Method and system for constructing rural cultural characteristic tourist attractions based on big data fusion

Abstract

The present application provides a method for constructing rural cultural characteristic tourist attractions based on big data fusion, the method comprising: obtaining first geographic information data of a target village, marking the type of scenic spots and the geographical location in the first geographic information data, obtaining second geographic information data, obtaining explicit rural cultural resources and implicit rural cultural resources and similarity values ​​between different types of rural cultural resources and similar types of cultural resources in other villages, obtaining the expression forms of different cultural resources, using the geographic information data for matching different cultural resources in the village as third geographic information data, integrating the expression forms and geographical location distribution of natural attractions, cultural attractions, ecological attractions, different cultural resources in the third geographic information data, and completing the construction of rural cultural characteristic tourist attractions for the target village. The present application creates attractive and competitive rural tourist attractions based on rural cultural characteristics to promote the sustainable development of rural tourism.

Description

Technical Field

[0001] The present application relates to the technical field of rural tourist attraction construction, and specifically to a method and system for constructing rural cultural characteristic tourist attractions based on big data fusion. Background Art

[0002] Rural tourism refers to a new market-oriented and commercialized form of tourism that takes rural areas as tourist destinations, uses the unique natural scenery, cultural traditions, human landscapes and agricultural resources of rural areas as attractions, and takes urban residents as the main source of tourists. In recent years, with the development of social economy and the improvement of people's living standards, rural tourism has gradually become an important form of tourism, but there are also some problems in its development process.

[0003] Rural tourism is a long-term project that requires long-term planning. The theme of rural tourism development should be based on local characteristics and cannot be directly copied from others. The homogeneity of rural tourism content is serious, and copying culture has become a trend in rural tourism construction. Most of the domestic rural tourism or leisure agriculture basically presents the problem of homogeneity, which cannot reflect the characteristic agriculture and culture of a place; most of my country's rural tourism products have the problem of homogeneity, which leads to aesthetic fatigue of tourists. Although the number of rural tourism websites is increasing, and the information and services provided are also increasing, the rural tourism products and experiences in most regions show a high degree of similarity. In terms of tourism activities, homestay style, local food, etc., many rural tourism products lack uniqueness and fail to fully display local culture and natural characteristics.

[0004] In the process of rural tourism construction, culture is the core competitiveness of development. Therefore, how to protect and inherit traditional culture, promote the protection and inheritance of traditional culture, let more people understand and recognize traditional culture, and enhance cultural confidence have become key issues that need to be addressed. Summary of the invention

[0005] In order to overcome the serious problem of homogeneity of rural tourism in the prior art, this application provides a method and system for constructing rural cultural characteristic tourist attractions based on big data fusion, which adopts the following technical solutions:

[0006] In the first aspect, the present application provides a method for constructing rural cultural characteristic tourist attractions based on big data fusion, including:

[0007] Acquire geographic information data of a target village as first geographic information data;

[0008] Based on the first geographic information data, natural attractions, cultural attractions, and ecological attractions of the target village are obtained, and the natural attractions, cultural attractions, and ecological attractions of the target village are marked with attraction types and geographical locations in the first geographic information data to obtain second geographic information data;

[0009] Mining rural cultural resources based on big data to obtain explicit and implicit rural cultural resources;

[0010] Based on different types of rural cultural resources, similarity analysis is performed on explicit rural cultural resource sets and implicit rural cultural resource sets to obtain the similarity values ​​between different types of rural cultural resources and similar types of cultural resources in other villages.

[0011] Based on the sorting of similarity values ​​of different types of rural cultural resources from low to high, obtain the explicit rural cultural resource dataset and implicit rural cultural resource dataset that meet the preset quantity of the target village;

[0012] Based on big data fusion technology, we obtain explicit rural cultural resource data sets and implicit rural cultural resource data sets, and the manifestations of different cultural resources in scenic spot construction;

[0013] According to the manifestation of scenic spot construction of different cultural resources and the second geographic information data of the target village, the geographical locations of different cultural resource scenic spots in the village are matched, and the geographic information data of the completed matching of different cultural resources in the village is used as the third geographic information data;

[0014] By integrating the third-party geographic information data of natural attractions, cultural attractions, ecological attractions, different cultural resources and their geographical locations, the construction of rural cultural characteristic tourist attractions in the target villages is completed.

[0015] Furthermore, the obtaining of geographic information data of the target village as first geographic information data includes:

[0016] Remote sensing technology is used to obtain remote sensing image data of the target village. After preprocessing the remote sensing image data of the target village, the pixels in the remote sensing image of the target village are classified into different land feature types based on the automatic interpretation algorithm based on machine learning. Spatial analysis and landscape analysis are performed on the different land feature types of the target village to obtain the geographic information data of the target village.

[0017] Further, the natural attractions, cultural attractions, and ecological attractions of the target village are obtained based on the first geographic information data, and the natural attractions, cultural attractions, and ecological attractions of the target village are marked with the type of attractions and the geographical location in the first geographic information data to obtain the second geographic information data, including:

[0018] Perform remote sensing image analysis on the first geographic information data to extract the characteristics of natural attractions, cultural attractions, and ecological attractions; classify and identify the extracted characteristics of natural attractions, cultural attractions, and ecological attractions based on machine learning algorithms; and associate the identified attraction location information with the geographic data based on geographic information system technology to achieve spatial analysis and geographic location annotation of natural attractions, cultural attractions, and ecological attractions; integrate the identified and annotated natural attractions, cultural attractions, and ecological attractions information into the geographic information system to generate second geographic information data.

[0019] Furthermore, based on different types of rural cultural resources, similarity analysis is performed on the explicit rural cultural resource set and the implicit rural cultural resource set to obtain similarity values ​​between different types of rural cultural resources and other similar types of rural cultural resources, including:

[0020] Feature extraction is performed on explicit and implicit rural cultural resources, the extracted features are represented by feature vectors, and a similarity calculation method is used to obtain the similarity value between the feature vectors of each type of rural cultural resources and similar cultural resources in other villages.

[0021] Furthermore, the big data fusion technology is used to obtain the explicit rural cultural resource data set and the implicit rural cultural resource data set, and the manifestations of different cultural resources for scenic spot construction include:

[0022] Conduct correlation analysis on the cultural resource data in the explicit rural cultural resource dataset and the implicit rural cultural resource dataset to obtain the correlation between explicit cultural resources and implicit cultural resources;

[0023] Based on the size of the correlation coefficient between explicit cultural resources and implicit cultural resources, determine whether there is a significant correlation between explicit cultural resources and implicit cultural resources;

[0024] When the correlation coefficient between explicit cultural resources and implicit cultural resources meets the preset threshold, that is, there is a significant correlation, the explicit cultural resources and implicit cultural resources are integrated and constructed;

[0025] When the correlation coefficient between explicit cultural resources and implicit cultural resources does not meet the preset threshold, that is, there is no significant correlation, scenic spots are constructed for explicit cultural resources and implicit cultural resources respectively.

[0026] Furthermore, when the correlation coefficient between the explicit cultural resources and the implicit cultural resources meets the preset threshold, that is, there is a significant correlation, the explicit cultural resources and the implicit cultural resources are integrated to obtain the expression form of the scenic spot construction by integrating the explicit cultural resources and the implicit cultural resources, including:

[0027] Association rule mining algorithms are used to mine association rules for explicit cultural resources and implicit cultural resources that have significant correlations; the potential connection patterns between explicit cultural resources and implicit cultural resources are obtained based on the mined association rules; the potential connections between different association rules are explained to obtain the deep connections between explicit cultural resources and implicit cultural resources; based on the deep connections between explicit cultural resources and implicit cultural resources, the expression form of the scenic spot is designed, and the explicit cultural resources and implicit cultural resources are integrated and displayed in the construction of the scenic spot.

[0028] Furthermore, the second geographic information data of the target village and the form of constructing scenic spots according to different cultural resources match the geographical locations of different cultural resource scenic spots in the village, and the geographic information data for completing the matching of different cultural resources in the village as the third geographic information data includes:

[0029] Classify the forms of expression of scenic spots constructed by different cultural resources to obtain the characteristics and forms of expression of different cultural resources; use geographic information system tools to perform spatial analysis on the characteristics and forms of expression of different cultural resources in the second geographic information data to determine their position and distribution in the geographic space; match the geographical locations of different cultural resource attractions based on the results of spatial distribution, so that the geographical locations of different cultural resource attractions are marked in the geographic information data to form the third geographic information data.

[0030] In the second aspect, the present application also provides a rural cultural characteristic tourist attraction construction system based on big data fusion, including:

[0031] A first geographic information data acquisition module, used to acquire geographic information data of a target village as first geographic information data;

[0032] A second geographic information data acquisition module is used to acquire the natural attractions, cultural attractions, and ecological attractions of the target village based on the first geographic information data, and to annotate the natural attractions, cultural attractions, and ecological attractions of the target village in the first geographic information data with the types of attractions and the geographical locations to acquire the second geographic information data;

[0033] The module for acquiring explicit and implicit rural cultural resources is used to mine rural cultural resources based on big data and acquire explicit and implicit rural cultural resources.

[0034] A module for obtaining similarity values ​​of different types of rural cultural resources is used to perform similarity analysis on explicit rural cultural resource sets and implicit rural cultural resource sets based on different types of rural cultural resources, and obtain similarity values ​​between different types of rural cultural resources and other rural cultural resources of similar types;

[0035] The explicit and implicit rural cultural resource screening module is used to sort the similarity values ​​of different types of rural cultural resources from low to high, and obtain the explicit rural cultural resource data set and implicit rural cultural resource data set that meet the preset number of target villages;

[0036] The module for obtaining the expression forms of different cultural resources is used to obtain the expression forms of different cultural resources in the explicit rural cultural resource data set and the implicit rural cultural resource data set based on big data fusion technology;

[0037] The third geographic information data acquisition module is used to match the geographical locations of different cultural resource attractions in the village according to the expression forms of different cultural resources and the second geographic information data of the target village, and use the geographic information data matching different cultural resources in the village as the third geographic information data;

[0038] The third geographic information data fusion module is used to complete the construction of rural cultural characteristic tourist attractions in the target village by integrating the manifestations and geographical location distribution of different cultural resources in the natural attractions, cultural attractions, ecological attractions, explicit rural cultural resource data sets and implicit rural cultural resource data sets in the third geographic information data.

[0039] In a third aspect, the present application provides an electronic device, including:

[0040] One or more processors; a memory; and one or more computer programs, wherein the one or more computer programs are stored in the memory, and the one or more computer programs include instructions, which, when executed by the device, cause the device to perform the method of the first aspect.

[0041] In a fourth aspect, the present application provides a computer-readable storage medium, in which a computer program is stored, and when the computer-readable storage medium is run on a computer, the computer executes the method of the first aspect.

[0042] In a fifth aspect, the present application provides a computer program, which, when executed by a computer, is used to execute the method of the first aspect.

[0043] In one possible design, the program in the fifth aspect may be stored in whole or in part on a storage medium packaged together with the processor, or may be stored in whole or in part on a memory not packaged together with the processor.

[0044] This application has the following beneficial effects:

[0045] 1. This application obtains the natural attractions, cultural attractions, and ecological attractions of the target village, which makes it easier to unify the natural attractions, cultural attractions, and ecological attractions of the target village into the rural tourism attraction planning, enriches the resources of rural tourism, promotes cultural heritage and ecological protection, and achieves sustainable development of rural tourism.

[0046] 2. This application obtains the explicit culture and implicit culture of the target village, so that the explicit culture and implicit culture of the target village are relevant and complementary, and together constitute the multi-dimensional appearance of the target village, comprehensively display the cultural heritage and characteristics of the village, and provide tourists with a richer and more diversified tourism experience.

[0047] 3. This application solves the serious problem of homogeneity of rural tourism content by obtaining the similarity values ​​of different types of rural cultural resources with similar types of cultural resources in other villages, and obtaining cultural resources with lower similarity values ​​with similar types of cultural resources in other villages for scenic spot construction. This makes the scenic spots constructed in the target villages more distinctive and personalized, explores the potential cultural value of the target villages, enhances the cultural appeal of the target villages, and provides tourists with a new cultural experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 is an exemplary system architecture diagram to which the embodiments of the present application can be applied;

[0049] Figure 2 This is a flow chart of a method for constructing rural cultural characteristic tourist attractions based on big data fusion according to an embodiment of the present application;

[0050] Figure 3 This is a flowchart of specific execution steps of step S6 in an embodiment of the present application;

[0051] Figure 4 A flow chart of constructing a scenic spot by integrating explicit culture and implicit culture in an embodiment of the present application;

[0052] Figure 5 This is a flow chart of the device according to an embodiment of the present application;

[0053] Figure 6 It is a schematic diagram of a computer device according to an embodiment of the present application. DETAILED DESCRIPTION

[0054] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0055] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0056] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0057] like Figure 1 As shown, the system architecture 100 may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0058] Users can use terminal devices 101, 102, 103 to interact with server 105 through network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0059] Terminal devices 101, 102, 103 can be various electronic devices with display screens and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III, Moving Picture Experts Group Audio Layer 3), MP4 (Moving Picture Experts Group Audio Layer IV, Moving Picture Experts Group Audio Layer 4) players, laptop computers and desktop computers, etc.

[0060] The server 105 may be a server that provides various services, such as a background server that provides support for web pages displayed on the terminal devices 101 , 102 , and 103 .

[0061] It should be noted that the method for constructing rural cultural characteristic tourist attractions based on big data fusion provided in the embodiment of the present application is generally executed by a server / terminal device, and accordingly, the system for constructing rural cultural characteristic tourist attractions based on big data fusion is generally set in the server / terminal device.

[0062] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0063] Continue to refer Figure 2 , the figure shows a flow chart of a method for constructing rural cultural characteristic tourist attractions based on big data fusion in the present application, the method comprising the following steps:

[0064] Step S1, obtaining geographic information data of a target village as first geographic information data.

[0065] In a possible implementation manner, the step of obtaining geographic information data of a target village as first geographic information data includes:

[0066] Remote sensing technology is used to obtain remote sensing image data of the target village. After preprocessing the remote sensing image data of the target village, the pixels in the remote sensing image of the target village are classified into different types of land objects based on the automatic interpretation algorithm of machine learning. Spatial analysis and landscape analysis are performed on different types of land objects in the target village to obtain the geographic information data of the target village.

[0067] Step S2, based on the first geographic information data, obtain the natural attractions, cultural attractions, and ecological attractions of the target village, mark the attraction type and geographical location of the natural attractions, cultural attractions, and ecological attractions of the target village in the first geographic information data, and obtain the second geographic information data.

[0068] In a possible implementation, the step of acquiring the natural attractions, cultural attractions, and ecological attractions of the target village based on the first geographic information data, marking the natural attractions, cultural attractions, and ecological attractions of the target village by their attraction types and geographic locations in the first geographic information data, and acquiring the second geographic information data includes:

[0069] Perform remote sensing image analysis on the first geographic information data to extract the characteristics of natural attractions, cultural attractions, and ecological attractions; classify and identify the extracted characteristics of natural attractions, cultural attractions, and ecological attractions based on machine learning algorithms; and associate the identified attraction location information with the geographic data based on geographic information system technology to achieve spatial analysis and geographic location annotation of natural attractions, cultural attractions, and ecological attractions; integrate the identified and annotated natural attractions, cultural attractions, and ecological attractions information into the geographic information system to generate second geographic information data.

[0070] For example, natural attractions may include mountains, lakes, rivers, etc.; cultural attractions may include ancient towns, temples, monuments, etc.; ecological attractions may include wetlands, forests, nature reserves, etc.

[0071] Step S3, mining rural cultural resources based on big data to obtain explicit rural cultural resources and implicit rural cultural resources.

[0072] For example, big data is used to mine the explicit cultural resources of the target village through the digital archives, cultural heritage databases, social media and Internet data of the village, and the explicit cultural resources of the target village are obtained, where the explicit cultural resources include but are not limited to the geographical location data, historical background data, image data, and text description data of the target village. Based on big data or field visits, local legends, traditional skills, folk activities, etc. of the target village are obtained, where implicit cultural resources include but are not limited to text content: text content of folk legends and rural stories, image data: related works of art, illustrations, etc.; recording data: audio materials such as oral history and folk songs.

[0073] Step S4, based on different types of rural cultural resources, similarity analysis is performed on the explicit rural cultural resource set and the implicit rural cultural resource set to obtain similarity values ​​between different types of rural cultural resources and other similar types of rural cultural resources.

[0074] In a possible implementation, the similarity analysis is performed on the explicit rural cultural resource set and the implicit rural cultural resource set based on different types of rural cultural resources to obtain the similarity values ​​of different types of rural cultural resources and other similar types of rural cultural resources, including:

[0075] Feature extraction is performed on explicit and implicit rural cultural resources, the extracted features are represented by feature vectors, and a similarity calculation method is used to obtain the similarity value between the feature vectors of each type of rural cultural resources and similar cultural resources in other villages.

[0076] Step S5, based on the sorting of the similarity values ​​of different types of rural cultural resources from low to high, obtain the explicit rural cultural resource dataset and implicit rural cultural resource dataset that meet the preset quantity of the target village.

[0077] Step S6, based on the big data fusion technology, obtain the explicit rural cultural resource data set and the implicit rural cultural resource data set, and the expression form of different cultural resources for scenic spot construction.

[0078] Based on the big data fusion technology, the explicit rural cultural resource data set and the implicit rural cultural resource data set are obtained. For the expression of different cultural resources in the construction of scenic spots, please refer to Figure 3 , the specific steps include:

[0079] Step S601, performing correlation analysis on the cultural resource data in the explicit rural cultural resource data set and the implicit rural cultural resource data set to obtain the correlation between the explicit cultural resources and the implicit cultural resources;

[0080] Step S602, based on the magnitude of the correlation coefficient between the explicit cultural resources and the implicit cultural resources, determining whether there is a significant correlation between the explicit cultural resources and the implicit cultural resources;

[0081] Step S603: when the correlation coefficient between the explicit cultural resources and the implicit cultural resources meets the preset threshold, that is, there is a significant correlation, the explicit cultural resources and the implicit cultural resources are integrated and constructed;

[0082] Step S604: when the correlation coefficient between the explicit cultural resources and the implicit cultural resources does not meet the preset threshold, that is, when there is no significant correlation, scenic spots are constructed for the explicit cultural resources and the implicit cultural resources respectively.

[0083] In a possible implementation, when the correlation coefficient between the explicit cultural resources and the implicit cultural resources meets the preset threshold, that is, there is a significant correlation, the explicit cultural resources and the implicit cultural resources are integrated and constructed. Please refer to Figure 4 The specific steps include:

[0084] Step S603a, using an association rule mining algorithm to mine association rules for explicit cultural resources and implicit cultural resources that have significant correlations;

[0085] Step S603b, obtaining the potential connection pattern between the explicit cultural resources and the implicit cultural resources based on the mined association rules;

[0086] Step S603c, interpreting the potential connections between different association rules, and obtaining the deep connections between explicit cultural resources and implicit cultural resources;

[0087] Step S603d, based on the deep connection between explicit cultural resources and implicit cultural resources, design the expression form of the scenic spot, and integrate the explicit cultural resources with the implicit cultural resources in the construction of the scenic spot.

[0088] Step S7, matching geographical locations for scenic spots with different cultural resources in the village according to the representation forms of scenic spots constructed with different cultural resources and the second geographic information data of the target village, and using the geographic information data for matching different cultural resources in the village as the third geographic information data.

[0089] The second geographic information data of the target village and the form of scenic spot construction based on different cultural resources match the geographical location of different cultural resource scenic spots in the village, and the geographic information data for completing the matching of different cultural resources in the village as the third geographic information data includes:

[0090] Classify the forms of expression of scenic spots constructed by different cultural resources to obtain the characteristics and forms of expression of different cultural resources; use geographic information system tools to perform spatial analysis on the characteristics and forms of expression of different cultural resources in the second geographic information data to determine their position and distribution in the geographic space; match the geographical locations of different cultural resource attractions based on the results of spatial distribution, so that the geographical locations of different cultural resource attractions are marked in the geographic information data to form the third geographic information data.

[0091] Step S8, by integrating the natural attractions, cultural attractions, ecological attractions, different cultural resources in the third geographic information data to construct the scenic spots and their geographical locations, the construction of rural cultural characteristic tourist attractions in the target village is completed.

[0092] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0093] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0094] Continue to refer Figure 5 The rural cultural characteristic tourist attraction construction system based on big data fusion of this embodiment includes:

[0095] The first geographic information data acquisition module 501 is used to acquire the geographic information data of the target village as the first geographic information data.

[0096] The second geographic information data acquisition module 502 is used to acquire the natural attractions, cultural attractions, and ecological attractions of the target village based on the first geographic information data, and to annotate the natural attractions, cultural attractions, and ecological attractions of the target village in the first geographic information data with the types of attractions and the geographical locations to acquire the second geographic information data.

[0097] The explicit and implicit rural cultural resources acquisition module 503 is used to mine rural cultural resources based on big data to acquire explicit rural cultural resources and implicit rural cultural resources.

[0098] The module 504 for obtaining similarity values ​​of different types of rural cultural resources is used to perform similarity analysis on explicit rural cultural resource sets and implicit rural cultural resource sets based on different types of rural cultural resources, and obtain similarity values ​​between different types of rural cultural resources and other similar types of rural cultural resources.

[0099] The explicit and implicit rural cultural resource screening module 505 is used to obtain a preset number of explicit rural cultural resource data sets and implicit rural cultural resource data sets for the target village based on the sorting of similarity values ​​of different rural cultural resource types from low to high.

[0100] The expression form acquisition module 506 of different cultural resources is used to obtain the expression forms of different cultural resources in the explicit rural cultural resource data set and the implicit rural cultural resource data set based on the big data fusion technology.

[0101] The third geographic information data acquisition module 507 is used to match the geographical locations of different cultural resource attractions in the village according to the expression forms of different cultural resources and the second geographic information data of the target village, and use the geographic information data that completes the matching of different cultural resources in the village as the third geographic information data.

[0102] The third geographic information data fusion module 508 is used to complete the construction of rural cultural characteristic tourist attractions in the target village by integrating the manifestations of different cultural resources and their geographical location distribution in the natural attractions, cultural attractions, ecological attractions, explicit rural cultural resource data sets and implicit rural cultural resource data sets in the third geographic information data.

[0103] To solve the above technical problems, the present application also provides a computer device. Figure 6 , Figure 6 This is a basic structural block diagram of the computer device in this embodiment.

[0104] The computer device 6 includes a memory 6a, a processor 6b, and a network interface 6c that are interconnected and communicated through a system bus. It should be noted that the figure only shows a computer device 6 having components 6a-6c, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable gate arrays (Field-Programmable Gate Array, FPGA), digital processors (Digital Signal Processor, DSP), embedded devices, etc.

[0105] Computer devices can be computing devices such as desktop computers, notebooks, PDAs, and cloud servers. Computer devices can interact with users through keyboards, mice, remote controls, touch pads, or voice control devices.

[0106] The memory 6a includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 6a can be an internal storage unit of the computer device 6, such as a hard disk or memory of the computer device 6. In other embodiments, the memory 6a can also be an external storage device of the computer device 6, such as a plug-in hard disk equipped on the computer device 6, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. Of course, the memory 6a can also include both the internal storage unit of the computer device 6 and its external storage device. In this embodiment, the memory 6a is generally used to store the operating system and various application software installed on the computer device 6, such as the program code of the method for constructing a rural cultural characteristic tourist attraction based on big data fusion. In addition, the memory 6a can also be used to temporarily store various types of data that have been output or are to be output.

[0107] The processor 6b may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments. The processor 6b is generally used to control the overall operation of the computer device 6. In this embodiment, the processor 6b is used to run the program code stored in the memory 6a or process data, such as running the program code of the method for constructing a rural cultural characteristic tourist attraction based on big data fusion.

[0108] The network interface 6c may include a wireless network interface or a wired network interface, and the network interface 6c is generally used to establish a communication connection between the computer device 6 and other electronic devices.

[0109] The present application also provides another implementation mode, namely, providing a non-volatile computer-readable storage medium, which stores a program of a method for constructing a rural cultural characteristic tourist attraction based on big data fusion, and the construction of a rural cultural characteristic tourist attraction based on big data fusion can be executed by at least one processor, so that at least one processor executes the steps of the method for constructing a rural cultural characteristic tourist attraction based on big data fusion as described above.

[0110] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a disk, or an optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods of each embodiment of the present application.

[0111] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. A method for constructing rural cultural tourist attractions based on big data fusion, characterized in that: include: Acquire geographic information data of a target village as first geographic information data; Based on the first geographic information data, natural attractions, cultural attractions, and ecological attractions of the target village are obtained, and the natural attractions, cultural attractions, and ecological attractions of the target village are marked with attraction types and geographical locations in the first geographic information data to obtain second geographic information data; Mining rural cultural resources based on big data to obtain explicit and implicit rural cultural resources; Based on different types of rural cultural resources, similarity analysis is performed on explicit rural cultural resource sets and implicit rural cultural resource sets to obtain the similarity values ​​between different types of rural cultural resources and similar types of cultural resources in other villages. Based on the sorting of similarity values ​​of different types of rural cultural resources from low to high, obtain the explicit rural cultural resource dataset and implicit rural cultural resource dataset that meet the preset quantity of the target village; Based on the big data fusion technology, the explicit rural cultural resource data set and the implicit rural cultural resource data set are obtained, and the expression form of different cultural resources for scenic spot construction is obtained; the expression form of different cultural resources for scenic spot construction based on the big data fusion technology, including: performing correlation analysis on the cultural resource data in the explicit rural cultural resource data set and the implicit rural cultural resource data set to obtain the correlation between the explicit cultural resources and the implicit cultural resources; based on the size of the correlation coefficient between the explicit cultural resources and the implicit cultural resources, determining whether there is a significant correlation between the explicit cultural resources and the implicit cultural resources; when the correlation coefficient between the explicit cultural resources and the implicit cultural resources meets the preset threshold, that is, there is a significant correlation, the explicit cultural resources and the implicit cultural resources are fused and constructed; when the correlation coefficient between the explicit cultural resources and the implicit cultural resources does not meet the preset threshold, that is, there is no significant correlation, the explicit cultural resources and the implicit cultural resources are constructed as scenic spots respectively; According to the manifestation of scenic spot construction of different cultural resources and the second geographic information data of the target village, the geographical locations of different cultural resource scenic spots in the village are matched, and the geographic information data of the completed matching of different cultural resources in the village is used as the third geographic information data; By integrating the third-party geographic information data of natural attractions, cultural attractions, ecological attractions, different cultural resources and their geographical locations, the construction of rural cultural characteristic tourist attractions in the target villages is completed.

2. The method for constructing rural cultural tourist attractions based on big data fusion according to claim 1 is characterized in that: The step of obtaining the geographic information data of the target village as the first geographic information data includes: Remote sensing technology is used to obtain remote sensing image data of the target village. After preprocessing the remote sensing image data of the target village, the pixels in the remote sensing image of the target village are classified into different land feature types based on the automatic interpretation algorithm based on machine learning. Spatial analysis and landscape analysis are performed on the different land feature types of the target village to obtain the geographic information data of the target village.

3. The method for constructing rural cultural characteristic tourist attractions based on big data fusion according to claim 1 is characterized in that: The method of obtaining the natural scenic spots, cultural scenic spots, and ecological scenic spots of the target village based on the first geographic information data, marking the scenic spot types and geographical locations of the natural scenic spots, cultural scenic spots, and ecological scenic spots of the target village in the first geographic information data, and obtaining the second geographic information data includes: Perform remote sensing image analysis on the first geographic information data to extract the characteristics of natural attractions, cultural attractions, and ecological attractions; classify and identify the extracted characteristics of natural attractions, cultural attractions, and ecological attractions based on machine learning algorithms; and associate the identified attraction location information with the geographic data based on geographic information system technology to achieve spatial analysis and geographic location annotation of natural attractions, cultural attractions, and ecological attractions; integrate the identified and annotated natural attractions, cultural attractions, and ecological attractions information into the geographic information system to generate second geographic information data.

4. The method for constructing rural cultural tourist attractions based on big data fusion according to claim 1 is characterized in that: Based on different types of rural cultural resources, similarity analysis is performed on explicit rural cultural resource sets and implicit rural cultural resource sets to obtain similarity values ​​between different types of rural cultural resources and other similar types of rural cultural resources, including: Feature extraction is performed on explicit and implicit rural cultural resources, the extracted features are represented by feature vectors, and a similarity calculation method is used to obtain the similarity value between the feature vectors of each type of rural cultural resources and similar cultural resources in other villages.

5. The method for constructing rural cultural characteristic tourist attractions based on big data fusion according to claim 1 is characterized in that: The second geographic information data of the target village and the form of scenic spot construction based on different cultural resources match the geographical location of different cultural resource scenic spots in the village, and the geographic information data for completing the matching of different cultural resources in the village as the third geographic information data includes: Classify the forms of expression of scenic spots constructed by different cultural resources to obtain the characteristics and forms of expression of different cultural resources; use geographic information system tools to perform spatial analysis on the characteristics and forms of expression of different cultural resources in the second geographic information data to determine their position and distribution in the geographic space; match the geographical locations of different cultural resource attractions based on the results of spatial distribution, so that the geographical locations of different cultural resource attractions are marked in the geographic information data to form the third geographic information data.

6. A rural cultural characteristic tourist attraction construction system based on big data fusion, characterized in that: include: A first geographic information data acquisition module, used to acquire geographic information data of a target village as first geographic information data; A second geographic information data acquisition module is used to acquire the natural attractions, cultural attractions, and ecological attractions of the target village based on the first geographic information data, and to annotate the natural attractions, cultural attractions, and ecological attractions of the target village in the first geographic information data with the types of attractions and the geographical locations to acquire the second geographic information data; The explicit and implicit rural cultural resource acquisition module is used to mine rural cultural resources based on big data and obtain explicit and implicit rural cultural resources; A module for obtaining similarity values ​​of different types of rural cultural resources is used to perform similarity analysis on explicit rural cultural resource sets and implicit rural cultural resource sets based on different types of rural cultural resources, and obtain similarity values ​​between different types of rural cultural resources and other rural cultural resources of similar types; The explicit and implicit rural cultural resource screening module is used to sort the similarity values ​​of different types of rural cultural resources from low to high, and obtain the explicit rural cultural resource data set and implicit rural cultural resource data set that meet the preset number of target villages; A module for acquiring the manifestations of different cultural resources is used to acquire the manifestations of different cultural resources in the explicit rural cultural resource data set and the implicit rural cultural resource data set based on big data fusion technology; the manifestations of different cultural resources for scenic spot construction in the explicit rural cultural resource data set and the implicit rural cultural resource data set based on big data fusion technology include: performing correlation analysis on the cultural resource data in the explicit rural cultural resource data set and the implicit rural cultural resource data set to acquire the correlation between the explicit cultural resources and the implicit cultural resources; determining whether there is a significant correlation between the explicit cultural resources and the implicit cultural resources based on the size of the correlation coefficient between the explicit cultural resources and the implicit cultural resources; when the correlation coefficient between the explicit cultural resources and the implicit cultural resources meets the preset threshold, that is, there is a significant correlation, the explicit cultural resources and the implicit cultural resources are fused and constructed; when the correlation coefficient between the explicit cultural resources and the implicit cultural resources does not meet the preset threshold, that is, there is no significant correlation, the explicit cultural resources and the implicit cultural resources are constructed as scenic spots respectively; The third geographic information data acquisition module is used to match the geographical locations of different cultural resource attractions in the village according to the expression forms of different cultural resources and the second geographic information data of the target village, and use the geographic information data matching different cultural resources in the village as the third geographic information data; The third geographic information data fusion module is used to complete the construction of rural cultural characteristic tourist attractions in the target village by integrating the manifestations and geographical location distribution of different cultural resources in the natural attractions, cultural attractions, ecological attractions, explicit rural cultural resource data sets and implicit rural cultural resource data sets in the third geographic information data.

7. An electronic device, characterized in that: include: one or more processors; Memory; And one or more computer programs, wherein the one or more computer programs are stored in a memory, and the one or more computer programs include instructions, which, when executed by the device, enable the device to perform the steps of the method for constructing a rural cultural characteristic tourist attraction based on big data fusion as claimed in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: A computer program is stored in a computer-readable storage medium, and when the computer-readable storage medium is run on a computer, the computer executes the steps of the method for constructing a rural cultural tourism attraction based on big data fusion as claimed in any one of claims 1 to 5.

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