A GIS-based method for creating zoning zones for tourism management across the Grand Canal Economic Belt

Abstract

This invention discloses a GIS-based method for mapping and managing the tourism resources of the Grand Canal Economic Belt, comprising: acquiring data of the entire Grand Canal Economic Belt; dividing the data into sub-data of multiple data types; mapping the sub-data onto a preset image of the entire Grand Canal Economic Belt to obtain sub-data point cloud images; performing cluster analysis on the sub-data in the sub-data point cloud images to obtain data-divided regional images of the entire Grand Canal Economic Belt; and converting the data-divided regional images into a tourism management zoning map of the entire Grand Canal Economic Belt and displaying it based on the spatial overlay and visualization functions of GIS. This invention, through data collection and processing, zoning standard formulation, and the application of GIS technology, achieves the rational planning and effective utilization of tourism resources in the Grand Canal Economic Belt.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and more specifically, to a method for mapping the zoning of tourism management across the Grand Canal Economic Belt based on GIS. Background Technology

[0002] With the development of the tourism economy, the tourism industry has achieved effective development. However, in the process of tourism development, if products are too similar, they cannot bring unique experiences to tourists. In particular, for tourist attractions with relatively lagging management systems, the uniformity and coordination of management work are affected, which in turn hinders the effective development of tourist attractions and the development of the tourism industry. Geographic Information System (GIS) technology can provide efficient data management capabilities and spatial analysis functions, and these features make GIS an effective management tool. Summary of the Invention

[0003] In view of the above problems, the purpose of this invention is to provide a GIS-based method for mapping the zoning of tourism management across the Grand Canal Economic Belt, which can realize the rational planning and effective utilization of tourism resources in the Grand Canal Economic Belt.

[0004] This invention provides a GIS-based method for creating zoning zones for tourism management across the Grand Canal Economic Belt, including:

[0005] Obtain comprehensive data for the entire Grand Canal Economic Belt;

[0006] The data of the entire canal economic belt is divided into sub-data of multiple data types;

[0007] The sub-data is mapped onto a pre-defined full-area image of the canal economic zone to obtain a sub-data point cloud image;

[0008] Cluster analysis is performed on the sub-data in the sub-data point cloud image to obtain a data division image of the entire canal economic belt.

[0009] Based on the spatial overlay and visualization functions of GIS, the data-divided regional images are transformed into a zoning map of the entire tourism management area of ​​the Grand Canal Economic Belt and then displayed.

[0010] In this solution, the step of mapping sub-data to a preset full-area image of the canal economic zone specifically includes:

[0011] Extract the names of key nodes from the pre-defined image of the entire canal economic zone;

[0012] Extract keywords from sub-data;

[0013] Determine the matching degree between the sub-data and the key node names based on the keywords and key node names in the sub-data;

[0014] If the matching degree between the sub-data and the key node name is greater than or equal to the preset first matching degree threshold, then the sub-data will be displayed at the position of the corresponding key node name.

[0015] In this solution, the step of determining the matching degree between sub-data and key node names based on keywords and key node names in the sub-data specifically includes:

[0016] When the keywords and key node names in the sub-data are the same, the matching degree between the keywords and key node names in the corresponding sub-data is 100%.

[0017] If the keywords and key node names in the sub-data are not the same, extract the correlation between the keywords and key node names in the corresponding sub-data and set the correlation as the matching degree between the keywords and key node names in the corresponding sub-data.

[0018] Iterate through the keywords in the entire sub-data to obtain the set of matching degrees between the keywords in the corresponding sub-data and the same key node name;

[0019] Extract the maximum matching score from the matching score set;

[0020] Multiply the maximum matching degree by the corresponding matching degree weight coefficient to obtain the matching degree between the corresponding sub-data and the key node name.

[0021] In this solution, the steps for obtaining the matching degree weight coefficient specifically include:

[0022] The matching scores in the matching score set are compared with the preset second matching score threshold in turn. If the matching score is greater than the preset second matching score threshold, the effective matching score is incremented by one.

[0023] Extract the total number of valid matches and the total number of matches in the match set;

[0024] Divide the total number of valid matches by the total number of matches in the match set to obtain the corresponding match weight coefficient.

[0025] In this solution, the step of extracting the correlation between keywords and key node names in the corresponding sub-data specifically includes:

[0026] Obtain historical data on tourism development across the entire Grand Canal Economic Belt;

[0027] Extract the number of times the keyword and key node name appear simultaneously in historical data, and set it as the first occurrence value;

[0028] Extract the frequency of the keyword or key node name in historical data and set it as the second value;

[0029] Divide the first value by the second value to obtain the correlation between keywords and key node names in the corresponding sub-data.

[0030] In this solution, the step of performing cluster analysis on the sub-data in the sub-data point cloud image to obtain the data region image of the entire canal economic belt specifically includes:

[0031] Extract the key node names corresponding to the sub-data;

[0032] Extract the data type of the sub-data, and obtain the sub-dataset of that data type based on the corresponding data type;

[0033] Traverse the subset of data of this data type to obtain the set of key node names of the corresponding data type;

[0034] Get the range of key nodes corresponding to the key node names;

[0035] Based on the set of key node names for the data type, determine the set of key node range regions for the corresponding data type;

[0036] Merge the range regions in the key node range set of the corresponding data type to obtain the range region of the corresponding data type;

[0037] By combining the ranges of all data types, the data division areas for the entire Grand Canal Economic Belt are obtained;

[0038] The data segmentation image of the entire canal economic belt includes the data segmentation area of ​​the entire canal economic belt.

[0039] In this solution, the step of merging the range regions in the key node range set corresponding to the data type specifically includes:

[0040] Taking any key node range region in the keyword node range set as the base point, extract the first distance value set between the key node range region of the base point and other key node range regions, extract the minimum value in the first distance value set, and set it as the first distance value.

[0041] If there exists a first distance value that is less than or equal to a preset first distance threshold, then the first distance value is set as the connecting link between the range areas of the corresponding two key nodes.

[0042] Based on the connecting link, the range areas of two corresponding key nodes are merged to obtain the initial partition area of ​​the corresponding data type;

[0043] Obtain a second set of distance values ​​from the range of other key nodes to the initial partitioned region, extract the minimum value in the second set of distance values, and set it as the second distance value;

[0044] If the second distance value is less than or equal to the preset first distance threshold, then the second distance value is set as the connecting link between the corresponding key node range area and the initial division area;

[0045] Based on the connecting link, the key node range area and the initial management partition are connected to obtain the updated initial partition area;

[0046] Traverse the entire set of key nodes, or if all second distance values ​​are greater than the preset first distance threshold, set the initial partition region as the partition region of the corresponding data type.

[0047] In this solution, after obtaining the data for the entire canal economic belt and dividing it into regions, the solution further includes:

[0048] Extract the boundary lines of the data regions;

[0049] Retrieve map elements within a preset first region based on the boundary line;

[0050] Determine whether the map element is the same as the preset map element. If so, revise the boundary line of the corresponding position according to the map element to obtain the revised data division area.

[0051] In this solution, the steps of converting the data-divided regional image into a zoning map for the overall tourism management of the Grand Canal Economic Belt and displaying it based on the spatial overlay and visualization functions of GIS specifically include:

[0052] Based on the visualization function of GIS, the data is divided into regions and displayed in map form to obtain the corresponding data management zoning map;

[0053] Based on the spatial overlay function of GIS, the data management zoning map is overlaid and calculated to obtain the overall tourism management map of the Grand Canal Economic Belt.

[0054] Based on a preset algorithm, the overall map of tourism management in the Grand Canal Economic Belt is divided into levels to obtain a zoning map of tourism management in the Grand Canal Economic Belt.

[0055] This invention discloses a GIS-based method for mapping and managing tourism zones along the Grand Canal Economic Belt. Through data collection and processing, the formulation of zoning standards, and the application of GIS technology, it enables the rational planning and effective utilization of tourism resources in the Grand Canal Economic Belt. Attached Figure Description

[0056] Figure 1 The flowchart of the present invention, which uses GIS to assist in the zoning of tourism management across the canal economic belt, is shown.

[0057] Figure 2 The flowchart illustrating how the present invention maps sub-data to a pre-defined image of the entire canal economic zone is shown. Detailed Implementation

[0058] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0059] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0060] Figure 1 The flowchart of the present invention, which uses GIS to assist in the zoning of tourism management in the canal economic belt, is shown.

[0061] S101, obtain data for the entire Grand Canal Economic Belt;

[0062] S102, divide the data of the entire canal economic belt into sub-data of multiple data types;

[0063] S103, map the sub-data onto the preset canal economic zone full-area image to obtain the sub-data point cloud image;

[0064] S104, perform cluster analysis on the sub-data in the sub-data point cloud image to obtain the data division area image of the entire canal economic belt.

[0065] S105, based on the spatial overlay and visualization functions of GIS, transforms the data-divided regional images into a zoning map of the canal economic belt's overall tourism management and displays it.

[0066] According to an embodiment of the present invention, the data for the entire canal economic belt includes at least geographical data, tourism resource data, and socio-economic data. The geographical data includes at least topography, water system distribution, transportation network, and land use type. The tourism resource data includes at least the distribution of scenic spots, historical and cultural sites, and nature reserves. The socio-economic data includes at least population density, economic development level, and tourism revenue. Through GIS technology, the entire tourism area of ​​the canal economic belt is divided into multiple management zones, thereby achieving rational planning and effective utilization of tourism resources in the canal economic belt.

[0067] Figure 2 The flowchart illustrating how the present invention maps sub-data to a pre-defined image of the entire canal economic zone is shown.

[0068] like Figure 2 As shown, the step of mapping sub-data to a preset full-area image of the canal economic zone specifically includes:

[0069] S201, Extract the names of key nodes from the preset image of the entire canal economic zone;

[0070] S202, Extract keywords from sub-data;

[0071] S203, Determine the matching degree between the sub-data and the key node names based on the keywords and key node names in the sub-data;

[0072] S104, If the matching degree between the sub-data and the key node name is greater than or equal to the preset first matching degree threshold, then the sub-data is displayed at the position of the corresponding key node name.

[0073] According to an embodiment of the present invention, the key node name is the name of a scenic spot or geographical name in the entire canal economic belt. The matching degree is determined by a preset first matching degree threshold, and the sub-data and key nodes are associated. When the matching degree of a sub-data and multiple key node names is greater than or equal to the preset first matching degree threshold, the corresponding sub-data is displayed at the positions of multiple key node names simultaneously. When the matching degree of a key node name and multiple sub-data is greater than or equal to the preset first matching degree threshold, the positions of the key node names display the corresponding multiple sub-data.

[0074] According to an embodiment of the present invention, the step of determining the matching degree between sub-data and key node names based on keywords and key node names in the sub-data specifically includes:

[0075] When the keywords and key node names in the sub-data are the same, the matching degree between the keywords and key node names in the corresponding sub-data is 100%.

[0076] If the keywords and key node names in the sub-data are not the same, extract the correlation between the keywords and key node names in the corresponding sub-data and set the correlation as the matching degree between the keywords and key node names in the corresponding sub-data.

[0077] Iterate through the keywords in the entire sub-data to obtain the set of matching degrees between the keywords in the corresponding sub-data and the same key node name;

[0078] Extract the maximum matching score from the matching score set;

[0079] Multiply the maximum matching degree by the corresponding matching degree weight coefficient to obtain the matching degree between the corresponding sub-data and the key node name.

[0080] It should be noted that a subdata set may contain multiple keywords, and these keywords are matched sequentially with the same key node name to determine the set of matching degrees between the keywords in the corresponding subdata set and the same key node name.

[0081] According to an embodiment of the present invention, the step of obtaining the matching degree weight coefficient specifically includes:

[0082] The matching scores in the matching score set are compared with the preset second matching score threshold in turn. If the matching score is greater than the preset second matching score threshold, the effective matching score is incremented by one.

[0083] Extract the total number of valid matches and the total number of matches in the match set;

[0084] Divide the total number of valid matches by the total number of matches in the match set to obtain the corresponding match weight coefficient.

[0085] It should be noted that the preset second matching degree threshold is less than the preset first matching degree threshold, and the preset second matching degree threshold reduces random errors during matching.

[0086] According to an embodiment of the present invention, the step of extracting the correlation between keywords and key node names in corresponding sub-data specifically includes:

[0087] Obtain historical data on tourism development across the entire Grand Canal Economic Belt;

[0088] Extract the number of times the keyword and key node name appear simultaneously in historical data, and set it as the first occurrence value;

[0089] Extract the frequency of the keyword or key node name in historical data and set it as the second value;

[0090] Divide the first value by the second value to obtain the correlation between keywords and key node names in the corresponding sub-data.

[0091] It should be noted that when the keyword and the key node name appear simultaneously in historical data, it is set as if the keyword or the key node name appears in historical data. In other words, if the keyword or the key node name appears in historical data, it includes the situation where the keyword and the key node name appear simultaneously in historical data.

[0092] According to an embodiment of the present invention, the step of performing cluster analysis on sub-data in the sub-data point cloud image to obtain a data-divided regional image of the entire canal economic belt specifically includes:

[0093] Extract the key node names corresponding to the sub-data;

[0094] Extract the data type of the sub-data, and obtain the sub-dataset of that data type based on the corresponding data type;

[0095] Traverse the subset of data of this data type to obtain the set of key node names of the corresponding data type;

[0096] Get the range of key nodes corresponding to the key node names;

[0097] Based on the set of key node names for the data type, determine the set of key node range regions for the corresponding data type;

[0098] Merge the range regions in the key node range set of the corresponding data type to obtain the range region of the corresponding data type;

[0099] By combining the ranges of all data types, the data division areas for the entire Grand Canal Economic Belt are obtained;

[0100] The data segmentation image of the entire canal economic belt includes the data segmentation area of ​​the entire canal economic belt.

[0101] It should be noted that each data type may have multiple sub-data. Conversely, the data type to which the corresponding sub-data belongs can be determined from the sub-data. Furthermore, all sub-data of the corresponding data type can be obtained and combined into a sub-data set of the corresponding data type. The key node names corresponding to each sub-data in the sub-data set of the corresponding data type can be collected to obtain the set of key node names. The key node range regions corresponding to each key node name can be combined to determine the set of key node range regions of the corresponding data type.

[0102] According to an embodiment of the present invention, the step of merging the range regions in the key node range set corresponding to the data type specifically includes:

[0103] Taking any key node range region in the keyword node range set as the base point, extract the first distance value set between the key node range region of the base point and other key node range regions, extract the minimum value in the first distance value set, and set it as the first distance value.

[0104] If there exists a first distance value that is less than or equal to a preset first distance threshold, then the first distance value is set as the connecting link between the range areas of the corresponding two key nodes.

[0105] Based on the connecting link, the range areas of two corresponding key nodes are merged to obtain the initial partition area of ​​the corresponding data type;

[0106] Obtain a second set of distance values ​​from the range of other key nodes to the initial partitioned region, extract the minimum value in the second set of distance values, and set it as the second distance value;

[0107] If the second distance value is less than or equal to the preset first distance threshold, then the second distance value is set as the connecting link between the corresponding key node range area and the initial division area;

[0108] Based on the connecting link, the key node range area and the initial management partition are connected to obtain the updated initial partition area;

[0109] Traverse the entire set of key nodes, or if all second distance values ​​are greater than the preset first distance threshold, set the initial partition region as the partition region of the corresponding data type.

[0110] It should be noted that if all first distance values ​​are greater than the preset first distance threshold, it indicates that the key node range area of ​​the base point is in a relatively independent position, and the corresponding key node range area of ​​the base point is set as a separate division area of ​​the corresponding data type. When there is a second distance value less than or equal to the preset first distance threshold, the initial division area is updated, and the remaining key node range areas in the key node range set are compared and analyzed one by one according to the updated initial division area to obtain the second distance value, and the judgment is made according to the corresponding preset first distance threshold, until the second distance value of the remaining key node range areas in the key node range set and the updated initial division area are both greater than the preset first distance threshold, or the key node range areas in the roller node range set are all merged with the updated initial division area.

[0111] According to an embodiment of the present invention, after obtaining the data region division of the entire canal economic zone, the method further includes:

[0112] Extract the boundary lines of the data regions;

[0113] Retrieve map elements within a preset first region based on the boundary line;

[0114] Determine whether the map element is the same as the preset map element. If so, revise the boundary line of the corresponding position according to the map element to obtain the revised data division area.

[0115] It should be noted that the preset map elements include at least terrain features or buildings with segmenting properties such as rivers, roads, fences, flower and grass fences, and isolation walls. The preset map elements in the entire canal economic belt are used to revise the boundary lines of the data division area, thereby improving the accuracy of the boundary lines of the data division area.

[0116] According to an embodiment of the present invention, the step of converting a data-divided regional image into a zoning map for the overall tourism management of the Grand Canal Economic Belt and displaying it based on the spatial overlay and visualization functions of GIS specifically includes:

[0117] Based on the visualization function of GIS, the data is divided into regions and displayed in map form to obtain the corresponding data management zoning map;

[0118] Based on the spatial overlay function of GIS, the data management zoning map is overlaid and calculated to obtain the overall tourism management map of the Grand Canal Economic Belt.

[0119] Based on a preset algorithm, the overall map of tourism management in the Grand Canal Economic Belt is divided into levels to obtain a zoning map of tourism management in the Grand Canal Economic Belt.

[0120] It should be noted that the preset algorithm described in this embodiment of the invention is the natural boundary method. By using GIS to assist in the mapping of the overall tourism management zones of the Grand Canal Economic Belt, the rational planning and effective utilization of tourism resources in the Grand Canal Economic Belt are improved.

[0121] This invention discloses a GIS-based method for mapping and managing tourism zones along the Grand Canal Economic Belt. Through data collection and processing, the formulation of zoning standards, and the application of GIS technology, it enables the rational planning and effective utilization of tourism resources in the Grand Canal Economic Belt.

[0122] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0123] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0124] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0125] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0126] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

Claims

1. A method for mapping the overall tourism management zones of the Grand Canal Economic Belt based on GIS, characterized in that: include: Obtain comprehensive data for the entire Grand Canal Economic Belt; The data of the entire canal economic belt is divided into sub-data of multiple data types; The sub-data is mapped onto a pre-defined full-area image of the canal economic zone to obtain a sub-data point cloud image; Cluster analysis is performed on the sub-data in the sub-data point cloud image to obtain a data division image of the entire canal economic belt. Based on the spatial overlay and visualization functions of GIS, the data-divided regional images are transformed into a zoning map of the entire tourism management area of ​​the Grand Canal Economic Belt and displayed. The step of mapping the sub-data to a preset full-area image of the canal economic zone specifically includes: Extract the names of key nodes from the pre-defined image of the entire canal economic zone; Extract keywords from sub-data; Determine the matching degree between the sub-data and the key node names based on the keywords and key node names in the sub-data; If the matching degree between the sub-data and the key node name is greater than or equal to the preset first matching degree threshold, then the sub-data will be displayed at the position of the corresponding key node name; The step of determining the matching degree between sub-data and key node names based on keywords and key node names in the sub-data specifically includes: When the keywords and key node names in the sub-data are the same, the matching degree between the keywords and key node names in the corresponding sub-data is 100%. If the keywords and key node names in the sub-data are not the same, extract the correlation between the keywords and key node names in the corresponding sub-data and set the correlation as the matching degree between the keywords and key node names in the corresponding sub-data. Iterate through the keywords in the entire sub-data to obtain the set of matching degrees between the keywords in the corresponding sub-data and the same key node name; Extract the maximum matching score from the matching score set; Multiply the maximum matching degree by the corresponding matching degree weight coefficient to obtain the matching degree between the corresponding sub-data and the key node name; The step of performing cluster analysis on sub-data in the sub-data point cloud image to obtain a data region image of the entire canal economic belt specifically includes: Extract the key node names corresponding to the sub-data; Extract the data type of the sub-data, and obtain the sub-dataset of that data type based on the corresponding data type; Traverse the subset of data of this data type to obtain the set of key node names of the corresponding data type; Obtain the range of key nodes corresponding to the key node names; Based on the set of key node names for the data type, determine the set of key node range regions for the corresponding data type; Merge the range regions in the key node range set of the corresponding data type to obtain the range region of the corresponding data type; By combining the ranges of all data types, the data division areas of the entire Canal Economic Belt are obtained; The data segmentation image of the entire canal economic belt includes the data segmentation area of ​​the entire canal economic belt.

2. The method for mapping the overall tourism management zones of the Grand Canal Economic Belt based on GIS, as described in claim 1, is characterized in that... The steps for obtaining the matching degree weight coefficient specifically include: The matching scores in the matching score set are compared with the preset second matching score threshold in turn. If the matching score is greater than the preset second matching score threshold, the effective matching score is incremented by one. Extract the total number of valid matches and the total number of matches in the match set; Divide the total number of valid matches by the total number of matches in the match set to obtain the corresponding match weight coefficient.

3. The method for mapping the overall tourism management zones of the Grand Canal Economic Belt based on GIS, as described in claim 1, is characterized in that... The step of extracting the correlation between keywords and key node names in the corresponding sub-data specifically includes: Obtain historical data on tourism development across the entire Grand Canal Economic Belt; Extract the number of times the keyword and key node name appear simultaneously in historical data, and set it as the first occurrence value; Extract the frequency of the keyword or key node name in historical data and set it as the second value; Divide the first value by the second value to obtain the correlation between keywords and key node names in the corresponding sub-data.

4. The method for mapping the overall tourism management zones of the Grand Canal Economic Belt based on GIS, as described in claim 1, is characterized in that... The step of merging the range regions in the key node range set of the corresponding data type specifically includes: Taking any key node range region in the keyword node range set as the base point, extract the first distance value set between the key node range region of the base point and other key node range regions, extract the minimum value in the first distance value set, and set it as the first distance value. If there exists a first distance value that is less than or equal to a preset first distance threshold, then the first distance value is set as the connecting link between the corresponding two key node range areas; Based on the connecting link, the range areas of two corresponding key nodes are merged to obtain the initial partition area of ​​the corresponding data type; Obtain a second set of distance values ​​from the range of other key nodes to the initial partitioned region, extract the minimum value in the second set of distance values, and set it as the second distance value; If the second distance value is less than or equal to the preset first distance threshold, then the second distance value is set as the connecting link between the corresponding key node range area and the initial division area; Based on the connecting link, the key node range area and the initial management partition are connected to obtain the updated initial partition area; Traverse the entire set of key nodes, or if all second distance values ​​are greater than the preset first distance threshold, set the initial partition region as the partition region of the corresponding data type.

5. The method for mapping the overall tourism management zones of the Grand Canal Economic Belt based on GIS, as described in claim 1, is characterized in that... After obtaining the data for the entire canal economic belt and dividing it into regions, the following is also included: Extract the boundary lines of the data regions; Retrieve map elements within a preset first region based on the boundary line; Determine whether the map element is the same as the preset map element. If so, revise the boundary line of the corresponding position according to the map element to obtain the revised data division area.

6. The method for mapping the overall tourism management zones of the Grand Canal Economic Belt based on GIS, as described in claim 1, is characterized in that... The steps of converting data-divided regional images into a zoning map for the overall tourism management of the Grand Canal Economic Belt and displaying it, based on the GIS-based spatial overlay and visualization functions, specifically include: Based on the visualization function of GIS, the data is divided into regions and displayed in map form to obtain the corresponding data management zoning map; Based on the spatial overlay function of GIS, the data management zoning map is overlaid and calculated to obtain the overall tourism management map of the Grand Canal Economic Belt. Based on a preset algorithm, the overall map of tourism management in the Grand Canal Economic Belt is divided into levels to obtain a zoning map of tourism management in the Grand Canal Economic Belt.

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