Big data-based multi-source information fusion system and application

Abstract

The invention relates to the technical field of data fusion, in particular to a multi-source information fusion system and application based on big data, and the system comprises a crawling module, a classification module, a data storage module, a data fusion module and a big data application module. The classification module identifies and judges the authenticity of the data and classifies the data according to region data and name data, the data storage module stores the classified data to obtain a large amount of tourism data, and the heterogeneous information resources of the tourism data are fused based on the tourism big data data fusion module. The problems that according to an existing multi-platform tourism data fusion system based on big data, due to the fact that the obtained data is directly issued to a data storage unit to be not classified, the data storage process is slow, all the data cannot be effectively classified, and the subsequent data fusion efficiency is affected are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of data fusion, and in particular to a multi-source information fusion system and application based on big data. Background Art

[0002] At present, tourism information is characterized by wide distribution, large data volume, and diverse data presentation forms. It is impossible to integrate tourism data from multiple platforms, which leads to great limitations for users when searching for tourism projects, and results in users being unable to effectively find the most cost-effective data that suits them when looking for tourism projects.

[0003] The prior art has a multi-platform tourism data fusion system based on big data, including a collection unit, a storage unit, a fusion unit and a comprehensive application unit. The data collection unit is used to collect multi-platform tourism data, the data storage unit is used to store the tourism data collected by the data collection unit, the data fusion unit is used to fuse the heterogeneous information resources of the tourism data, and the big data comprehensive application unit is used to analyze the data set fused by the data fusion unit to form a tourism data aggregation and fusion platform.

[0004] However, in the above method, since the acquired data is directly sent to the data storage unit without being classified, the data storage process may be slow and the data cannot be effectively classified, affecting the subsequent data fusion efficiency. Summary of the invention

[0005] The purpose of the present invention is to provide a multi-source information fusion system and application based on big data, aiming to solve the problem that the existing multi-platform tourism data fusion system based on big data may cause the data storage process to be relatively slow and unable to effectively classify each data, thereby affecting the subsequent data fusion efficiency because the acquired data is directly sent to the data storage unit without being classified.

[0006] To achieve the above-mentioned purpose, in a first aspect, the present invention provides a multi-source information fusion system based on big data, including a crawling module, a classification module, a data storage module, a data fusion module and a big data application module;

[0007] The classification module is connected to the crawling module; the data storage module is connected to the classification module; the data fusion module is connected to the data storage module; the big data application module is connected to the data fusion module;

[0008] The crawling module is used to crawl and collect tourism data based on multiple platforms to obtain corresponding tourism data;

[0009] The classification module is used to classify the acquired tourism data and place data into corresponding categories according to name types;

[0010] The data storage module is used to store the tourism data sorted by the classification module, and to store the data by confirming the storage method according to different tourism data requirements;

[0011] The data fusion module is used to fuse the heterogeneous information resources of tourism data and associate the tourism data stored in the data storage module;

[0012] The big data application module is used to analyze the fused data of the data fusion module and then form a fusion platform for tourism data.

[0013] Wherein, the classification module includes a data acquisition submodule, a data name identification submodule and a data classification submodule, wherein the data name identification submodule is connected to the data acquisition submodule; the data classification submodule is connected to the data name identification submodule;

[0014] The data acquisition submodule is used to obtain the corresponding tourism data and regional data obtained by the crawling module on each platform;

[0015] The data name identification submodule is used to identify the acquired tourism data and regional data to determine whether there is false or erroneous information;

[0016] The data classification submodule is used to classify and store the recognized true and correct tourism data and regional data according to the name and place data.

[0017] Wherein, the data storage module includes a data receiving submodule, a data analysis submodule, a mesh storage submodule and a tree storage submodule, the data receiving submodule is connected to the classification module; the data analysis submodule is connected to the data receiving submodule; the mesh storage submodule is connected to the data analysis submodule; the tree storage submodule is connected to the data analysis submodule;

[0018] The data receiving submodule is used to obtain the tourism data classified in the classification module;

[0019] The data analysis submodule is used to re-analyze the classified tourism data to determine whether it is suitable for a tree-type storage method or a mesh storage module;

[0020] The mesh storage submodule is used to receive travel data suitable for mesh storage and store the data in mesh form;

[0021] The tree storage submodule is used to receive tourism data suitable for tree storage and store the data in the tree format.

[0022] Wherein, the data fusion module includes a data association submodule, an information difference calculation submodule and a Gaussian mixture submodule, the data association submodule is connected to the data storage module; the information difference calculation submodule is connected to the data association submodule; the Gaussian mixture submodule is connected to the information difference calculation submodule;

[0023] The data association submodule is used to realize the merging and sharing of tourism data information resources, and to realize the connection, exchange and sharing between different databases;

[0024] The information difference calculation submodule is used to calculate the differences between different tourism data;

[0025] The Gaussian mixture submodule is used to establish a Gaussian mixture model to integrate mobile network and multi-source tourism data information and calculate the function of all tourism information.

[0026] Wherein, the multi-source information fusion system based on big data further includes a data security access module, and the data security access module is connected to the big data application module;

[0027] The data security access module is used by the client to view the content of the big data application module using ciphertext security access technology to increase data security.

[0028] The data security access module includes a login submodule, an encryption submodule and a data transfer submodule. The login submodule is connected to the big data application module; the encryption submodule is connected to the login submodule; and the data transfer submodule is connected to the encryption submodule.

[0029] The login submodule is used to log in to the tourism data query client;

[0030] The encryption submodule is used to receive the encrypted information to form an encryption key;

[0031] The data transmission submodule is used to transmit signals to the data processor.

[0032] Wherein, the multi-source information fusion system based on big data further includes a cloud data sending module; the cloud data sending module is connected to the big data application module;

[0033] The cloud data sending module is used to send corresponding travel data to the mobile terminals used by each user based on the cloud server.

[0034] In a second aspect, the present invention also provides a multi-source information fusion system based on big data, which is applied to tourism big data.

[0035] The present invention discloses a multi-source information fusion system based on big data. The crawling module selects data seeds and then analyzes them. Through the hyperlink information in the webpage, the data of other webpages on the network is continuously obtained to collect tourism data of multiple platforms. The classification module obtains the tourism data of multiple platforms captured by the crawling module, identifies and judges the authenticity of the data, and classifies them according to the regional data and the name data to obtain a series of classified tourism data. The classified data is then stored through the data storage module to obtain a large amount of tourism data. Then, based on the tourism big data, the data fusion module fuses the heterogeneous information resources of the tourism data and associates the stored tourism data. Finally, the fused tourism data is analyzed through the big data application module to form a fusion platform for tourism data, so as to solve the problem that the existing multi-platform tourism data fusion system based on big data may cause the data storage process to be relatively slow because the acquired data is directly sent to the data storage unit without being classified, and each data cannot be effectively classified, thereby affecting the subsequent data fusion efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art are briefly introduced below.

[0037] Figure 1 It is a schematic diagram of the overall structure of a first embodiment of a multi-source information fusion system based on big data of the present invention.

[0038] Figure 2 It is a structural diagram of a big data module of a first embodiment of a multi-source information fusion system based on big data of the present invention.

[0039] Figure 3 It is a structural schematic diagram of a data storage module of a first embodiment of a multi-source information fusion system based on big data of the present invention.

[0040] Figure 4 It is a schematic diagram of the overall structure of a second embodiment of a multi-source information fusion system based on big data of the present invention.

[0041] Figure 5 It is a structural diagram of a data security access module of a second embodiment of a multi-source information fusion system based on big data of the present invention.

[0042] Figure 6 It is a schematic diagram of the overall structure of a third embodiment of a multi-source information fusion system based on big data of the present invention.

[0043] 101-crawling module, 102-classification module, 103-data storage module, 104-data fusion module, 105-big data application module, 106-data acquisition submodule, 107-data name identification submodule, 108-data classification submodule, 109-data receiving submodule, 110-data analysis submodule, 111-network storage submodule, 112-tree storage submodule, 113-data association submodule, 114-information difference calculation submodule, 115-Gaussian mixture submodule, 116-information release submodule, 117-scenic area management submodule, 118-data prediction submodule, 119-tourist feature submodule, 201-data security access module, 202-login submodule, 203-encryption submodule, 204-data transmission submodule, 301-cloud data sending module. DETAILED DESCRIPTION

[0044] In a first aspect, the present invention provides a multi-source information fusion system based on big data. A first embodiment of a multi-source information fusion system based on big data of the present application is:

[0045] See also Figure 1-Figure 3 ,in, Figure 1 is a schematic diagram of the overall structure of a first embodiment of a multi-source information fusion system based on big data of the present invention, Figure 2 It is a structural diagram of a big data module of a first embodiment of a multi-source information fusion system based on big data of the present invention. Figure 3 It is a structural schematic diagram of a data storage module of a first embodiment of a multi-source information fusion system based on big data of the present invention.

[0046] The present invention discloses a multi-source information fusion system based on big data; it comprises a crawling module 101, a classification module 102, a data storage module 103, a data fusion module 104 and a big data application module 105, wherein the classification module 102 comprises a data acquisition submodule 106, a data name recognition submodule 107 and a data classification submodule 108, the data storage module 103 comprises a data receiving submodule 109, a data analysis submodule 110, a mesh storage submodule 111 and a tree storage submodule 112, the data fusion module 104 comprises a data association submodule 113, an information difference calculation submodule 114 and a Gaussian mixture submodule 115, the big data application module 105 comprises an information release submodule 116, a scenic area management submodule 117, a data prediction submodule 118 and a tourist feature submodule 119, and the above-mentioned scheme solves the problem that the existing multi-platform tourism data fusion system based on big data, because the acquired data is directly sent to the data storage unit without being classified, may cause the data storage process to be relatively slow, and cannot effectively classify each data, thereby affecting the subsequent data fusion efficiency.

[0047] According to this specific implementation, the classification module 102 is connected to the crawling module 101; the data storage module 103 is connected to the classification module 102; the data fusion module 104 is connected to the data storage module 103; the big data application module 105 is connected to the data fusion module 104, the crawling module 101 selects data seeds and then parses them through the hyperlink information in the web page, continuously obtains data from other web pages on the network to collect tourism data from multiple platforms, the classification module 102 obtains the tourism data from multiple platforms captured by the crawling module 101, identifies and judges the authenticity of the data, and parses them according to the regional data and name data. A series of classified tourism data are obtained through classification, and then the classified data are stored through the data storage module 103 to obtain a large amount of tourism data. Then, based on the tourism big data, the data fusion module 104 fuses the heterogeneous information resources of the tourism data and associates the stored tourism data. Finally, the integrated tourism data is analyzed through the big data application module 105 to form a fusion platform for tourism data, so as to solve the problem that the existing multi-platform tourism data fusion system based on big data may cause the data storage process to be relatively slow, and the data cannot be effectively classified, thereby affecting the subsequent data fusion efficiency.

[0048] Among them, the data name identification submodule 107 is connected to the data acquisition submodule 106; the data classification submodule 108 is connected to the data name identification submodule 107, and the data acquisition submodule 106 is used to obtain the corresponding tourism data and regional data obtained by the crawling module 101 on each platform, and then use the data name identification submodule 107 to identify the acquired tourism data and regional data to determine whether there is false or erroneous information. If the data information is correct, the data classification submodule 108 will classify and store the correct tourism data and regional data according to the name and local data, so as to facilitate the subsequent call by the data storage module 103.

[0049] Secondly, the data receiving submodule 109 is connected to the classification module 102; the data analysis submodule 110 is connected to the data receiving submodule 109; the mesh storage submodule 111 is connected to the data analysis submodule 110; the tree storage submodule 112 is connected to the data analysis submodule 110, the data receiving submodule 109 obtains the tourism data classified and sorted in the classification module 102, and then analyzes the tourism data classified by the data analysis submodule 110 again to determine the suitable storage format, the mesh storage submodule 111 receives the tourism data suitable for mesh storage and stores it in mesh data, and the tree storage submodule 112 is used to receive the tourism data suitable for tree storage and store it in tree data.

[0050] Again, the data association submodule 113 is connected to the data storage module 103; the information difference calculation submodule 114 is connected to the data association submodule 113; the Gaussian mixture submodule 115 is connected to the information difference calculation submodule 114. The data association submodule 113 is used to realize the merging and sharing of tourism data information resources, and realize the connection, exchange and sharing between different databases. Then, the information difference calculation submodule 114 is used to calculate the differences between different tourism data, and cooperate with the Gaussian mixture submodule 115 to establish a Gaussian mixture model to integrate the mobile network and multi-source tourism data information, and calculate the function of all tourism information.

[0051] In addition, the big data application module 105 includes an information release submodule 116, a scenic area management submodule 117 and a tourist feature submodule 119, wherein the information release submodule 116 is connected to the data fusion module 104; the scenic area management submodule 117 is connected to the information release submodule 116; and the tourist feature submodule 119 is connected to the scenic area management submodule 117;

[0052] The information publishing submodule 116 is used to publish travel information;

[0053] The scenic area management submodule 117 is used to detect tourist scenic area data;

[0054] The tourist characteristics submodule 119 is used to analyze the tourist distribution data of the tourism data.

[0055] Furthermore, the big data application module 105 also includes a data prediction submodule 118, and the data prediction submodule 118 is connected to the tourist feature submodule 119;

[0056] The data prediction submodule 118 is used to predict the tourism data of the next quarter based on the scenic spot management submodule 117 and the tourist feature submodule 119 in previous years and recent scenic spots and tourist data, so as to facilitate unified management of tourist attractions based on big data.

[0057] In the use of the multi-source information fusion system based on big data described in the present invention, the crawling module 101 selects data seeds and then parses them. Through the hyperlink information in the web page, the data of other web pages on the network is continuously obtained to collect tourism data from multiple platforms. The classification module 102 obtains the tourism data of multiple platforms captured by the crawling module 101, identifies and judges the authenticity of the data, and classifies it according to the regional data and the name data to obtain a series of classified tourism data. Then, the classified data is stored through the data storage module 103 to obtain a large amount of tourism data. Then, based on the tourism big data, the data fusion module 104 fuses the heterogeneous information resources of the tourism data and associates the stored tourism data. Finally, the fused tourism data is analyzed through the big data application module 105 to form a fusion platform for tourism data, so as to solve the problem that the existing multi-platform tourism data fusion system based on big data, because the acquired data is directly sent to the data storage unit without being classified, it may cause the data storage process to be relatively slow, and each data cannot be effectively classified, affecting the subsequent data fusion efficiency.

[0058] A second embodiment of a multi-source information fusion system based on big data of the present application is:

[0059] See also Figure 4-Figure 5 ,in, Figure 4 is a schematic diagram of the overall structure of a second embodiment of a multi-source information fusion system based on big data of the present invention, Figure 5 It is a structural diagram of a data security access module of a second embodiment of a multi-source information fusion system based on big data of the present invention.

[0060] Based on the first embodiment, the present invention provides a multi-source information fusion system and application based on big data. The multi-source information fusion system based on big data also includes a data security access module 201, and the data security access module 201 includes a login sub-module 202, an encryption sub-module 203 and a data transmission sub-module 204.

[0061] The data security access module 201 is connected to the big data application module 105, and the data security access module 201 is used by the client to view the content of the big data application module 105 by means of ciphertext security access technology, thereby increasing data security.

[0062] Secondly, the login submodule 202 is connected to the big data application module 105; the encryption submodule 203 is connected to the login submodule 202; the data transmission submodule 204 is connected to the encryption submodule 203. The login submodule 202 is used to log in to the tourism data query client, and the encrypted information is received through the encryption submodule 203 to form an encryption key, and then the signal is transmitted to the data processor through the data transmission submodule 204, thereby increasing the security of tourism data.

[0063] The present embodiment describes a multi-source information fusion system based on big data, wherein the login submodule 202 is used to log in to a tourism data query client, the encrypted information is received through the encryption submodule 203 to form an encryption key, and then the signal is transmitted to the data processor through the data transmission submodule 204, thereby increasing the security of tourism data.

[0064] A third embodiment of a multi-source information fusion system based on big data of the present application is:

[0065] See also Figure 6 ,in, Figure 6 It is a schematic diagram of the overall structure of a third embodiment of a multi-source information fusion system based on big data of the present invention.

[0066] On the basis of the first embodiment, the present invention provides a multi-source information fusion system and application based on big data. The multi-source information fusion system based on big data also includes a cloud data sending module 301.

[0067] The cloud data sending module 301 is connected to the big data application module 105, and the cloud data sending module 301 is used to send corresponding travel data to the mobile terminal used by each user based on the cloud server.

[0068] In the multi-source information fusion system based on big data described in this embodiment, the cloud data sending module 301 is used to send corresponding travel data to the mobile terminals used by each user based on the cloud server.

[0069] In a second aspect, the present invention also provides a multi-source information fusion system based on big data, which is applied to tourism big data.

[0070] What is disclosed above is only one or more preferred embodiments of the present application, and cannot be used to limit the scope of rights of the present application. Ordinary technicians in this field can understand that all or part of the processes of implementing the above embodiments and equivalent changes made according to the claims of the present application are still within the scope covered by the present application.

Claims

1. A multi-source information fusion system based on big data, characterized by: It includes crawling module, classification module, data storage module, data fusion module and big data application module; The classification module is connected to the crawling module; the data storage module is connected to the classification module; the data fusion module is connected to the data storage module; the big data application module is connected to the data fusion module; The crawling module is used to crawl and collect tourism data based on multiple platforms to obtain corresponding tourism data; The classification module is used to classify and organize the acquired tourism data and place data according to the name type; The data storage module is used to store the tourism data sorted by the classification module, and to complete the data storage by confirming the storage method according to different tourism data requirements; The data fusion module is used to fuse the heterogeneous information resources of tourism data and associate the tourism data stored in the data storage module; The big data application module is used to analyze the fusion data of the data fusion module and then form a fusion platform for tourism data.

2. A multi-source information fusion system based on big data according to claim 1, characterized in that: The classification module includes a data acquisition submodule, a data name identification submodule and a data classification submodule, wherein the data name identification submodule is connected to the data acquisition submodule; the data classification submodule is connected to the data name identification submodule; The data acquisition submodule is used to obtain the corresponding tourism data and regional data obtained by the crawling module on each platform; The data name identification submodule is used to identify the acquired tourism data and regional data to determine whether there is any false or erroneous information; The data classification submodule is used to classify and store the identified real and correct tourism data and regional data according to the name and place data.

3. A multi-source information fusion system based on big data according to claim 2, characterized in that: The data storage module includes a data receiving submodule, a data analysis submodule, a network storage submodule and a tree storage submodule, wherein the data receiving submodule is connected to the classification module; the data analysis submodule is connected to the data receiving submodule; the network storage submodule is connected to the data analysis submodule; and the tree storage submodule is connected to the data analysis submodule. The data receiving submodule is used to obtain the tourism data classified in the classification module; The data analysis submodule is used to re-analyze the classified tourism data to determine whether it is suitable for a tree-type storage method or a mesh storage module; The mesh storage submodule is used to receive travel data suitable for mesh storage and store the data in mesh form; The tree-type storage submodule is used to receive travel data suitable for tree-type storage and store the data in the tree-type.

4. The multi-source information fusion system based on big data according to claim 3, characterized in that: The data fusion module includes a data association submodule, an information difference calculation submodule and a Gaussian mixture submodule, wherein the data association submodule is connected to the data storage module; the information difference calculation submodule is connected to the data association submodule; and the Gaussian mixture submodule is connected to the information difference calculation submodule. The data association submodule is used to realize the merging and sharing of tourism data information resources and to realize the connection, exchange and sharing between different databases; The information difference calculation submodule is used to calculate the differences between different tourism data; The Gaussian mixture submodule is used to establish a Gaussian mixture model to integrate mobile network and multi-source tourism data information and calculate the function of all tourism information.

5. The multi-source information fusion system based on big data according to claim 1, characterized in that: The multi-source information fusion system based on big data further includes a data security access module, which is connected to the big data application module; The data security access module is used by the client to view the content of the big data application module using ciphertext security access technology, thereby increasing data security.

6. A multi-source information fusion system based on big data according to claim 5, characterized in that: The data security access module includes a login submodule, an encryption submodule and a data transfer submodule, wherein the login submodule is connected to the big data application module; the encryption submodule is connected to the login submodule; and the data transfer submodule is connected to the encryption submodule. The login submodule is used to log in to the tourism data query client; The encryption submodule is used to receive the encrypted information to form an encryption key; The data transmission submodule is used to transmit signals to the data processor.

7. The multi-source information fusion system based on big data according to claim 1, characterized in that: The multi-source information fusion system based on big data further includes a cloud data sending module; the cloud data sending module is connected to the big data application module; The cloud data sending module is used to send corresponding travel data to the mobile terminals used by each user based on the cloud server.

8. A multi-source information fusion system based on big data as described in any one of claims 1 to 7, applied to tourism big data.

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