CN108108432B - Metaphor map construction generation method considering semantic data hierarchical features - Google Patents
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Abstract
The invention discloses a metaphor map construction generation method considering semantic data hierarchical features, which comprises the following steps: a data preparation stage; a map construction stage: step 1, constructing a metaphor map frame; and 2, carrying out map finishing on the metaphor map frame. The invention can map display and express semantic data with hierarchical relation characteristics, highlight the structural characteristics of the data, visually express data contents and display the data attribute characteristics; the comprehension cognition of abstract data is promoted by fully utilizing the visual cognition talent of a person and the familiarity with a map; a new data visualization mode is provided, a metaphorical map is used as a carrier, and introduction of map correlation techniques and methods is facilitated to assist analysis of abstract data without positioning features.
Description
Technical Field
The invention relates to visual expression of non-positioning semantic data, in particular to a metaphor map construction generation method considering semantic data hierarchical features.
Background
In the big data era, with the development of various software and hardware technologies, channels for acquiring data are more various and correspondingly, the data volume is greatly enriched. The data are effectively expressed, so that useful characteristics, rules and patterns can be extracted from a large amount of data, and the understanding and analysis of the data are promoted. The key point is to construct a proper data expression vector, and the excellent data expression vector not only needs to correctly express data, but also needs to be displayed to an audience in a proper form so as to facilitate the understanding of information.
According to whether the data contains the space positioning attribute or not, the data can be divided into space data and non-space data, and the latter is semantic data without space position characteristics. For spatial data, a map is generally used as a vector for expression analysis, and expression of non-spatial data is often performed by means of various graphic charts. When the graphic chart is used for data expression, the artistic aesthetic feeling is often lacked, or a certain professional basis is needed for analyzing and reading the expression content. Human beings have unique spatial cognitive advantages, face strange, abstract concepts or relations, often with the help of tangible spatial things to assist understanding thinking, use image thinking instead of abstract thinking. The map is a common spatial data expression vector, the map has been used as an important tool for human to know and understand spatial objects for hundreds of years, and the human establishes sufficient familiarity with the map in the process of reading and recognizing the map. Besides being used for expressing spatial data, the map can also be used as an expression vector of non-spatial data, namely a metaphorical map. The metaphor map reflects elements of the unfamiliar field into element objects in the map field familiar to people to establish mapping relation between a source target (map object) and an object target (expression object), so that the unfamiliar and abstract contents are displayed in a familiar and sensible form.
The map is a classical cognitive model, which can reflect the adjacency of elements in the transverse direction and can express the hierarchical relationship characteristics of data through surface domain registration. The metaphor map is used for expressing abstract hierarchical data without positioning features, data structures, relations and the like can be displayed obviously, and in addition, abundant technical methods in the cartography can provide powerful support for expression analysis of the data. However, at present, a systematic and systematic metaphor map construction method is not available, and popularization and use of the metaphor map are hindered.
Disclosure of Invention
The invention aims to solve the technical problem of providing a metaphor map structure generation method considering semantic data hierarchical features aiming at the defect that a systematic construction method of a metaphor map without positioning feature hierarchical data is lacked in the prior art.
The technical scheme adopted by the invention for solving the technical problems is as follows:
the invention provides a metaphor map construction generation method considering semantic data hierarchical features, which comprises the following steps:
a data preparation stage:
modeling the structural characteristics of hierarchical data, uniformly organizing the hierarchical data in a multi-branch tree form, and storing each hierarchical data item on one tree node of the multi-branch tree;
a map construction stage:
step 1, constructing a metaphor map frame: constructing a regular hexagon map base map, distributing space areas for all leaf nodes of the hierarchical data according to a linear guide sequence, and completing space area division of the rest nodes from bottom to top in a mode of combining child surface areas to generate parent surface areas according to the attribution relation among the hierarchical data to complete metaphor map frame construction;
step 2, carrying out map finishing on the metaphor map frame: symbolizing map elements in the metaphor map frame, expressing the hierarchical structure characteristics of data, and realizing the display of the property characteristics of the data.
Further, the method for constructing the metaphor map frame in the step 1 specifically comprises the following steps:
step 1.1, constructing a discrete point set used for generating a base map of a regular hexagon;
step 1.2, taking each discrete point of the discrete point set in the step 1.1 as a generating element, and implementing a Thiessen polygon construction algorithm to obtain a map base map with a unit form of a regular hexagon;
step 1.3, constructing a fractal curve for guiding map area division according to a fractal generation rule;
step 1.4, under the guidance of the fractal curve constructed in the step 1.3, distributing corresponding areas for leaf nodes of the hierarchical data;
and step 1.5, combining the leaf node areas in the step 1.4 according to the attribution inclusion relationship in the hierarchical data structure to generate a father node area, continuously repeating the process, completing the construction work of all the node areas, and further obtaining the metaphor map frame.
Further, the method for constructing the discrete point set used as the base map for generating the regular hexagon map in step 1.1 of the present invention specifically comprises:
step 1.1.1, setting an initial point coordinate as L (x, y), setting an incremental step length along a y-axis direction as sy, setting an incremental step length along an x-axis direction as sx, setting a set point set longitudinal width as n, setting a set point set transverse width as m, setting a transverse pedometer i and a longitudinal pedometer j, initializing the pedometer, setting i as 0 and j as 0, and starting a discrete point construction process;
step 1.1.2, longitudinal point formation is carried out, and the coordinate of a new point formation is set to be Lnew(xa,ya)，xa＝x,ya＝y+(j*sy) Updating the value j of the longitudinal pedometer to be j + 1;
step 1.1.3, continuously repeating step 1.1.2 until j is equal to n, namely completing the construction of a row of points; updating the value i of the horizontal pedometer to be i +1, j to be 0, and updating the initial point abscissa x to be x + sx; the vertical coordinate of the initial point is adjusted adaptively, when i is odd number, the value is changed, namely y is equal to y-syAnd/2, when i is an even number, changing the value of y to y + sy/2；
And step 1.1.4, continuously repeating the step 1.1.3 until the pedometer value meets the termination condition, namely i is m, and finishing the construction of the discrete point set.
Further, in step 1.3 of the present invention, a fractal curve for guiding map area division is constructed, a Gosper curve is selected, and a character drawing method is used for constructing the Gosper curve, and the method specifically comprises:
step 1.3.1, constructing a character replacement rule F, and performing one-to-many type replacement on characters appearing in a character sequence R; the specific replacement rule is f (a) { a-B + a + + AA + B- }, i.e., replacing the character "a" with the character set "a-B + a + + AA + B-", and f (B) { + a-BB-B-a + + a + B }, i.e., replacing the character "B" with the character set "+ a-BB-B-a + + a + B"; wherein, the characters "A" and "B" are drawing characters, and the characters "+", "" are turning characters; after the characters A and B in the character sequence are replaced for one time, the iterative process is considered to be completed for one time;
step 1.3.2, setting the initial value of the character sequence R as 'A', and performing n iterations on the character sequence R according to the set iteration times n and the rule in the step 1.3.1;
step 1.3.3, drawing a curve according to the generated result character string sequence R; the drawing process is as follows:
step A, setting a drawing point p (x, y), setting a set L for storing curve break points, adding an initial point p (x, y) into the set L, and setting a drawing step length s and s in step 1.1.1ySetting an initial value theta of a drawing angle when the two values are equal;
step B, reading the character contents in the sequence R one by one, updating the coordinates of the drawing points when the characters are ' A ' or ' B ', wherein x is x + s cos (theta), y is y-s sin (theta), adding the updated drawing points into the set L, updating the drawing angle when the characters are ' + ' or ' -, when the characters meet with ' + ' or theta is theta + pi/3, and when the characters meet with ' - ', theta is theta-pi/3;
and C, for the point set L obtained in the step B, connecting points in the L in series according to the adding sequence of the points to form a line, and obtaining a Gosper curve.
Further, in step 1.4 of the present invention, under the guidance of the fractal curve, the method for allocating corresponding regions to leaf nodes of hierarchical data specifically includes:
step 1.4.1, determining the total number n of hexagon units required by composition, determining the total number v of value attributes required to be expressed, and calculating the number u of the value attributes represented by each hexagon unit as v/n;
step 1.4.2, extracting all leaf nodes of the hierarchical data in sequence, calculating the number of hexagons required to be distributed according to k which is g/u for each leaf node according to the attribute value g of the required expression value, wherein k needs to be rounded, distributing hexagons with corresponding number for the leaf nodes along the Gosper curve guiding sequence, and fusing the distributed hexagons to form corresponding areas of the leaf nodes.
Further, the method for generating the parent node area by combining the leaf node areas in step 1.5 of the present invention specifically includes:
step 1.5.1, according to the inclusion relationship of the parent node and the child node, merging the areas corresponding to the leaf nodes belonging to the same parent node, and taking the merged result area as the corresponding area of the parent node;
and step 1.5.2, for the father node areas obtained in the step 1.5.1, generating corresponding areas of higher-level father nodes according to the inclusion relation of father and son nodes, and constructing the high-level areas from bottom to top by combining the low-level areas until the topmost father nodes are reached.
Further, the method for performing map finishing on the metaphor map frame in step 2 of the present invention specifically comprises:
2.1, designing a metaphor map boundary form according to the hierarchical relation corresponding to different regional objects in the metaphor map frame;
2.2, performing character marking design on the metaphor map according to the hierarchical relation;
and 2.3, performing hierarchical rendering or classified rendering on the map area according to the hierarchy or classification corresponding to the objects in different areas.
Further, the method for designing the metaphor map boundary form according to the hierarchical relationship in step 2.1 of the present invention specifically comprises:
the boundary of each level region in the metaphor map frame, namely the outline outside the region, is extracted, the boundary is designed from the aspects of shape, size, brightness and color according to the level relationship corresponding to the region object and by combining a map symbol visual parameter method, and the region level relationship is distinguished through a boundary pattern.
Further, the method for rendering the map area in step 2.3 of the present invention specifically includes:
and (3) performing map area hierarchical rendering: selecting value attributes required to be expressed in map rendering, selecting rendering color types, rendering colors with different brightness sizes in different areas according to the size of each area attribute value, and reflecting the size of the attribute values through the size of color brightness;
and (3) map area classification rendering: selecting attributes for distinguishing the types of the regional objects, binding colors with different hues for different attribute values, and establishing a mapping relation between the attribute values and the colors with the hues; and aiming at each map area, setting colors corresponding to the hues according to the attribute values for identifying the types of the map areas, so as to realize the purpose of distinguishing the types of the area objects through the hue types.
The invention has the following beneficial effects: the metaphor map construction generation method considering the semantic data hierarchical features has the following advantages: 1. the method can be used for carrying out map display expression on semantic data with hierarchical relation characteristics, highlighting the structural characteristics of the data, carrying out visual expression on data contents and displaying the characteristics of data attributes. 2. The comprehension and cognition of abstract data are promoted by fully utilizing the visual cognition talent of a person and the familiarity with a map. 3. A new data visualization mode is provided, a metaphorical map is used as a carrier, and introduction of map correlation techniques and methods is facilitated to assist analysis of abstract data without positioning features.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a schematic flow diagram of the process of the present invention.
FIG. 2 is a schematic diagram of a discrete point generator in the present invention.
Fig. 3 is a schematic view of a hexagonal base pattern according to the present invention.
FIG. 4 is a schematic diagram of the Gosper curve of the present invention.
Fig. 5 is a schematic diagram of the registration of the Gosper curve and the hexagonal base map in the invention.
FIG. 6 is a leaf node map region of hierarchical data in accordance with the present invention.
FIG. 7 is a map region of a parent node of a leaf node of hierarchical data in the present invention.
FIG. 8 is a schematic diagram of a map region of a hierarchical data root node according to the present invention.
Fig. 9 is a schematic diagram of a metaphorical map framework in accordance with the present invention.
Fig. 10 is a schematic diagram of a metaphorical map boundary pattern in accordance with the present invention.
FIG. 11 is a schematic diagram of metaphorical map labeling in accordance with the present invention.
Fig. 12 is a schematic diagram of hierarchical rendering of a metaphorical map in accordance with the present invention.
Fig. 13 is a schematic diagram of the metaphor map classification rendering in the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
As shown in fig. 1, a metaphorical map structure generation method considering semantic data hierarchy features according to an embodiment of the present invention includes the following steps:
a data preparation stage:
modeling the structural characteristics of hierarchical data, uniformly organizing the hierarchical data in a multi-branch tree form, and storing each hierarchical data item on one tree node of the multi-branch tree;
a map construction stage:
step 1, constructing a metaphor map frame: constructing a regular hexagon map base map, distributing space areas for all leaf nodes of the hierarchical data according to a linear guide sequence, and completing space area division of the rest nodes from bottom to top in a mode of combining child surface areas to generate parent surface areas according to the attribution relation among the hierarchical data to complete metaphor map frame construction;
step 2, carrying out map finishing on the metaphor map frame: symbolizing map elements in the metaphor map frame, expressing the hierarchical structure characteristics of data, and realizing the display of the property characteristics of the data.
In another embodiment of the present invention, the method comprises the following steps:
1. and constructing a discrete point generator.
The construction method of the discrete point generating element comprises the following steps:
(1 initial point coordinates are set to L (x, y) and the longitudinal (i.e. along the y-axis) incremental step size is set to sySetting the incremental step size in the transverse direction (i.e., along the x-axis) to sxThe set point set has a longitudinal width n, the set point set has a transverse width m,setting a horizontal pedometer i and a vertical pedometer j, initializing the pedometers, setting i to be 0 and j to be 0, and starting the construction process of discrete points.
(2, carrying out longitudinal point formation, setting the new point formation coordinate as Lnew(xa,ya)，xa＝x,ya＝y+(j*sy) And updating the value j of the vertical pedometer to be j + 1.
(3, repeating the step (2) continuously until j equals n, namely completing the construction of a row of points, updating the horizontal pedometer value i equals i +1, j equals 0, and updating the initial point abscissa x equals x + sx. The vertical coordinate of the initial point is adjusted adaptively, when i is odd number, the value is changed, namely y is equal to y-syAnd/2, when i is an even number, changing the value of y to y + sy/2。
(4, repeating the step (3) continuously until the pedometer value meets the termination condition, namely i is m.
2. And (3) inputting the discrete point set obtained in the step (1) into a Thiessen polygon construction algorithm to obtain a Thiessen polygon set. And eliminating polygons which are positioned at the edge part of the Thiessen polygon set and are not regular hexagons in shape to obtain a seamless spliced regular hexagon set, wherein the result is shown in figure 3.
The Thiessen polygon is also called Voronoi Diagram (Georgy Voronoi), named Georgy Voronoi, and is composed of a set of continuous polygons composed of perpendicular bisectors connecting two adjacent point straight lines. The Thiessen polygon is a subdivision of a spatial plane, and is characterized in that any position in the polygon is closest to a sampling point (such as a residential point) of the polygon and is far from the sampling point in an adjacent polygon, and each polygon contains only one sampling point.
3. And constructing a Gosper fractal curve for guiding map area division.
The method comprises the following steps of (1) constructing a character replacement rule F, and performing one-to-many replacement on characters appearing in a character sequence R, wherein the specific replacement rule is F (A) ({ A-B + A + + AA + B- }, namely replacing the character "A" with a character set of 'A-B- + A + + AA + B-', and F (B) { + A-BB-B-A + + A + B }, namely replacing the character "B" with a character set of '+ A-BB-B-A + + A + B'.
(2, setting the initial value of the character sequence R as 'A', and according to the set iteration number n, carrying out n iterations on the character sequence R according to the rule in the step (1).
(3, drawing a curve according to the generated result character string sequence R, wherein the drawing process comprises the following steps:
step A: setting a drawing point p (x, y), setting a set L for storing curve break points, adding an initial point p (x, y) into the set L, setting a drawing step s to be equal to sy in the step 1 to construct a discrete point generator (1), and setting a drawing angle initial value theta.
And B: reading the character contents in the sequence R one by one, updating the coordinates of the drawing points when the characters are ' A ' or ' B ', wherein x is x + s cos (theta), y is y-s sin (theta), adding the updated drawing points into the set L, updating the drawing angle when the characters are ' + ' or ' -, when the characters meet ' + ' or theta is theta + pi/3, and when the characters meet ' - ', theta is theta-pi/3.
And C: and (4) for the point set L obtained in the step B, connecting points in the L in series according to the adding sequence of the points to form a line, and obtaining a Gosper curve.
Fig. 4 shows the constructed Gosper curve in the above process, which is matched with the regular hexagon base map (fig. 3) obtained in step 2, so that each curve break point coincides with the hexagon center point (i.e. the taison polygon generating element point), and the matching result is shown in fig. 5.
4. And (4) defining the allocation of the node areas.
(1, determining the total number n of hexagon units required by the composition, determining the total number v of value attributes required to be expressed, and calculating the number u of the value attributes represented by each hexagon unit as v/n.
(2, extracting all leaf nodes of the hierarchical data in sequence, calculating the number k of hexagons required to be allocated to each leaf node according to the attribute value g of the required expression value, allocating the hexagons of the corresponding number to each leaf node along the Gosper curve guide sequence, and fusing the allocated hexagons to form corresponding areas of the leaf nodes, as shown in FIG. 6.
(3, according to the inclusion relationship of the parent node and the child node, merging the regions corresponding to the leaf nodes belonging to one parent node, and using the merged result region as the corresponding region of the parent node, as shown in fig. 7.
(4, for the parent node region obtained in step (3), generating a corresponding region of a higher-level parent node according to the inclusion relationship of parent and child nodes, and so on, constructing the higher-level region by combining the lower-level regions from bottom to top until reaching the topmost parent node, as shown in fig. 8.
After the distribution of all the node areas is completed from bottom to top, the metaphorical map frame shown in fig. 9 can be obtained.
5. And (5) finishing the bottom drawing.
Firstly, extracting the boundary of each hierarchical region, namely the outer contour of the region, and designing the boundary from the aspects of shape, size, brightness, color and the like according to the hierarchical relationship corresponding to the region object and combining with Bertin map symbol visual parameter theory, as shown in FIG. 10, the region hierarchical relationship can be distinguished through the boundary style.
(2, the areas in the graph are labeled with characters, firstly, the attribute which needs to be labeled in each area is determined, generally the name of the area object or other self-defined attributes, according to the hierarchical level of the labeled area, the labeled size, style, color and the like are designed to ensure that the hierarchical relationship among labels is highlighted, and the result is shown in FIG. 11.
The rendering operation is developed to each area belonging to the same level by combining specific rendering requirements, and the specific description is as follows:
and (3) performing map area hierarchical rendering: selecting the value attribute required to be expressed by map rendering, selecting the type of rendering color, rendering colors with different brightness sizes for different areas according to the size of the attribute value of each area, reflecting the size of the attribute value through the size of the brightness of the color, and showing the grading rendering result in figure 12.
And (3) map area classification rendering: selecting the attribute for distinguishing the type of each region object, binding colors with different hues for different attribute values, and establishing the mapping relation between the attribute values and the colors with various hues. For each map area, according to the attribute value for identifying the type of the map area, the color of the corresponding hue is set for the map area, so that the object types of the area are distinguished through the hue type, and the classified rendering result can be shown in fig. 12.
The method of the invention has the following advantages: 1. the method can be used for carrying out map display expression on semantic data with hierarchical relation characteristics, highlighting the structural characteristics of the data, carrying out visual expression on data contents and displaying the characteristics of data attributes. 2. The comprehension and cognition of abstract data are promoted by fully utilizing the visual cognition talent of a person and the familiarity with a map. 3. A new data visualization mode is provided, a metaphorical map is used as a carrier, and introduction of map correlation techniques and methods is facilitated to assist analysis of abstract data without positioning features.
It will be understood that modifications and variations can be made by persons skilled in the art in light of the above teachings and all such modifications and variations are intended to be included within the scope of the invention as defined in the appended claims.
Claims (7)
1. A metaphor map construction generation method considering semantic data hierarchy features is characterized by comprising the following steps:
a data preparation stage:
modeling the structural characteristics of hierarchical data, uniformly organizing the hierarchical data in a multi-branch tree form, and storing each hierarchical data item on one tree node of the multi-branch tree;
a map construction stage:
step 1, constructing a metaphor map frame: constructing a regular hexagon map base map, distributing space areas for all leaf nodes of the hierarchical data according to a linear guide sequence, and completing space area division of the rest nodes from bottom to top in a mode of combining child surface areas to generate parent surface areas according to the attribution relation among the hierarchical data to complete metaphor map frame construction;
step 2, carrying out map finishing on the metaphor map frame: symbolizing map elements in a metaphor map frame, expressing the hierarchical structure characteristics of data, and realizing the display of the property characteristics of the data;
the method for constructing the metaphor map frame in the step 1 specifically comprises the following steps:
step 1.1, constructing a discrete point set used for generating a base map of a regular hexagon;
step 1.2, taking each discrete point of the discrete point set in the step 1.1 as a generating element, and implementing a Thiessen polygon construction algorithm to obtain a map base map with a unit form of a regular hexagon;
step 1.3, constructing a fractal curve for guiding map area division according to a fractal generation rule;
step 1.4, under the guidance of the fractal curve constructed in the step 1.3, distributing corresponding areas for leaf nodes of the hierarchical data;
step 1.5, combining the leaf node areas in the step 1.4 according to the attribution inclusion relationship in the hierarchical data structure to generate a father node area, continuously repeating the process, completing the construction work of all the node areas, and then obtaining a metaphor map frame;
the method for constructing the discrete point set used for generating the base map of the regular hexagon map in the step 1.1 specifically comprises the following steps:
step 1.1.1, setting an initial point coordinate as L (x, y), setting an incremental step length along a y-axis direction as sy, setting an incremental step length along an x-axis direction as sx, setting a set point set longitudinal width as n, setting a set point set transverse width as m, setting a transverse pedometer i and a longitudinal pedometer j, initializing the pedometer, setting i as 0 and j as 0, and starting a discrete point construction process;
step 1.1.2, longitudinal point formation is carried out, and the coordinate of a new point formation is set to be Lnew(xa,ya)，xa＝x,ya＝y+(j*sy) Updating the value j of the longitudinal pedometer to be j + 1;
step 1.1.3, continuouslyRepeating the step 1.1.2 until j is equal to n, and completing the construction of a list of points; updating the value i of the horizontal pedometer to be i +1, j to be 0, and updating the initial point abscissa x to be x + sx; the vertical coordinate of the initial point is adjusted adaptively, when i is odd number, the value is changed, namely y is equal to y-syAnd/2, when i is an even number, changing the value of y to y + sy/2；
And step 1.1.4, continuously repeating the step 1.1.3 until the pedometer value meets the termination condition, namely i is m, and finishing the construction of the discrete point set.
2. The metaphor map construction generation method considering semantic data hierarchical features of claim 1, wherein a fractal curve for guiding map area division is constructed in step 1.3, a gossper curve is selected, and a character drawing method is used for constructing the gossper curve, and the method specifically comprises the following steps:
step 1.3.1, constructing a character replacement rule F, and performing one-to-many type replacement on characters appearing in a character sequence R; the specific replacement rule is f (a) { a-B + a + + AA + B- }, i.e., replacing the character "a" with the character set "a-B + a + + AA + B-", and f (B) { + a-BB-B-a + + a + B }, i.e., replacing the character "B" with the character set "+ a-BB-B-a + + a + B"; wherein, the characters "A" and "B" are drawing characters, and the characters "+", "" are turning characters; after the characters A and B in the character sequence are replaced for one time, the iterative process is considered to be completed for one time;
step 1.3.2, setting the initial value of the character sequence R as 'A', and performing n iterations on the character sequence R according to the set iteration times n and the rule in the step 1.3.1;
step 1.3.3, drawing a curve according to the generated result character string sequence R; the drawing process is as follows:
step A, setting a drawing point p (x, y), setting a set L for storing curve break points, adding an initial point p (x, y) into the set L, and setting a drawing step length s and s in step 1.1.1ySetting an initial value theta of a drawing angle when the two values are equal;
step B, reading the character contents in the sequence R one by one, updating the coordinates of the drawing points when the characters are ' A ' or ' B ', wherein x is x + s cos (theta), y is y-s sin (theta), adding the updated drawing points into the set L, updating the drawing angle when the characters are ' + ' or ' -, when the characters meet with ' + ' or theta is theta + pi/3, and when the characters meet with ' - ', theta is theta-pi/3;
and C, for the point set L obtained in the step B, connecting points in the L in series according to the adding sequence of the points to form a line, and obtaining a Gosper curve.
3. The metaphorical map construction generation method considering semantic data hierarchical features according to claim 2, characterized in that, in step 1.4, under the guidance of a fractal curve, the method for allocating corresponding areas to leaf nodes of hierarchical data specifically includes:
step 1.4.1, determining the total number n of hexagon units required by composition, determining the total number v of value attributes required to be expressed, and calculating the number u of the value attributes represented by each hexagon unit as v/n;
step 1.4.2, extracting all leaf nodes of the hierarchical data in sequence, calculating the number of hexagons required to be distributed according to k which is g/u for each leaf node according to the attribute value g of the required expression value, wherein k needs to be rounded, distributing hexagons with corresponding number for the leaf nodes along the Gosper curve guiding sequence, and fusing the distributed hexagons to form corresponding areas of the leaf nodes.
4. The metaphorical map construction generation method considering semantic data hierarchical features according to claim 1, wherein the method for generating the parent node region by merging leaf node regions in step 1.5 specifically comprises:
step 1.5.1, according to the inclusion relationship of the parent node and the child node, merging the areas corresponding to the leaf nodes belonging to the same parent node, and taking the merged result area as the corresponding area of the parent node;
and step 1.5.2, for the father node areas obtained in the step 1.5.1, generating corresponding areas of higher-level father nodes according to the inclusion relation of father and son nodes, and constructing the high-level areas from bottom to top by combining the low-level areas until the topmost father nodes are reached.
5. The metaphor map structure generation method considering semantic data hierarchy features according to claim 1, wherein the method for performing map finishing on the metaphor map frame in step 2 specifically comprises:
2.1, designing a metaphor map boundary form according to the hierarchical relation corresponding to different regional objects in the metaphor map frame;
2.2, performing character marking design on the metaphor map according to the hierarchical relation;
and 2.3, performing hierarchical rendering or classified rendering on the map area according to the hierarchy or classification corresponding to the objects in different areas.
6. The metaphor map construction generation method considering semantic data hierarchical features according to claim 5, wherein the method for designing the metaphor map boundary form according to the hierarchical relationship in step 2.1 specifically comprises:
the boundary of each level region in the metaphor map frame, namely the outline outside the region, is extracted, the boundary is designed from the aspects of shape, size, brightness and color according to the level relationship corresponding to the region object and by combining a map symbol visual parameter method, and the region level relationship is distinguished through a boundary pattern.
7. The metaphor map structure generation method considering semantic data hierarchy features according to claim 5, wherein the method for rendering the map area in step 2.3 is specifically:
and (3) performing map area hierarchical rendering: selecting value attributes required to be expressed in map rendering, selecting rendering color types, rendering colors with different brightness sizes in different areas according to the size of each area attribute value, and reflecting the size of the attribute values through the size of color brightness;
and (3) map area classification rendering: selecting attributes for distinguishing the types of the regional objects, binding colors with different hues for different attribute values, and establishing a mapping relation between the attribute values and the colors with the hues; and aiming at each map area, setting colors corresponding to the hues according to the attribute values for identifying the types of the map areas, so as to realize the purpose of distinguishing the types of the area objects through the hue types.
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