Spatial raster data acquisition method, apparatus and device, and readable storage medium

Abstract

The invention provides a spatialized raster data acquisition method and device, equipment and a readable storage medium. The method comprises the following steps: determining a first category of scenic spots and a second category of scenic spots in a preset spatial region; obtaining first spatialization raster data based on first tourism related data in the first type of scenic spots; obtaining second spatialization raster data corresponding to the point-of-interest data based on the point-of-interest data in the second type of scenic spots; summing all the second spatialization raster data to obtain the sum of the second spatialization raster data; obtaining total tourism related data in the preset space region; based on the first tourism related data, the second spatialization raster data, the sum of the second spatialization raster data and the total tourism related data, obtaining third spatialization raster data; and based on the first spatialization raster data and the third spatialization raster data, obtaining a total amount of tourism-related data corresponding to the preset space region, so as to obtain richer and more visual tourism-related data.

Description

Technical Field

[0001] The present application belongs to the field of data processing and relates to a method for acquiring spatialized raster data, and in particular to a method, device, equipment and readable storage medium for acquiring spatialized raster data. Background Art

[0002] Spatial rasterization is a technology that converts continuous geographic data into raster format, which is widely used in the analysis and visualization of tourism-related data. By converting tourism-related data into raster format, spatial information can be effectively combined with attribute data, which is convenient for Geographic Information System (GIS) analysis. For example, data such as tourist hotspots, scenic spot distribution and tourist flow can be processed by rasterization to form images and maps that are easy to understand and operate. In addition, rasterization helps to achieve data standardization and unified management, so that tourism data from different sources and formats can be analyzed on the same platform. However, the existing spatialization technology of tourism-related data has many defects and limitations. Summary of the invention

[0003] The present application provides a spatialized raster data acquisition method, device, equipment and readable storage medium, which are used to solve the technical problems in the prior art of single data acquisition method and low data processing efficiency.

[0004] In a first aspect, an embodiment of the present application provides a method for acquiring spatialized raster data, the method comprising: determining a first category scenic spot and a second category scenic spot in a preset spatial area; obtaining first spatialized raster data corresponding to the first tourism-related data based on first tourism-related data in the first category scenic spot; obtaining second spatialized raster data corresponding to the interest point data based on point of interest data in the second category scenic spot; summing all of the second spatialized raster data to obtain the sum of the second spatialized raster data; acquiring total tourism-related data in the preset spatial area; obtaining third spatialized raster data based on the first tourism-related data, the second spatialized raster data, the sum of the second spatialized raster data and the total tourism-related data; obtaining the total amount of tourism-related data corresponding to the preset spatial area based on the first spatialized raster data and the third spatialized raster data.

[0005] In an implementation of the first aspect, the number of scenic spots in the first category is at least one, and the obtaining of first spatialized raster data corresponding to the first tourism-related data based on the first tourism-related data in the scenic spots in the first category includes: determining the unit pixel area of ​​the scenic spots in the first category and the scenic area area of ​​the scenic spots in the first category; determining the unit tourism-related data corresponding to the unit pixel area based on the unit pixel area, the scenic area area and the first tourism-related data; inputting the unit pixel area and the unit tourism-related data corresponding to the unit pixel area into a vector-to-raster tool to obtain the spatialized data corresponding to the scenic spots in the first category; and overlaying the spatialized data of all the scenic spots in the first category to obtain the first spatialized raster data corresponding to the first tourism-related data.

[0006] In an implementation of the first aspect, obtaining second spatialized raster data corresponding to the point of interest data based on the point of interest data in the second category of scenic spots includes: obtaining a calculation frequency weight of the point of interest data based on a weight analysis method; determining an output pixel area of ​​the second category of scenic spots; and determining the second spatialized raster data corresponding to the point of interest data based on the calculation frequency weight and the output pixel area.

[0007] In an implementation of the first aspect, determining the second spatialized raster data corresponding to the interest point data based on the calculation frequency weight and the output pixel area includes: obtaining the spatialized raster data corresponding to the calculation frequency weight and the output pixel area based on the calculation frequency weight and the output pixel area; and eliminating the first spatialized raster data from the spatialized raster data to obtain the second spatialized raster data corresponding to the interest point data.

[0008] In an implementation manner of the first aspect, the calculation frequency weight is divided into 1 to 5 levels from low to high.

[0009] In an implementation of the first aspect, the first tourism-related data includes: the proportion of tourists from different source areas arriving at the first category of scenic spots, the traffic value of different source areas to the first category of scenic spots, the consumption value of the first category of scenic spots, the tickets of the first category of scenic spots and / or the parking value of the first category of scenic spots.

[0010] In an implementation of the first aspect, the spatialized raster data acquisition method further includes: acquiring each item of the first tourism-related data based on a network cloud big data platform.

[0011] A spatialized raster data acquisition method provided by an embodiment of the present application includes: determining a first category scenic spot and a second category scenic spot in a preset spatial area; obtaining first spatialized raster data corresponding to the first tourism-related data based on first tourism-related data in the first category scenic spot; obtaining second spatialized raster data corresponding to the interest point data based on interest point data in the second category scenic spot; summing all the second spatialized raster data to obtain the sum of the second spatialized raster data; obtaining total tourism-related data in the preset spatial area; obtaining third spatialized raster data based on the first tourism-related data, the second spatialized raster data, the sum of the second spatialized raster data and the total tourism-related data; obtaining the total amount of tourism-related data corresponding to the preset spatial area based on the first spatialized raster data and the third spatialized raster data, and dividing the scenic spots in the preset spatial area into first category scenic spots and second category scenic spots based on specific spatialized raster data. The method for determining grid data respectively determines the first spatialized raster data corresponding to the first category scenic spot and the second spatialized raster data corresponding to the second category scenic spot, thereby avoiding the previous method of directly obtaining data in a preset spatial area without dividing the scenic spot categories, resulting in a relatively single method of obtaining data. The spatialized raster data acquisition method provided by the present application improves the richness of data acquisition and realizes multi-level coverage of various types and levels of scenic spots and attractions in the preset spatial area; by obtaining the first spatialized raster data corresponding to the first category scenic spot and the second spatialized raster data corresponding to the second category scenic spot, and obtaining the total amount of tourism-related data based on the first spatialized raster data and the second spatialized raster data, the corresponding data structure in the preset spatial area is simpler, and a series of data processing can be performed on the obtained first spatialized raster data and the second spatialized raster data more intuitively to obtain the total amount of tourism-related data, thereby improving the efficiency of data processing and providing feasibility for spatial data processing of multiple groups of data in the preset spatial area.

[0012] In a second aspect, an embodiment of the present application provides a spatialized raster data acquisition device, the spatialized raster data acquisition device comprising: a scenic spot category determination module, used to determine a first category scenic spot and a second category scenic spot in a preset spatial area; a first spatialized raster data determination module, used to obtain first spatialized raster data corresponding to the first tourism-related data based on the first tourism-related data in the first category scenic spot; a second spatialized raster data determination module, used to obtain second spatialized raster data corresponding to the interest point data based on the interest point data in the second category scenic spot; a second spatialized raster data sum determination module, used to sum all the second spatialized raster data to obtain the sum of the second spatialized raster data; a total tourism-related data acquisition module, used to obtain the total tourism-related data in the preset spatial area; a third spatialized raster data determination module, used to obtain third spatialized raster data based on the first tourism-related data, the second spatialized raster data, and the sum of the second spatialized raster data; a tourism-related data total amount determination module, used to obtain the total tourism-related data corresponding to the preset spatial area based on the first spatialized raster data and the third spatialized raster data.

[0013] In a third aspect, an embodiment of the present application provides an electronic device, comprising a memory and a processor; the memory is used to store a computer program; the processor is used to execute the computer program and implement the spatialized raster data acquisition method as described in any one of the first aspects of the embodiments of the present application when executing the computer program.

[0014] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the spatialized raster data acquisition method described in any one of the first aspects of the embodiments of the present application is implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 Shown is a flow chart of a method for acquiring spatialized raster data provided by an embodiment of the present application.

[0016] Figure 2 Shown is a flowchart of obtaining first spatialized raster data corresponding to first tourism-related data in one embodiment of the present application.

[0017] Figure 3 Shown is a flowchart of determining the second spatialized raster data in one embodiment of the present application.

[0018] Figure 4 Shown is a flowchart of specific steps corresponding to determining the second spatialized raster data in an embodiment of the present application.

[0019] Figure 5 Shown is a schematic diagram of a spatialized raster data acquisition device in an embodiment of the present application.

[0020] Figure 6 Shown is a schematic diagram of the structure of an electronic device in an embodiment of the present application.

[0021] Component number description

[0022] Steps S11 to S17

[0023] Steps S21 to S24

[0024] Steps S31 to S33

[0025] Steps S41 to S42

[0026] 50 Spatialized grid data acquisition device

[0027] 51 Scenic spot category determination module

[0028] 52 First spatialized raster data determination module

[0029] 53 Second spatialized raster data determination module

[0030] 54 Second spatialized grid data sum determination module

[0031] 55 Total tourism related data acquisition module

[0032] 56 Third spatialization grid data determination model

[0033] 57 Tourism related data total amount determination module

[0034] 60 Electronic equipment

[0035] 61 Processor

[0036] 62 Non-volatile storage media

[0037] 63 System Bus

[0038] 64 Internal Memory

[0039] 65 Network Interface DETAILED DESCRIPTION

[0040] The following describes the embodiments of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0041] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present application, and thus the drawings only show components related to the present application rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the form, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0042] The embodiment of the present application provides a method for acquiring spatialized raster data. The method determines a first category scenic spot and a second category scenic spot in a preset spatial area; based on the first tourism-related data in the first category scenic spot, obtains the first spatialized raster data corresponding to the first tourism-related data; based on the point of interest data in the second category scenic spot, obtains the second spatialized raster data corresponding to the point of interest data; sums all the second spatialized raster data to obtain the sum of the second spatialized raster data; obtains the total tourism-related data in the preset spatial area; based on the first tourism-related data, the second spatialized raster data, the sum of the second spatialized raster data and the total tourism-related data, obtains the third spatialized raster data; based on the first spatialized raster data and the third spatialized raster data, obtains the total amount of tourism-related data corresponding to the preset spatial area, which can solve the technical problems of single data acquisition method and low data processing efficiency in the prior art.

[0043] The technical solutions in the embodiments of the present application will be described in detail below in conjunction with the drawings in the embodiments of the present application.

[0044] Figure 1 The flowchart of the spatialized raster data acquisition method provided by one embodiment of the present application is shown. Figure 1 As shown, the spatialized raster data acquisition method provided in the embodiment of the present application includes the following steps S11 to S17.

[0045] S11. Determine a first category scenic spot and a second category scenic spot in a preset spatial area.

[0046] Among them, the first category scenic spots are different from the second category scenic spots.

[0047] Among them, the first category scenic spot may be an A-level scenic spot, and the second category scenic spot may be a non-A-level scenic spot.

[0048] For example, based on the classification standard of tourist attractions in the preset spatial area, the A-level scenic spots in the preset spatial area can be determined as the first category scenic spots, and the non-A-level scenic spots in the preset spatial area can be determined as the second category scenic spots.

[0049] In addition, the first category scenic spot may also be a park scenic spot, and the second category scenic spot may also be a non-park scenic spot, etc. According to the name of the scenic spot in the preset spatial area, the park scenic spot in the preset spatial area may be determined as the first category scenic spot, and the non-park scenic spot in the preset spatial area may be determined as the first category scenic spot.

[0050] It should be noted that the above-mentioned division method of determining the first category scenic spot and the second category scenic spot in the preset spatial area can also be any other appropriate division method. In practical applications, the preset spatial area can also be reasonably divided according to specific circumstances, which will not be repeated in this application.

[0051] S12. Based on the first tourism-related data in the first category of scenic spots, obtain first spatialized raster data corresponding to the first tourism-related data.

[0052] In some embodiments, the first tourism-related data may include: the proportion of tourists from different tourist source areas arriving at the first category of scenic spots, traffic value, satisfaction, stay time, revisit rate, scenic spot rating, online reservation volume, social media mention volume, scenic spot facility utilization rate, scenic spot safety accident rate, scenic spot activity participation rate and / or scenic spot recommendation index, etc. The traffic value may include, for example, high-speed rail mileage, air mileage and / or self-driving mileage.

[0053] In some embodiments, the spatialized raster data acquisition method further includes: acquiring each item of the first tourism-related data based on a network cloud big data platform.

[0054] S13: Based on the point of interest data in the second category of scenic spots, obtain second spatialized raster data corresponding to the point of interest data.

[0055] S14. Sum all the second spatialized raster data to obtain a sum of the second spatialized raster data.

[0056] Before summing all the second spatialized raster data, it is ensured that all the second spatialized raster data are available and have the same format. If the format of some of the second spatialized raster data is incorrect, the format of the part of the second spatialized raster data is adjusted so that the adjusted part of the second spatialized raster data can be summed with other second spatialized raster data.

[0057] Specifically, in the process of summing all the second spatialized raster data, it is necessary to ensure that the data in all the second spatialized raster data are spatially aligned, so as to facilitate the subsequent summing of the data of the same category in all the second spatialized raster data. For example, in the process of summing the second spatialized raster data A and the second spatialized raster data B, the number of tourists in the second spatialized raster data A is added to the number of tourists in the second spatialized raster data B, and the tourist attractions in the second spatialized raster data A are added to the tourist attractions in the second spatialized raster data B.

[0058] S15. Obtaining total tourism-related data within the preset spatial area.

[0059] The total tourism-related data within the preset spatial area may be the total tourism-related data of this year, or may be the total tourism-related data obtained through forecasting and analysis based on the total tourism-related data within the preset spatial area in previous years.

[0060] Exemplarily, the total tourism-related data in the preset spatial area may be directly obtained based on the relevant websites of the preset spatial area.

[0061] As another example, the total tourism-related data in the preset spatial area can also be analyzed and predicted based on a big data platform, wherein the big data platform can be: Apache Hadoop, Apache Spark, Hortonworks, etc.

[0062] It should be noted that the big data platforms listed above are only used for illustrative purposes. In actual applications, any other suitable big data platform can be selected according to specific circumstances, and this application does not impose any restrictions on this.

[0063] S16. Obtain third spatialized raster data based on the first tourism-related data, the second spatialized raster data, the sum of the second spatialized raster data and the total tourism-related data.

[0064] Among them, the third spatialized raster data table reflects the tourism-related data corresponding to each point of interest in the second category of scenic spots.

[0065] The formula corresponding to the third spatialized raster data obtained based on the first tourism-related data, the second spatialized raster data, the sum of the second spatialized raster data and the total tourism-related data is:

[0066]

[0067] Raster3 represents the third spatialized raster data, V0 represents the total tourism related data, V i represents the i-th first tourism related data, Raster2 represents the second spatialized raster data, R s represents the sum of the second spatialized raster data, and n represents the number of the first tourism-related data.

[0068] S17: Based on the first spatialized raster data and the third spatialized raster data, obtain the total amount of tourism-related data corresponding to the preset spatial area.

[0069] The total amount of tourism-related data represents tourism-related data corresponding to each scenic spot in a preset spatial area.

[0070] Specifically, each item of data in the first spatialized raster data and each item of data processed at a corresponding position in the third spatialized raster data are summed up respectively to obtain the total amount of tourism-related data corresponding to the preset spatial area.

[0071] The corresponding expression is: Raster4 = Raster1 + Raster3

[0072] Among them, Raster1 represents the first spatialized raster data, and Raster4 represents the total amount of tourism-related data.

[0073] In the spatialized raster data acquisition method provided in the embodiment of the present application, a first category scenic spot and a second category scenic spot are determined in a preset spatial area; first spatialized raster data corresponding to the first tourism-related data is obtained based on the first tourism-related data in the first category scenic spot; second spatialized raster data corresponding to the interest point data is obtained based on the interest point data in the second category scenic spot; all the second spatialized raster data are summed to obtain the sum of the second spatialized raster data; the total tourism-related data in the preset spatial area is obtained; third spatialized raster data is obtained based on the first tourism-related data, the second spatialized raster data, the sum of the second spatialized raster data and the total tourism-related data; the total amount of tourism-related data corresponding to the preset spatial area is obtained based on the first spatialized raster data and the third spatialized raster data, and the first category scenic spot and the third spatialized raster data are determined in the preset spatial area, and the data in the first category scenic spot and the second category scenic spot are processed respectively, so that the richness of data acquisition is improved, the multi-level coverage of various types and levels of scenic spots and attractions in the preset spatial area is achieved, the accuracy of the total amount of tourism-related data is improved, and feasibility is provided for spatial data processing of multiple groups of data in the preset spatial area.

[0074] Figure 2The flowchart of obtaining the first spatialized raster data corresponding to the first tourism-related data in one embodiment of the present application is shown. Figure 2 As shown, the process of obtaining the first spatialized raster data corresponding to the first tourism-related data in the embodiment of the present application includes the following steps S21 to S24.

[0075] S21, determining the unit pixel area of ​​the first category scenic spot and the scenic spot area of ​​the first category scenic spot.

[0076] The unit pixel area can be reasonably set according to specific application requirements, and this application does not limit this. For example, the unit pixel area can be 30x30m, 50x50m, etc.

[0077] Specifically, the scenic area of ​​the first category of scenic spots can be directly obtained based on official data.

[0078] S22: Determine the unit tourism-related data corresponding to the unit pixel area based on the unit pixel area, the scenic area area and the first tourism-related data.

[0079] Among them, the expression corresponding to the unit tourism related data is:

[0080]

[0081] Among them, S0 represents the unit pixel area, S i represents the area of ​​the first category scenic spot i, V i Represents the first tourism related data, V ip Represents unit tourism related data.

[0082] S23, inputting the unit pixel area and the unit tourism related data corresponding to the unit pixel area into a vector-to-raster tool to obtain spatialized data corresponding to the first category of scenic spots.

[0083] Among them, the vector-to-raster tool can be ArcGIS, QGIS, GRASS GIS, Python, etc.

[0084] It should be noted that the vector-to-raster tools listed above are only used for illustrative purposes. In actual applications, other vector-to-raster tools may be selected according to specific circumstances, and this application does not impose any restrictions on this.

[0085] S24, performing superposition processing on the spatialized data of all the first-category scenic spots to obtain the first spatialized raster data corresponding to the first tourism-related data.

[0086] The superimposing process on the spatialized data of all the first-category scenic spots is: superimposing the spatialized data of corresponding categories in all the first-category scenic spots respectively.

[0087] Specifically, before the spatialized data of all the first-category scenic spots are overlaid, the above steps of obtaining the spatialized data corresponding to the first-category scenic spots are performed on each of the first-category scenic spots in turn to obtain the spatialized data corresponding to each of the first-category scenic spots. After the spatialized data corresponding to all the first-category scenic spots are obtained, the spatialized data of all the first-category scenic spots are overlaid to obtain the first spatialized raster data corresponding to the first tourism-related data.

[0088] In the method for obtaining first spatialized raster data corresponding to first tourism-related data provided in an embodiment of the present application, the unit pixel area of ​​the first category scenic spot and the scenic area area of ​​the first category scenic spot are determined; based on the unit pixel area, the scenic area area and the first tourism-related data, the unit tourism-related data corresponding to the unit pixel area is determined; the unit pixel area and the unit tourism-related data corresponding to the unit pixel area are input into a vector-to-raster tool to obtain the spatialized data corresponding to the first category scenic spot; the spatialized data of all the first category scenic spots are superimposed to obtain the first spatialized raster data corresponding to the first tourism-related data, and the first spatialized raster data obtained by inputting the unit pixel area and the unit tourism-related data into the vector-to-raster tool can more intuitively reflect the data structure of each first category scenic spot, and then the superposition processing of each spatialized data in all the first category scenic spots can be more simply realized to obtain the first spatialized raster data corresponding to the first tourism-related data, thereby improving the efficiency of obtaining the first spatialized raster data.

[0089] Figure 3 The flowchart of determining the second spatialized raster data in one embodiment of the present application is shown. Figure 3 As shown, the process of determining the second spatialized raster data in the embodiment of the present application includes the following steps S31 to S33.

[0090] S31, based on the weight analysis method, obtaining the calculation frequency weight of the interest point data.

[0091] Specifically, before obtaining the calculation frequency weight of the interest point data based on the weight analysis method, the number of interest point data in the second category scenic spot and the category of the interest point data are determined.

[0092] For example, each point of interest in the second category scenic area may be determined based on the kernel density estimation method, wherein each point of interest in the second category scenic area includes: scenic spots, parks and squares, religious temples, memorial pavilions, and the like.

[0093] Exemplarily, the data of points of interest in the above-mentioned second category scenic spots include: the proportion of tourists from different source areas arriving at the second category scenic spots, traffic value, satisfaction, stay time, revisit rate, scenic spot rating, online booking volume, social media mention volume, scenic spot facility utilization rate, scenic spot safety accident rate, scenic spot activity participation rate and / or scenic spot recommendation index, etc. The traffic value may include, for example, high-speed rail mileage, air mileage and / or self-driving mileage.

[0094] It should be noted that the types and / or quantity of the POI data in the second category scenic spot may be the same as or different from the types and / or quantity of the first tourism-related data in the first category scenic spot.

[0095] In some embodiments, the calculation frequency weights are divided into 1 to 5 levels from low to high.

[0096] It should be noted that the calculation frequency weight can also be divided into levels 1 to 4 or 1 to 6 according to actual application needs. The specific classification of the calculation frequency weight of the point of interest data can be reasonably determined according to the actual application scenario, and this application does not impose any restrictions on this.

[0097] S32: Determine the output pixel area of ​​the second category scenic spot.

[0098] Among them, the output pixel area can be a circular area obtained with the search radius interval R, wherein the radius interval R can be any radius distance between 3km and 8km. The size of the specific radius interval R can be reasonably set according to the actual area of ​​the second category scenic spot, and this application does not impose any restrictions on this.

[0099] In addition, the output pixel area can also be a rectangle or a square. In practical applications, any other suitable shape of the output pixel area can be selected according to the specific application scenario. This application does not limit the specific shape of the output pixel area.

[0100] S33: Determine second spatialized raster data corresponding to the point of interest data based on the calculation frequency weight and the output pixel area.

[0101] Specifically, after obtaining the calculated frequency weights of each point of interest in the second category scenic spot, a new raster layer can be created based on GIS software, wherein the GIS software includes ArcGIS or QGIS. In the new raster layer, based on the calculated frequency weights and the output pixel area, the second spatialized raster data corresponding to the point of interest data is determined.

[0102] It should be noted that the above method of obtaining a new raster layer is only for exemplary purposes. In practical applications, any other suitable method can be selected to obtain a new raster layer according to the actual application scenario. Then, in the new raster layer, the second spatialized raster data corresponding to the interest point data is determined. This application does not limit this.

[0103] An embodiment of the present application provides a method for determining second spatialized raster data, in which the calculation frequency weight of the point of interest data is obtained based on a weight analysis method; the output pixel area of ​​the second category scenic spot is determined; based on the calculation frequency weight and the output pixel area, the second spatialized raster data corresponding to the point of interest data is determined. The second spatialized rasterized data obtained based on the above method has continuity, which is convenient for more intuitively displaying the distribution and change trend of the data on the second category scenic spot, and the obtained second spatialized rasterized data is convenient for spatial analysis and geographic modeling on the second category scenic spot, thereby improving the user experience.

[0104] Figure 4 The flowchart is shown as a specific step of determining the corresponding second spatialized raster data in one embodiment of the present application. Figure 4 As shown, the process of determining the second spatialized raster data in the embodiment of the present application includes the following steps S41 to S42.

[0105] S41, based on the calculation frequency weight and the output pixel area, obtaining spatialized raster data corresponding to the calculation frequency weight and the output pixel area.

[0106] Specifically, the frequency weight and the output pixel area can be interpolated based on the spatial interpolation tool of GIS software, wherein the spatial interpolation tool of GIS software includes: Inverse Distance Weighting (IDW), Kriging, etc.

[0107] Exemplarily, the calculated frequency weight and the output pixel area may be used as input data in sequence, and the input data may be input into an IDW tool to generate spatialized raster data.

[0108] S42: Eliminate the first spatialized raster data from the spatialized raster data to obtain the second spatialized raster data corresponding to the point of interest data.

[0109] Before removing the first spatialized raster data from the spatialized raster data, it is ensured that the first spatialized raster data and the second spatialized raster data have the same geographic coordinate system, resolution and coverage.

[0110] Specifically, in the process of removing the first spatialized raster data from the spatialized raster data, if the second spatialized raster data is a subset of the first spatialized raster data, that is, if the second spatialized raster data only contains part of the information of the first spatialized raster data, the first spatialized raster data can be removed by simple raster subtraction. For example, the second spatialized raster data = spatialized raster data - the first spatialized raster data.

[0111] On the contrary, if the second spatialized raster data needs to completely eliminate the information in the first spatialized raster data, it is necessary to initialize a new raster array with the same size and resolution as the second spatialized raster data, traverse each pixel, and check the value of the corresponding position in the first spatialized raster data. If the value of the first spatialized raster data is 0 or no data (for example, NoData value), the value of the second spatialized raster data is copied to the new array. If the value of the first spatialized raster data is not 0, the value of the position in the new array is set to No Data or 0, and the obtained new raster array is saved as a new raster file, which is the second spatialized raster data.

[0112] It should be noted that the above-mentioned method of removing the first spatialized raster data from the spatialized raster data is only for illustrative purposes. In practical applications, any other suitable removal method can be selected according to actual needs, and this application does not limit the comparison.

[0113] In one embodiment of the present application, based on the calculation frequency weight and the output pixel area, spatialized raster data corresponding to the calculation frequency weight and the output pixel area are obtained; the first spatialized raster data is eliminated from the spatialized raster data to obtain the second spatialized raster data corresponding to the point of interest data, and the spatialized raster data is subjected to elimination processing. After the elimination processing, the spatialized raster data is subjected to elimination processing to obtain the second spatialized raster data that does not contain the first spatialized raster data, so that the second spatialized raster data fully reflects the data characteristics of the second category of scenic spots, reduces data redundancy, avoids data errors caused by the mixed first spatialized raster data, and improves the accuracy of the second spatialized raster data in reflecting the second category of scenic spots.

[0114] The protection scope of the spatialized raster data acquisition method of the embodiment of the present application is not limited to the execution order of the steps listed in the present embodiment. All solutions implemented by adding, reducing or replacing steps in the prior art based on the principles of the present application are included in the protection scope of the present application.

[0115] Next, the spatialized raster data acquisition method provided in the embodiment of the present application will be exemplarily introduced through two specific application scenarios.

[0116] In an application scenario, suppose there are 5 first-category scenic spots in the preset spatial area A, namely, first-category scenic spot 1 to first-category scenic spot 5, and 10 second-category scenic spots, namely, second-category scenic spot 1 to second-category scenic spot 10, and the points of interest data are m 1_1 To m 1_10 . In this application scenario, the first tourism-related data includes the first tourism-related data including the proportion of tourists from different source areas arriving at the first category scenic spot, satisfaction, stay time, revisit rate, scenic spot score, social media mentions, scenic spot facility utilization rate and / or scenic spot recommendation index, etc. In this application scenario, the corresponding first spatialized raster data can be obtained according to the first tourism-related data of the first category scenic spot 1 to the first category scenic spot 5. The specific steps can be: sequentially perform the above steps of obtaining spatialized value data on the first category scenic spot 1 to obtain the spatialized value data corresponding to the first category scenic spot 1; perform the above steps of obtaining spatialized value data on the first category scenic spot 2 to obtain the spatialized value data corresponding to the first category scenic spot 2; perform the above steps of obtaining spatialized value data on the first category scenic spot 3 to obtain the spatialized value data corresponding to the first category scenic spot 3; perform the above steps of obtaining spatialized value data on the first category scenic spot 4 to obtain the spatialized value data corresponding to the first category scenic spot 4; perform the above steps of obtaining spatialized value data on the first category scenic spot 5 to obtain the spatialized value data corresponding to the first category scenic spot 5. After obtaining the spatialized value data corresponding to all five first-category scenic spots, the spatialized value data corresponding to the five first-category scenic spots are superimposed to obtain the first spatialized raster data corresponding to the first tourism-related data. 1_1 To m 1_10 The corresponding second spatialized raster data Raster2_1 to Raster2_10 are obtained. The sum of Raster2_1 to Raster2_10 is obtained to obtain the second spatialized raster data sum Rs. Based on the first tourism-related data corresponding to the first category scenic spots 1 to 5, the point of interest data m 1_1 To m 1_10 The corresponding second spatialized raster data, the sum of the second spatialized raster data and the total tourism-related data are used to obtain the third spatialized raster data Raster3. The third spatialized raster data is used to represent the tourism value data corresponding to each point of interest in the second category scenic spots 1 to 10. Based on the first spatialized raster data and the third spatialized raster data, the total amount of tourism-related data corresponding to the preset spatial area A is obtained, and the total amount of tourism-related data is used to represent the total amount of tourism-related data in the preset spatial area A. Based on the total amount of tourism-related data, the satisfaction of tourists with the preset spatial area A can be obtained.

[0117] In another application scenario, there are 5 first-category scenic spots in the preset spatial area B, namely, first-category scenic spot 1, first-category scenic spot 2, first-category scenic spot 3, first-category scenic spot 4, and first-category scenic spot 5, and there are 10 second-category scenic spots, namely, second-category scenic spots 1 to 10, and the points of interest data are m 2_1 To m 2_10 In this application scenario, the first tourism-related data includes the proportion of tourists from different source areas arriving at the first category of scenic spots, the traffic value of different source areas to the first category of scenic spots, the consumption value of the first category of scenic spots, the tickets of the first category of scenic spots and / or the parking value of the first category of scenic spots.

[0118] Exemplarily, the transportation value may include: high-speed rail fees, air fees and / or self-driving fees. For example, when the distance from a tourist to the first category of scenic spots in the preset spatial area is less than 100km, the tourist can choose to travel by self-driving, and the corresponding self-driving fee is the transportation value of the tourist to the first category of scenic spots; if the distance from the tourist to the first category of scenic spots in the preset spatial area is greater than 100km and less than 1000km, the tourist can choose to travel by high-speed rail, and the corresponding high-speed rail fee is the transportation value of the tourist to the first category of scenic spots; if the distance from the tourist to the first category of scenic spots in the preset spatial area is greater than 1000km, the tourist can choose to travel by air, and the corresponding air fee is the transportation value of the tourist to the first category of scenic spots.

[0119] It should be noted that the travel mode selected according to the distance from the above-mentioned tourists to the first category of scenic spots in the preset spatial area is only for illustrative purposes. In actual applications, the appropriate travel mode can be selected according to the specific distance. This application does not impose any restrictions on the travel mode selected according to the distance from the above-mentioned tourists to the first category of scenic spots in the preset spatial area.

[0120] In addition, the consumption value may include: the cost of buying souvenirs, meals, etc. in the first category scenic spot. For example, the consumption value of buying souvenirs in the first category scenic spot A is 100 yuan, and the corresponding consumption value of meals is 200 yuan. The consumption value of buying souvenirs in the first category scenic spot B is 150 yuan, and the corresponding consumption value of meals is 400 yuan.

[0121] It should be noted that the specific numerical value of the consumption value in the first category scenic spot can be determined according to different first category scenic spots, and this application comparison is not limited.

[0122] For example, the parking value may be the charge for parking in a parking lot corresponding to the second category scenic spot when a tourist drives to the second category scenic spot by himself. For example, the parking charge for the parking lot in the second category scenic spot is 20 yuan / hour.

[0123] In addition, it should be noted that the parking fee standards of parking lots corresponding to different second-category scenic spots may be different. The parking fees of parking lots corresponding to other second-category scenic spots are 30 yuan / hour, 50 yuan / hour, etc.

[0124] Based on the above-mentioned traffic values, consumption values, parking values, etc. in the first category of scenic spots, the first spatialized raster data is obtained. Based on the above-mentioned traffic values, consumption values, parking values, etc. in the second category of scenic spots, the third spatialized raster data is obtained. Based on the first spatialized raster data and the third spatialized raster data, the total amount of tourism-related data is obtained. Based on the total amount of tourism-related data, the total amount of tourism value of the preset spatial area B can be obtained. This scheme not only innovates the calculation method of leisure tourism value in the existing single travel cost method accounting theory, but also provides specific operations and spatial methods for leisure tourism value accounting from a practical perspective, creating conditions for promoting the rationality and wide application of leisure tourism value accounting.

[0125] Exemplarily, in the second category scenic area, the calculation frequency weight is divided into 1 to 5 levels from low to high, which can be: based on the number of tourists, ticket prices, transportation, consumption, tourists' comments, number of shared photos, geographical location, etc. in the above-mentioned point of interest data, determine the calculation frequency weight of the point of interest data corresponding to each scenic spot in the second category scenic area. For example, if the number of daily tourists at scenic spot A in the second category scenic area is 100 people, the average consumption per person is 200 yuan, and there are direct subways and buses to scenic spot A, then the calculation frequency weight of scenic spot A in the second category scenic area is listed as level 5; if the number of daily tourists at scenic spot B in the second category scenic area is 80 people, the average consumption per person is 150 yuan, and there are direct subways and buses to scenic spot B, then the calculation frequency weight of scenic spot B in the second category scenic area is listed as level 4; if the number of daily tourists at scenic spot C in the second category scenic area is 100 people, the average consumption per person is 10 0 yuan, and scenic spot A is directly accessible by subway or bus, etc., then the calculation frequency weight of scenic spot C in the second category of scenic spots is listed as level 3; if the daily number of tourists of scenic spot D in the second category of scenic spots is 80 people, the average consumption per person is 50 yuan, and there is no subway near scenic spot D, then the calculation frequency weight of scenic spot D in the second category of scenic spots is listed as level 2; if the daily number of tourists of scenic spot E in the second category of scenic spots is 50 people, the average consumption per person is 50 yuan, and scenic spot E is directly accessible by subway or bus, etc., then the calculation frequency weight of scenic spot E in the second category of scenic spots is listed as level 1.

[0126] It should be noted that the above-mentioned division of the calculation frequency weight from low to high into 1 to 5 levels is only for illustrative purposes. In actual applications, the frequency weight of each scenic spot in the second category scenic spot can be calculated from low to high into 1 to 5 levels according to other appropriate methods. This application does not impose any restrictions on this.

[0127] The embodiment of the present application also provides a spatialized raster data acquisition device, which can implement the spatialized raster data acquisition method of the present application. However, the implementation device of the spatialized raster data acquisition method of the present application includes but is not limited to the structure of the spatialized raster data acquisition device listed in the present embodiment. All structural deformations and replacements of the prior art made according to the principles of the present application are included in the protection scope of the present application.

[0128] like Figure 5 As shown, in one embodiment, the spatialized raster data acquisition device 50 of the present application includes a scenic area category determination module 51, a first spatialized raster data determination module 52, a second spatialized raster data determination module 53, a second spatialized raster data sum determination module 54, a total tourism-related data acquisition module 55, a third spatialized raster data determination module 56, and a tourism-related data total amount determination module 57.

[0129] The scenic spot category determination module 51 is used to determine a first category scenic spot and a second category scenic spot in a preset spatial area.

[0130] The first spatialized raster data determining module 52 is used to obtain first spatialized raster data corresponding to the first tourism-related data based on the first tourism-related data in the first category of scenic spots.

[0131] The second spatialized raster data determining module 53 is used to obtain the second spatialized raster data corresponding to the interest point data based on the interest point data in the second category scenic spot.

[0132] The second spatialized raster data sum determination module 54 is configured to sum all the second spatialized raster data to obtain the second spatialized raster data sum.

[0133] The total tourism related data acquisition module 55 is used to acquire the total tourism related data in the preset spatial area.

[0134] The third spatialized raster data determination module 56 is used to obtain the third spatialized raster data based on the first tourism-related data, the second spatialized raster data, the sum of the second spatialized raster data, the total tourism-related data, and the third spatialized raster data.

[0135] The tourism-related data total amount determination module 57 is used to obtain the total amount of tourism-related data corresponding to the preset spatial area based on the first spatialized raster data and the third spatialized raster data.

[0136] Among them, the structures and principles of the scenic spot category determination module 51, the first spatialized raster data determination module 52, the second spatialized raster data determination module 53, the second spatialized raster data sum determination module 54, the total tourism-related data acquisition module 55, the third spatialized raster data determination module 56, and the tourism-related data total amount determination module 57 correspond one by one to the steps in the above-mentioned spatialized raster data acquisition method, so they will not be repeated here.

[0137] In the several embodiments provided in the present application, it should be understood that the disclosed device or method can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of modules / units is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules or units, which can be electrical, mechanical or other forms.

[0138] The modules / units described as separate components may or may not be physically separated, and the components displayed as modules / units may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules / units may be selected according to actual needs to achieve the purpose of the embodiments of the present application. For example, the functional modules / units in the various embodiments of the present application may be integrated into one processing module, or each module / unit may exist physically separately, or two or more modules / units may be integrated into one module / unit.

[0139] Those of ordinary skill in the art should further appreciate that the units and steps of each example described in conjunction with the embodiments disclosed herein can be implemented with electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0140] The present application embodiment also provides a computer-readable storage medium. A person of ordinary skill in the art can understand that all or part of the steps in the method for implementing the above embodiment can be completed by instructing the processor through a program, and the program can be stored in a computer-readable storage medium, and the storage medium is a non-transitory medium, such as a random access memory, a read-only memory, a flash memory, a hard disk, a solid-state hard disk, a magnetic tape, a floppy disk, an optical disc, and any combination thereof. The above storage medium can be any available medium that a computer can access or a data storage device such as a server or a data center that includes one or more available media integrated. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a digital video disc (DVD)), or a semiconductor medium (e.g., a solid-state disk (SSD)), etc.

[0141] An embodiment of the present application also provides an electronic device. Figure 6 The schematic diagram of the structure of the electronic device 60 in one embodiment of the present application is shown. The spatialized raster data acquisition method provided in the embodiment of the present application can be applied to Figure 6 The electronic device 60 is shown, but is not limited thereto. Figure 6 As shown, the electronic device 60 includes a processor 61 , a memory, a system bus 63 , and a network interface 65 , wherein the memory may include a non-volatile storage medium 62 and an internal memory 64 .

[0142] The non-volatile storage medium 62 can store an operating system and a computer program. The computer program includes program instructions, and when the program instructions are executed, the processor can execute any one of the spatialized raster data acquisition methods provided in the embodiments of the present application.

[0143] The processor is used to provide computing and control capabilities and support the operation of the entire computer equipment.

[0144] The internal memory 64 provides an environment for the operation of the computer program in the non-volatile storage medium. When the computer program is executed by the processor, the processor can execute any one of the spatialized raster data acquisition methods provided in the embodiments of the present application.

[0145] The network interface 65 is used for network communication, such as sending assigned tasks, etc. Those skilled in the art will appreciate that Figure 6The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0146] It should be understood that the processor 61 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0147] The electronic device 60 in the embodiment of the present application may include terminal devices such as tablet computers, laptop computers, mobile phones, supercomputers, smart wearable devices, etc., and can also be applied to databases, servers, and service response systems based on terminal artificial intelligence. The embodiment of the present application does not impose any restrictions on the specific type of electronic device.

[0148] For example, the electronic device can be a station (STAION, ST) in a WLAN, a cellular phone, a cordless phone, a Session Initiation Protocol (SIP) phone, a Wireless Local Loop (WLL) station, a handheld device with wireless communication function, a computing device or other processing device connected to a wireless modem, a computer, a laptop computer, a handheld communication device, a handheld computing device, and / or other devices for communicating on wireless systems and next-generation communication systems, such as mobile terminals in 5G networks, mobile terminals in future evolved public land mobile networks (Public Land Mobile Network, PLMN) or mobile terminals in future evolved non-terrestrial networks (Non-terrestrial Network, NTN), etc.

[0149] As an example but not limitation, when the electronic device is a wearable device, the wearable device can also be a general term for wearable devices that are intelligently designed and developed using wearable technology for daily wear, such as gloves and watches equipped with near-field communication modules. A wearable device is a portable device that is worn directly on the body or integrated into the user's clothes or accessories. It is attached to the user and performs payment, authentication and other operations through a pre-bound electronic card. Wearable devices are not only hardware devices, but also powerful functions achieved through software support, data interaction, and cloud interaction. Broadly speaking, wearable smart devices include those that are fully functional, large in size, and can achieve complete or partial functions without relying on smartphones, such as smart watches or smart glasses, as well as those that only focus on a certain type of application function and need to be used in conjunction with other devices such as smartphones, such as various types of smart watches and smart bracelets with display screens.

[0150] The descriptions of the processes or structures corresponding to the above-mentioned figures have different emphases. For parts that are not described in detail in a certain process or structure, please refer to the relevant descriptions of other processes or structures.

[0151] The above embodiments are merely illustrative of the principles and effects of the present application and are not intended to limit the present application. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present application. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed in the present application shall still be covered by the claims of the present application.

Claims

1. A spatialized raster data acquisition method, characterized in that: The method comprises: Determine a first category scenic spot and a second category scenic spot in a preset spatial area; Based on the first tourism-related data in the first category of scenic spots, obtaining first spatialized raster data corresponding to the first tourism-related data; Based on the point of interest data in the second category of scenic spots, obtaining second spatialized raster data corresponding to the point of interest data; Summing all the second spatialized raster data to obtain a second spatialized raster data sum; Acquire total tourism-related data within the preset spatial area; obtaining third spatialized raster data based on the first tourism-related data, the second spatialized raster data, the sum of the second spatialized raster data, and the total tourism-related data; Based on the first spatialized raster data and the third spatialized raster data, a total amount of tourism-related data corresponding to the preset spatial area is obtained.

2. The spatialized raster data acquisition method according to claim 1, characterized in that: The number of the first category scenic spots is at least one, and the acquiring, based on the first tourism-related data in the first category scenic spots, first spatialized raster data corresponding to the first tourism-related data comprises: Determining the unit pixel area of ​​the first category scenic spot and the scenic area area of ​​the first category scenic spot; Determine the unit tourism related data corresponding to the unit pixel area based on the unit pixel area, the scenic area area and the first tourism related data; Input the unit pixel area and the unit tourism related data corresponding to the unit pixel area into a vector-to-raster tool to obtain spatialized data corresponding to the first category of scenic spots; The spatialized data of all the first-category scenic spots are overlaid to obtain the first spatialized raster data corresponding to the first tourism-related data.

3. The spatialized raster data acquisition method according to claim 1, characterized in that: The obtaining, based on the interest point data in the second category scenic spot, second spatialized raster data corresponding to the interest point data includes: Based on the weight analysis method, the calculation frequency weight of the interest point data is obtained; Determine the output pixel area of ​​the second category scenic spot; Based on the calculation frequency weight and the output pixel area, second spatialized raster data corresponding to the interest point data is determined.

4. The spatialized raster data acquisition method according to claim 3, characterized in that: The determining, based on the calculation frequency weight and the output pixel area, the second spatialized raster data corresponding to the point of interest data comprises: Based on the calculation frequency weight and the output pixel area, obtaining spatialized raster data corresponding to the calculation frequency weight and the output pixel area; The first spatialized raster data is eliminated from the spatialized raster data to obtain the second spatialized raster data corresponding to the interest point data.

5. The spatialized raster data acquisition method according to claim 3, characterized in that: The calculation frequency weights are divided into 1 to 5 levels from low to high.

6. The spatialized raster data acquisition method according to claim 1, characterized in that: The first tourism-related data include: the proportion of tourists from different source areas arriving at the first category of scenic spots, the traffic value of tourists from different source areas to the first category of scenic spots, the consumption value of the first category of scenic spots, the tickets of the first category of scenic spots and / or the parking value of the first category of scenic spots.

7. The spatialized raster data acquisition method according to claim 6, characterized in that: The spatialized raster data acquisition method also includes: acquiring various items of the first tourism-related data based on a network cloud big data platform.

8. A spatialized grid data acquisition device, characterized in that: include: A scenic spot category determination module, used to determine a first category scenic spot and a second category scenic spot in a preset spatial area; A first spatialized raster data determination module, configured to obtain first spatialized raster data corresponding to the first tourism-related data based on the first tourism-related data in the first category of scenic spots; A second spatialized raster data determination module, configured to obtain second spatialized raster data corresponding to the interest point data based on the interest point data in the second category scenic spot; A second spatialized raster data sum determination module, configured to sum all the second spatialized raster data to obtain a second spatialized raster data sum; A total tourism related data acquisition module, used to acquire the total tourism related data in the preset spatial area; A third spatialized raster data determination module, configured to obtain third spatialized raster data based on the first tourism-related data, the second spatialized raster data, the sum of the second spatialized raster data, and the total tourism-related data; The tourism-related data total amount determination module is used to obtain the total amount of tourism-related data corresponding to the preset spatial area based on the first spatialized raster data and the third spatialized raster data.

9. An electronic device, characterized in that: The electronic device comprises: A memory storing a computer program; A processor is communicatively connected to the memory, and executes the spatialized raster data acquisition method according to any one of claims 1 to 7 when calling the computer program.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the spatialized raster data acquisition method according to any one of claims 1 to 7 is implemented.

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