GIS (Geographic Information System)-based power-assisted canal economic belt global tourism management partition drawing method

Abstract

The invention discloses a GIS-assisted canal economic belt global tourism management partition drawing method. The method comprises the following steps: acquiring canal economic belt global data; dividing the canal economic belt global data to obtain sub-data of a plurality of data types; mapping the sub-data to a preset canal economic belt global image to obtain a sub-data point cloud image; performing clustering analysis on sub-data in the sub-data point cloud image to obtain a data division area image of the whole area of the canal economic zone; and based on a spatial superposition function and a visualization function of a GIS (Geographic Information System), converting a data division region image into a canal economic belt global tourism management partition map and displaying the partition map. According to the invention, through data collection and processing, partition standard formulation and GIS technology application, reasonable planning and effective utilization of the tourism resources of the economic zone of the canal are realized.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and more specifically, to a method for drawing zoning areas for overall tourism management of the Grand Canal Economic Zone based on GIS. Background Art

[0002] With the development of tourism economy, the tourism industry has been effectively developed; however, in the process of tourism development, if the products are too similar, it will not bring unique experience to tourists, especially for tourist attractions with relatively backward management systems, which affects the unity and coordination of management work, and will hinder 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, these features make GIS an effective management tool. Summary of the invention

[0003] In view of the above problems, the purpose of the present invention is to provide a method for drawing tourism management zoning zones in the canal economic belt based on GIS, which can realize the rational planning and effective utilization of tourism resources in the canal economic belt.

[0004] The present invention provides a method for drawing zoning for global tourism management of the Grand Canal Economic Belt based on GIS, including:

[0005] Obtain the full-area data of the Canal Economic Belt;

[0006] Divide the whole data of the canal economic zone to obtain sub-data of multiple data types;

[0007] Map the sub-data to the preset global image of the canal economic zone to obtain a sub-data point cloud image;

[0008] 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 zone;

[0009] Based on the spatial overlay and visualization functions of GIS, the data division area image is converted into a comprehensive tourism management zoning map of the Canal Economic Belt and displayed.

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

[0011] Extract the key node names from the preset global image of the canal economic zone;

[0012] Extract keywords from sub-data;

[0013] According to the keywords and key node names in the sub-data, determining the matching degree between the sub-data and the key node names;

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

[0015] In this solution, the step of determining the matching degree between the sub-data and the key node name according to the 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 of 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] Traverse the keywords in the entire sub-data to obtain a set of matching degrees between the keywords in the corresponding sub-data and the same key node name;

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

[0020] The maximum matching degree is multiplied by the corresponding matching degree weight coefficient to obtain the matching degree of the corresponding sub-data and the key node name.

[0021] In this solution, the step of obtaining the matching weight coefficient specifically includes:

[0022] Compare and analyze the matching degrees in the matching degree set with the preset second matching degree threshold in turn, and if the matching degree is greater than the preset second matching degree threshold, record the effective matching degree plus one;

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

[0024] The total number of valid matches is divided 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 the keywords and key node names in the corresponding sub-data specifically includes:

[0026] Obtain historical data on tourism in the Grand Canal Economic Belt;

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

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

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

[0030] In this solution, the step of clustering the sub-data in the sub-data point cloud image to obtain the data division area image of the entire canal economic zone specifically includes:

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

[0032] Extract the data type to which the sub-data belongs, and obtain the sub-data set of the data type according to the corresponding data type;

[0033] Traverse the sub-datasets of the data type to obtain the key node name set of the corresponding data type;

[0034] Get the key node range area corresponding to the key node name;

[0035] According to the key node name set of the data type, determine the key node range area set of the corresponding data type;

[0036] Merge the range areas in the key node range set of the corresponding data type to obtain the range area of ​​the corresponding data type;

[0037] Combine the range areas of all data types to obtain the data division areas of the entire canal economic zone;

[0038] The data division area image of the entire canal economic belt contains the data division areas of the entire canal economic belt.

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

[0040] Taking any key node range area in the keyword node range set as a base point, extracting a first distance value set between the key node range area based on the base point and other key node range areas, extracting the minimum value in the first distance value set, and setting it as the first distance value;

[0041] If there is a first distance value that is less than or equal to a preset first distance threshold, the first distance value is set as a connection link between the corresponding two key node range areas;

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

[0043] Obtain a second distance value set from other key node range areas to the initial divided area, extract the minimum value in the second distance value set, 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, the second distance value is set as a connection link between the corresponding key node range area and the initial divided area;

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

[0046] The entire key node range set is traversed, or all second distance values ​​are greater than the preset first distance threshold, and the initial partition area is set to the partition area of ​​the corresponding data type.

[0047] In this solution, after obtaining the data of the entire canal economic zone and dividing the area, it also includes:

[0048] Extract the boundary lines of data partition areas;

[0049] Obtaining map elements within a preset first area based on the boundary line;

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

[0051] In this scheme, the spatial overlay function and visualization function based on GIS, the steps of converting the data division area image into the canal economic belt full-region tourism management zoning map and displaying it specifically include:

[0052] Based on the visualization function of GIS, the data is divided into regions and displayed in the form of a map 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 canal economic zone;

[0054] Based on the preset algorithm, the overall tourism management map of the Canal Economic Belt is graded to obtain the overall tourism management zoning map of the Canal Economic Belt.

[0055] The present invention discloses a method for drawing zoning zones for overall tourism management in the Grand Canal Economic Belt based on GIS. Through data collection and processing, formulation of zoning standards and application of GIS technology, reasonable planning and effective utilization of tourism resources in the Grand Canal Economic Belt are achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 A flowchart of a method for drawing zoning areas for global tourism management of the Grand Canal Economic Belt based on GIS is shown;

[0057] Figure 2 A flow chart of the present invention for mapping sub-data to a preset global image of the canal economic zone is shown. DETAILED DESCRIPTION

[0058] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0059] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0060] Figure 1 A flow chart of the method for drawing zoning areas for overall tourism management of the canal economic belt based on GIS is shown.

[0061] S101, obtain the whole area data of the canal economic zone;

[0062] S102, dividing the whole data of the canal economic zone to obtain sub-data of multiple data types;

[0063] S103, mapping the sub-data to a preset global image of the canal economic zone to obtain a sub-data point cloud image;

[0064] S104, performing cluster analysis on the sub-data in the sub-data point cloud image to obtain a data division area image of the entire canal economic zone;

[0065] S105, based on the spatial overlay function and visualization function of GIS, the data division area image is converted into a regional tourism management zoning map of the Grand Canal Economic Belt and displayed.

[0066] According to an embodiment of the present invention, the global data of the 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 scenic spot distribution, historical and cultural relics and nature reserves. The socio-economic data includes at least population density, economic development level and tourism income. Through GIS technology, the global tourism area of ​​the canal economic belt is divided into multiple management zones to achieve reasonable planning and effective utilization of tourism resources in the canal economic belt.

[0067] Figure 2 A flow chart of the present invention for mapping sub-data to a preset global image of the canal economic zone is shown.

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

[0069] S201, extracting key node names from a preset global image of the canal economic zone;

[0070] S202, extracting keywords from the sub-data;

[0071] S203, determining the matching degree between the sub-data and the key node name according to 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 a preset first matching degree threshold, the sub-data is displayed at a position corresponding to the key node name.

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

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

[0075] When the keywords and key node names in the sub-data are the same, the matching degree of 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] Traverse the keywords in the entire sub-data to obtain a set of matching degrees between the keywords in the corresponding sub-data and the same key node name;

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

[0079] The maximum matching degree is multiplied by the corresponding matching degree weight coefficient to obtain the matching degree of the corresponding sub-data and the key node name.

[0080] It should be noted that there may be multiple keywords in one sub-data, and multiple keywords in one sub-data are matched with the same key node name in sequence to determine the matching degree set of the keywords in the corresponding sub-data and the same key node name.

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

[0082] Compare and analyze the matching degrees in the matching degree set with the preset second matching degree threshold in turn, and if the matching degree is greater than the preset second matching degree threshold, record the effective matching degree plus one;

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

[0084] The total number of valid matches is divided 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 smaller than the preset first matching degree threshold, and the preset second matching degree threshold is used to reduce accidental errors during matching.

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

[0087] Obtain historical data on tourism in the Grand Canal Economic Belt;

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

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

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

[0091] It should be noted that when the keyword and the key node name appear in the historical data at the same time, it is set as the keyword or key node name appearing in the historical data, that is, the keyword or key node name appearing in the historical data includes the situation where the keyword and the key node name appear at the same time in the historical data.

[0092] According to an embodiment of the present invention, the step of performing 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 zone specifically includes:

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

[0094] Extract the data type to which the sub-data belongs, and obtain the sub-data set of the data type according to the corresponding data type;

[0095] Traverse the sub-datasets of the data type to obtain the key node name set of the corresponding data type;

[0096] Get the key node range area corresponding to the key node name;

[0097] According to the key node name set of the data type, determine the key node range area set of the corresponding data type;

[0098] Merge the range areas in the key node range set of the corresponding data type to obtain the range area of ​​the corresponding data type;

[0099] Combine the range areas of all data types to obtain the data division areas of the entire canal economic zone;

[0100] The data division area image of the entire canal economic belt contains the data division areas 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 in the corresponding data type are obtained and combined into a sub-data set of the corresponding data type. The key node name corresponding to each sub-data in the sub-data set of the corresponding data type is collected to obtain a key node name set, and the key node range areas corresponding to each key node name are combined to determine the key node range area set of the corresponding data type.

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

[0103] Taking any key node range area in the keyword node range set as a base point, extracting a first distance value set between the key node range area based on the base point and other key node range areas, extracting the minimum value in the first distance value set, and setting it as the first distance value;

[0104] If there is a first distance value that is less than or equal to a preset first distance threshold, the first distance value is set as a connection link between the corresponding two key node range areas;

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

[0106] Obtain a second distance value set from other key node range areas to the initial divided area, extract the minimum value in the second distance value set, 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, the second distance value is set as a connection link between the corresponding key node range area and the initial divided area;

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

[0109] The entire key node range set is traversed, or all second distance values ​​are greater than the preset first distance threshold, and the initial partition area is set to the partition area of ​​the corresponding data type.

[0110] It should be noted that if all the first distance values ​​are greater than the preset first distance threshold, it means that the key node range area based on the base point is in a relatively independent position, and the key node range area corresponding to the base point is set as a separate partitioning 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 partitioning 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 partitioning area to obtain the second distance value, and the judgment is made according to the corresponding preset first distance threshold until the second distance values ​​of the remaining key node range areas in the key node range set and the updated initial partitioning area are both greater than the preset first distance threshold or the key node range areas in the inter-roller node range set are merged with the updated initial partitioning area.

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

[0112] Extract the boundary lines of data division areas;

[0113] Obtaining map elements within a preset first area based on the boundary line;

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

[0115] It should be noted that the preset map elements include at least terrain features or buildings with dividing properties such as rivers, roads, fences, flower and grass guardrails and isolation walls. The boundary lines of the data division areas are revised through the preset map elements in the entire canal economic zone to improve the accuracy of the boundary lines of the data division areas.

[0116] According to an embodiment of the present invention, the steps of converting the data division area image into a regional tourism management zoning map of the Grand Canal Economic Belt and displaying the regional tourism management map based on the spatial overlay function and visualization function of GIS specifically include:

[0117] Based on the visualization function of GIS, the data is divided into regions and displayed in the form of a map 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 canal economic zone;

[0119] Based on the preset algorithm, the overall tourism management map of the Canal Economic Belt is graded to obtain the overall tourism management zoning map of the Canal Economic Belt.

[0120] It should be noted that the preset algorithm described in the embodiment of the present invention is the natural boundary method, which assists in the drawing of the overall tourism management zoning of the Canal Economic Belt through GIS, thereby improving the rational planning and effective utilization of tourism resources in the Canal Economic Belt.

[0121] The present invention discloses a method for drawing zoning zones for overall tourism management in the Grand Canal Economic Belt based on GIS. Through data collection and processing, formulation of zoning standards and application of GIS technology, reasonable planning and effective utilization of tourism resources in the Grand Canal Economic Belt are achieved.

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

[0123] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0124] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0125] Those skilled in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, and other media that can store program codes.

[0126] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

Claims

1. A method for drawing zoning for the overall tourism management of the Grand Canal Economic Belt based on GIS, characterized in that: include: Obtain the full-area data of the Canal Economic Belt; Divide the whole data of the canal economic zone to obtain sub-data of multiple data types; Map the sub-data to the preset global image of the canal economic zone to obtain a sub-data point cloud image; 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 zone; Based on the spatial overlay and visualization functions of GIS, the data division area image is converted into a comprehensive tourism management zoning map of the Canal Economic Belt and displayed.

2. According to claim 1, the method for drawing zoning for global tourism management of the Grand Canal Economic Belt based on GIS is characterized in that: The step of mapping the sub-data to the preset global image of the canal economic zone specifically includes: Extract the key node names from the preset global image of the canal economic zone; Extract keywords from sub-data; According to the keywords and key node names in the sub-data, determining the matching degree between the sub-data and the key node names; If the matching degree between the sub-data and the key node name is greater than or equal to a preset first matching degree threshold, the sub-data is displayed at the position corresponding to the key node name.

3. The method for drawing zoning for global tourism management of the Grand Canal Economic Belt based on GIS according to claim 2 is characterized in that: The step of determining the matching degree between the sub-data and the key node name according to the keywords and the key node name in the sub-data specifically includes: When the keywords and key node names in the sub-data are the same, the matching degree of 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; Traverse the keywords in the entire sub-data to obtain a set of matching degrees between the keywords in the corresponding sub-data and the same key node name; Extract the maximum matching degree from the matching degree set; The maximum matching degree is multiplied by the corresponding matching degree weight coefficient to obtain the matching degree of the corresponding sub-data and the key node name.

4. The method for drawing zoning for global tourism management of the Grand Canal Economic Belt based on GIS according to claim 3 is characterized in that: The step of obtaining the matching weight coefficient specifically includes: Compare and analyze the matching degrees in the matching degree set with the preset second matching degree threshold in turn, and if the matching degree is greater than the preset second matching degree threshold, record the effective matching degree plus one; Extract the total number of valid matches and the total number of matches in the match set; The total number of valid matches is divided by the total number of matches in the match set to obtain the corresponding match weight coefficient.

5. According to claim 3, the method for drawing zoning for global tourism management of the Grand Canal Economic Belt based on GIS is characterized in that: The step of extracting the correlation between the keywords and key node names in the corresponding sub-data specifically includes: Obtain historical data on tourism in the Grand Canal Economic Belt; Extract the number of times the keyword and key node name appear simultaneously in the historical data and set it as the first value; Extract the number of times the keyword or key node name appears in the historical data and set it as the second value; Divide the first value by the second value to obtain the correlation between the keyword and the key node name in the corresponding sub-data.

6. The method for drawing zoning for global tourism management of the Grand Canal Economic Belt based on GIS according to claim 1 is characterized in that: The step of clustering the sub-data in the sub-data point cloud image to obtain the data division area image of the entire canal economic zone specifically includes: Extract the key node names corresponding to the sub-data; Extract the data type to which the sub-data belongs, and obtain the sub-data set of the data type according to the corresponding data type; Traverse the sub-datasets of the data type to obtain the key node name set of the corresponding data type; Get the key node range area corresponding to the key node name; According to the key node name set of the data type, determine the key node range area set of the corresponding data type; Merge the range areas in the key node range set of the corresponding data type to obtain the range area of ​​the corresponding data type; Combine the range areas of all data types to obtain the data division areas of the entire canal economic zone; The data division area image of the entire canal economic belt contains the data division areas of the entire canal economic belt.

7. The method for drawing zoning for global tourism management of the Grand Canal Economic Belt based on GIS according to claim 6 is characterized in that: The step of merging the range areas in the key node range set of the corresponding data type specifically includes: Taking any key node range area in the keyword node range set as a base point, extracting a first distance value set between the key node range area based on the base point and other key node range areas, extracting the minimum value in the first distance value set, and setting it as the first distance value; If there is a first distance value that is less than or equal to a preset first distance threshold, the first distance value is set as a connection link between the corresponding two key node range areas; Based on the connection link, the corresponding two key node range areas are merged to obtain the initial division area of ​​the corresponding data type; Obtain a second distance value set from other key node range areas to the initial divided area, extract the minimum value in the second distance value set, and set it as the second distance value; If the second distance value is less than or equal to the preset first distance threshold, the second distance value is set as a connection link between the corresponding key node range area and the initial divided area; Based on the connection link, the key node range area and the initial management partition are connected to obtain the updated initial partition area; The entire key node range set is traversed, or all second distance values ​​are greater than the preset first distance threshold, and the initial partition area is set to the partition area of ​​the corresponding data type.

8. The method for drawing zoning for global tourism management of the Grand Canal Economic Belt based on GIS according to claim 6 is characterized in that: After the data of the whole area of ​​the canal economic zone is divided into regions, the following is also included: Extract the boundary lines of data division areas; Obtaining map elements within a preset first area based on the boundary line; It is determined whether the map element is the same as a preset map element. If so, the boundary line of the corresponding position is revised according to the map element to obtain a revised data division area.

9. The method for drawing zoning for global tourism management of the Grand Canal Economic Belt based on GIS according to claim 1 is characterized in that: The steps of converting the data division area image into the overall tourism management zoning map of the Grand Canal Economic Belt and displaying it based on the spatial overlay function and visualization function of GIS specifically include: Based on the visualization function of GIS, the data is divided into regions and displayed in the form of a map 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 canal economic zone; Based on the preset algorithm, the overall tourism management map of the Canal Economic Belt is graded to obtain the overall tourism management zoning map of the Canal Economic Belt.

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