line is processed; circulating y from y1 to y3, enabling K1[ x1, y ] to be the region code of the region, and filling contour points;

when y1 is equal to y3, belonging to special cases, a horizontal line is processed;

cycling x from x1 to x3, enabling K1[ x, y1] to be the region code of the region, and filling contour points;

step 2.2, jumping the rotor, and processing the next contour point until L1 is empty;

to this end, the broken contour is closed.

The visual data processing method of financial risk control data, wherein,

Searching for the coordinates of the center of gravity point in the contour line by the following steps:

obtaining a center point coordinate PC (x, y), wherein x=x0+ (x 1-x 0)/2, and y=y0+ (y 1-y 0)/2, wherein (x 0, y 0) is the upper left corner coordinate of the contour, and (x 1, y 1) is the lower right corner coordinate of the contour;

square scanning from a central point to an X-axis, wherein if the X-axis passes through the contour line for 1 time, the X-coordinate of the current center is the X-coordinate of the center of gravity point of the contour, if the X-axis passes through the contour line for 2 times, the X-axis coordinates xx1 and xx2 of the two passing contour points are recorded, and the X-coordinate of the center of gravity point is xx=xx1+ (xx 2-xx 1), and the obtained coordinates [ xx, y ] are the X-coordinate of the center of gravity point of the contour:

step 3.1, let k=x-axis coordinate X of the center point PC, let nc=0;

step 3.2, k=k+1;

step 3.3, if k2[ K, y ] =q, if nc=0, let xx1=k, nc=nc+1, loop k=k+1 until k2[ K, y ] =0; if nc=1, let xx2=k, nc=nc+1, loop k=k+1 until k2[ K, y ] =0;

step 3.4, jump to step 3.2 until k > = Wi;

if nc=2, the center of gravity Pg abscissa xx=xx1+ (xx 2-xx 1);

if nc=1, the abscissa xx of the gravity center pg=the X-axis coordinate X of the center point PC is obtained;

if nc=0, the contour is not on the right side of the center point, the algorithm ends, and the lower X-axis negative direction scanning, Y-axis square and negative direction scanning is performed;

The X coordinate of the current center is the X coordinate of the profile gravity point if the current point passes through the profile line for 1 time, the X coordinate of the current center is the X coordinate of the profile gravity point if the current point passes through the profile line for 2 times, the X coordinates xx1 and xx2 of the two profile points passing through are recorded, and the X coordinate of the gravity point is xx=xx1+ (xx 2-xx 1) to obtain coordinates [ xx, y ] which are the X coordinate of the profile gravity point;

scanning from the center point to the positive direction of the Y axis and the negative direction of the Y axis to obtain a center of gravity point Pg (x, Y);

after the coordinates Pg (x, y) of the center of gravity point in the contour are obtained, the values of the region in the contour are filled according to the following steps:

step 4.1, establishing an array variable S, wherein the number of elements of the array is Wi-H i, each element is an x-y coordinate, and adding Pg (x, y) into the array S after zeroing each element in the array variable S;

step 4.2, establishing a reference matrix:

step 4.3, fetching the first element P1 (x, y) in the array S and removing from S;

the P1 (x, y) coordinates (x, y) are added to each element in the reference matrix to obtain the following addition matrix:

judging whether K2[ xx, yy ] is zero or not for each element [ xx, yy ] in the addition matrix, if so, enabling K2[ xx, yy ] to be the region code of the region, and adding PP (xx, yy) to the array S;

Step 4.3, jumping until the array S is empty, and finishing filling the inside of the outline;

each contour region is circularly processed to obtain a lattice data series K3[ 1..n ]; wherein n is the number of regions;

and merging K3[ 1..n ] to map lattice data D1.

The visual data processing method of financial risk control data, wherein,

for each region code in D1, extracting and generating certain region multiplexing lattice data D2[ Qn ] specifically comprises the following steps, wherein Qn is a set of Q values for different region codes:

for one element Q of Qn in the set;

scanning D1 at each point [ X, Y ], if D1[ X, Y ] =q, calculating the maximum and minimum values on the X-axis and Y-axis: xmin, xmax, ymin, ymax;

let xmin=xmi n-1, xmax=xmax+1, ymi n=ymi n-1, ymax=ymax+1;

W＝Xmax-Xmi n,H＝Ymax-Ymi n；

creating lattice D2[ Q ] with the size of [ W, H ];

rectangular region data corresponding to D1[ Rect (Xmi n, xmax, ymi n, ymax) ] is copied to D2[ Q ] [ Rect (0, W, H) ] where Rect represents a rectangular region.

Compared with the prior art, the invention has the following advantages: the invention can realize the multiplexing of different layers such as the provincial area and the like of the map lattice data and the map fusion data based on the map information visualization of the data in the financial field.

Drawings

Various other advantages and benefits of the present invention will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. It is evident that the figures described below are only some embodiments of the invention, from which other figures can be obtained without inventive effort for a person skilled in the art. Also, like reference numerals are used to designate like parts throughout the figures.

In the drawings:

FIG. 1 is a flow chart of a method for processing visual data of financial risk control data according to one embodiment of the present invention;

fig. 2 is a schematic flow chart of extracting reusable lattice data D2 of a visual data processing method of financial risk control data according to an embodiment of the present invention;

FIG. 3 is a schematic diagram of an area-binning map of a visual data processing method of financial risk control data according to one embodiment of the present invention;

FIG. 4 is a schematic diagram of the regional liability of a method of visual data processing of financial risk control data according to one embodiment of the present invention;

FIG. 5 is a schematic diagram of H1 coordinate transformed H2 coordinates of a visual data processing method of financial risk control data according to an embodiment of the present invention;

FIG. 6 is a schematic diagram of a K1 lattice data visualization of a method for processing visual data of financial risk control data according to one embodiment of the present invention;

FIG. 7A is a schematic diagram of a discontinuous outline of K1 lattice data of a visual data processing method of financial risk control data according to an embodiment of the present invention;

FIG. 7B is a schematic diagram of a closed continuous contour line K2 of a visual data processing method of financial risk control data according to an embodiment of the present invention;

FIGS. 8A, 8B, and 8C are schematic diagrams of filling contour points on horizontal lines, vertical lines, and other cases, respectively, of a visual data processing method of financial risk control data according to an embodiment of the present invention;

FIG. 9 is a schematic diagram of the center and center of gravity of the Sichuan outline of the visual data processing method of financial risk control data according to one embodiment of the present invention;

FIG. 10 is a schematic diagram of the center and center of gravity of the Gansu outline of the visualized data processing method of financial risk control data according to one embodiment of the invention;

FIG. 11 is a schematic diagram of the center and center of gravity of the inner Monte contours of a visualized data processing method of financial risk control data according to one embodiment of the invention;

fig. 12A to 12E are a plurality of schematic diagrams of centers and barycenters of other outlines of a visualized data processing method of financial risk control data according to an embodiment of the present invention.

The invention is further explained below with reference to the drawings and examples.

Detailed Description

Specific embodiments of the present invention will be described in more detail below with reference to fig. 1 to 12E. While specific embodiments of the invention are shown in the drawings, it should be understood that the invention may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

It should be noted that certain terms are used throughout the description and claims to refer to particular components. Those of skill in the art will understand that a person may refer to the same component by different names. The description and claims do not identify differences in terms of components, but rather differences in terms of the functionality of the components. As used throughout the specification and claims, the terms "include" and "comprise" are used in an open-ended fashion, and thus should be interpreted to mean "include, but not limited to. The description hereinafter sets forth a preferred embodiment for practicing the invention, but is not intended to limit the scope of the invention, as the description proceeds with reference to the general principles of the description. The scope of the invention is defined by the appended claims.

For the purpose of facilitating an understanding of the embodiments of the present invention, reference will now be made to the drawings, by way of example, and specific examples of which are illustrated in the accompanying drawings.

For better understanding, as shown in fig. 1 to 12E, and in particular in fig. 1 to 4, the visualized data processing method of financial risk control data includes,

and importing a financial data set A, processing and summarizing the data set A to generate a data set B, and matching the data set B with the region mapping table T to further generate a data set C. And finally, map fusion data F1 of the area related to the financial product risk control business data are generated by loading map dot matrix data D1 of the designated area and a wind control display rule R1. When a user mouse moves on the interactable map fusion data F1, according to the mouse position [ x, y ], the region code Q=D1 [ x, y ] is obtained, the data set C is queried according to the Q value, and the corresponding data item in the C is obtained for display. When the user clicks the mouse at F1, the region code Q=D1 [ x, y ] is obtained according to the mouse position [ x, y ], the map lattice data D2[ Q ] of the designated region is loaded according to the Q value, and map fusion data F2 of the region is generated in a similar way. The regional map lattice data D1 and D2 Qn are generated by obtaining the latitude and longitude data of the outline gps of each administrative region from the online map, and performing data mapping, coordinate transformation, contour closure, filling, and the like on these latitude and longitude data to the computer image. The data multiplexing is realized by the localized persistent storage of the data D1, D2[ Qn ] for the use of various financial business risk control data map modules.

It can be seen that fig. 1 and 2 illustrate two flowcharts of the method disclosed in the present invention, and fig. 3 to 4 illustrate a schematic diagram of the regional holding map and a schematic diagram of the regional liability rate in the final visualization, respectively.

In the aspect of bank financial management business risk control, different indexes of nationwide, provinces and cities are often required to be monitored on a block map. Such as national (province, city) bond holding maps, regional GDP, regional ten year population acceleration, regional financial self-supply rates, etc. Because operations such as positioning, navigation and line planning are not needed, the invention only needs to use a nationwide regional schematic map, and the invention is simple and easy to use in view of cost and localization. While figures 3 to 4 illustrate the technical effects of the invention in a very visual way: the reusable map can combine different banking financial wind control indexes such as holding data, liability data and the like, namely different data sets, and realize the multiplexing of map data. Moreover, such multiplexing is not only multiplexing in terms of map lattice data of a nationwide area, but also multiplexing in terms of map lattice data of an area. In addition, in the aspect of map fusion data, after macroscopic display of nationwide data, the map fusion data of the region can be extracted and generated according to region codes, so that the map fusion data has reusability in the nationwide and provincial layers, and the same can be understood in the provincial and urban two-level region layers.

In one embodiment, a method for processing visual data of financial risk control data includes:

Still further, still include:

The map dot matrix data D1 is reusable map dot matrix data.

For this embodiment, since at least two levels of provincial regions are involved, all provincial data constitute national data, and then the map fusion data F2 naturally embody the reusable feature of the map fusion data of the present invention since it is directly extracted from the map fusion data F1. It can be found that this is closely related to the map dot matrix data D1 itself being reusable map dot matrix data, which makes the map dot matrix data D2 also map dot matrix data of regional nature that can be extracted by the map dot matrix data D1.

In one embodiment, the financial management and wind control data visualization data processing method comprises the following steps:

the first step, importing a data set A, processing and assembling the data set B according to financial wind control indexes, and generating a data set C by combining a configured area mapping table T, wherein the specific example is as follows:

{ [ Guangdong province, guangzhou, south China aviation group Co., ltd., 2700000.00],

[ Shenzhen City, shenzhen development investment Co., ltd., 1000000.00],

[ Beijing, national development Bank, 710000.00],

[ Beijing, national development Bank, 9980000.00],

[ Beijing, medium-credit stock Co., ltd., 500000.00],

[ Shaanxi, western An City, shaanxi traffic control group Co., ltd., 5700000.00],

[ Shandong province, jinan, shandong energy group Co., ltd., 10000000.00],

[ Jiangsu province, nanjing, jiangsu province national information service group Co., ltd., 8100000.00],

.....}

the initial data set a contains province, city, debt subject, and amount held.

{ [ Guangdong province, 3700000.00],

[ Guangdong province, guangzhou City, 2700000.00],

[ Shenzhen City, guangdong, 1000000.00],

[ Beijing, 11190000.00],

[ Shanxi province, 5700000.00],

[ Shaanxi, western An City, 5700000.00],

.....}

data set B, including province, city, total hold amount.

{ [ Beijing, 10],

[ Shaanxi province, 20], [ Shaanxi province, western America, 2001], [ Shaanxi province, salt-sun, 2002],

[ Fujian province, 33], [ Fujian province, xiamen City, 3301], [ Fujian province, longyan City, 3322],

[ Sichuan province, 43], [ Sichuan province, city 4301], [ Sichuan province, mianyang City, 4302],

[ Guangdong province, 47], [ Guangdong province, guangzhou City, 4701], [ Guangdong province, shenzhen City, 4702],

[ Guangdong province, shanzhong Kou City, 4703], [ Guangdong province, shaoyang City, 4704],

....}

the region mapping table T includes province, city, and code Q.

{ [ Guangdong province, 3700000.00,47],

[ Guangdong province, guangzhou City, 2700000.00,4701],

[ Shenzhen City, guangdong, 1000000.00,4702],

[ Beijing, 11190000.00,10],

[ Shanxi province, 5700000.00,20],

[ Shaanxi, western An City, 5700000.00,2001],

[ Guangdong province, shandong, 0.00,4703],// is contained in T but not in B, filled with 0

[ Guangdong province, kaiyang City, 0.00,4704],// is contained in T but not in B, filled with 0

[ Fujian, xiamen City, 0.00,3301],// is contained in T but not in B, filled with 0

[ Fujian, longyan, 0.00,3322],// is contained in T but not in B, filled with 0

.....}

[ dataset C ].

And a second step of loading the wind control display rule R1, taking the data set C as input, taking the wind control display rule R1 as a mapping relation, generating a color configuration data set E1, loading the national map lattice data D1, and generating map fusion data F1 related to the data set C according to the E1 and the D1. It can be appreciated that one typical implementation of a mapping relationship is a functional expression relationship formed by fitting. In the financial industry, the mapping relationship may further utilize rules between existing financial data.

{ [ holding amount=0, gray ],

[0< holding amount < = 1000000, blue ],

1000000< = holding amount <10000000, orange),

10000000< = holding amount <100000000, red),

.....}

[ wind control display rule R1 ].

{ [ Guangdong province, 3700000.00,47, orange ],

[ Guangdong province, guangzhou City, 2700000.00,4701, orange ],

[ Shenzhen City, guangdong, 1000000.00,4702, orange ],

[ Beijing, 11190000.00,10, red ],

[ Shanxi province, 5700000.00,20, orange ],

[ Shaanxi, western An, 5700000.00,2001, orange ],

[ Guangdong province, shanzhong, 0.00,4703, grey ],

[ Guangdong province, revealing Yang City, 0.00,4704, gray ],

[ Fujian province, xiamen City, 0.00,3301, gray ],

[ Fujian province, longyan City, 0.00,3322, grey ]

.....}

[ data set E1 ]

Map dot matrix data D1.

And thirdly, when a user mouse moves on the map, according to the position (x, y) of the mouse, map lattice data Q=D1 (x, y) corresponding to the position of the mouse is obtained, a data set C is inquired according to the Q value, and corresponding data items in the C are obtained and displayed.

And fourthly, when a user clicks the map fusion data F1 by a mouse, acquiring region codes Q=D1 [ x, y ] according to the positions [ x, y ], loading and loading map lattice data D2[ Q ] of the designated region according to the Q value, and generating map fusion data F2 of the region in a similar way.

The initial data includes provincial name two-stage region information. The data set C is a region code Q containing an integer type, and C contains all regions nationwide (filled with 0 for the region contained in the region map T but not contained in the data set B) so as to be directly applicable to the subsequent map dot matrix data D1, D2, etc. The map lattice data D1 and D2 are two-dimensional array matrixes based on shaping under a computer screen coordinate system, and each point in the lattice data is a pixel on the computer screen under the condition that an image is not zoomed.

The map lattice data D1 is computationally generated as:

firstly, loading a region mapping table T, and acquiring boundary gps data of an administrative region through an open online interface 'http:// api.map.baidu.com/apiv=1.3' of a hundred-degree map to acquire longitude and latitude data G of a contour point gps.

{ "name": "Beijing", [117.348611,40.581141], [117.348611,40.581141], [117.269771,40.560684], [117.247597,40.539766], [117.262995,40.512927], [117.208793,40.501552], [..,

{ "name": "Tianjin", [117.765602,39.400527], [117.699696,39.407463], [117.673211,39.386652], [117.668899,39.412087], [117.614081,39.407001], [117.601146,39.419485], [..

Contour point gps longitude and latitude data G

And a second step of establishing a right-hand coordinate system CR1 taking the computer pixels as units in the Y-axis direction by taking the lower left corner as an origin, and the X-axis to the right. The longitude and latitude data G of the contour point gps are mapped into a coordinate system CR1, and a data set H1 is obtained. Because the minimum unit of the computer image is 1 pixel, and the computer screen has a certain resolution range, the longitude and latitude data G of the contour points gps can not be directly applied to the calculation and display of the computer image. The data thereof needs to be mapped.

Let Xg_min be the minimum longitude value in the longitude and latitude data G of the contour point gps, xg_max be the maximum longitude value in the longitude and latitude data G of the contour point gps, yg_min be the minimum latitude value in the longitude and latitude data G of the contour point gps, and Yg_max be the maximum latitude value in the longitude and latitude data G of the contour point gps.

Let xi_min be the image X-axis start coordinate in coordinate system CR1, (left margin left), yi_min be the image Y-axis start coordinate in coordinate system CR1, (lower margin left), where left represents setting of 0 for the corresponding data point in the lattice, xi_min represents setting of 0 for all the data points in each column from 0 to xi_min in the lattice, yi_min represents setting of 0 for all the data points in each row from 0 to yi_min in the lattice.

Longitude and latitude (X axis) in the longitude and latitude data G of the contour point gps is calculated:

Wg＝Xg_max-Xg_min；

and similarly calculating latitude and longitude height (Y axis) in the longitude and latitude data G of the contour point gps:

Hg＝Yg_max-Yg_min；

calculating the longitude and latitude aspect ratio of gps in G:

Rg＝Wg÷Hg

calculating an image height Hi (Y axis) by setting a generated image width (X axis) as Wt, and an image width of wi=wt+2X xi_min (left and right sides are left with xi_min);

Wi＝Wt+2×Xi_min

Ht＝Wt÷Rg

Hi＝Ht+2×Yi_min

for each point Pg (x, y) of the longitude and latitude data G of the contour point gps, calculating to obtain a data set H1 in a coordinate system CR1, wherein each point coordinate in the H1 is H1 (x, y), and obtaining the data set H1:

H1(x)＝Floor((Pg(x)-Xg_min)×Wt÷Wg)+Xi_min

[ formula 1 ]

H1(y)＝Floor((Pg(y)-Yg_min)×Ht÷Hg)+Yi_min

[ formula 2 ]

Floor is a rounding function in the above formula.

In the third step, the H1 data is transformed into a data set H2 in the screen coordinate system CR2 (the left-hand coordinate system with the upper left corner as the origin, the X-axis to the right and the Y-axis to the bottom). The coordinates obtained from the longitude and latitude of the GPS are the right-hand coordinate system in the Y-axis direction, and the data are in the first quadrant (the fourth quadrant under the computer screen coordinate system CR 2). The computer screen coordinates are the left hand coordinate system with the Y axis downward, and the coordinate system of H1 needs to be converted into H2. This step is shown in fig. 5.

H2(x)＝H1(x)

Firstly, H1 (y) is downwards translated into Ht+2 x Yi_min to obtain y2=H2 (y) +Ht+2 x Yi_min,

h2 (y) = -y2, transformed to:

H2(y)＝Ht+2*Yi_mi n-H1(y)＝H i-H1(y)

fourth, creating lattice data K1 of [ Wi, H i ] size, initializing each point data to 0, and according to the region code Q in the region mapping table T corresponding to the currently processed region, for each coordinate point (x, y) in the H2 data set, let K1[ x, y ] =q. It can be found that each element of the H2 data set is a composition of coordinate points (x, y).

Referring to fig. 6, the solid dot value is Q and the blank portion is 0.

For example, the number of the cells to be processed,

{(50,10),(52,10),(53,11),(56,14),(57,14),(58,14),(59,14).......}

[ H1 dataset ]

{(50,90),(52,90),(53,89),(56,86),(57,86),(58,86),(59,86).......}

[ H2 dataset ].

In the fifth step, in the formulas 1 and 2, the contour line break may be caused by the operation on the floating point data and the rounding operation. For example, the X-axis calculation results in [1.82,3.16], rounded [1,3], which results in a 2-contour point loss on the X-axis. This requires a dot-sealing process on the non-sealing contour to generate the dot matrix K2. As in [ discontinuous contour ], P1, P2, P3, P4 are points of fracture on the contour.

It is necessary to find an adjacent breaking point P3 for P1 and then fill the contour point between P1 and P3. The implementation algorithm is as follows:

by scanning the lattice information, a broken contour point list L1[ P1, P2, P3, P4] is obtained, and whether the broken contour point P (x, y) is judged as follows:

Step a, establishing a matrix

Step b, setting t=0

Step c P (x, y) coordinates (x, y) plus a matrixEach element is obtained byFor each element [ xx, yy ] divided by the center point in the upper matrix]For example K1[ xx, yy]=q, i.e. t=t+1

Step d, if t > =2, the point P (x, y) is a continuous contour point, if t=1, the point P (x, y) is a broken contour point, if t=0, the point P (x, y) is an isolated point, let K1[ xx, yy ] =0, and directly remove the point.

Through the above steps, broken contour points L1[ P1, P2, P3, P4] can be obtained, and it is possible that all the broken contour points P1, [ P2, P3, P4] need to be filled with the contour so that the contour is not broken completely. For P2 to P1, the two points are themselves connected, with a full contour between them. No padding of P1, P2 is needed. The two points P1 and P3 break between them, and the two points P1 and P4 break; it may be necessary to fill back the profile of the broken portion. Thus, the shortest distance method is needed to determine that the filling between P1 and P3 is not the filling between P1 and P4. The distance between P1 and P3 is less than the distance between P1 and P4. It can be determined that the fill profile is needed between the two points P1 and P3. The algorithm below further describes how to determine whether two points are connected (connection is that there is a complete contour between the two points, and no filling of the contour is needed) or broken (breaking is that there is a missing complete contour between the two points, which means that it is necessary to see which point is closest to the current point and which other breaking point, filling the contour between it and the closest breaking point, such that the two broken contour points fill back with a complete contour).

See fig. 7A, 7B, where the hollow circle between P1, P3 in fig. 7B is the filled-in profile. This leaves the contour between P1, P3 intact.

Step e, taking out the first broken outline point P1 (x, y) from L1, and removing P1 (x, y) from L1;

step f, the first data P3 is fetched from the L1;

step g, judging whether the P1 is communicated with other contour points P3 and the like, if so, jumping to step f, judging the next contour point, otherwise, executing downwards, and the communication judging flow is as follows:

step g1, establishing a matrix

Step g2, setting an array variable S, adding P1 (x, y) into the array S

Step g3, fetching the first element PP1 (x, y) in the array S

Step g4 PP1 (x, y) coordinates (x, y) plus matrixEach element is obtained byFor each element [ xx, yy ] divided by the center point in the upper matrix]If [ xx, yy]The coordinates of P3, P1 and P3 represent connectivity, and the algorithm ends.

Step g5 if K1[ xx, yy ] =q, if yes, add PP (xx, yy) to array S.

Step g6, jumping to step g3 until the array S is empty, and enabling the P1 and the P3 not to be communicated.

Step h, calculating the distance between P1 and other contour points, and obtaining a point P3 (x 3, y 3) corresponding to the shortest distance:

(x 1, y 1) is the point P1 coordinate, (xn, yn) is other contour point coordinates, n is 1..the number of L1 elements-1, d represents the shortest distance from P1 to other contour points.

Step i, when x1 is not equal to x3, circulating x (x 1 to x 3), calculating y value, and making K1[ x, y ] =Q fill the contour point;

x3≠x1，x1≤x≤x3

step j, when the slope isAbove 1, a missing point coordinate may be generated for the Y-axis, such as y=2x, when x=1, y=2; when x steps 1, x=2, y=4, then y [2,4]The coordinate point 3 is left empty, resulting in the contour still not being closed. Thus, cycling y (y 1 to y 3), calculating the value of x, let K1[ x, y ]]=q fill contour points

y3≠y1，y1≤y≤y3，

And step k, when x1 is equal to x3, processing a vertical line under special conditions. Cycling y (y 1 to y 3), let K1[ x1, y ] =q fill the contour points;

and step l, when y1 is equal to y3, processing a horizontal line under special conditions. The contour points are filled with K1[ x, y1] =q by a loop x (x 1 to x 3)

Step e is skipped, the next contour point is processed until L1 is empty.

The algorithm ends, closing the broken contour.

It can be found that by the present invention, the contour points need to be filled between P1 and P3, which is divided into the following 3 cases:

p1 and P3 are on a horizontal line (i.e. the Y coordinates of P1 and P3 are the same);

p1 and P3 are on a perpendicular line (i.e. the X coordinates of P1 and P3 are the same);

3. in addition to the two cases described above (i.e., P1 and P3 are sloped).

For P1 and P3 being on a horizontal line (i.e., the Y coordinates of P1 and P3 are the same), x (x 1 to x 3) is cycled, let K1[ x, Y1] =q fill the contour point, x1 is the x coordinate of P1, and x3 is the x coordinate of P3. y1 is the y coordinate of P1. x steps from x1 to x3, 1 step at a time. Fig. 8A illustrates contour points where two hollow circles fill on a horizontal line.

P1 and P3 are on a vertical line (i.e., the X coordinates of P1 and P3 are the same), and y (y 1 to y 3) is cycled, such that K1[ X1, y ] =q fills the contour point. y1 is the y coordinate of P1 and y3 is the y coordinate of P3. x1 is the x coordinate of P1. y steps from y1 to y3, 1 step at a time. Fig. 8B illustrates contour points filled by two hollow circles on a vertical line.

In addition to the two cases described above (i.e., P1 and P3 are sloped):

when x1 is not equal to x3, cycling x (x 1 to x 3), calculating y value, let K1[ x, y ] =q fill the contour point;

/>

x3≠x1，x1≤x≤x3

when the slope isAbove 1, a missing point coordinate may be generated for the Y-axis, such as y=2x, when x=1, y=2; when x steps 1, x=2, y=4, then y [2,4]The coordinate point 3 is left empty, resulting in the contour still not being closed. Thus, cycling y (y 1 to y 3), calculating the value of x, let K1[ x, y ]]=q fill contour points;

y3≠y1，y1≤y≤y3；

see figure 8C for details on two outline points filled with hollow circles. The hollow dots are the filling outline dots.

And a sixth step, after the continuous contour line is obtained according to the fifth step, the inner area of the contour is still data 0, which represents a blank. It needs to be filled with a value Q. It is necessary to find the center of gravity point within the contour to achieve filling. Let the center point PC (x, y), x=x0+ (x 1-x 0)/2, y=y0+ (y 1-y 0)/2, (x 0, y 0) be the upper left angular position of the contour and (x 1, y 1) be the lower right angular position of the contour.

In the convex contour line, both the center point PC and the center point Pg are within the contour. But in a concave profile, the center point PC may be outside the profile.

The coordinates of the center of gravity point in the contour line need to be found, and the following is realized:

the center point coordinates PC (x, y) are obtained, x=x0+ (x 1-x 0)/2, y=y0+ (y 1-y 0)/2, (x 0, y 0) being the upper left angular coordinates of the contour and (x 1, y 1) being the lower right angular coordinates of the contour.

And (3) square scanning from the center point to the X axis, wherein if the square scanning passes through the contour line for 1 time, the X coordinate of the current center is the X coordinate of the center of gravity point of the contour. If the contour line passes 2 times, the current point is shown on the left side of the contour line, the X-axis coordinates xx1 and xx2 of the two passing contour points are recorded, and the X-axis coordinates of the gravity center point are obtained as xx=xx1+ (xx 2-xx 1). And obtaining coordinates [ xx, y ] which are coordinates of the gravity center point of the profile. The algorithm is as follows:

step n1 let k=x-axis coordinate of center point PC, let nc=0

Step n2:k=k+1

Step n3, if k2[ K, y ] =q, if nc=0, let xx1=k, nc=nc+1, loop k=k+1 until k2[ K, y ] =0; if nc=1, let xx2=k, nc=nc+1, loop k=k+1 until k2[ K, y ] =0

Step n4 step n2 is skipped until k > = Wi

Step n5, if nc=2, obtaining the abscissa xx=xx1+ (xx2-xx1) of the gravity center point Pg

Step n6, if nc=1, the abscissa xx of the gravity center Pg=the X-axis coordinate X of the center PC is obtained

Step n 7-if nc=0, the contour is not to the right of the center point. The algorithm ends, performing the X-axis negative scan below, the Y-axis square and negative scan.

Similarly, if the current point passes through the contour line 1 time from the center point to the negative direction of the X axis, the X coordinate of the current center is the X coordinate of the center point of gravity of the contour. If the contour line passes 2 times, the current point is shown on the right side of the contour line, the X-axis coordinates xx1 and xx2 of the two passing contour points are recorded, and the X-axis coordinate of the gravity center point is xx=xx1+ (xx 2-xx 1). And obtaining coordinates [ xx, y ] which are coordinates of the gravity center point of the profile.

Similarly, the center point Pg (x, Y) is obtained by scanning in the positive direction of the Y axis from the center point and in the negative direction of the Y axis.

After the coordinates Pg (x, y) of the center of gravity point in the contour are obtained, the numerical values of the region in the contour are filled according to the following algorithm:

step m1, establishing an array S, and adding Pg (x, y) into the array S

Step m2, establishing a matrix

Step m3, fetch the first element P1 (x, y) in array S and remove from S

Step m 4P 1 (x, y) coordinates [ x, y]Adding matrixEach element is obtained by

Step m5, for each element [ xx, yy ] in the upper matrix, judging K2[ xx, yy ] =0, letting K2[ xx, yy ] =Q, adding PP (xx, yy) to the array S.

And step m6, jumping to the step m3 until the array S is empty, and finishing filling the inside of the outline.

Each contour region is processed in a loop to obtain a lattice data series K3[ 1..n ]. n is the number of regions. K3[ 1..n ] are combined to generate reusable national lattice data D1.

For each region code in D1, extracting and generating a certain region multiplexing lattice data D2[ Qn ]. Qn is a set of Q values encoding different regions.

The extraction algorithm is as follows:

for one element Q of Qn in the collection

Scanning each point [ X, Y ] of D1, and if D1[ X, Y ] =Q, calculating maximum and minimum values Xmin, xmax, ymin and Ymax on the X axis and the Y axis.

Xmin＝Xmin-1，Xmax＝Xmax+1Ymin＝Ymin-1,Ymax＝Ymax+1

W＝Xmax-Xmin,H＝Ymax-Ymin

Creating lattice D2[ Q ] with the size of [ W, H ];

copying the region data corresponding to D1[ Rect (Xmin, xmax, ymin, ymax) ] into D2[ Q ] [ Rect (0, W, H) ] and wherein Rect represents a rectangular region.

The relevant lattice data of the present invention are exemplified as follows:

/>

map lattice data K3[1]

Map dot matrix data D1

Map lattice data D2 10

Map dot matrix data D2[20 ].

So far, it can be found that the invention has the following characteristics in technical aspect:

1. obtaining L1[ P1, P2, P3, P4] by using a contour point algorithm (broken contour point algorithm) for judging whether the contour point is broken or not;

2. taking out P1 from L1, wherein L1 is L1[ P2, P3, P4], and carrying out communication judgment (using a communication judgment algorithm) on each point [ P2, P3, P4] in L1 by using P1, wherein the two points (P1, P2) are communicated, the points (P1, P3) are not communicated by breaking, and the points (P1, P4) are also not communicated by breaking. Which way (P1, P3) and (P1, P4) are obtained may require filling the profile between the two segments, so that the profile is closed. However, how to determine whether to use the (P1, P3) between the two points to fill the contour instead of using the (P1, P4) between the two points to fill the contour requires a calculation of the shortest distance (shortest distance calculation algorithm). The distance between (P1, P3) is calculated by the shortest distance to be less than the distance between (P1, P4). This confirms that the broken contour points are to be replenished back from the point P1 to the point P3 (contour point filling algorithm). The hollow circle between the right sides P1, P3 is the filled-in profile. So that the contour line between P1 and P3 is not broken completely;

3. After the above-mentioned P1 is taken out for processing, at this time, L1[ P2, P3, P4] is taken out from L1, and similarly, L1 becomes L1[ P3, P4], and similarly, P2 carries out the above-mentioned processing on each element [ P2, P3] in L1, and so on, and the processing is circulated until L1 is empty.

In specific embodiments, as shown in fig. 9-12E, the find center of gravity algorithm is: after the continuous contour line is obtained, the contour inner area is still data 0, which represents a blank. It needs to be filled with a value Q. It is necessary to find the center of gravity point within the contour to achieve filling. Let the center point PC (x, y), x=x0+ (x 1-x 0)/2, y=y0+ (y 1-y 0)/2, (x 0, y 0) be the upper left angular position of the contour and (x 1, y 1) be the lower right angular position of the contour.

In the convex contour line, both the center point PC and the center point Pg are within the contour. But in a concave profile, the center point PC may be outside the profile. In the case of Sichuan as shown in FIG. 9, both the center point and the center of gravity are within the outline, but the inner Mongolian isocenter as shown in FIG. 11 is not within the outline like Gansu as shown in FIG. 10. Other cases, as shown in fig. 12A to 12E.

In one embodiment, the coordinates of the center of gravity point in the contour line need to be found, which is achieved as follows:

a. the center point coordinates PC (x, y) are obtained, x=x0+ (x 1-x 0)/2, y=y0+ (y 1-y 0)/2, (x 0, y 0) being the upper left angular coordinates of the contour and (x 1, y 1) being the lower right angular coordinates of the contour.

b. And (3) square scanning from the center point to the X axis, wherein if the square scanning passes through the contour line for 1 time, the X coordinate of the current center is the X coordinate of the center of gravity point of the contour. If the contour line passes 2 times, the current point is shown on the left side of the contour line, the X-axis coordinates xx1 and xx2 of the two passing contour points are recorded, and the X-axis coordinates of the gravity center point are obtained as xx=xx1+ (xx 2-xx 1). And obtaining coordinates [ xx, y ] which are coordinates of the gravity center point of the profile. The algorithm is as follows:

a) Let k=x-axis coordinate X of center point PC, let nc=0

b) k=k+1 (second step)

c) If k2[ K, y ] =q, if nc=0, let xx1=k, nc=nc+1, loop k=k+1 until k2[ K, y ] =0; if nc=1, let xx2=k,

nc=nc+1, cycling k=k+1 until k2[ K, y ] =0

d) Jump to the second step until k > = Wi

e) If nc=2, the center of gravity Pg abscissa xx=xxx1+ (xx 2-xx 1)

f) If nc=1, the abscissa xx of the gravity center pg=the X-axis coordinate X of the center point PC is obtained

g) If nc=0, the contour is not to the right of the center point. The algorithm ends, performing the X-axis negative scan below, the Y-axis square and negative scan.

c. Similarly, if the current point passes through the contour line 1 time from the center point to the negative direction of the X axis, the X coordinate of the current center is the X coordinate of the center point of gravity of the contour. If the contour line passes 2 times, the current point is shown on the right side of the contour line, the X-axis coordinates xx1 and xx2 of the two passing contour points are recorded, and the X-axis coordinate of the gravity center point is xx=xx1+ (xx 2-xx 1). And obtaining coordinates [ xx, y ] which are coordinates of the gravity center point of the profile. Similarly, the center point Pg (x, Y) is obtained by scanning in the positive direction of the Y axis from the center point and in the negative direction of the Y axis.

Although the embodiments of the present invention have been described above with reference to the accompanying drawings, the present invention is not limited to the above-described specific embodiments and application fields, and the above-described specific embodiments are merely illustrative, and not restrictive. Those skilled in the art, having the benefit of this disclosure, may effect numerous forms of the invention without departing from the scope of the invention as claimed.

Claims

1. A visual data processing method of financial risk control data is characterized by comprising the following steps,

2. The method for processing visual data of financial risk control data according to claim 1, further comprising, preferably:

the map dot matrix data D1 is reusable map dot matrix data.

3. The visual data processing method of financial risk control data according to claim 1, wherein,

4. The visual data processing method of financial risk control data according to claim 1, wherein the map lattice data D1 or the regional map lattice data D2 is based on a shaped two-dimensional array matrix in a computer screen coordinate system, and each point in the lattice data is one pixel on the computer screen without scaling of the image.

5. The visual data processing method of financial risk control data according to claim 1, wherein the map dot matrix data D1 is calculated by:

creating lattice data K1 with matrix size of [ Wi, hi ] and initializing each data in the K1 to be 0, wherein Wi represents width, hi represents height, wi is the maximum abscissa of a coordinate system CR2, and Hi is the maximum ordinate of the coordinate system CR 2;

processing each coordinate Point of the data set H2 according to the region coding in the region mapping table T corresponding to the current region _i (x, y) such that the K1 value of each point in the same region is a region code of the region to which the corresponding point belongs;

6. The visual data processing method of financial risk control data according to claim 5, wherein,

A certain contour point of the break in the lattice data K1;

firstly, finding broken contour points adjacent to the contour points;

7. The method for processing visual data of financial risk control data according to claim 6, comprising the following sub-steps,

the reference matrix is established as follows:

the variable t=0 is set and,

if t > =2, it means that the point P (x, y) is a continuous contour point;

if t=1, it means that point P (x, y) is a broken contour point;

the communication judging flow is as follows:

sub-step 2.4.1, establish a reference matrix:

8. The method for processing visual data of financial risk control data according to claim 7, further comprising the substep of, after substep 2.4,

x3≠x1，x1≤x≤x3

when the slope isAbove 1, missing point coordinates may be generated for the Y-axis, specifically: y=2x, there is x=1, y=2; when x steps 1, x=2, y=4, then y [2,4]3 coordinate points are left in between, so that the contour is still not closed;

y3≠y1-y1≤y≤y3，

to this end, the broken contour is closed.

9. The visual data processing method of financial risk control data according to claim 8, wherein,

step 3.1, let k=x-axis coordinate X of the center point PC, let nc=0;

step 3.2, k=k+1;

step 3.4, jump to step 3.2 until k > = Wi;

if nc=2, the center of gravity Pg abscissa xx=xx1+ (xx 2-xx 1);

step 4.1, establishing an array variable S, wherein the number of elements of the array is Wi x Hi, each element is an x and y coordinate, and adding Pg (x, y) into the array S after zeroing each element in the array variable S;

step 4.2, establishing a reference matrix:

and merging K3[ 1..n ] to map lattice data D1.

10. The visual data processing method of financial risk control data according to claim 2, wherein,

for one element Q of Qn in the set;

let xmin=xmin-1, xmax=xmax+1, ymin=ymin-1, ymax=ymax+1;

W＝Xmax-Xmin,H＝Ymax-Ymin；

creating lattice D2[ Q ] with the size of [ W, H ];

and copying rectangular area data corresponding to D1[ Rect (Xmin, xmax, ymin, ymax) ] into D2[ Q ] [ Rect (0, W, H) ], wherein Rect represents a rectangular area.