Cloud Exhibition Hall Personalized Recommendation System and Method Based on Big Data Analysis

Abstract

The present invention provides a personalized recommendation system and method for a cloud exhibition hall based on big data analysis, including: obtaining user browsing data in big data, constructing the browsing preference information of users according to the user browsing data, performing personalized rendering on each exhibit presentation area of the cloud exhibition hall, respectively analyzing the browsing information corresponding to each visitor in different exhibit presentation areas, recommending corresponding exhibits to the corresponding visitors according to the browsing information, after the visitors select the interested exhibits, constructing the current browsing preference of the visitors according to the browsing information of the visitors in the cloud exhibition hall, deeply beautifying the interested exhibits based on the current browsing preference, retrieving the corresponding explanation method according to the current browsing preference, using the explanation method to explain the interested exhibits, and using big data analysis technology to provide personalized exhibit recommendation services for cloud exhibition hall users according to the user browsing habits and preferences, so as to improve user satisfaction and the attractiveness of the exhibition hall.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent product recommendation, and particularly to a personalized recommendation system and method for a cloud exhibition hall based on big data analysis. Background Art

[0002] A cloud exhibition hall is an online exhibition platform based on the Internet and virtual reality technology, which provides a digital exhibition experience that allows visitors to visit exhibitions, view exhibits, and interact with other participants in a virtual environment.

[0003] Virtual exhibition space: The cloud exhibition hall creates a highly realistic exhibition space through virtual reality technology. Visitors can enter the virtual exhibition hall through a computer or mobile device to explore different exhibition areas and exhibition content.

[0004] Exhibit display: In the cloud exhibition hall, exhibits are presented in digital form. Visitors can view high-definition pictures, videos or models to understand the details and background information of the exhibits.

[0005] Interactive experience: The cloud exhibition hall usually provides rich interactive functions. Visitors can interact with the exhibits, such as zooming in, rotating or touching the displayed items. At the same time, visitors can also communicate and discuss with other online visitors or exhibition organizers in real time.

[0006] No geographical restrictions: One of the features of the cloud exhibition hall is that it can break through geographical restrictions, enabling visitors worldwide to participate in the exhibition. This provides a wider audience and more display opportunities for exhibition organizers.

[0007] Data statistics and analysis: The cloud exhibition hall usually records visitors' behavior data, such as browsing time, click times, etc. These data can help exhibition organizers understand the interests and behaviors of the audience, and then optimize exhibition planning and design.

[0008] Customizability: The cloud exhibition hall can be customized according to the needs of the exhibition. Exhibition organizers can customize the appearance and functions of the exhibition hall to adapt to different types of exhibitions and activities.

[0009] Generally speaking, the cloud exhibition hall provides an innovative digital form for exhibitions, bringing a more convenient, rich and diverse exhibition experience to visitors, and at the same time providing more display and interaction opportunities for exhibition organizers.

[0010] However, existing cloud exhibition halls can only display different exhibits in sequence within a specified time, and visitors can only visit mechanically without being able to obtain more knowledge.

[0011] Therefore, the present invention provides a personalized recommendation system and method for a cloud exhibition hall based on big data analysis. Summary of the Invention

[0012] The cloud exhibition hall personalized recommendation system of the present invention is based on big data analysis. It uses big data analysis technology to provide personalized exhibit recommendation services for cloud exhibition hall users according to user browsing habits and preferences, thereby improving user satisfaction and the attractiveness of the exhibition hall.

[0013] The present invention provides a cloud exhibition hall personalized recommendation system based on big data analysis, including:

[0014] A personal collection module is used to obtain user browsing data from big data and construct user browsing preference information based on the user browsing data;

[0015] An exhibition hall rendering module, used to perform personalized rendering of each exhibit presentation area of ​​the cloud exhibition hall according to the exhibition hall function of the cloud exhibition hall and the browsing preference information;

[0016] An exhibit recommendation module is used to analyze the browsing information corresponding to each visitor in different exhibit presentation areas, and recommend corresponding exhibits to the corresponding visitors according to the browsing information;

[0017] An exhibit beautification module is used to construct the visitor's browsing preferences according to the visitor's browsing information in the cloud exhibition hall after the visitor selects an exhibit of interest, and to beautify the exhibit of interest based on the browsing preferences;

[0018] The exhibit explanation module is used to call the corresponding explanation method according to the current browsing preference, and use the explanation method to explain the exhibit of interest.

[0019] In one practicable manner,

[0020] The personality collection module includes:

[0021] A data collection unit is used to collect real-time data from the Internet, construct a number of screening matching words according to the current exhibit information of the cloud exhibition hall and the basic information of the exhibition hall, and respectively construct a data form set corresponding to each of the screening matching words;

[0022] A data screening unit, used to establish screening rules corresponding to each screening matching word according to each data form set, and use each screening rule to screen the real-time data to obtain a plurality of available data;

[0023] A data classification unit, used to respectively determine the screening matching words corresponding to each of the available data, classify the available data according to the meanings of the screening matching words, and construct user browsing data according to each data class;

[0024] A preference analysis unit for constructing the public browsing information of the user in the Internet based on the user browsing data, and constructing the browsing preference information of the user based on the similarity information between different pieces of the public browsing information.

[0025] In an implementable manner,

[0026] The exhibition hall rendering module includes:

[0027] A function analysis unit for obtaining the current exhibit information and the basic information of the exhibition hall of the cloud exhibition hall, constructing the initial function of the cloud exhibition hall by using the basic information of the exhibition hall, establishing the display characteristics of the corresponding exhibit according to each piece of the current exhibit information, dividing the area of the basic information of the exhibition hall by using the display characteristics, and combining the initial function to obtain the initial area function corresponding to each exhibit area;

[0028] A color rendering unit for determining the user's preferred color list according to the browsing preference information, determining the exhibit color distribution corresponding to each exhibit according to the current exhibit information, performing color fusion on the preferred color list and the exhibit color distribution to obtain a plurality of color fusion schemes, respectively obtaining the color contrast corresponding to each color fusion scheme, determining the user's preference degree for each color contrast according to the browsing preference, and performing color rendering on the corresponding exhibit area by using the preferred color fusion scheme with the highest preference degree;

[0029] A personalized rendering unit for determining the user's preferred elements according to the browsing preference information, constructing a scoring matrix corresponding to each preferred element by performing a fusion score on each preferred element and each exhibit according to the association degree between each preferred element and each exhibit, transmitting the scoring matrix to the Internet to perform gradient analysis on each preferred element respectively, obtaining the fusion preference characteristics between different preferred elements and the exhibits, and performing element rendering on each exhibit area based on the fusion preference characteristics.

[0030] In an implementable manner,

[0031] The exhibit recommendation module includes:

[0032] A browsing collection unit for collecting the appearance information corresponding to each visitor when each visitor enters the cloud exhibition hall, respectively establishing a virtual visit code for each visitor, collecting the visit path corresponding to each visitor and establishing legal visit information in combination with the corresponding virtual visit code;

[0033] An area analysis unit, configured to determine the passenger flow information corresponding to each exhibit presentation area based on the legal visit information, and respectively establish the passenger flow gender distribution and passenger flow age distribution corresponding to each exhibit presentation area within different browsing time periods;

[0034] A visit distribution unit, configured to adjust the recommended order of each exhibit presentation area according to the passenger flow gender distribution and passenger flow age distribution corresponding to each exhibit presentation area, obtain the current appearance information of the current visitor corresponding to the exhibit presentation area to determine the gender and age of the current visitor, and find the corresponding recommended exhibits in the recommended order and recommend them to the corresponding current visitors in sequence.

[0035] In an implementable manner,

[0036] A virtual visit code is established for each visitor respectively, including:

[0037] Construct a virtual visit image corresponding to the visitor according to the appearance information corresponding to each visitor, perform face analysis and height analysis on the virtual visit image to obtain the facial features and height features corresponding to the visitor;

[0038] Assign corresponding gender labels to the corresponding visitors according to the facial features, and assign corresponding adult labels / minor labels to the corresponding visitors according to the height features;

[0039] Use AI to perform clothing analysis on the virtual visit image to obtain several clothing details of the corresponding visitor, and judge whether the labels assigned to the corresponding visitor are reasonable based on the clothing details;

[0040] When the assigned labels are unreasonable, adjust the labels of the corresponding visitors;

[0041] Obtain the labels corresponding to each visitor and the order in which each visitor enters the cloud exhibition hall, and establish a virtual visit code for the corresponding visitor based on the labels and the order.

[0042] In an implementable manner,

[0043] The exhibit beautification module includes:

[0044] An information scheduling unit, configured to, when the visitor selects an interesting exhibit, retrieve the corresponding legal visit information according to the virtual visit code corresponding to the visitor, construct the visit path of the visitor in the cloud exhibition hall according to the legal visit information, as well as the residence duration of the visitor corresponding to each exhibit presentation area, and establish the time-domain browsing trajectory of the visitor.

[0045] A service analysis unit, configured to mark the exhibition schedule corresponding to each exhibition area on the time-domain browsing trajectory, obtain the product information obtained by the visitor in each exhibition area, and establish the visit information chain of the visitor;

[0046] A service execution unit, configured to construct the browsing preferences of the visitor in the cloud exhibition hall according to the visit information chain, and according to the detail level of the interest exhibits, respectively perform iterative beautification on each detail level of the exhibits by using the browsing preferences, and obtain the beautified exhibit detail levels corresponding to each detail level after each beautification, and construct several beautification results;

[0047] A beautification analysis unit, configured to perform similarity analysis on the beautification results, obtain the similarity degree between different beautification results, input several similarity degrees into a Gaussian model for probability analysis to obtain a probability distribution table, and extract the target beautification result corresponding to the highest similarity degree in the probability distribution table;

[0048] A beautification execution unit, configured to perform in-depth beautification on each exhibit detail level of the interest exhibits by using the target beautification result.

[0049] In an implementable manner,

[0050] The exhibit explanation module includes:

[0051] A method matching unit, configured to determine the preferred tone color and intonation of the visitor according to the current browsing preferences of the visitor, and retrieve the corresponding explanation method according to the preferred tone color and intonation;

[0052] An explanation preparation unit, configured to obtain the exhibit data of the interest exhibits, construct several semantic descriptions of the interest exhibits according to the exhibit data, screen and reorganize the semantic descriptions according to the explanation method, and obtain an explanation manuscript;

[0053] An explanation execution unit, configured to explain the interest exhibits by using the explanation method and the explanation manuscript.

[0054] This example provides a cloud exhibition hall personalized recommendation system based on big data analysis, including:

[0055] Step 1: Obtain the user browsing data in the big data, and construct the browsing preference information of the user according to the user browsing data;

[0056] Step 2: Perform personalized rendering on each exhibition area of the cloud exhibition hall according to the exhibition hall functions of the cloud exhibition hall in combination with the browsing preference information;

[0057] Step 3: Analyze the browsing information of each visitor corresponding to different exhibition area of the exhibits respectively, and recommend corresponding exhibits to the corresponding visitors according to the browsing information;

[0058] Step 4: After the visitor selects the interested exhibits, construct the current browsing preferences of the visitor based on the browsing information of the visitor in the cloud exhibition hall, and deeply beautify the interested exhibits based on the current browsing preferences;

[0059] Step 5: Retrieve the corresponding explanation method according to the current browsing preferences, and use the explanation method to explain the interested exhibits.

[0060] In an implementable manner,

[0061] The step 3 includes:

[0062] Step 31: After each visitor enters the cloud exhibition hall, collect the appearance information corresponding to each visitor, establish a virtual visit code for each visitor respectively, collect the visit path corresponding to each visitor, and establish legal visit information in combination with the corresponding virtual visit code;

[0063] Step 32: Based on the legal visit information, determine the passenger flow information corresponding to each exhibition area of the exhibits, and establish the corresponding passenger flow gender distribution and passenger flow age distribution of each exhibition area of the exhibits in different browsing time periods respectively;

[0064] Step 33: Adjust the recommendation order of each exhibition area of the exhibits according to the passenger flow gender distribution and passenger flow age distribution corresponding to each exhibition area of the exhibits, obtain the current appearance information of the current visitor corresponding to the exhibition area of the exhibits, determine the gender and age of the current visitor, search for the corresponding recommended exhibits in the recommendation order, and recommend them to the corresponding current visitors in turn.

[0065] In an implementable manner,

[0066] Establishing a virtual visit code for each visitor respectively includes:

[0067] Construct a virtual visit image of the corresponding visitor according to the appearance information corresponding to each visitor, perform face analysis and height analysis on the virtual visit image, and obtain the face features and height features corresponding to the visitor;

[0068] Assign corresponding gender labels to the corresponding visitors according to the face features, and assign corresponding adult labels / minor labels to the corresponding visitors according to the height features;

[0069] Use AI to analyze the clothing of the virtual tour image, obtain several clothing details of the corresponding visitor, and judge whether the label assigned to the corresponding visitor is reasonable based on the clothing details;

[0070] When the assigned label is unreasonable, adjust the label of the corresponding visitor;

[0071] Obtain the label corresponding to each visitor and the order in which each visitor enters the cloud exhibition hall, and establish a virtual tour code for the corresponding visitor based on the label and the order.

[0072] The achievable beneficial effects of the above technical solution are as follows: In order to enhance the interest of visitors, analyze the user browsing data in the big data to determine the browsing preference information of the current user, and then use the browsing preference information to perform corresponding rendering on the cloud exhibition hall, thereby improving the interest of visitors. In order to further enhance the interest of visitors, during the process of visitors' browsing, deepen and adjust the interest exhibits selected by the visitors according to the visitors' browsing interests, and explain the interest exhibits in a corresponding way of explanation. In this way, not only can the cloud exhibition hall keep up with the times and incorporate novel elements in the Internet into it, but also adjust the exhibition method of the interest exhibits according to the interests of the visitors, thereby improving the interest of the visitors and enabling the visitors to understand the exhibits more deeply.

[0073] Other features and advantages of the present invention will be described in the subsequent description, and, in part, will be obvious from the description, or will be understood by implementing the present invention. The objectives and other advantages of the present invention can be realized and obtained by the structures specifically pointed out in the written description, claims, and drawings.

[0074] The following will further describe the technical solution of the present invention in detail through the drawings and embodiments. Description of the Drawings

[0075] The drawings are used to provide a further understanding of the present invention and constitute a part of the description. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation to the present invention. In the drawings:

[0076] Figure 1 It is a schematic diagram of the composition of the cloud exhibition hall personalized recommendation system based on big data analysis in the embodiment of the present invention;

[0077] Figure 2 It is a schematic diagram of the working process of the cloud exhibition hall personalized recommendation method based on big data analysis in the embodiment of the present invention. Detailed Embodiments

[0078] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0079] Embodiment 1

[0080] This embodiment provides a personalized recommendation system for a cloud exhibition hall based on big data analysis. As Figure 1 shown, it includes:

[0081] A personality collection module, which is used to obtain user browsing data in big data and construct browsing preference information of the user according to the user browsing data;

[0082] An exhibition hall rendering module, which is used to perform personalized rendering on each exhibit presentation area of the cloud exhibition hall according to the exhibition hall functions of the cloud exhibition hall in combination with the browsing preference information;

[0083] An exhibit recommendation module, which is used to analyze the browsing information corresponding to each visitor in different exhibit presentation areas respectively, and recommend corresponding exhibits to the corresponding visitors according to the browsing information;

[0084] An exhibit beautification module, which is used to construct the current browsing preference of the visitor according to the browsing information of the visitor in the cloud exhibition hall after the visitor selects an interested exhibit, and deeply beautify the interested exhibit based on the current browsing preference;

[0085] An exhibit explanation module, which is used to retrieve the corresponding explanation method according to the current browsing preference and explain the interested exhibit by using the explanation method.

[0086] In this example, the user browsing data represents the data generated by the user's interaction on the Internet;

[0087] In this example, the browsing preference information represents the information of the things that the user likes during the browsing process;

[0088] In this example, the personalized rendering represents the process of beautifying the exhibit presentation area;

[0089] In this example, the interested exhibit represents the exhibit that the visitor is interested in.

[0090] Working principle and beneficial effects of the above technical solution: To enhance the interest of visitors, user browsing data is analyzed in big data to determine the current user's browsing preference information, and then the cloud exhibition hall is rendered accordingly using the browsing preference information, thereby improving the interest of visitors. To further enhance the interest of visitors, during the browsing process of visitors, the interest exhibits selected by the visitors are deeply adjusted according to the visitors' browsing interests, and the interest exhibits are explained in a corresponding way of explanation. In this way, not only can the cloud exhibition hall keep up with the times and incorporate novel elements from the Internet into it, but also the exhibition method of the interest exhibits can be adjusted according to the interests of visitors, thereby improving the interest of visitors and enabling visitors to understand the exhibits more deeply.

[0091] Example 2

[0092] Based on Example 1, in the personalized recommendation system of the cloud exhibition hall based on big data analysis, the personality collection module includes:

[0093] A data collection unit, which is used to collect real-time data on the Internet, construct several screening and matching words according to the current exhibit information and the basic information of the exhibition hall of the cloud exhibition hall, and respectively construct a data form set corresponding to each of the screening and matching words;

[0094] A data screening unit, which is used to respectively establish a screening rule corresponding to each screening and matching word according to each data form set, and respectively use each screening rule to screen the real-time data to obtain several available data;

[0095] A data classification unit, which is used to respectively determine the screening and matching word corresponding to each available data, classify the available data according to the meaning of the screening and matching word corresponding to it, and construct user browsing data according to each data category;

[0096] A preference analysis unit, which is used to construct the public browsing information of the user on the Internet for the user browsing data, and construct the browsing preference information of the user based on the similarity information between different public browsing information.

[0097] In this example, the current exhibit information represents the basic information of the exhibits displayed in the cloud exhibition hall;

[0098] In this example, the basic information of the exhibition hall includes: the exhibition hall specifications and distribution information;

[0099] In this example, one piece of information can correspond to one or more screening and matching words;

[0100] In this example, the data form set represents the expression way of describing the screening and matching words in the form of data, and how many data forms can correspond to one description of the screening and matching word, so the data form set is constructed;

[0101] In this example, the publicly browsed information refers to the information that is publicly available on the Internet and does not involve the user's privacy.

[0102] The working principle and beneficial effects of the above technical solution: In order to make the cloud exhibition hall keep up with the popular elements, screening and matching words are constructed according to the current exhibit information and the basic information of the exhibition hall of the cloud exhibition hall, so as to screen the real-time data collected from the Internet, extract several available data from it, and then match the available data with the screening words, so as to construct the user browsing data according to the matching results, and further conduct similarity analysis on the user browsing data, so as to construct the browsing preference information of the user. In this way, not only can the current popular elements be grasped in real time, but also the guiding direction of the popular elements can be classified to a certain extent according to the user's preference information, which is convenient for selecting suitable data for the cloud exhibition hall in the future.

[0103] Embodiment 3

[0104] Based on Embodiment 1, in the personalized recommendation system of the cloud exhibition hall based on big data analysis, the exhibition hall rendering module includes:

[0105] A function analysis unit, configured to obtain the current exhibit information and the basic information of the exhibition hall of the cloud exhibition hall, use the basic information of the exhibition hall to construct the initial function of the cloud exhibition hall, establish the display characteristics of the corresponding exhibits according to each piece of the current exhibit information, use the display characteristics to divide the area of the basic information of the exhibition hall, and combine the initial function to obtain the initial area function corresponding to each exhibit area;

[0106] A color rendering unit, configured to determine the user's preferred color list according to the browsing preference information, determine the exhibit color distribution corresponding to each exhibit according to the current exhibit information, perform color fusion on the preferred color list and the exhibit color distribution to obtain several color fusion schemes, and respectively obtain the color contrast corresponding to each color fusion scheme, determine the user's preference degree for each color contrast according to the browsing preference, and use the optimal color fusion scheme with the highest preference degree to perform color rendering on the corresponding exhibit area;

[0107] A personality rendering unit, configured to determine the user's preferred elements according to the browsing preference information, fuse and score each preferred element with each exhibit according to the association degree between each preferred element and each exhibit to construct a scoring matrix corresponding to each preferred element, transmit the scoring matrix to the Internet to perform gradient analysis on each preferred element respectively, obtain the fusion preference characteristics between the user's different preferred elements and the exhibits, and perform element rendering on each exhibit area based on the fusion preference characteristics.

[0108] In this example, the initial function represents the functions that the cloud exhibition hall can perform;

[0109] In this example, the display features include: display method, display occupied area, and display duration;

[0110] In this example, the initial area function represents the functions that the exhibit area needs to perform because the exhibits are within the exhibit area. For example, when displaying Exhibit A, different sound effects need to be coordinated at different display angles;

[0111] In this example, the preferred color list represents a ranking list of the colors that the user likes;

[0112] In this example, the color distribution of the exhibit represents the distribution of different colors in an exhibit;

[0113] In this example, the color contrast table represents the contrast presented after different colors are combined together;

[0114] In this example, the degree of preference represents the degree of preference of the user for different color combinations;

[0115] In this example, the preferred color fusion scheme is selected by analyzing the degree of preference, and it is the color fusion scheme with the highest degree of preference among users on the Internet;

[0116] In this example, the preferred elements represent the popular elements that users on the Internet like;

[0117] In this example, the degree of association represents the association relationship between the preferred elements and the exhibits. For example, if the theme of Exhibit B is fire safety, the preferred element 1 is a first aid sign, and the preferred element 2 is a food store visit, then the degree of association between the preferred element 1 and Exhibit B is higher than the degree of association between the preferred element 2 and Exhibit B;

[0118] In this example, the gradient analysis represents the process of gradually analyzing the scoring matrix in the field where the preferred elements are located;

[0119] In this example, the fusion preference feature represents the user's favorite features when the preferred elements are fused with the exhibits.

[0120] Working principle and beneficial effects of the above technical solution: In order to attract visitors to move around in the cloud exhibition hall and increase their interest, exhibit characteristics are constructed based on the information of the exhibits in this exhibition, and the basic information of the exhibition hall is constructed to divide the cloud exhibition hall into regions, thereby establishing the initial regional functions corresponding to each exhibit area. Further, the browsing preference information is used to determine the user's preferred color list, and color fusion experiments are carried out in combination with the color distribution of the exhibits. By analyzing the color contrast corresponding to different color fusions, the user's preference degree for each color fusion is determined. Thus, a color fusion scheme can be selected to render the color of the exhibit area. Then, the user's preferred elements are determined according to the browsing preference information, and a scoring matrix is constructed using the degree of association between the preferred elements and the exhibits. Then, a large number of users on the Internet rate the exhibits to determine the user's fusion preference characteristics. Thus, the fusion preference characteristics are used to render the elements of the exhibit area. In this way, the colors liked by the users and the elements that the users are interested in can be added to the exhibit area, making the exhibits in line with the trend and increasing the interest of the visitors.

[0121] Example 4

[0122] Based on Example 1, for the cloud exhibition hall personalized recommendation system based on big data analysis, the exhibit recommendation module includes:

[0123] A browsing collection unit, which is used to collect the appearance information corresponding to each visitor when each visitor enters the cloud exhibition hall, establish a virtual visit code for each visitor respectively, collect the visit path corresponding to each visitor, and establish legal visit information in combination with the corresponding virtual visit code;

[0124] A regional analysis unit, which is used to determine the passenger flow information corresponding to each exhibit presentation area based on the legal visit information, and establish the passenger flow gender distribution and passenger flow age distribution corresponding to each exhibit presentation area during different browsing time periods respectively;

[0125] A visit distribution unit, which is used to adjust the recommendation order of each exhibit presentation area according to the passenger flow gender distribution and passenger flow age distribution corresponding to each exhibit presentation area, obtain the current appearance information of the current visitor corresponding to the exhibit presentation area to determine the gender and age of the current visitor, and find the corresponding recommended exhibits in the recommendation order and recommend them to the corresponding current visitors in turn.

[0126] In this example, the appearance information represents the appearance of the visitor, that is, the visitor is photographed after entering the cloud exhibition hall, and the visitor knows that they are being photographed currently;

[0127] In this example, the virtual visit code corresponds to the visitor one by one;

[0128] In this example, the gender distribution of the passenger flow represents the proportional distribution of the genders of the visitors viewing the exhibition areas where exhibits are presented.

[0129] In this example, the age distribution of the passenger flow represents the proportional distribution of the ages of the visitors viewing the exhibition areas where exhibits are presented.

[0130] In this example, the legal visit information represents the information presented by the user during the visit.

[0131] The working principle and beneficial effects of the above technical solution: In order to achieve precise recommendation, when a visitor enters the cloud exhibition hall, the appearance information of the visitor is collected to establish a unique virtual visit code for them, and then the legal visit information is established, so as to analyze the passenger flow information of each exhibition area where exhibits are presented, and recommend corresponding exhibits according to the gender and age of the passenger flow, thus achieving precise recommendation.

[0132] Embodiment 5

[0133] Based on Embodiment 4, the cloud exhibition hall personalized recommendation system based on big data analysis establishes a virtual visit code for each of the visitors, including:

[0134] Construct a virtual visit image corresponding to the visitor according to the appearance information corresponding to each visitor, perform facial analysis and height analysis on the virtual visit image, and obtain the facial features and height features corresponding to the visitor;

[0135] Assign corresponding gender labels to the corresponding visitors according to the facial features, and assign corresponding adult labels / minor labels to the corresponding visitors according to the height features;

[0136] Use AI to perform clothing analysis on the virtual visit image to obtain several clothing details of the corresponding visitor, and judge whether the labels assigned to the corresponding visitor are reasonable based on the clothing details;

[0137] When the assigned label is unreasonable, adjust the label of the corresponding visitor;

[0138] Obtain the label corresponding to each visitor and the order in which each visitor enters the cloud exhibition hall, and establish a virtual visit code for the corresponding visitor based on the label and the order.

[0139] The working principle and beneficial effects of the above technical solution: In order to distinguish different visitors, after a visitor enters the cloud exhibition hall, a virtual visit image is constructed according to their appearance information, and then the virtual visit image is analyzed and different distinguishing labels are added to it. In order to ensure the effectiveness of label classification, the labels are reviewed and reorganized according to the clothing details of the visitor, and finally a virtual visit label is established according to the marked labels of the visitor combined with their admission order.

[0140] Example 6

[0141] Based on Example 1, for the personalized recommendation system of the cloud exhibition hall based on big data analysis, the exhibit beautification module includes:

[0142] An information scheduling unit, which is used to, after the visitor selects an interesting exhibit, retrieve corresponding legal visit information according to the virtual visit code corresponding to the visitor, construct the visit path of the visitor in the cloud exhibition hall based on the legal visit information, and the residence duration of the visitor in each exhibit presentation area, and establish the time-domain browsing trajectory of the visitor;

[0143] A service analysis unit, which is used to mark the exhibit display schedule corresponding to each exhibit presentation area in the time-domain browsing trajectory, obtain the product information obtained by the visitor in each exhibit presentation area, and establish the visit information chain of the visitor;

[0144] A service execution unit, which is used to construct the browsing preference of the visitor in the cloud exhibition hall according to the visit information chain, and according to the exhibit detail level of the interesting exhibit, use the browsing preference to iteratively beautify each exhibit detail level respectively, and obtain the beautified exhibit detail level corresponding to each exhibit detail level after each beautification, and construct several beautification results;

[0145] A beautification analysis unit, which is used to perform similarity analysis on the beautification results, obtain the similarity degree between different beautification results, input several similarity degrees into a Gaussian model for probability analysis to obtain a probability distribution table, and extract the target beautification result corresponding to the highest similarity degree in the probability distribution table;

[0146] A beautification execution unit, which is used to deeply beautify each exhibit detail level of the interesting exhibit by using the target beautification result.

[0147] In this example, the time-domain browsing trajectory represents the result of representing the browsing trajectory of the visitor in chronological order;

[0148] In this example, the visit information chain represents the information learned by the visitor during the visit;

[0149] In this example, iterative beautification represents the process of beautifying multiple times;

[0150] In this example, the probability distribution table contains all similarity degrees.

[0151] Working principle and beneficial effects of the above technical solution: In order to further improve the external performance of the exhibits and deepen the impression of visitors, when a visitor selects an interesting exhibit, the legal visit information of the visitor is retrieved, the visit path of the visitor in the cloud exhibition hall is further constructed, the corresponding stay duration of the visitor in each exhibit presentation area is determined, so as to establish his time-domain browsing trajectory, and then combined with the exhibit display schedule to analyze the exhibit information learned by the visitor during the browsing process, thus constructing a visit information chain, then constructing the browsing preferences of the visitor in the cloud exhibition hall, using the browsing preferences to beautify the exhibit detail level of the interesting exhibit, and finally selecting a target beautification result through the Gaussian model, so as to deeply beautify the exhibit with the selected target beautification result. Through the above technical means, not only can different characteristics of the exhibits be presented to different visitors, but also a balance can be sought during the beautification process to avoid over-beautification.

[0152] Embodiment 7

[0153] Based on the cloud exhibition hall personalized recommendation system of big data analysis in Embodiment 1, the exhibit explanation module includes:

[0154] The method matching unit is used to determine the preferred tone color and preferred intonation of the visitor according to the visitor's current browsing preferences, and retrieve the corresponding explanation method according to the preferred tone color and preferred intonation;

[0155] The explanation preparation unit is used to obtain the exhibit data of the interesting exhibit, construct several semantic descriptions of the interesting exhibit according to the exhibit data, screen and reorganize the semantic descriptions according to the explanation method to obtain an explanation manuscript;

[0156] The explanation execution unit is used to explain the interesting exhibit by using the explanation method and the explanation manuscript.

[0157] Working principle and beneficial effects of the above technical solution: By analyzing the preferences of visitors to match corresponding explanation methods, different explanations are given to different visitors, and the focus of the explanation is adjusted, thereby improving the interest of visitors.

[0158] Embodiment 8

[0159] This embodiment provides a cloud exhibition hall personalized recommendation method based on big data analysis, as Figure 2 shown, including:

[0160] Step 1: Obtain the user browsing data in the big data, and construct the browsing preference information of the user according to the user browsing data;

[0161] Step 2: Perform personalized rendering on each exhibit presentation area of the cloud exhibition hall according to the exhibition hall functions of the cloud exhibition hall in combination with the browsing preference information;

[0162] Step 3: Analyze the browsing information corresponding to each visitor in different exhibition area of the exhibits respectively, and recommend corresponding exhibits for the corresponding visitors according to the browsing information;

[0163] Step 4: After the visitor selects the interested exhibits, construct the visitor's current browsing preferences according to the browsing information of the visitor in the cloud exhibition hall, and deeply beautify the interested exhibits based on the current browsing preferences;

[0164] Step 5: Retrieve the corresponding explanation method according to the current browsing preferences, and use the explanation method to explain the interested exhibits.

[0165] In this example, the user browsing data represents the data generated by the user's interaction on the Internet;

[0166] In this example, the browsing preference information represents the information of the things that the user likes during the browsing process;

[0167] In this example, the personalized rendering represents the process of beautifying the exhibition area of the exhibits;

[0168] In this example, the interested exhibits represent the exhibits that the visitors are interested in.

[0169] The working principle and beneficial effects of the above technical solutions: In order to enhance the interest of the visitors, analyze the user browsing data in the big data, so as to determine the browsing preference information of the current user, and then use the browsing preference information to perform corresponding rendering on the cloud exhibition hall, thereby improving the interest of the visitors. In order to further enhance the interest of the visitors, during the browsing process of the visitors, deepen and adjust the interested exhibits selected by the visitors according to the browsing interest of the visitors, and explain the interested exhibits in a corresponding explanation method. In this way, not only can the cloud exhibition hall keep up with the times and incorporate novel elements in the Internet into it, but also the exhibition method of the interested exhibits can be adjusted according to the interests of the visitors, thereby improving the interest of the visitors and enabling the visitors to understand the exhibits more deeply.

[0170] Embodiment 9

[0171] Based on Embodiment 8, for the cloud exhibition hall personalized recommendation method based on big data analysis, Step 3 includes:

[0172] Step 31: After each visitor enters the cloud exhibition hall, collect the appearance information corresponding to each visitor, and establish a virtual visit code for each visitor respectively, and collect the visit path corresponding to each visitor and establish legal visit information in combination with the corresponding virtual visit code;

[0173] Step 32: Determine the passenger flow information corresponding to each exhibit presentation area based on the legal visit information, and respectively establish the passenger flow gender distribution and passenger flow age distribution corresponding to each exhibit presentation area within different browsing time periods;

[0174] Step 33: Adjust the recommended order of each exhibit presentation area according to the passenger flow gender distribution and passenger flow age distribution corresponding to each exhibit presentation area, obtain the current appearance information of the current visitor corresponding to the exhibit presentation area to determine the gender and age of the current visitor, and find the corresponding recommended exhibits in the recommended order and recommend them to the corresponding current visitors in sequence.

[0175] In this example, the appearance information represents the appearance of the visitor, that is, the visitor takes a photo after entering the cloud exhibition hall, and the visitor knows that they are being photographed currently;

[0176] In this example, the virtual visit code corresponds to each visitor one by one;

[0177] In this example, the passenger flow gender distribution represents the gender distribution ratio of the visitors browsing the exhibit presentation area;

[0178] In this example, the passenger flow age distribution represents the age distribution ratio of the visitors browsing the exhibit presentation area;

[0179] In this example, the legal visit information represents the information presented by the user during the visit.

[0180] The working principle and beneficial effects of the above technical solution: In order to achieve accurate recommendation, when the visitor enters the cloud exhibition hall, the appearance information of the visitor is collected to establish a unique virtual visit code for them, and then the legal visit information is established, so as to analyze the passenger flow information of each exhibit presentation area, and recommend the corresponding exhibits according to the passenger flow gender and age, realizing accurate recommendation.

[0181] Embodiment 10

[0182] Based on Embodiment 9, the personalized recommendation method for the cloud exhibition hall based on big data analysis, which respectively establishes a virtual visit code for each visitor, includes:

[0183] Construct a virtual visit image corresponding to the visitor according to the appearance information corresponding to each visitor, perform facial analysis and height analysis on the virtual visit image, and obtain the facial features and height features corresponding to the visitor;

[0184] Assign the corresponding gender label to the corresponding visitor according to the facial features, and assign the corresponding adult label / minor label to the corresponding visitor according to the height features;

[0185] Use AI to analyze the clothing of the virtual visit image, obtain several clothing details of the corresponding visitor, and judge whether the label assigned to the corresponding visitor is reasonable based on the clothing details;

[0186] When the assigned label is unreasonable, adjust the label of the corresponding visitor;

[0187] Obtain the label corresponding to each visitor and the order in which each visitor enters the cloud exhibition hall, and establish a virtual visit code for the corresponding visitor based on the label and the order.

[0188] The working principle and beneficial effects of the above technical solution: In order to distinguish different visitors, after the visitors enter the cloud exhibition hall, a virtual visit image is constructed according to their appearance information, and then the virtual visit image is analyzed and different distinguishing labels are added to it. In order to ensure the effectiveness of label classification, the labels are reviewed and reorganized according to the clothing details of the visitors. Finally, a virtual visit label is established based on the labels marked by the visitors and their admission order.

[0189] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. The cloud exhibition hall personalized recommendation system based on big data analysis is characterized by: include: A personal collection module is used to obtain user browsing data from big data and construct user browsing preference information based on the user browsing data; An exhibition hall rendering module, used to perform personalized rendering of each exhibit presentation area of ​​the cloud exhibition hall according to the exhibition hall function of the cloud exhibition hall and the browsing preference information; An exhibit recommendation module is used to analyze the browsing information corresponding to each visitor in different exhibit presentation areas, and recommend corresponding exhibits to the corresponding visitors according to the browsing information; An exhibit beautification module is used to construct the visitor's browsing preferences according to the visitor's browsing information in the cloud exhibition hall after the visitor selects an exhibit of interest, and to beautify the exhibit of interest based on the browsing preferences; An exhibit explanation module is used to call a corresponding explanation method according to the current browsing preference, and explain the exhibit of interest using the explanation method; The exhibit recommendation module includes: A browsing collection unit is used to collect appearance information corresponding to each visitor after each visitor enters the cloud exhibition hall, and respectively establish a virtual visit code for each visitor, collect the visit path corresponding to each visitor and establish legal visit information in combination with the corresponding virtual visit code; A regional analysis unit, used to determine the passenger flow information corresponding to each exhibit presentation area based on the legal visit information, and respectively establish the passenger flow gender distribution and passenger flow age group distribution corresponding to each exhibit presentation area in different browsing time periods; The visitor distribution unit is used to adjust the recommendation order of each of the exhibit presentation areas according to the gender distribution of the passenger flow and the age group distribution of the passenger flow corresponding to each of the exhibit presentation areas, obtain the current appearance information corresponding to the current visitor of the exhibit presentation area to determine the gender and age group corresponding to the current visitor, search for the corresponding recommended exhibits in the recommendation order and recommend them to the corresponding current visitor in turn.

2. The cloud exhibition hall personalized recommendation system based on big data analysis as claimed in claim 1, characterized in that: The personality collection module includes: A data collection unit is used to collect real-time data from the Internet, construct a number of screening matching words according to the current exhibit information of the cloud exhibition hall and the basic information of the exhibition hall, and respectively construct a data form set corresponding to each of the screening matching words; A data screening unit, used to establish screening rules corresponding to each screening matching word according to each data form set, and use each screening rule to screen the real-time data to obtain a plurality of available data; A data classification unit, used to respectively determine the screening matching words corresponding to each of the available data, classify the available data according to the meanings of the screening matching words, and construct user browsing data according to each data class; The preference analysis unit is used to construct the user's public browsing information on the Internet based on the user's browsing data, and to construct the user's browsing preference information based on similar information between different public browsing information.

3. The cloud exhibition hall personalized recommendation system based on big data analysis as claimed in claim 1, characterized in that: The exhibition hall rendering module includes: A function analysis unit is used to obtain the current exhibit information and basic exhibition hall information of the cloud exhibition hall, construct the initial function of the cloud exhibition hall using the basic exhibition hall information, establish the display characteristics of the corresponding exhibits according to each of the current exhibit information, divide the basic exhibition hall information into regions using the display characteristics, and obtain the initial region function corresponding to each exhibit region in combination with the initial function; A color rendering unit is used to determine the user's favorite color list according to the browsing preference information, determine the exhibit color distribution corresponding to each of the exhibits according to the current exhibit information, perform color fusion on the favorite color list and the exhibit color distribution to obtain a plurality of color fusion schemes, and respectively obtain the color contrast corresponding to each of the color fusion schemes, determine the user's preference for each of the color contrasts according to the browsing preference, and perform color rendering on the corresponding exhibit area using the preferred color fusion scheme with the highest preference; A personalized rendering unit is used to determine the user's favorite elements according to the browsing preference information, and to integrate and score each of the favorite elements with each of the exhibits according to the degree of association between each of the favorite elements and each of the exhibits to construct a scoring matrix corresponding to each of the favorite elements, and to transmit the scoring matrix to the Internet to perform gradient analysis on each of the favorite elements to obtain the user's integrated preference features between different favorite elements and the exhibits, and to perform element rendering for each of the exhibit areas based on the integrated preference features.

4. The cloud exhibition hall personalized recommendation system based on big data analysis as claimed in claim 1, characterized in that: A virtual visit code is established for each visitor, including: Constructing a virtual visitor image of the corresponding visitor according to the appearance information corresponding to each of the visitors, performing facial analysis and height analysis on the virtual visitor image to obtain facial features and height features corresponding to the visitor; Assigning a corresponding gender label to the corresponding visitor according to the facial features, and assigning a corresponding adult label / minor label to the corresponding visitor according to the height features; Using AI to analyze the clothing of the virtual visitor image, obtain a number of clothing details of the corresponding visitor, and determine whether the label assigned to the corresponding visitor is reasonable based on the clothing details; When the labels assigned are unreasonable, adjust the labels of the corresponding visitors; The tag corresponding to each of the visitors and the order in which each of the visitors enters the cloud exhibition hall are obtained, and a virtual visit code is established for the corresponding visitor based on the tag and the order.

5. The cloud exhibition hall personalized recommendation system based on big data analysis as claimed in claim 1, characterized in that: The exhibit beautification module includes: An information scheduling unit is used to retrieve corresponding legal visit information according to the virtual visit code corresponding to the visitor after the visitor selects an exhibit of interest, and to construct a visit path of the visitor in the cloud exhibition hall and the corresponding stay time of the visitor in each exhibit presentation area according to the legal visit information, so as to establish a time-domain browsing trajectory of the visitor; A service analysis unit, used to mark the exhibit display timetable corresponding to each of the exhibit presentation areas in the time domain browsing trajectory, obtain the product information obtained by the visitor in each of the exhibit presentation areas, and establish a visit information chain of the visitor; A service execution unit, configured to construct the visitor's browsing preferences in the cloud exhibition hall according to the visit information chain, and iteratively beautify each of the exhibit detail levels according to the exhibit detail levels of interest using the browsing preferences, and obtain a beautified exhibit detail level corresponding to each of the exhibit detail levels after each beautification, and construct a plurality of beautification results; A beautification analysis unit is used to perform similarity analysis on the beautification results to obtain similarity between different beautification results, input a plurality of similarity levels into a Gaussian model for probability analysis to obtain a probability distribution table, and extract a target beautification result corresponding to the highest similarity level from the probability distribution table; The beautification execution unit is used to perform in-depth beautification on each exhibit detail level of the interest exhibit using the target beautification result.

6. The cloud exhibition hall personalized recommendation system based on big data analysis as claimed in claim 1, characterized in that: The exhibit explanation module includes: A method matching unit, used to determine the visitor's preferred timbre and preferred intonation according to the visitor's current browsing preferences, and to select a corresponding explanation method according to the preferred timbre and preferred intonation; An explanation preparation unit, used for acquiring the exhibit data of the exhibit of interest, constructing several semantic descriptions of the exhibit of interest according to the exhibit data, screening and reorganizing the semantic descriptions according to the explanation method, and obtaining an explanation manuscript; The explanation execution unit is used to explain the exhibits of interest using the explanation method and the explanation manuscript.

7. A personalized recommendation method for cloud exhibition halls based on big data analysis, characterized in that: include: Step 1: Obtain user browsing data from big data, and construct user browsing preference information based on the user browsing data; Step 2: performing personalized rendering on each exhibit presentation area of ​​the cloud exhibition hall according to the exhibition hall function of the cloud exhibition hall and the browsing preference information; Step 3: Analyze the browsing information of each visitor in different exhibit presentation areas respectively, and recommend corresponding exhibits to the corresponding visitors according to the browsing information; Step 4: After the visitor selects an exhibit of interest, the visitor's current browsing preferences are constructed according to the visitor's browsing information in the cloud exhibition hall, and the exhibit of interest is further beautified based on the current browsing preferences; Step 5: Retrieve a corresponding explanation method according to the current browsing preference, and explain the exhibits of interest using the explanation method; The step 3 comprises: Step 31: When each visitor enters the cloud exhibition hall, the appearance information corresponding to each visitor is collected, and a virtual visit code is established for each visitor respectively. The visit path corresponding to each visitor is collected and combined with the corresponding virtual visit code to establish legal visit information; Step 32: Determine the passenger flow information corresponding to each exhibit presentation area based on the legal visit information, and respectively establish the passenger flow gender distribution and passenger flow age group distribution corresponding to each exhibit presentation area in different browsing time periods; Step 33: Adjust the recommendation order of each of the exhibit presentation areas according to the gender distribution and age distribution of the visitors corresponding to each of the exhibit presentation areas, obtain the current appearance information corresponding to the current visitor of the exhibit presentation area to determine the gender and age group corresponding to the current visitor, search for the corresponding recommended exhibits in the recommendation order and recommend them to the corresponding current visitor in turn.

8. The cloud exhibition hall personalized recommendation method based on big data analysis according to claim 7, characterized in that: A virtual visit code is established for each visitor, including: Constructing a virtual visitor image of the corresponding visitor according to the appearance information corresponding to each of the visitors, performing facial analysis and height analysis on the virtual visitor image to obtain facial features and height features corresponding to the visitor; Assigning a corresponding gender label to the corresponding visitor according to the facial features, and assigning a corresponding adult label / minor label to the corresponding visitor according to the height features; Using AI to analyze the clothing of the virtual visitor image, obtain a number of clothing details of the corresponding visitor, and determine whether the label assigned to the corresponding visitor is reasonable based on the clothing details; When the labels assigned are unreasonable, adjust the labels of the corresponding visitors; The tag corresponding to each of the visitors and the order in which each of the visitors enters the cloud exhibition hall are obtained, and a virtual visit code is established for the corresponding visitor based on the tag and the order.

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