Visualization method, device and electronic device for coupling geographical name tags with TreeMap dissection

Abstract

The present invention provides a visualization method, device and electronic device for coupling geographical name tags with TreeMap dissection, which relates to the field of geographic information science and visualization. The method includes: extracting all scenic spots in a city, determining the font size of the scenic spots according to the popularity of the scenic spots, and determining the weight of the city according to the weighted character count of all scenic spots and the font size of the scenic spots; according to the weight of each city in the complete list of cities, distributing each city in the form of a node to the shortest side direction of a preset canvas to obtain the canvas after layout; sequentially placing the scenic spot labels corresponding to each scenic spot until the layout of all scenic spots in the city in the rectangular space is completed, traversing all cities until the visualization layout of all cities in the canvas after layout is completed, and displaying the visualization layout. The present invention uses the Squarified layout algorithm, combines geographical name tags, and maps the locations along the spatial path to the canvas space according to the weights, solving the problem of the imbalance between global and local information expression in traditional path maps.

Description

Technical Field

[0001] The present invention relates to the field of geographic information science and visualization, and particularly to a visualization method, device, and electronic device that couple place name tags with TreeMap dissection. Background Art

[0002] With the rapid development of information and communication technology (ICT), the expression form of geographic information has gradually expanded from traditional standard maps to the field of pan-maps. The function of maps is no longer limited to the accurate presentation of spatial positions, but has begun to incorporate diverse semantic information. As a core component of geographic information, the visualization form of points of interest (POIs) has become particularly important in scenarios such as travel navigation, dining recommendations, and commercial site selection. However, traditional POI visualization methods mostly focus on the display of POIs in a single area, ignoring the spatial relationships and semantic connections between multiple areas, that is, the visualization of spatial paths.

[0003] Visualizing spatial paths not only requires showing the starting point and the ending point, but also needs to include a series of waypoints along the path and their related information. The essence of a path is a data structure with spatial sequence attributes. By visualizing the spatial structure of the path and the semantic features of the areas along the way, it can help users understand the geographical distribution, important nodes, and the associations between multi-dimensional information along the path. For example, in the travel planning scenario, visualizing spatial paths can not only provide a global perspective of the route, but also refine and show the distribution and characteristics of scenic spots in the cities along the way, providing comprehensive support for user decision-making.

[0004] However, existing spatial path visualization technologies generally face two main challenges: First, traditional maps are difficult to balance the expression of global and local information. At a small scale, although the global structure of the path can be shown, the distribution of POIs within the city is difficult to reflect. At a large scale, although the POI information in the local area of the city can be refined, the global perspective is often lost, making it difficult to show the overall relationship between the cities involved in the path. When users obtain global and local information, they need to frequently switch perspectives, reducing the overall usability. Second, the spatial utilization efficiency in current path visualization is relatively low. Traditional path visualization methods mostly use polylines as the core elements, only presenting the geometric information of the path, but ignoring the attributes and semantic information of the areas along the way, resulting in a large waste of map spatial resources and insufficient information density. Currently, there is no technical solution that can solve the above technical problems, nor a visualization method, device, and electronic device that couple place name tags with TreeMap dissection. Summary of the Invention

[0005] The present invention provides a visualization method, apparatus, and electronic device for coupling place name tags with TreeMap dissection. Through the coupled expression of the semantic information and spatial distribution of path data, the comprehensive visualization of cities and point-of-interest information along the path is realized.

[0006] In a first aspect, the present invention provides a visualization method for coupling place name tags with TreeMap dissection, including:

[0007] Receiving and responding to user input, processing the starting point and the ending point according to a preset navigation software to obtain a sequence of spatial coordinate points corresponding to the starting point, all waypoints, and the ending point, processing the sequence of spatial coordinate points using a preset polyline densification algorithm to obtain densified fold points, using the Turf library to perform city matching on the densified fold points, identifying the city to which each densified fold point belongs, and generating a complete list of cities, where the user input is generated after the user inputs the starting point and the ending point in the preset navigation software;

[0008] For each city in the complete list of cities, extracting all scenic spots of the city, determining the font size of the scenic spots according to the popularity of each scenic spot, and determining the weight of the city according to the weighted character count and font size of all scenic spots;

[0009] According to the weight of each city in the complete list of cities, using the Squarified layout algorithm to assign each city to the shortest side direction of a preset canvas in the form of a node one by one until the layout of all cities in the preset canvas is completed, obtaining a post-layout canvas containing the rectangular space corresponding to each city;

[0010] For each city in the post-layout canvas, sorting the scenic spots in the city from largest to smallest according to the popularity of each scenic spot to obtain a scenic spot popularity ranking, and starting from the center position of the rectangular space corresponding to the city according to the scenic spot popularity ranking, placing the scenic spot labels corresponding to each scenic spot in sequence until the layout of all scenic spots in the rectangular space of the city is completed. Traverse all cities until the visualization layout of all cities in the post-layout canvas is completed, and display the visualization layout.

[0011] According to the visualization method for coupling place name tags with TreeMap dissection provided by the present invention, the processing of the sequence of spatial coordinate points using a preset polyline densification algorithm to obtain densified fold points includes:

[0012] Using a preset polyline densification algorithm to perform thinning operation on the sequence of spatial coordinate points to obtain densified fold points;

[0013] Wherein, the distance between each coordinate point in the densified fold points is the same.

[0014] According to the visualization method of coupling place name tags and TreeMap dissection provided by the present invention, determining the weight of the city according to the weighted number of characters of all scenic spots and the font size of the scenic spots includes:

[0015]

[0016] Among them, is the total weight of the city, represents the total number of scenic spots included in the city, represents the number of characters in the POI label of the th scenic spot, represents the font size of the

[0017] According to the visualization method of coupling place name tags and TreeMap dissection provided by the present invention, using the Squarified layout algorithm to assign each city to the shortest side direction of the preset canvas in the form of a node one by one until the layout of all cities in the preset canvas is completed, and obtaining the layout canvas containing the rectangular space corresponding to each city includes:

[0018] For the weight of any city, in the process of assigning the city to the shortest side direction of the preset canvas in the form of a node, obtain the first city assignment layout in the largest rectangular space corresponding to the rectangular space where the previous city has been assigned, and obtain the second city assignment layout of the city in the remaining space to be assigned. The remaining space to be assigned is the remaining space in the preset canvas after removing all the assigned rectangular spaces;

[0019] Calculate the first aspect ratio corresponding to the first city assignment layout, calculate the second aspect ratio corresponding to the second city assignment layout, and determine the city assignment layout with the smallest absolute value of the difference from 1 in the first aspect ratio and the second aspect ratio as the target assignment layout of the city, and assign the city to the preset canvas in the form of a node according to the target assignment layout;

[0020] Traverse all cities until the layout of all cities in the preset canvas is completed, and obtain the layout canvas containing the rectangular space corresponding to each city.

[0021] According to the visualization method of coupling place name tags and TreeMap dissection provided by the present invention, starting from the central position of the rectangular space corresponding to the city, place the scenic spot labels corresponding to each scenic spot in turn until the layout of all scenic spots in the city in the rectangular space is completed, including:

[0022] For any scenic spot label, when placing it, perform a collision detection with the labels already placed in the rectangular space. In the case of overlap, move it outward along the Archimedes spiral path and re-detect until there is no overlap, then the scenic spot label is fixed;

[0023] Traverse all scenic spots sorted by the scenic spot popularity until all scenic spot labels are placed, and complete the layout of all scenic spots in the city in the rectangular space.

[0024] According to the visualization method of coupling place name labels and TreeMap dissection provided by the present invention, before performing a collision detection with the labels already placed in the rectangular space when placing, the method further includes:

[0025] For any scenic spot, process the scenic spot label corresponding to the scenic spot according to the font size of the scenic spot, and obtain the scenic spot label after processing the font size;

[0026] Traverse all scenic spots to obtain the scenic spot labels after processing the font size corresponding to each scenic spot.

[0027] According to the visualization method of coupling place name labels and TreeMap dissection provided by the present invention, after traversing all cities until the visual layout of all cities in the layout canvas is completed, the method further includes:

[0028] For the rectangular space corresponding to any city in the layout canvas, select any current color from a preset number of coloring schemes for assignment, and judge whether the current color conflicts with the colors of adjacent rectangular spaces based on the adjacency matrix;

[0029] If a color conflict occurs, abandon the current color, select the next color from the preset number of coloring schemes for assignment until the current color does not conflict with the colors of adjacent rectangular spaces;

[0030] If all colors in the preset number of coloring schemes conflict with the colors of adjacent rectangular spaces, then backtrack to the coloring of the rectangular space corresponding to the previous city and re-adjust the color matching until the rectangular spaces corresponding to all cities do not conflict with the colors of adjacent rectangular spaces, and obtain the layout canvas after color coloring.

[0031] According to the visualization method of coupling place name labels and TreeMap dissection provided by the present invention, after obtaining the layout canvas after color coloring, the method further includes:

[0032] Adopt a Catmull-Rom spline curve to sequentially connect the central positions of each city in the rectangular space according to the order of cities in the complete list of cities, and obtain the connected layout canvas.

[0033] In a second aspect, a visualization device for coupling place name tags with TreeMap dissection is provided, including:

[0034] A receiving unit, which is configured to receive and respond to user input, process a starting point and an ending point according to a preset navigation software, obtain a sequence of spatial coordinate points corresponding to the starting point, all waypoints, and the ending point, process the sequence of spatial coordinate points by using a preset polyline densification algorithm to obtain encrypted break points, perform city matching on the encrypted break points by using a Turf library, identify the city to which each encrypted break point belongs, and generate a complete list of cities. The user input is generated after the user inputs the starting point and the ending point in the preset navigation software;

[0035] An extraction unit, which is configured to, for each city in the complete list of cities, extract all scenic spots of the city, determine the font size of the scenic spots according to the popularity of each scenic spot, and determine the weight of the city according to the weighted number of characters of all scenic spots and the font size of the scenic spots;

[0036] An allocation unit, which is configured to, according to the weight of each city in the complete list of cities, use the Squarified layout algorithm to allocate each city to the shortest side direction of a preset canvas in the form of a node one by one until the layout of all cities in the preset canvas is completed, and obtain a post-layout canvas including a rectangular space corresponding to each city;

[0037] A display unit, which is configured to, for each city in the post-layout canvas, sort the scenic spots in the city from largest to smallest according to the popularity of each scenic spot to obtain a scenic spot popularity ranking, and starting from the center position of the rectangular space corresponding to the city, sequentially place the scenic spot labels corresponding to each scenic spot until the layout of all scenic spots in the rectangular space of the city is completed. Traverse all cities until the visual layout of all cities in the post-layout canvas is completed, and display the visual layout.

[0038] In a third aspect, an electronic device is provided, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the visualization method for coupling place name tags with TreeMap dissection is implemented.

[0039] The present invention has introduced a number of innovations in the field of path visualization, comprehensively improving the spatial utilization rate of path data, semantic expression ability, and user interaction experience. By means of the TreeMap rectangular tree map technology, it has achieved an efficient expression of the importance of cities along the path. Through the Squarified layout algorithm, the city weights are mapped into the canvas area, solving the problem of the imbalance between global and local information expression in traditional path maps. The area of the city rectangular region not only clearly reflects the weight of its points of interest but also provides an accurate spatial segmentation structure, enhancing the readability of the global structure of the path; the introduction of the tag cloud technology has broken through the limitation of only displaying geometric information in traditional path visualization. Through visual variables such as the font size and color of the tag cloud, the popularity attributes and distribution characteristics of the points of interest are intuitively encoded. The compact arrangement of the tag cloud within each city rectangular region effectively avoids tag overlap through the spiral layout algorithm, improving the clarity and expression efficiency of local semantic information;

[0040] The four-color method is used for color matching and combined with the recursive backtracking algorithm of the adjacency matrix to ensure a clear distinction in the colors of adjacent rectangular regions, while restricting the number of color types to optimize the coordination of the overall layout. In addition, Catmull-Rom spline curves are used to connect the city rectangular regions to smooth the visual effect of the path curve, further enhancing the expression of path coherence and directionality; this method can be widely applied to scenarios such as tourism planning, business analysis, and regional traffic optimization, providing more intuitive data support for path planning and point-of-interest selection. At the same time, in the future, it can be combined with user behavior data and dynamic path generation technology to achieve intelligent and personalized visualization expressions of multiple categories of points of interest, providing new possibilities for the technological development of path visualization. Brief Description of the Drawings

[0041] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0042] Figure 1 It is one of the flow schematic diagrams of the visualization method for coupling place name tags and TreeMap dissection provided by the present invention;

[0043] Figure 2 It is the second of the flow schematic diagrams of the visualization method for coupling place name tags and TreeMap dissection provided by the present invention;

[0044] Figure 3 It is the schematic diagram of the polyline density algorithm for retaining break points provided by the present invention;

[0045] Figure 4It is a schematic diagram of the fixed-spacing polyline density algorithm provided by the present invention;

[0046] Figure 5 It is a schematic diagram of obtaining a city list provided by the present invention;

[0047] Figure 6 It is a traditional treemap provided by the present invention;

[0048] Figure 7 It is a rectangular treemap TreeMap provided by the present invention;

[0049] Figure 8 It is an example diagram of the Squarified algorithm provided by the present invention;

[0050] Figure 9 It is a schematic diagram of generating an adjacency matrix provided by the present invention;

[0051] Figure 10 It is a schematic diagram of the recursive backtracking algorithm provided by the present invention;

[0052] Figure 11 It is a schematic diagram of a polyline path provided by the present invention;

[0053] Figure 12 It is a schematic diagram of a curved path provided by the present invention;

[0054] Figure 13 It is a schematic diagram of the structure of a visualization device that couples place name labels with the dissection of TreeMap;

[0055] Figure 14 It is a schematic diagram of the structure of an electronic device provided by the present invention. Detailed implementation manners

[0056] To make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the present invention. Obviously, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present invention without creative efforts shall fall within the protection scope of the present invention.

[0057] The present invention proposes an innovative path visualization method. By combining a place name label cloud with the TreeMap space dissection technology, a new path visualization framework is constructed. This framework can not only efficiently express the global structure of the path but also detail the distribution of local interest points of cities along the path, thereby improving the spatial utilization efficiency and information carrying capacity of the canvas and being applicable to scenarios such as tourism planning, business analysis, and regional traffic optimization. Figure 1It is one of the schematic flowcharts of the visualization method for coupling geographical name tags with the dissection of TreeMap provided by the present invention. The visualization method for coupling geographical name tags with the dissection of TreeMap includes:

[0058] Step 101: Receive and respond to user input. Process the starting point and the ending point according to a preset navigation software to obtain a sequence of spatial coordinate points corresponding to the starting point, all waypoints, and the ending point. Process the sequence of spatial coordinate points using a preset polyline densification algorithm to obtain densified breakpoints. Use the Turf library to perform city matching on the densified breakpoints, identify the city to which each densified breakpoint belongs, and generate a complete list of cities. The user input is generated after the user inputs the starting point and the ending point in the preset navigation software.

[0059] In step 101, the process of using a preset polyline densification algorithm to process the sequence of spatial coordinate points to obtain densified breakpoints includes: performing a thinning operation on the sequence of spatial coordinate points using a preset polyline densification algorithm to obtain densified breakpoints; wherein, the distance between each coordinate point in the densified breakpoints is the same. The acquisition of path data is the basis of the technical method of the present invention. After the user inputs the starting point and the ending point of the path, the system generates detailed path data including the starting point, the ending point, and waypoints by calling the Gaode path navigation API, and extracts the sequence of spatial coordinate points of the path. For the efficiency of subsequent calculations and the uniformity of node information, the present invention adopts an optimized polyline densification (thickening) algorithm to process the path.

[0060] Traditional thickening algorithms usually retain the original breakpoints of the path. However, due to the complexity of the path, directly retaining the original breakpoints will cause the data volume to grow rapidly, increasing the computational burden of subsequent processing. The present invention adopts an improved strategy to discard the original breakpoints during the densification process and strictly ensure that the distances between the densified breakpoints are consistent. This method can not only retain the geometric features of the path but also significantly reduce redundant data, thereby improving the computational efficiency.

[0061] Figure 3 It is a schematic diagram of the polyline densification algorithm that retains breakpoints provided by the present invention. Figure 4It is a schematic diagram of the fixed-spacing polyline densification algorithm provided by the present invention. The purpose of densifying the polyline is to obtain the list of cities passed by the spatial path. Therefore, in order to further improve the efficiency on the basis of accurately and completely extracting cities, it is necessary to densify the polyline. For the densification algorithm, as a relatively basic algorithm in mature spatial data processing software such as ArcGIS Pro, it can densify each straight line segment according to the distance / maximum offset angle / maximum offset parameter. The "densification" of the present invention can also be called the "polyline simplification (thinning)" operation. On the premise of ensuring that cities are not missed in extraction, some fold points are removed, which will achieve the same purpose and effect as "densification". Existing algorithms for the "polyline simplification" operation include Douglas-Peucker, Douglas-Peucker, Visvalingam-Whyatt, Zhou-Jones, etc. After testing, for the spatial path from Xi'an to Beijing, the original number of fold points is 14,753. If the densification algorithm that retains fold points is used, since the distance between each fold point is less than the threshold of 5 km, the number of fold points remains unchanged; if the densification process that discards fold points is used, the number of fold points will drop to 253.

[0062] Figure 5 It is a schematic diagram of obtaining a list of cities provided by the present invention. After densification is completed, the system uses the Turf library to match cities for the densified fold points, identify the city to which each fold point belongs, and generate a complete list of cities passed by the path. Compared with the method of directly using the geocoding API, using the Turf library can effectively avoid the loss of node information caused by API request quota problems and ensure the integrity and reliability of data acquisition. Through the above series of processes, the spatial path is accurately mapped from a two-dimensional spatial curve to a set of discrete city nodes, laying a foundation for subsequent point-of-interest screening and weight calculation.

[0063] Step 102: For each city in the complete list of cities, extract all scenic spots of the city, determine the font size of the scenic spots according to the popularity of each scenic spot, and determine the weight of the city according to the weighted character count of all scenic spots and the font size of the scenic spots.

[0064] In step 102, after generating the list of cities along the path, the system extracts the scenic spot data of each city from the database and sorts it according to the popularity information of the scenic spots. The present invention converts the popularity of each scenic spot into a font size through a mapping formula, thereby realizing the visual encoding of the importance of the scenic spots. The formula is as follows:

[0065]

[0066] In the formula, represents the The font size of each point of interest label represents the heat ranking of the th point of interest label. And represent the minimum and maximum font sizes for displaying on the map respectively, and represent the minimum and maximum rankings of all labels respectively. In the present invention, the heat value is mapped to the font size, and the higher the heat of the scenic spot label, the larger the font size it will have.

[0067] On this basis, calculate the weight of each city. The weight value is determined by the weighted character count and font size of all scenic spots within the city. The formula is as follows:

[0068]

[0069] In the formula, is the total weight of the city, represents the total number of scenic spots included in the city, represents the number of characters in the th scenic spot POI label, and

[0070]

[0071]

[0072] Figure 6 represents the font size of the

[0070] th scenic spot label. Through this method, the system not only considers the heat of the scenic spots but also combines the length of their names to comprehensively calculate the weight, making the weight distribution more in line with the actual distribution of points of interest and more accurate than the traditional method that only relies on the number of points of interest.

[0070] In the present invention, for some cities, although the number of scenic spots is large, their heat is not very high, the font sizes of their labels are relatively small, and the total canvas space required for them is not very large. However, due to the single indicator of the large number of scenic spots, the treemap algorithm allocates a large canvas space for it, resulting in a waste of space. On the contrary, for some cities, although the number of scenic spots is small, their heat is high, the label font sizes will be relatively large, and the total canvas space required will be larger than usual. However, due to the small number of scenic spots, a smaller space is allocated. This is the conflict between the number of city labels and the space to be allocated. Therefore, the present invention takes into account the character count and font size of each label, can more precisely obtain the weights of each city, and allocate a more reasonable area of space on the canvas.

[0071] Step 103: According to the weight of each city in the complete list of cities, use the Squarified layout algorithm to allocate each city to the shortest side direction of the preset canvas in the form of a node one by one until the layout of all cities in the preset canvas is completed, and obtain a layout canvas containing the corresponding rectangular space for each city.

[0072] Figure 6is the traditional tree diagram provided by the present invention, while Figure 7 is a schematic diagram of the TreeMap of the rectangular tree diagram provided by the present invention. It uses the classic SND algorithm. The SND algorithm is the first algorithm of TreeMap in history. After the weight calculation is completed, the system inputs the cities passed by the path and their weights into the TreeMap rectangular tree diagram algorithm for canvas space division. TreeMap originates from the traditional tree diagram and is a space-filling visualization technology that represents node weights by area. It can effectively improve the canvas utilization rate by mapping node information to a two-dimensional rectangular area. In the selection of the layout algorithm, the present invention adopts the Squarified layout algorithm. This algorithm takes optimizing the aspect ratio of the rectangle as the core and preferentially selects a rectangle segmentation strategy close to a square based on the greedy principle to ensure that the layout result has high visual recognition and interaction friendliness. Specifically, the Squarified algorithm will, based on the weights of each city node, allocate the nodes to the shortest side direction of the canvas one by one. Each time a new node is added, the algorithm will calculate the average aspect ratio (AAR) of the rectangle row or column to select the optimal layout solution, and finally generate a rectangular arrangement close to a square. After the layout is completed, the system maps each city to the corresponding rectangular space to realize the distribution expression of the cities along the path.

[0073] Optionally, the step of using the Squarified layout algorithm to allocate each city to the shortest side direction of the preset canvas in the form of a node one by one until the layout of all cities in the preset canvas is completed, and obtaining the post-layout canvas including the rectangular space corresponding to each city, includes:

[0074] For the weight of any city, during the process of allocating the city to the shortest side direction of the preset canvas in the form of a node, obtain the first city allocation layout in the largest rectangular space corresponding to the rectangular space already allocated by the previous city of the city, and obtain the second city allocation layout of the city in the remaining space to be allocated. The remaining space to be allocated is the remaining space in the preset canvas after removing all the already allocated rectangular spaces;

[0075] Calculate the first aspect ratio corresponding to the first city allocation layout, calculate the second aspect ratio corresponding to the second city allocation layout, determine the city allocation layout with the smallest absolute value of the difference from 1 in the first aspect ratio and the second aspect ratio as the target allocation layout of the city, and allocate the city to the preset canvas in the form of a node according to the target allocation layout;

[0076] Traverse all cities until the layout of all cities in the preset canvas is completed, and obtain the post-layout canvas including the rectangular space corresponding to each city.

[0077] Figure 8It is an example diagram of the Squarified algorithm provided by the present invention. The node order and its weights used are: 6, 6, 4, 3, 2, 2, 1. It can be seen that this algorithm can obtain a layout with a relatively low average aspect ratio. Figure 8 Only an example in the case where the node order is sorted from largest to smallest according to the weights is shown. In the original algorithm, all nodes will be sorted according to the weight size, and the nodes with larger weights will be placed preferentially. However, this does not mean that the present invention needs to perform layout according to such a weight sorting. Since the spatial path visualization of the present invention has requirements for the order between cities, the Squarified algorithm of the present invention does not sort the node weights and directly constructs the layout. The core of the present invention lies in the combination of the treemap and the word cloud. The role of the treemap is only to divide the space, facilitating the user to obtain the order information of the spatial path on the global scale.

[0078] Step 104: For each city in the canvas after layout, sort the popularity of each scenic spot in the city from largest to smallest to obtain the scenic spot popularity sorting. According to the scenic spot popularity sorting, starting from the center position of the rectangular space corresponding to the city, place the scenic spot labels corresponding to each scenic spot in sequence until all the scenic spots in the city are laid out in the rectangular space. Traverse all cities until the visualization layout of all cities in the canvas after layout is completed, and display the visualization layout.

[0079] Optionally, the process of starting from the center position of the rectangular space corresponding to the city and placing the scenic spot labels corresponding to each scenic spot in sequence until all the scenic spots in the city are laid out in the rectangular space includes:

[0080] For any scenic spot label, when placing it, perform collision detection with the labels already placed in the rectangular space. In the case of overlap, move outward along the Archimedean spiral path and re-detect until there is no overlap, and then the scenic spot label is fixed.

[0081] Traverse all scenic spots according to the scenic spot popularity sorting until all scenic spot labels are placed, and complete the layout of all scenic spots in the city in the rectangular space.

[0082] After completing the layout of the treemap, the present invention further fills the rectangular areas corresponding to each city with a placename tag cloud to visually display the semantic information of the scenic spots within the city. The generation of the tag cloud is based on the d3-cloud library, and its core uses a spiral layout algorithm to optimize the spatial distribution of the tags. The core logic of the spiral layout algorithm is to sort the words according to their weights from large to small, and place the tags one by one starting from the center of the canvas. When placing each tag, a collision detection is performed with the tags that have already been placed. If an overlap is found, the tag is moved one step outward along the path of the Archimedean spiral and the detection is repeated; when there is no overlap, the tag can be fixed, and this process is repeated until all tags have been placed. This algorithm not only ensures the compactness of the tag cloud but also effectively avoids the overlap of tags.

[0083] Optionally, before performing the collision detection with the tags already placed in the rectangular space during placement, the method further includes:

[0084] For any scenic spot, process the scenic spot label corresponding to the scenic spot according to the font size of the scenic spot to obtain the processed scenic spot label with the font size of the scenic spot;

[0085] Traverse all scenic spots to obtain the processed scenic spot labels corresponding to each scenic spot.

[0086] The present invention will use multiple visual variables to display the multi-dimensional attribute information of the scenic spots within each city, specifically including: using the font size of the label to identify the popularity attribute of the scenic spot. The higher the popularity of the scenic spot, the larger the font size of the label, thereby enhancing the visual prominence of high-popularity scenic spots. At the same time, to facilitate the distinction of the points of interest in different cities, the tag clouds within the rectangular space of the same city are identified with a unified color, thereby enhancing the readability of the city dimension in the overall visual layout. In addition, the city name is used as the core label in the tag cloud for display, and its font size is set to the maximum value in the tag cloud. Therefore, under the spiral layout algorithm, it will ensure that the city name is always located at the center of the tag cloud. This design not only strengthens the centrality of the overall semantics of the city but also visually highlights the identification role of the city.

[0087] Optionally, after traversing all cities until the visual layout of all cities in the canvas after the layout is completed, the method further includes:

[0088] For the rectangular space corresponding to any one of the cities in the canvas after the layout, select any current color from a preset number of coloring schemes for assignment, and judge whether the current color conflicts with the colors of adjacent rectangular spaces based on the adjacency matrix;

[0089] If a color conflict occurs, discard the current color and select the next color from a preset number of coloring schemes for assignment until the current color does not conflict with the colors of adjacent rectangular spaces;

[0090] If all the colors in the preset number of coloring schemes conflict with the colors of adjacent rectangular spaces, then backtrack to the coloring of the rectangular space corresponding to the previous city and re - adjust the color matching until all the rectangular spaces corresponding to the cities do not conflict with the colors of adjacent rectangular spaces, obtaining the layout canvas after color coloring.

[0091] Figure 9 is the schematic diagram of the adjacency matrix provided by the present invention, Figure 10 is the schematic diagram of the recursive backtracking algorithm provided by the present invention. To avoid visual confusion caused by the same color in adjacent rectangular areas, the present invention adopts a recursive backtracking algorithm based on the adjacency matrix to implement the four - color method for color matching. The adjacency matrix is a mathematical representation for recording the adjacent relationships of each rectangular area, which can clearly reflect the spatial adjacency relationship between each rectangle and other rectangles. On this basis, using the recursive backtracking algorithm, by assigning colors layer by layer, it can be ensured that the color of each rectangle is different from that of its adjacent rectangles, while restricting the total number of color types, thereby improving the overall layout aesthetics and readability while optimizing the efficiency and logic of color matching.

[0092] Specifically, for a certain rectangular space, the algorithm will sequentially select a color from a preset n (in the present invention, n = 4) coloring schemes for assignment and immediately determine whether this color conflicts with the colors of its adjacent rectangles based on the adjacency matrix. If a color overlap occurs, the algorithm will discard the current color selection and instead try the next color for assignment; if all n colors cannot meet the requirements, the algorithm will backtrack to the previous rectangular space and re - adjust the color matching. Through this recursive backtracking method, the algorithm completes the color assignment for each rectangular space in turn until all rectangles meet the non - overlapping color matching requirements.

[0093] Optionally, after obtaining the layout canvas after color coloring, the method further includes:

[0094] Adopt Catmull - Rom spline curves and sequentially connect the central positions of each city in the rectangular space according to the order of cities in the complete list of cities, obtaining the connected layout canvas.

[0095] Figure 11 is the schematic diagram of the polyline path provided by the present invention, Figure 12It is a schematic diagram of the curved path provided by the present invention. To show the spatial sequence relationship of the cities along the path, the present invention uses Catmull-Rom spline curves to connect the urban rectangular areas. Such curves are smooth and have good visual continuity, and can highlight the coherence and directionality of the path on a global scale, thus helping users quickly understand the overall structure of the path.

[0096] The present invention aims to provide an innovative method that can effectively improve the path visualization effect by coupling the place name tag cloud and the TreeMap dissection technology, solve problems such as low information density and imbalance between global and local expressions in traditional path visualization. Different from the traditional linear path expression form, this method maps the cities along the path and their weights to a two-dimensional canvas through the TreeMap rectangular tree map algorithm, making full use of the canvas space. At the same time, through the place name tag cloud technology, the semantic attributes of the points of interest are encoded into visual variables, including font size, color, etc., to intuitively present the distribution and characteristics of the scenic spots inside the city in a multi-dimensional way. On the basis of the unified expression of the global spatial path structure and local point-of-interest details, it can enhance the user's cognitive efficiency of the multi-level information of the path.

[0097] Figure 2 It is the second schematic diagram of the process of the visualization method that couples place names and TreeMap dissection provided by the present invention. The present invention is carried out around four main steps: path data acquisition and processing, point-of-interest screening and weight calculation, TreeMap space dissection, and tag cloud filling and visual optimization, aiming to efficiently integrate the global structure of the path and the semantic information of local points of interest to achieve a multi-level path visualization expression.

[0098] In path data acquisition and processing, after the user inputs the starting point and the ending point, the system calls the Amap path navigation API to generate detailed path data, and optimizes the spatial curve structure of the path through an improved polyline densification algorithm. During the densification process, the system adopts a polyline densification strategy with a fixed spacing to balance the retention of path geometric features and the optimization of data volume, ensuring the uniformity and processing efficiency of node information. Further, through the Turf library, the densified breakpoints are matched with cities to generate a list of cities passed by the path, and the spatial sequence information of the path is completely extracted, laying a foundation for subsequent analysis.

[0099] In point-of-interest screening and weight calculation, based on the list of cities passed by the path, the point-of-interest data of each city is extracted from the database, sorted according to the scenic spot popularity, and the popularity information is converted into font size through a mapping formula to visually express the importance of the scenic spots. At the same time, the overall weight of the city is calculated by combining the popularity weights and the number of characters of all scenic spots in the city, and an importance index of each city in the global path is given. This method not only considers the number of scenic spots, but also integrates the name length and popularity distribution, providing a more accurate measurement method for weight calculation.

[0100] In the spatial partitioning of the TreeMap, through the TreeMap rectangular tree map algorithm, the canvas is divided into regions according to the city weights. The Squarified layout algorithm is used to optimize the segmentation results to ensure that the rectangular regions corresponding to each city are close to squares, enhancing visual recognition and interaction friendliness. The area of each rectangular region directly reflects the weight size of the city and provides a clear layout basis for subsequent tag cloud filling.

[0101] In tag cloud filling and visual optimization, after the TreeMap partitioning is completed, the system generates a tag cloud within the rectangular region of each city. The spiral layout algorithm is used to optimize the tag arrangement to avoid tag overlap. Visual variables such as font size and color are used to encode the multi-dimensional attribute information of the points of interest, strengthening the semantic expression effect of the tag cloud. The system also uses the four-color method for color matching to distinguish adjacent rectangular regions, avoiding color confusion, and connects the city rectangular regions with smooth Catmull-Rom spline curves to show the spatial sequence relationship and directionality of the cities along the path, overall enhancing the layout aesthetics and the clarity of path expression.

[0102] The core point of this invention is to combine the traditional rectangular tree map with the word cloud for application in the visualization field of spatial paths. The specific combination method is as follows: The spatial partitioning method of the rectangular tree map is adopted to map the one-dimensional city sequence into the two-dimensional space to view the cities passed by the path at the global scale; at the same time, the word cloud is filled inside the space of each city to obtain the semantic information inside each city at the local scale. Compared with the traditional visualization of spatial paths, because it relies on absolute spatial reference and positioning, it can only be observed at the global scale and it is difficult to know its local details; after magnifying the observation, although more detailed information can be obtained, at the same time, the overall view is lost, which is the classic "user lost" problem in multi-scale maps. The visualization method of this invention combines the rectangular tree map and the word cloud to achieve the visualization effect of taking into account both the global context and local details.

[0103] The present invention introduces a number of innovations in the field of path visualization, comprehensively improving the spatial utilization rate, semantic expression ability of path data, and user interaction experience. With the help of the TreeMap technology, it realizes the efficient expression of the importance of cities along the path. Through the Squarified layout algorithm, the city weights are mapped into the canvas area, solving the problem of the imbalance between global and local information expression in traditional path maps. The area of the city rectangle not only clearly reflects the weight of its points of interest, but also provides an accurate spatial segmentation structure, enhancing the readability of the global path structure; the introduction of the tag cloud technology breaks through the limitation of only showing geometric information in traditional path visualization. Through visual variables such as the font size and color of the tag cloud, the popularity attributes and distribution characteristics of the points of interest are intuitively encoded. The compact arrangement of the tag cloud within each city rectangle effectively avoids tag overlap through the spiral layout algorithm, improving the clarity and expression efficiency of local semantic information; the four-color method is used for color matching and combined with the recursive backtracking algorithm of the adjacency matrix to ensure clear distinction of colors in adjacent rectangle areas, while restricting the number of color types to optimize the coordination of the overall layout. In addition, Catmull-Rom spline curves are used to connect the city rectangle areas to smooth the visual effect of the path curve, further enhancing the expression of path coherence and directionality; this method can be widely applied to scenarios such as tourism planning, business analysis, and regional traffic optimization, providing more intuitive data support for path planning and point-of-interest selection. At the same time, in the future, by combining user behavior data and dynamic path generation technology, intelligent and personalized visual expression of multiple categories of points of interest can be achieved, providing new possibilities for the technical development of path visualization. The present invention significantly improves the spatial utilization rate and semantic expression ability of path visualization, solves the problems of insufficient information density and imbalance between global and local perspectives in traditional path maps, and provides an innovative solution for path expression in geographical information science.

[0104] Figure 13 It is a schematic structural diagram of a visualization device that couples place name tags and TreeMap dissection. The visualization device that couples place name tags and TreeMap dissection includes a receiving unit 1. The receiving unit 1 is used to receive and respond to user input, process the starting point and the ending point according to a preset navigation software, obtain a sequence of spatial coordinate points corresponding to the starting point, all waypoints, and the ending point, process the sequence of spatial coordinate points using a preset polyline densification algorithm to obtain encrypted breakpoints, perform city matching on the encrypted breakpoints using the Turf library, identify the city to which each encrypted breakpoint belongs, and generate a complete list of cities. The user input is generated after the user inputs the starting point and the ending point in the preset navigation software. The working principle of the receiving unit 1 can refer to the aforementioned step 101 and will not be elaborated here.

[0105] The visualization device for coupling geographical name tags with TreeMap dissection further includes an extraction unit 2. The extraction unit 2 is configured to, for each city in the complete list of cities, extract all scenic spots of the city, determine the font size of the scenic spots according to the popularity of each scenic spot, and determine the weight of the city according to the weighted number of characters of all scenic spots and the font size of the scenic spots. The working principle of the extraction unit 2 can refer to the foregoing step 102 and will not be elaborated here.

[0106] The visualization device for coupling geographical name tags with TreeMap dissection further includes an allocation unit 3. The allocation unit 3 is configured to, according to the weight of each city in the complete list of cities, use the Squarified layout algorithm to allocate each city to the shortest side direction of a preset canvas in the form of a node one by one until the layout of all cities in the preset canvas is completed, obtaining a post-layout canvas containing the rectangular space corresponding to each city. The working principle of the allocation unit 3 can refer to the foregoing step 103 and will not be elaborated here.

[0107] The visualization device for coupling geographical name tags with TreeMap dissection further includes a display unit 4. The display unit 4 sorts the scenic spots of each city in the post-layout canvas from largest to smallest according to the popularity of each scenic spot in the city, obtaining a scenic spot popularity ranking. According to the scenic spot popularity ranking, starting from the center position of the rectangular space corresponding to the city, the scenic spot labels corresponding to each scenic spot are placed in sequence until the layout of all scenic spots of the city in the rectangular space is completed. All cities are traversed until the visual layout of all cities in the post-layout canvas is completed, and the visual layout is displayed. The working principle of the display unit 4 can refer to the foregoing step 104 and will not be elaborated here.

[0108] Figure 14 It is a schematic structural diagram of the electronic device provided by the present invention. As Figure 14As shown, the electronic device may include: a processor 110, a communications interface 120, a memory 130, and a communication bus 140. Among them, the processor 110, the communications interface 120, and the memory 130 complete mutual communication through the communication bus 140. The processor 110 may call the logical instructions in the memory 130 to execute a visualization method for coupling place name tags with the dissection of a TreeMap. The method includes: receiving and responding to user input, processing a starting point and an ending point according to a preset navigation software, obtaining a sequence of spatial coordinate points corresponding to the starting point, all waypoints, and the ending point, processing the sequence of spatial coordinate points using a preset polyline densification algorithm to obtain encrypted fold points, performing city matching on the encrypted fold points using the Turf library, identifying the city to which each encrypted fold point belongs, and generating a complete list of cities. The user input is generated after the user inputs the starting point and the ending point in the preset navigation software; for each city in the complete list of cities, extracting all scenic spots in the city, determining the font size of the scenic spots according to the popularity of each scenic spot, and determining the weight of the city according to the weighted character count and the font size of all scenic spots; according to the weight of each city in the complete list of cities, using the Squarified layout algorithm to assign each city to the shortest side direction of a preset canvas in the form of a node one by one until the layout of all cities in the preset canvas is completed, obtaining a post-layout canvas containing the rectangular space corresponding to each city; for each city in the post-layout canvas, sorting the scenic spots in the city from largest to smallest according to the popularity of each scenic spot to obtain a scenic spot popularity ranking, and starting from the center position of the rectangular space corresponding to the city, placing the scenic spot labels corresponding to each scenic spot in sequence until the layout of all scenic spots in the rectangular space of the city is completed, traversing all cities until the visualization layout of all cities in the post-layout canvas is completed, and displaying the visualization layout.

[0109] In addition, when the logical instructions in the above-mentioned memory 130 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0110] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0111] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the technical solution, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disks, optical discs, etc., and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments.

[0112] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments or perform equivalent replacements for some of the technical features. These modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of various embodiments of the present invention.

Claims

1. A visualization method coupling geographical name tags with TreeMap dissection, characterized in that, Including: Receiving and responding to user input, processing the starting point and the ending point according to a preset navigation software to obtain a sequence of spatial coordinate points corresponding to the starting point, all waypoints, and the ending point, processing the sequence of spatial coordinate points using a preset polyline densification algorithm to obtain encrypted fold points, performing city matching on the encrypted fold points using the Turf library, identifying the city to which each encrypted fold point belongs, and generating a complete list of cities, where the user input is generated after the user inputs the starting point and the ending point in the preset navigation software; For each city in the complete list of cities, extracting all scenic spots of the city, determining the font size of the scenic spots according to the popularity of each scenic spot, and determining the weight of the city according to the weighted character count of all scenic spots and the font size of the scenic spots; According to the weight of each city in the complete list of cities, using the Squarified layout algorithm to assign each city to the shortest side direction of a preset canvas in the form of a node one by one until the layout of all cities in the preset canvas is completed, obtaining a post-layout canvas containing a rectangular space corresponding to each city; For each city in the post-layout canvas, sorting the scenic spots in the city according to the popularity of each scenic spot from large to small to obtain a scenic spot popularity ranking, and starting from the center position of the rectangular space corresponding to the city according to the scenic spot popularity ranking, placing the scenic spot labels corresponding to each scenic spot in turn until the layout of all scenic spots in the rectangular space of the city is completed, traversing all cities until the visual layout of all cities in the post-layout canvas is completed, and displaying the visual layout.

2. The visualization method for coupling geographical name tags with TreeMap dissection according to claim 1, characterized in that The processing of the sequence of spatial coordinate points using a preset polyline densification algorithm to obtain encrypted fold points includes: Performing thinning operation on the sequence of spatial coordinate points using a preset polyline densification algorithm to obtain encrypted fold points; Wherein, the distance between each coordinate point in the encrypted fold points is the same.

3. The visualization method for coupling geographical name tags with TreeMap dissection according to claim 1, characterized in that, The determining the weight of the city according to the weighted character count of all scenic spots and the font size of the scenic spots includes: ； Among them, is the total weight of the city, represents the total number of scenic spots contained in the city, represents the number of characters in the POI label of the th scenic spot, and represents the font size of the th scenic spot label.

4. The visualization method for coupling geographical name tags with TreeMap dissection according to claim 1, characterized in that, The using the Squarified layout algorithm to assign each city to the shortest side direction of a preset canvas in the form of a node one by one until the layout of all cities in the preset canvas is completed, obtaining a post-layout canvas containing a rectangular space corresponding to each city, includes: For the weight of any city, in the process of assigning the city to the shortest side direction of the preset canvas in the form of a node, obtaining the first city assignment layout of the city in the largest rectangular space corresponding to the rectangular space where the previous city has been assigned, and obtaining the second city assignment layout of the city in the remaining space to be assigned, where the remaining space to be assigned is the remaining space in the preset canvas after removing all the assigned rectangular spaces; Calculate the first aspect ratio corresponding to the first urban allocation layout, calculate the second aspect ratio corresponding to the second urban allocation layout, and determine the urban target allocation layout as the urban allocation layout with the smallest absolute value of the difference from 1 between the first aspect ratio and the second aspect ratio. Allocate the city to the preset canvas in the form of a node according to the target allocation layout; Traverse all cities until the layout of all cities in the preset canvas is completed, and obtain the layout canvas containing the rectangular space corresponding to each city.

5. The visualization method for coupling geographical name tags with TreeMap dissection according to claim 1, wherein Starting from the central position of the rectangular space corresponding to the city, place the scenic spot labels corresponding to each scenic spot in sequence until the layout of all scenic spots in the rectangular space of the city is completed, including: For any scenic spot label, perform a collision detection with the labels already placed in the rectangular space during placement. In the case of overlap, move outward along the Archimedean spiral path and re-detect until there is no overlap, and the scenic spot label is fixed; Traverse all scenic spots in the order of the scenic spot heat until all scenic spot labels are placed, and complete the layout of all scenic spots in the rectangular space of the city.

6. The visualization method for coupling geographical name tags with the TreeMap dissection according to claim 5, characterized in that Before performing the collision detection with the labels already placed in the rectangular space during placement, the method further includes: For any scenic spot, process the scenic spot label corresponding to the scenic spot according to the font size of the scenic spot to obtain the scenic spot label after processing the font size of the scenic spot; Traverse all scenic spots to obtain the scenic spot labels after processing the font size of each scenic spot.

7. The visualization method for coupling geographical name tags with TreeMap dissection according to claim 1, characterized in that, After traversing all cities until the visual layout of all cities in the layout canvas is completed, the method further includes: For the rectangular space corresponding to any city in the layout canvas, select any current color from a preset number of coloring schemes for assignment, and judge whether the current color conflicts with the colors of adjacent rectangular spaces based on the adjacency matrix; If a color conflict occurs, discard the current color and select the next color from the preset number of coloring schemes for assignment until the current color does not conflict with the colors of adjacent rectangular spaces; If all colors in the preset number of coloring schemes conflict with the colors of adjacent rectangular spaces, then backtrack to the coloring of the rectangular space corresponding to the previous city and re-adjust the color matching until the rectangular spaces corresponding to all cities do not conflict with the colors of adjacent rectangular spaces, and obtain the layout canvas after color coloring.

8. The visualization method for coupling geographical name tags with TreeMap dissection according to claim 7, characterized in that, After obtaining the layout canvas after color coloring, the method further includes: Adopt a Catmull-Rom spline curve to sequentially connect the central positions of each city in the rectangular space in the order of the cities in the complete list of cities, and obtain the connected layout canvas.

9. A visualization device coupling a geographical name tag with a TreeMap dissection, characterized in that, Including: A receiving unit, which is configured to receive and respond to user input, process a starting point and an ending point according to a preset navigation software, obtain a sequence of spatial coordinate points corresponding to the starting point, all waypoints, and the ending point, process the sequence of spatial coordinate points by using a preset polyline densification algorithm to obtain encrypted fold points, perform city matching on the encrypted fold points by using a Turf library, identify the city to which each encrypted fold point belongs, and generate a complete list of cities, where the user input is generated after the user inputs the starting point and the ending point in the preset navigation software; An extraction unit, which is configured to, for each city in the complete list of cities, extract all scenic spots of the city, determine the font size of the scenic spots according to the popularity of each scenic spot, and determine the weight of the city according to the weighted character count of all scenic spots and the font size of the scenic spots; An allocation unit, which is configured to, according to the weight of each city in the complete list of cities, use the Squarified layout algorithm to allocate each city to the shortest side direction of a preset canvas in the form of a node one by one until the layout of all cities in the preset canvas is completed, and obtain a post-layout canvas containing a rectangular space corresponding to each city; A display unit, which is configured to, for each city in the post-layout canvas, sort the scenic spots in the city from largest to smallest according to the popularity of each scenic spot to obtain a scenic spot popularity sorting, and starting from the center position of the rectangular space corresponding to the city, sequentially place the scenic spot labels corresponding to each scenic spot according to the scenic spot popularity sorting until the layout of all scenic spots in the rectangular space of the city is completed, traverse all cities until the visual layout of all cities in the post-layout canvas is completed, and display the visual layout.

10. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the visualization method for coupling a geographical name label and a TreeMap dissection as described in any one of claims 1 to 8.

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