A method and system for data interaction display in a digital exhibition hall

Abstract

The present invention provides a data interaction display method and system for a digital exhibition hall, which relates to the technical field of display content recommendation. The method includes: collecting historical display interaction behavior data of multiple display devices in the exhibition hall, extracting the browsing behavior preference characteristics of users to determine multiple browsing behavior preference patterns; extracting the browsing time period preference characteristics and determining the time period distribution pattern to generate a first interaction display strategy; collecting historical display environment data of multiple display devices and performing content-environment correlation analysis, constructing a content-environment analysis model and generating a second interaction display strategy; fusing to generate a target interaction display strategy and performing interaction display optimization; obtaining the real-time display interaction behavior data of the display devices and performing user behavior collaborative influence analysis, generating multiple interaction collaborative optimization strategies to perform interaction display collaborative optimization. The present invention realizes dynamic and personalized display content optimization.

Description

Technical Field

[0001] The present invention relates to the technical field of display content recommendation, and particularly to a data interaction display method and system for a digital exhibition hall. Background Art

[0002] With the continuous development of digital technology, the digital exhibition hall, as a new type of exhibition method, has gradually become an important carrier for various exhibitions, product displays, and brand promotions. By leveraging technologies such as computer graphics and virtual reality, the digital exhibition hall can not only provide a more rich and interactive display experience but also optimize the display content through real-time analysis of data to improve the display effect.

[0003] During the process of promoting exhibits through the digital exhibition hall system, different audience groups may correspond to different time periods during the exhibition, resulting in a phenomenon of time-varying demands and concerns. For example, in the morning period, there may be more enterprise users interested in certain high-tech products, while in the afternoon period, there may be more interactive experience demands for ordinary consumers. If the preferences of the audience at different time periods cannot be effectively identified and analyzed, it is easy to lead to low adaptability of the display content.

[0004] Moreover, during the process of display through the display interface and interaction mode in the digital exhibition hall, since the spatial layout of the display devices has been pre-set and it may be relatively troublesome to make changes, and the display environment has a certain impact on the display effect to a certain extent. For example, some exhibits may be more attractive in a stronger light environment, while other exhibits have better display effects in a darker environment. Therefore, during the process of product display through multiple display devices, it is necessary to effectively combine different environmental factors to improve the display effect of different exhibits.

[0005] There are also behavioral influences among different users in the actual display scenario, that is, the browsing behavior of the previous user may cause changes in the tendency of the observing user during the actual browsing of the exhibits. Mining the collaborative behavior among different users in a local scenario can further enhance the browsing interaction experience of users. Summary of the Invention

[0006] To solve the above technical problems, the present invention proposes a data interaction display method and system for a digital exhibition hall. By collecting and deeply analyzing the historical display data of the digital exhibition hall, data analysis is carried out from multiple dimensions such as the user's behavior pattern, display time period, and display environment, fully considering the impacts of multiple factors such as time period changes, user behavior patterns, environmental characteristics, and behavioral synergy on the display effect, so as to achieve dynamic and personalized optimization of the display content and improve the display effect and user experience of the digital exhibition hall.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] In a first aspect, the present invention provides a method for data interaction display in a digital exhibition hall, including:

[0009] Collecting historical display interaction behavior data of multiple display devices in the exhibition hall, extracting browsing behavior preference features corresponding to multiple users from the historical display interaction behavior data, analyzing the browsing behavior preferences of multiple users according to the browsing behavior preference features, and determining multiple browsing behavior preference patterns;

[0010] Determining browsing behavior labels corresponding to each user according to the browsing behavior preference patterns to which the users belong, extracting browsing time period preference features regarding multiple browsing behavior preference patterns according to the browsing behavior labels, analyzing the browsing time period distribution of multiple browsing behavior preference patterns according to the browsing time period preference features, and determining a time period distribution pattern regarding multiple browsing behavior preference patterns;

[0011] Performing time period behavior correlation analysis on the historical display interaction behavior data according to the time period distribution pattern corresponding to multiple browsing behavior preference patterns, extracting preference interaction operation features under each browsing behavior preference pattern, and determining a first interaction display strategy regarding multiple display devices in the exhibition hall according to the time period distribution pattern corresponding to multiple browsing behavior preference patterns;

[0012] Collecting historical display environment data of multiple display devices, performing content environment correlation analysis on the historical display interaction behavior data based on the historical display environment data, extracting content environment correlation features and constructing a content environment analysis model, and generating a second interaction display strategy regarding multiple display devices in the exhibition hall through the content environment analysis model;

[0013] Fusing the first interaction display strategy and the second interaction display strategy to generate a target interaction display strategy, and optimizing the interaction display of multiple display devices in the exhibition hall based on the target interaction display strategy;

[0014] Obtaining the real-time display interaction behavior data of each display device, performing user behavior collaborative influence analysis on the real-time display interaction behavior data, extracting the behavior collaborative influence features of each browsing behavior preference pattern, generating an interaction collaborative optimization strategy for each browsing behavior preference pattern, and performing interaction display collaborative optimization on multiple display devices based on the interaction collaborative optimization strategy.

[0015] Preferably, for determining the time period distribution pattern regarding multiple browsing behavior preference patterns, it further includes:

[0016] The browsing behavior preference features include multiple browsing behavior feature parameters. After constructing a browsing behavior preference feature vector for each user through the multiple browsing behavior feature parameters, clustering the multiple users according to the browsing behavior preference feature vector to determine multiple browsing behavior preference patterns;

[0017] Divide the global display period of the exhibition hall into time periods to determine multiple local display periods, extract multiple groups of user distribution data for each local display period regarding multiple browsing behavior preference patterns. The user distribution data includes the user quantity characteristics of each browsing behavior preference pattern within the local display period, and obtain the browsing time period preference characteristics regarding multiple browsing behavior preference patterns;

[0018] Analyze the multiple groups of user distribution data within each local display period, determine the pedestrian flow characteristic parameters of each group of user distribution data within the local display period, and fuse the multiple user quantity characteristics of each browsing behavior preference pattern within the local display period according to the pedestrian flow characteristic parameters to obtain the behavior preference distribution data of each local display period. Generate the time period distribution pattern regarding multiple browsing behavior preference patterns based on the behavior preference distribution data of multiple local display periods.

[0019] Preferably, extract the preference interaction operation characteristics under each browsing behavior preference pattern, and determine the first interaction display strategy regarding multiple display devices in the exhibition hall according to the time period distribution pattern corresponding to multiple browsing behavior preference patterns, including:

[0020] Extract the browsing interaction data of multiple users under each browsing behavior preference pattern from the historical display interaction behavior data, determine the interaction operation preference data of the users according to the browsing interaction data of the users, and generate the preference interaction operation characteristics under the browsing behavior preference pattern according to the interaction operation preference data of multiple users under the browsing behavior preference pattern, including the group preference list regarding multiple display interaction operations;

[0021] Among them, the browsing interaction data includes the individual preference scores corresponding to multiple display interaction operations. Generate the group preference score of each display interaction operation by fusing the interaction operation preference data of multiple users, and sort the multiple display interaction operations according to the group preference score to obtain the group preference list;

[0022] Analyze the preference interaction operation characteristics under each browsing behavior preference pattern according to the time period distribution pattern regarding multiple browsing behavior preference patterns, determine the allocation parameters of each browsing behavior preference pattern within the local display period according to the behavior preference distribution data of the local display period, determine the allocation strategy regarding the preset number of interaction operations according to the allocation parameters, including the number of interaction operations allocated for each browsing behavior preference pattern. Determine the local display interaction operation recommendation plan regarding multiple browsing behavior preference patterns under the local display period according to the preference interaction operation characteristics and the number of allocated interaction operations under the browsing behavior preference pattern. Generate the first interaction display strategy under the global display period according to the local display interaction operation recommendation plans under multiple local display periods.

[0023] Preferably, content environment association features are extracted and a content environment analysis model is constructed. A second interactive display strategy for multiple display devices in the exhibition hall is generated through the content environment analysis model, including:

[0024] Extract multiple groups of display environment features of each display device from the historical display environment data. According to the time information of the display environment features, extract the exhibit type popularity data of each group of display environment features from the historical display interaction behavior data. Associate each group of display environment features with the corresponding exhibit type popularity data to construct a sample data set. Use the multiple groups of display environment features in the sample data set as the input of the content environment analysis model, and use the exhibit type popularity data corresponding to each group of display environment features in the sample data set as the training target of the content environment analysis model. Train the content environment analysis model through the sample data set;

[0025] After collecting the target display environment data for multiple display devices, input the target display environment data into the trained content environment analysis model to generate the exhibit type popularity distribution data for multiple display devices, and generate a second interactive display strategy for multiple display devices according to the target popularity distribution data.

[0026] Preferably, the first interactive display strategy and the second interactive display strategy are fused to generate a target interactive display strategy, and the interactive display of multiple display devices in the exhibition hall is optimized based on the target interactive display strategy, including:

[0027] Set multiple recommended exhibit types for each display device according to the exhibit type popularity distribution data of multiple display devices. For the target display device, after determining the target exhibit currently browsed by the user, determine the local display interaction operation recommendation scheme of the target display device according to the local display period to which the current moment belongs, and set multiple recommended display interaction operations for the target display device according to the local display interaction operation recommendation scheme.

[0028] Preferably, the behavior collaborative influence features of each browsing behavior preference pattern are extracted, and an interactive collaboration optimization strategy for each browsing behavior preference pattern is generated, including:

[0029] Segment multiple groups of candidate collaborative interaction data from the real-time display interaction behavior data, perform temporal correlation screening on the multiple groups of candidate collaborative interaction data to determine multiple groups of target collaborative interaction data, extract the interaction operation evolution path of each user in each group of target collaborative interaction data, perform collaborative influence analysis on the multiple interaction operation evolution paths of each group of target collaborative interaction data, and calculate the behavior collaborative influence parameters corresponding to multiple display interaction operations under each group of target collaborative interaction data;

[0030] Perform browsing behavior preference pattern matching for each evolution path of interaction operations, generate pattern matching results, and construct an operation collaborative influence set under each browsing behavior preference pattern, including multiple behavior collaborative influence parameters corresponding to each display interaction operation. Determine multiple interaction collaborative influence operations for the browsing behavior preference pattern based on the operation collaborative influence set, and generate an interactive collaborative optimization strategy for the browsing behavior preference pattern according to the multiple interaction collaborative influence operations.

[0031] In a second aspect, the present invention provides a data interaction display system for a digital exhibition hall. The system is used to implement the data interaction display method for a digital exhibition hall described above, and includes:

[0032] A browsing behavior preference analysis module, configured to collect historical display interaction behavior data of multiple display devices in the exhibition hall, extract browsing behavior preference characteristics corresponding to multiple users from the historical display interaction behavior data, perform browsing behavior preference analysis on the multiple users according to the browsing behavior preference characteristics, and determine multiple browsing behavior preference patterns;

[0033] A time period distribution analysis module, configured to determine browsing behavior labels corresponding to each user according to the browsing behavior preference pattern to which the user belongs, extract browsing time period preference characteristics regarding multiple browsing behavior preference patterns according to the browsing behavior labels, perform browsing time period distribution analysis on the multiple browsing behavior preference patterns according to the browsing time period preference characteristics, and determine a time period distribution pattern regarding the multiple browsing behavior preference patterns;

[0034] A first interactive display optimization module, configured to perform time period behavior correlation analysis on the historical display interaction behavior data according to the time period distribution pattern corresponding to the multiple browsing behavior preference patterns, extract preference interactive operation characteristics under each browsing behavior preference pattern, and determine a first interactive display strategy for multiple display devices in the exhibition hall according to the time period distribution pattern corresponding to the multiple browsing behavior preference patterns;

[0035] A second interactive display optimization module, configured to collect historical display environment data of multiple display devices, perform content environment correlation analysis on the historical display interaction behavior data based on the historical display environment data, extract content environment correlation characteristics and construct a content environment analysis model, and generate a second interactive display strategy for multiple display devices in the exhibition hall through the content environment analysis model;

[0036] An interactive display management module, configured to fuse the first interactive display strategy and the second interactive display strategy to generate a target interactive display strategy, and perform interactive display optimization on multiple display devices in the exhibition hall based on the target interactive display strategy;

[0037] An interactive collaboration optimization module is used to obtain the real-time display interaction behavior data of each display device, conduct user behavior collaborative impact analysis on the real-time display interaction behavior data, extract the behavior collaborative impact features of each browsing behavior preference pattern, generate the interactive collaboration optimization strategy for each browsing behavior preference pattern, and perform interactive display collaboration optimization on multiple display devices based on the interactive collaboration optimization strategy.

[0038] Preferably, the time period distribution analysis module includes:

[0039] A browsing time period preference analysis unit is used to determine the browsing behavior labels corresponding to each user according to the browsing behavior preference pattern to which the user belongs, and extract the browsing time period preference features regarding multiple browsing behavior preference patterns according to the browsing behavior labels;

[0040] A browsing time period distribution analysis unit is used to conduct browsing time period distribution analysis on multiple browsing behavior preference patterns according to the browsing time period preference features, and determine the time period distribution pattern regarding multiple browsing behavior preference patterns.

[0041] The present invention has the following beneficial effects:

[0042] By analyzing the historical display interaction behavior data of the exhibition hall, the present invention determines the browsing behavior preference information of different users during the process of browsing exhibits to determine multiple browsing behavior preference patterns, conducts time period feature analysis based on browsing time period division on multiple browsing behavior preference patterns to obtain the time period distribution pattern regarding multiple browsing behavior preference patterns, generates the first interactive display strategy by integrating user behavior pattern features and time period change features, further considers the influence of environmental features on the display effect, models the association between the display environment and the popularity of exhibit types, and generates the second interactive display strategy according to the actual display environment data of the exhibition hall. Finally, the target interactive display strategy is fused to achieve the interactive display optimization of the exhibition hall, and according to the real-time display record data, the collaborative impact characteristics of browsing behaviors among different users in local scenarios are analyzed, and short-term interactive collaboration optimization strategies are introduced in different display scenarios to enhance the short-term diversified experience of users. By generating targeted interactive operation recommendations, it is ensured that each display device can provide a better display effect at different time periods, while maximizing the improvement of user experience and realizing dynamic and personalized display content optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a schematic flowchart of a data interactive display method for a digital exhibition hall provided for one implementation process of the present invention.

[0044] Figure 2 It is a schematic structural diagram of a data interactive display system for a digital exhibition hall provided for one implementation process of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0045] To enable those skilled in the art to better understand the technical solutions in the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0046] Figure 1 The flowchart of a data interaction display method for a digital exhibition hall provided in one implementation process of the present invention is shown. Specifically, a data interaction display method for a digital exhibition hall provided in an embodiment of the present invention includes the following steps:

[0047] Step S1: Collect the historical display interaction behavior data of multiple display devices in the exhibition hall, extract the browsing behavior preference characteristics corresponding to multiple users from the historical display interaction behavior data, perform browsing behavior preference analysis on the multiple users according to the browsing behavior preference characteristics, and determine multiple browsing behavior preference patterns.

[0048] Specifically, for the historical display interaction behavior data of multiple display devices, it specifically includes the interaction operation data of different users on different display devices, such as residence time, click times, interaction methods, etc., which reflects the browsing behavior of users on various exhibits provided by the display devices. By analyzing the historical display interaction behavior data, the browsing behavior preference characteristics corresponding to multiple users can be extracted, which includes multiple browsing behavior characteristic parameters during a certain browsing process of the user, such as browsing speed, interaction frequency, interaction duration, etc. For example, some users may prefer to browse quickly and skip the exhibits quickly; while some users may conduct in-depth browsing and view the content of each exhibit in detail.

[0049] Based on the extracted browsing behavior preference characteristics of each user, further perform browsing behavior preference analysis on multiple users, and identify different browsing behavior preference patterns, which is convenient to understand the behavior trends and habits of different types of users during the exhibition in the exhibition hall. In this process, through the multiple browsing behavior characteristic parameters included in the browsing behavior preference characteristics of the user, the browsing behavior preference feature vector of the user can be constructed to fully reflect the interaction mode and behavior preference of the user in the exhibition hall, etc. And a clustering algorithm, such as a distance-based or density-based clustering scheme, is adopted. In this embodiment, the DBSCAN clustering algorithm is taken as an example. The DBSCAN clustering algorithm is used to analyze and process the browsing behavior preference feature vectors of different users, and cluster multiple users according to the browsing behavior preference feature vectors, so as to classify users with similar browsing behaviors into one category, thereby forming multiple browsing behavior preference patterns, which are used to reflect the common characteristics or behavior patterns shown by different users during browsing.

[0050] Step S2: Determine the browsing behavior tags corresponding to each user according to the browsing behavior preference patterns to which the users belong. Extract the browsing time period preference features regarding multiple browsing behavior preference patterns based on the browsing behavior tags. Conduct a browsing time period distribution analysis on the multiple browsing behavior preference patterns, and determine the time period distribution pattern regarding the multiple browsing behavior preference patterns.

[0051] Specifically, after determining the different browsing behavior preference patterns to which multiple users belong, further analyze the browsing behavior characteristics of users at different time periods. In this process, first determine the browsing behavior tags of users according to the browsing behavior preference patterns to which the users belong. These tags can be, for example, quick browsing type, in-depth browsing type, interest bias type, etc., in order to distinguish the behavior preferences corresponding to different user groups. Then, divide the historical display interaction behavior data by time period, and analyze the activity of different users at different time periods. For example, some browsing behavior patterns are more frequent in the morning, while some patterns are more common in the afternoon or evening. By analyzing the personnel distribution regarding the browsing behavior preference patterns at different time periods, extract the browsing time period preference features regarding multiple browsing behavior preference patterns, which are used to characterize the time period distribution of users in the actual process of browsing exhibits under each browsing behavior preference pattern. Finally, analyze the time period distribution pattern regarding the multiple browsing behavior preference patterns to identify and quantify the time period distribution characteristics of each browsing behavior preference pattern, clarify the concentration degree of a certain behavior pattern at different time periods, and provide a basis for subsequent adjustment of display content.

[0052] Step S3: Conduct a time period behavior correlation analysis on the historical display interaction behavior data according to the time period distribution pattern corresponding to multiple browsing behavior preference patterns, extract the preference interaction operation features under each browsing behavior preference pattern, and determine the first interactive display strategy regarding multiple display devices in the exhibition hall according to the time period distribution pattern corresponding to the multiple browsing behavior preference patterns.

[0053] Specifically, the purpose of performing time period behavior association analysis on historical display interaction behavior data is to analyze the interactive operation habits preferred by users under different browsing behavior preference modes. For example, when a user prefers to browse quickly, he may simply browse the brief information of the exhibits, and quickly browse the structural information of the exhibits through some simple interactive operations such as zooming and flipping. When a user prefers to browse in depth, he may view the detailed introduction information of the exhibits, and view the structural design of some details of the exhibits through more complex interactive operations. By analyzing the interactive data of different browsing behavior modes in different time periods, the preferred interactive operation features in each browsing behavior preference mode can be extracted. These features include the interactive operations that users prefer in the specific browsing process, and further according to the browsing behavior preference modes to which the user groups belong in different time periods, it is possible to provide users with simple and convenient interactive interfaces in different time periods in a targeted manner, so as to avoid affecting the actual experience of users due to the numerous interactive operations. In this way, suitable interactive strategies can be designed for different display time periods, thereby determining the first interactive display strategy for multiple display devices in the exhibition hall.

[0054] Step S4: collect historical display environment data of multiple display devices, perform content environment association analysis on historical display interaction behavior data based on the historical display environment data, extract content environment association features and construct a content environment analysis model, and generate a second interactive display strategy for multiple display devices in the exhibition hall through the content environment analysis model.

[0055] Specifically, considering that the display environment data will affect the display effect of some exhibits to a certain extent, the historical display environment data of multiple display devices in the exhibition hall are further collected, and the content environment association analysis is performed on the historical display interaction behavior data. Among them, the historical display environment data can be data such as the light intensity and noise level of the area where the display device is located, so as to determine the light conditions, noise conditions, etc. of the area where the display device is located. For example, some exhibits may require sound or voice interaction, and the area near the wall or corner may be less affected by noise than the central area. Through the analysis of these environmental data, the correlation between the heat of different types of exhibits and the display environment can be extracted, so as to extract the corresponding content environment association features, and establish a content environment analysis model based on these features. The content environment analysis model learns the heat change rules of different types of exhibits under different display environments to achieve the inference of the display effects of different exhibits in the new environment, so that the display environment data can be analyzed based on the model to generate a second interactive display strategy that can improve the display effect, which is used to indicate how to adjust the display mode of multiple types of exhibits according to the environmental changes of the display equipment.

[0056] Step S5: Integrate the first interactive display strategy and the second interactive display strategy to generate a target interactive display strategy, and optimize the interactive display of multiple display devices in the exhibition hall based on the target interactive display strategy.

[0057] Specifically, the previously determined first interactive display strategy optimizes the display from the perspectives of time period and user behavior patterns. The second interactive display strategy further considers the changes in the display effect brought by the differences in the display environment and optimizes the display process. Finally, by integrating the two strategies, more accurate display content optimization can be achieved, that is, according to factors such as the lighting and noise in the environment where the display device is located, different types of exhibits suitable for display are determined, and further according to the display time period, the corresponding user group is determined, so as to provide a personalized interactive operation interface according to the preferences of the user group and intelligently adjust the display content of each display device in the exhibition hall. Exemplarily, in the morning period, a certain display area has strong lighting and since the current user group prefers to browse quickly, exhibits with strong visual impact and simplicity are preferentially recommended, and a relatively simple interactive operation interface is provided, such as providing some commonly used operation options for the current group to facilitate the user to quickly browse the exhibits; while in the evening period, a certain display area has a dim and quiet environment, and the user group prefers in-depth browsing, so exhibits suitable for this environment such as electronic audio equipment are preferentially recommended, and more operation options to understand the details and quality in detail are provided, such as the sound quality experience, etc., to actually display the playback effect. Based on the integrated target interactive display strategy, a more intelligent, flexible and accurate interactive display solution is provided for the display devices in the exhibition hall, improving the display effect and the audience experience.

[0058] Step S6: Obtain the real-time display interaction behavior data of each display device, conduct an analysis of the collaborative influence of user behavior on the real-time display interaction behavior data, extract the behavior collaborative influence characteristics of each browsing behavior preference pattern, generate the interactive collaborative optimization strategy for each browsing behavior preference pattern, and perform interactive display collaborative optimization on multiple display devices based on the interactive collaborative optimization strategy.

[0059] Specifically, the target interactive display strategy provides strategies for optimizing the interactive operation interface during the display process from the perspective of the preference habits of different user groups, and provides the exhibit content suitable for display in different areas according to the actual environmental changes in different exhibition areas, providing a personalized interactive display experience for different user groups. On this basis, further considering the behavioral impacts of users in the exhibition hall, for example, the interactive choices of user A for certain display interactions may attract user B to pay more attention, and there are certain differences in this attractiveness within different groups. For example, a certain research-oriented group is more interested in some technical points of the exhibits. When a user observes the browsing process of the previous user, they may be affected to some extent, so that they will prefer to choose some operation habits of the previous user during the process of browsing the exhibits. The above-mentioned target interactive display strategy more realizes some options with high popularity within the group from a global perspective, and pays little attention to some relatively unpopular interactive operations. The short-term interactive behavior impacts may cause significant local changes in the popularity of some display interactions with relatively low popularity. In this embodiment, the real-time display interaction behavior data of different display devices is further obtained, and the collaborative characteristics of some operation behaviors between two adjacent users browsing the exhibits are analyzed according to the alternation situation of the users, so as to further extract the behavior collaborative influence characteristics of each user group, that is, each browsing behavior preference pattern, and thus generate the corresponding interactive collaborative optimization strategy. For example, if it is found that a relatively unpopular display interaction operation shows a short-term popularity upsurge within a certain group in the short term, the interactive display of the display device is optimized collaboratively based on the real-time situation, and the recommendation strategy in the short term is dynamically adjusted to further improve the browsing and interaction experience of users.

[0060] Optionally, in step S2 of the above solution, for the process of determining the time period distribution pattern regarding multiple browsing behavior preference patterns, it includes the following contents:

[0061] The global display time period of the exhibition hall is divided into time periods to determine multiple local display time periods, and multiple groups of user distribution data regarding multiple browsing behavior preference patterns are extracted. The user distribution data includes the user quantity characteristics of each browsing behavior preference pattern within the local display time period, and the browsing time period preference characteristics regarding multiple browsing behavior preference patterns are obtained.

[0062] Specifically, in order to analyze the behavioral characteristics of users more precisely at different time periods, the global display time period of the exhibition hall, such as the time period from 8 am to 10 pm, is further divided into time periods to determine multiple local display time periods. For example, the display time period of a day is divided into multiple local display time periods at two-hour intervals. It should be noted that this is only an exemplary solution here, and those skilled in the art can also make reasonable divisions according to factors such as the opening hours of the exhibition hall and the fluctuation of passenger flow.

[0063] For each locally displayed period obtained by partitioning, multiple sets of user distribution data regarding multiple browsing behavior preference patterns are extracted. For example, within the past week, the proportion of the number of users under each browsing behavior preference pattern during a certain locally displayed period each day in the exhibition hall is used as the browsing period preference characteristics corresponding to the multiple browsing behavior preference patterns, indicating the overall distribution of the number of users in each pattern at different times.

[0064] Analyze the multiple sets of user distribution data within each locally displayed period, determine the pedestrian flow characteristic parameters of each set of user distribution data within the locally displayed period, and fuse the multiple user quantity characteristics of each browsing behavior preference pattern within the locally displayed period according to the pedestrian flow characteristic parameters to obtain the behavior preference distribution data for each locally displayed period. Generate a period distribution pattern regarding multiple browsing behavior preference patterns based on the behavior preference distribution data of multiple locally displayed periods.

[0065] Specifically, in order to more accurately analyze the user behavior at different times and further consider the influence of pedestrian flow on user behavior. The pedestrian flow characteristic parameters of different locally displayed periods can be determined according to the user distribution data of each locally displayed period. It can be understood as the parameter obtained by normalizing the total number of people in the exhibition hall at different times. The larger the pedestrian flow characteristic parameter of a certain set of user distribution data within the locally displayed period, the more users there are in the exhibition hall during that period. Thus, fuse the multiple sets of user distribution data under each locally displayed period according to the pedestrian flow characteristic parameters, making the reference significance of the user proportion data of different browsing behavior preference patterns greater in the scenario with a larger total number of people. After fusing the multiple sets of user distribution data under the locally displayed period, the behavior preference distribution data for each locally displayed period can be obtained, which is used to indicate the proportion of different types of users during that period.

[0066] Finally, based on the behavior preference distribution data of multiple locally displayed periods, a period distribution pattern regarding multiple browsing behavior preference patterns is constructed to reflect the specific user distribution of each browsing behavior preference pattern in the exhibition hall at different times. It can be used to provide data reference for adjusting the interactive content displayed by the display device at different times, so as to adjust the display strategy according to the needs of different user groups.

[0067] Optionally, in step S3 of the above solution, extract the preference interaction operation characteristics under each browsing behavior preference pattern, and determine the first interactive display strategy regarding multiple display devices in the exhibition hall according to the period distribution pattern corresponding to the multiple browsing behavior preference patterns, including the following content:

[0068] Step S31: Extract the browsing interaction data of multiple users under each browsing behavior preference pattern from the historical display interaction behavior data. Determine the user's interaction operation preference data based on the user's browsing interaction data, and generate the preference interaction operation characteristics under the browsing behavior preference pattern according to the interaction operation preference data of multiple users under the browsing behavior preference pattern, including the group preference list for multiple display interaction operations.

[0069] Specifically, the browsing interaction data includes data related to the specific interaction methods of users on the display device, that is, the specific operation records of users on the display device. For example, the click selection and duration of users on different interaction options, etc. According to the duration of different interaction options, that is, the time information staying in the content provided by the interaction option, the preference information of users for different interaction operations can be initially determined. For example, after normalizing the duration of different interaction options, it is used as the preference score data of the interaction operation, so as to determine the individual preference scores corresponding to multiple display interaction operations of users. By fusing the interaction operation preference data of multiple users, the group preference score of each display interaction operation is generated, so as to generate the preference interaction operation characteristics under the browsing behavior preference pattern according to the interaction operation preference data of multiple users under the browsing behavior preference pattern. Among them, the preference interaction operation characteristics include the group preference list for multiple display interaction operations.

[0070] In this process, the mean value of multiple individual preference scores of different display interaction operations can be calculated as the group preference score of the display interaction operation, and the multiple display interaction operations are sorted according to the group preference score of the display interaction operation, so as to generate the preference interaction operation characteristics under the browsing behavior preference pattern, that is, to obtain the group preference list for multiple display interaction operations, which is used to indicate the relative importance or popularity of various display interaction operations for this user group under the browsing behavior preference pattern.

[0071] Step S32: Analyze the preference interaction operation characteristics under each browsing behavior preference pattern according to the time period distribution pattern of multiple browsing behavior preference patterns. Determine the allocation parameters of each browsing behavior preference pattern within the local display time period according to the behavior preference distribution data of the local display time period, and determine the allocation strategy for the preset number of interaction operations according to the allocation parameters.

[0072] Specifically, after obtaining the preference interaction operation characteristics under each browsing behavior preference pattern, it is analyzed in combination with the time period distribution pattern of multiple browsing behavior preference patterns. The preference interaction operation characteristics reflect the preference of the user group for different display interaction operations under the browsing behavior preference pattern, and the time period distribution pattern reflects the distribution of the user group at different time periods. Combining the two can further determine the allocation parameters of each browsing behavior preference pattern within the local display time period.

[0073] In this process, considering that there are certain limitations in the size of the interaction interface of the display device, it is difficult to simply introduce the detailed content of the exhibits through a small number of interaction options. Instead, it is more necessary for users to further perform in-depth interaction operations according to the prompt content to view more detailed content. Considering the user groups with different browsing behavior preference patterns, there are differences in the preferred interaction options during the actual viewing of the exhibits. Therefore, if, according to the distribution of user groups at different times, the most popular part of the interaction options within each group is displayed on the main interface of the interaction operation, it can help users view their favorite content more quickly.

[0074] Therefore, by analyzing the quantity distribution of user groups at different times, the preset number of interaction operations to be allocated is actually allocated. For example, if the display device can provide six large icon display interaction operations for selection, after determining the allocation ratio of different user groups at a certain time, that is, the allocation parameters for each browsing behavior preference pattern, the preset number of interaction operations can be actually allocated according to the allocation parameters, so as to obtain the allocation strategy for the preset number of interaction operations, specifically including the number of interaction operations allocated for each browsing behavior preference pattern. In the allocation process, in the scenario where rounding is not possible, the group with a higher user quantity ratio can be preferentially satisfied. By this way, the number of interaction operations corresponding to different browsing behavior preference patterns is determined. Then, according to the preferred interaction operation characteristics and the number of interaction operations allocated under the browsing behavior preference pattern, the local display interaction operation recommendation scheme for multiple browsing behavior preference patterns under the local display period is determined. Specifically, according to the number of interaction operations allocated, multiple display interaction operations corresponding to the corresponding number are selected from the preferred interaction operation characteristics, that is, the corresponding number of the most popular display interaction operations in the group. These display interaction operations are used as the recommended options for this browsing behavior preference pattern. By this way, the local display interaction operation recommendation scheme for multiple browsing behavior preference patterns can be generated. Finally, according to the local display interaction operation recommendation schemes under multiple local display periods, the first interaction display strategy under the global display period is generated.

[0075] Optionally, in step S4 of the above solution, the content environment association features are extracted and the content environment analysis model is constructed. The second interaction display strategy for multiple display devices in the exhibition hall is generated through the content environment analysis model, including the following content:

[0076] Multiple groups of display environment features of each display device are extracted from the historical display environment data. According to the time information of the display environment features, the exhibit type popularity data of each group of display environment features is extracted from the historical display interaction behavior data. Each group of display environment features is associated with the corresponding exhibit type popularity data to construct a sample data set.

[0077] Specifically, the method for generating the second interactive display strategy focuses on the correlation analysis between the display environment characteristics of the display devices in the exhibition hall and the popularity of exhibit types. The purpose is to optimize the matching between the exhibit content and the environment by deeply analyzing the exhibit display effects and user interaction patterns in different display environments, thereby further improving the display effect and user experience of the exhibition hall. In this process, multiple groups of display environment characteristics of each display device are first extracted from the historical display environment data. Among them, each group of display environment characteristics can specifically be the overall environmental conditions during a certain period, such as the average brightness and average noise decibels in the display area during a certain period. Specifically, the environmental level of the display area can be monitored by devices such as light sensors and microphones.

[0078] The division of time periods can be the same as the aforementioned multiple local display time periods, or considering that information such as light or noise in the environment does not change significantly within a short period, a longer duration can be selected for division. There is no specific limitation on this here. For the popularity data of exhibit types corresponding to the display environment characteristics, it can specifically be the browsing frequency characteristics of each type of exhibit during the corresponding period, or data comprehensively measured by characteristics such as the interaction frequency and participation degree of the exhibits. Those skilled in the art can choose a suitable method to measure the popularity of each type of exhibit at different times, so as to obtain the popularity data of exhibit types corresponding to each group of display environment characteristics. Finally, a sample data set for training the content environment analysis model is constructed through multiple groups of display environment characteristics and the corresponding popularity data of exhibit types.

[0079] During the process of training the content environment analysis model with the sample data set, multiple groups of display environment characteristics in the sample data set will be used as the input of the content environment analysis model, while the popularity data of exhibit types corresponding to multiple groups of display environment characteristics in the sample data set will be used as the training target of the content environment analysis model to guide the model to learn the association pattern between different environmental characteristics and exhibit popularity. By training the model with the sample data set, the content environment analysis model will be able to learn the popularity performance of different exhibit types in a specific display environment. During the training process, the model analyzes the variation law of exhibit popularity in different environments and establishes a mapping relationship between the exhibit display effect and environmental factors. Among them, the content environment analysis model is specifically constructed by selecting a multi-layer perceptron model to extract deep-level association information between environmental characteristics and exhibit popularity and conduct identification and modeling.

[0080] After the content environment analysis model is trained, when the target display environment data of multiple display devices in the exhibition hall is collected, for example, data including the current lighting, noise and other environmental feature information of each display device, the target display environment data is input into the trained content environment analysis model. The content environment analysis model generates the exhibit type heat distribution data of multiple display devices according to these input environmental features, which is used to indicate the expected heat of displaying different types of exhibits in a specific display environment. Then, a second interactive display strategy for multiple display devices is generated according to the target heat distribution data, which is used to indicate the exhibit types recommended for display by different display devices. For example, in an environment where the lighting of a certain display device is weak and the space is relatively small, displaying a certain audio electronic product may obtain a higher display heat.

[0081] Optionally, in step S5 of the above solution, the first interactive display strategy and the second interactive display strategy are fused to generate a target interactive display strategy, and the interactive display of multiple display devices in the exhibition hall is optimized based on the target interactive display strategy, including the following content:

[0082] Set multiple recommended exhibit types for each display device according to the exhibit type heat distribution data of multiple display devices. For the target display device, after determining the target exhibit currently browsed by the user, determine the local display interaction operation recommendation scheme of the target display device according to the local display period to which the current moment belongs, and set multiple recommended display interaction operations of the target display device according to the local display interaction operation recommendation scheme.

[0083] Specifically, the target interactive display strategy generated by fusing the first interactive display strategy and the second interactive display strategy comprehensively considers the influence of multiple factors such as user behavior patterns, time period changes, and environmental features on the display effect. In the process of optimizing the interactive display of multiple display devices in the exhibition hall according to the target interactive display strategy, multiple recommended exhibit types for each display device can be set first according to the exhibit type heat distribution data of multiple display devices, that is, the best multiple types of exhibits for different display devices regarding exhibits in the current display environment. This process is only used to indicate which types of exhibits may obtain better effects, while in the actual display process, the exhibition hall can provide multiple browsing options for key types of exhibits according to actual needs, or provide one of the specific exhibits as a representative of these types. This embodiment does not specifically limit it. On the target display device, when it is detected that the user selects the target exhibit, analyze the local display period to which the current moment belongs to determine the display interaction operations that multiple users are more likely to select during the local display period.

[0084] Since the user has not yet performed specific browsing operations, it is difficult to determine the specific browsing behavior preference pattern corresponding to the user. Through the previously determined first interactive display strategy, it is possible to analyze and predict in advance the browsing behavior preference pattern that the user may correspond to, so as to recommend multiple display interactive operations that are preferred by the group under these browsing behavior preference patterns. For example, during certain periods, the user group may be more inclined to view product details, while during other periods, they may be inclined to interact with the product, such as rotating, zooming in, virtual trial, etc. After determining the local display interactive operation recommendation scheme for the target display device, set multiple recommended display interactive operations for the target display device according to the local display interactive operation recommendation scheme, that is, implement the interactive options that are more likely to be selected by multiple users on the interactive interface, which is convenient for users to quickly select their intended interactive operations and avoid being disturbed during the actual browsing of exhibits due to the large number of interface functions.

[0085] Through the above method, the target interactive display strategy can be implemented into specific exhibit display and interactive operations through the display device control system, and the display content can be adjusted in real time and dynamically. This way of generating targeted interactive operation recommendations can ensure that each display device can provide a good display effect at each time period, while maximizing the improvement of the user experience, and finally realizing the optimization of dynamic and personalized display content.

[0086] Optionally, in step S6 of the above solution, extract the behavior co-influence characteristics of each browsing behavior preference pattern and generate the interactive co-optimization strategy for each browsing behavior preference pattern, including the following content:

[0087] Segment multiple groups of candidate co-interactive data from the real-time display interactive behavior data, perform time-series correlation screening on the multiple groups of candidate co-interactive data to determine multiple groups of target co-interactive data, extract the interactive operation evolution path of each user in each group of target co-interactive data, perform co-influence analysis on the multiple interactive operation evolution paths of each group of target co-interactive data, and calculate the behavior co-influence parameters corresponding to multiple display interactive operations under each group of target co-interactive data.

[0088] Specifically, the candidate collaborative interaction data is specifically the actual operation data of any two adjacent users in the display interaction behavior data of the display device. To improve the reference degree of collaboration, those skilled in the art can further limit the number of users involved in the candidate collaborative interaction data. For example, the data corresponding to three or five consecutive users who browse exhibits based on the same display device is not specifically limited in this embodiment. Considering the timeliness of behavior influence, multiple groups of candidate collaborative interaction data can be screened for temporal correlation, that is, in-depth analysis is performed on some data with an adjacent interval lower than a pre-set duration threshold. For example, within 15 seconds after a user leaves the current display device, the next user starts a new browsing. Furthermore, those skilled in the art can also perform more detailed population limit on the target collaborative interaction data according to the pedestrian flow data related to the display device, that is, analyze the scenario where multiple users gather, and further limit the pedestrian flow corresponding to the target collaborative interaction data to reach a specific threshold, so as to extract the behavior collaborative influence characteristics with strong relevance.

[0089] After screening out multiple groups of target collaborative interaction data, the interaction operation evolution path of each user in the target collaborative interaction data is extracted, such as multiple display interaction operations selected by the user during the process of browsing exhibits, and the timestamps corresponding to different display interaction operations. Then, collaborative influence analysis is performed on adjacent users through the interaction operation evolution path of the user, specifically for the behavior collaborative influence parameters of each display interaction operation. Exemplarily, for each display interaction operation, the difference between timestamps is analyzed. Considering that the timestamps of different display interaction operations are different, the order between display operations is further considered, and an order weight is introduced to correct the difference between timestamps. For example, the first operation of user B selects the same operation x because it is affected by the third-to-last display interaction operation of user A, and user A spends a long time on the last two operations, resulting in a large timestamp difference between operation x for user A and user B, resulting in low collaboration. However, a strong collaboration is still shown under a large order difference. Therefore, the weight of correction is determined by the order, so that the finally calculated behavior collaborative influence parameter can reduce the negative impact brought by the single operation duration.

[0090] Perform browsing behavior preference pattern matching on each interaction operation evolution path, generate a pattern matching result, and construct an operation collaborative influence set under each browsing behavior preference pattern, including multiple behavior collaborative influence parameters corresponding to each display interaction operation. Determine multiple interaction collaborative influence operations of the browsing behavior preference pattern according to the operation collaborative influence set, and generate an interaction collaborative optimization strategy for the browsing behavior preference pattern according to the multiple interaction collaborative influence operations.

[0091] Specifically, after analyzing the behavior collaboration influence parameters corresponding to multiple display interaction operations under different target collaborative interaction data, group identification analysis is performed on the users in the different target collaborative interaction data, that is, the users are matched with different browsing behavior preference patterns according to the evolution path of their interaction operations. Specifically, it can be based on multiple display interaction operations involved in the evolution path of the interaction operation, and match with the popular operations in the browsing behavior preference pattern. Finally, the browsing behavior preference pattern with the highest matching degree is selected as the browsing behavior preference pattern to which the user belongs. After obtaining the matching results of each user in this way, the candidate collaborative interaction data under each browsing behavior preference pattern is screened out, that is, the candidate collaborative interaction data whose multiple interaction operation evolution paths belong to the same group are divided into the corresponding group. Thus, according to the behavior collaboration influence parameters corresponding to multiple display interaction operations in the interaction operation evolution path, an operation collaboration influence set under each browsing behavior preference pattern is constructed, which includes the quantitative collaboration characteristics of different display interaction operations in the short term. Based on the average coordination level of the display interaction operations, such as the mean value of the behavior collaboration influence parameters, multiple display interaction operations with higher coordination in the browsing behavior preference pattern are determined and recorded as interaction collaboration influence operations, and based on the determined multiple interaction collaboration influence operations, the order of the display interaction operations used for recommendation by this group in the short term is re-optimized. The high collaboration in the short term indicates that some display interaction operations also meet the preferences of a large number of users in this group to a certain extent. In the analysis process of historical data, some display interaction operations do not comprehensively consider the collaborative influence of the user group, which may lead to some display interaction operations with high recommendation value being ignored due to the analysis based solely on popularity, resulting in the actual display effect being affected. Through the above user behavior collaboration influence analysis, the interaction display strategy of different display devices can be dynamically adjusted according to real-time data, and the adaptability of the display optimization strategy to local scenarios is well improved by capturing real-time collaborative behavior, achieving a better interaction display effect.

[0092] Figure 2 The figure shows a schematic structural diagram of a data interaction display system for a digital exhibition hall provided in one implementation process of the present invention. A data interaction display system for a digital exhibition hall provided in an embodiment of the present invention is specifically used to implement the above-mentioned data interaction display method for a digital exhibition hall. The system includes:

[0093] A browsing behavior preference analysis module 11, configured to collect historical display interaction behavior data of multiple display devices in the exhibition hall, extract browsing behavior preference characteristics corresponding to multiple users from the historical display interaction behavior data, and perform browsing behavior preference analysis on the multiple users according to the browsing behavior preference characteristics to determine multiple browsing behavior preference patterns;

[0094] The time period distribution analysis module 12 is used to determine the browsing behavior labels corresponding to each user according to the browsing behavior preference patterns to which the user belongs, extract the browsing time period preference characteristics of multiple browsing behavior preference patterns according to the browsing behavior labels, and perform browsing time period distribution analysis on multiple browsing behavior preference patterns to determine the time period distribution patterns of multiple browsing behavior preference patterns;

[0095] Among them, the time period distribution analysis module includes a browsing time period preference analysis unit and a browsing time period distribution analysis unit. The browsing time period preference analysis unit is used to determine the browsing behavior labels corresponding to each user according to the browsing behavior preference patterns to which the user belongs, and extract the browsing time period preference characteristics of multiple browsing behavior preference patterns according to the browsing behavior labels; the browsing time period distribution analysis unit is used to perform browsing time period distribution analysis on multiple browsing behavior preference patterns according to the browsing time period preference characteristics to determine the time period distribution patterns of multiple browsing behavior preference patterns.

[0096] The first interactive display optimization module 13 is used to perform time period behavior correlation analysis on the historical display interactive behavior data according to the time period distribution patterns corresponding to multiple browsing behavior preference patterns, extract the preference interactive operation characteristics under each browsing behavior preference pattern, and determine the first interactive display strategy for multiple display devices in the exhibition hall according to the time period distribution patterns corresponding to multiple browsing behavior preference patterns;

[0097] The second interactive display optimization module 14 is used to collect the historical display environment data of multiple display devices, perform content environment correlation analysis on the historical display interactive behavior data based on the historical display environment data, extract the content environment correlation characteristics and construct a content environment analysis model, and generate the second interactive display strategy for multiple display devices in the exhibition hall through the content environment analysis model;

[0098] The interactive display management module 15 is used to fuse the first interactive display strategy and the second interactive display strategy to generate a target interactive display strategy, and perform interactive display optimization on multiple display devices in the exhibition hall based on the target interactive display strategy;

[0099] The interactive collaboration optimization module 16 is used to obtain the real-time display interactive behavior data of each display device, perform user behavior collaborative influence analysis on the real-time display interactive behavior data, extract the behavior collaborative influence characteristics of each browsing behavior preference pattern, generate the interactive collaboration optimization strategy of each browsing behavior preference pattern, and perform interactive display collaborative optimization on multiple display devices based on the interactive collaboration optimization strategy.

[0100] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The parts not described in detail in this specification belong to the prior art well known to those skilled in the art.

Claims

1. A data interactive display method for a digital exhibition hall, characterized in that: include: Collect historical display interaction behavior data of multiple display devices in the exhibition hall, extract browsing behavior preference features corresponding to multiple users from the historical display interaction behavior data, perform browsing behavior preference analysis on multiple users according to the browsing behavior preference features, and determine multiple browsing behavior preference patterns; Determine a browsing behavior tag corresponding to each user according to the browsing behavior preference pattern to which the user belongs, extract browsing time period preference features of multiple browsing behavior preference patterns according to the browsing behavior tags, perform browsing time period distribution analysis on the multiple browsing behavior preference patterns according to the browsing time period preference features, and determine a time period distribution pattern for the multiple browsing behavior preference patterns; Performing a time period behavior association analysis on the historical display interaction behavior data according to the time period distribution patterns corresponding to the multiple browsing behavior preference patterns, extracting the preferred interactive operation features under each browsing behavior preference pattern, and determining the first interactive display strategy for multiple display devices in the exhibition hall according to the time period distribution patterns corresponding to the multiple browsing behavior preference patterns; Collect historical display environment data of multiple display devices, perform content environment association analysis on historical display interaction behavior data based on the historical display environment data, extract content environment association features and construct a content environment analysis model, and generate a second interactive display strategy for multiple display devices in the exhibition hall through the content environment analysis model; The first interactive display strategy and the second interactive display strategy are integrated to generate a target interactive display strategy, and the interactive display of multiple display devices in the exhibition hall is optimized based on the target interactive display strategy; Acquire the real-time interactive display behavior data of each display device, perform user behavior collaborative impact analysis on the real-time interactive display behavior data, extract the behavioral collaborative impact characteristics of each browsing behavior preference pattern, generate an interactive collaborative optimization strategy for each browsing behavior preference pattern, and perform interactive display collaborative optimization on multiple display devices based on the interactive collaborative optimization strategy; For determining the time period distribution pattern of multiple browsing behavior preference patterns, it also includes: The browsing behavior preference feature includes a plurality of browsing behavior characteristic parameters. After constructing a browsing behavior preference characteristic vector of each user through the plurality of browsing behavior characteristic parameters, clustering processing is performed on the plurality of users according to the browsing behavior preference characteristic vectors to determine a plurality of browsing behavior preference patterns. The global display period of the exhibition hall is divided into time periods to determine multiple local display periods, and multiple groups of user distribution data about multiple browsing behavior preference patterns are extracted for each local display period, wherein the user distribution data includes the user quantity characteristics of each browsing behavior preference pattern in the local display period, and the browsing period preference characteristics about the multiple browsing behavior preference patterns are obtained; Analyze multiple groups of user distribution data in each local display period, determine the traffic characteristic parameters of each group of user distribution data in the local display period, fuse multiple user quantity characteristics of each browsing behavior preference pattern in the local display period according to the traffic characteristic parameters, obtain the behavior preference distribution data of each local display period, and generate a time period distribution pattern for multiple browsing behavior preference patterns according to the behavior preference distribution data of multiple local display periods; Extracting the preferred interactive operation features under each browsing behavior preference mode, and determining the first interactive display strategy for multiple display devices in the exhibition hall according to the time period distribution patterns corresponding to the multiple browsing behavior preference modes, including: Extracting browsing interaction data of multiple users in each browsing behavior preference mode from the historical display interaction behavior data, determining the user's interactive operation preference data according to the user's browsing interaction data, and generating the preferred interactive operation features in the browsing behavior preference mode according to the interactive operation preference data of multiple users in the browsing behavior preference mode, including a group preference list for multiple display interactive operations; The browsing interaction data includes individual preference scores corresponding to multiple display interaction operations, and the group preference score of each display interaction operation is generated by fusing the interaction operation preference data of multiple users, and the multiple display interaction operations are sorted according to the group preference scores to obtain a group preference list; Analyzing the preferred interactive operation characteristics under each browsing behavior preference mode according to the time period distribution mode of the multiple browsing behavior preference modes, determining the allocation parameters of each browsing behavior preference mode in the local display period according to the behavior preference distribution data of the local display period, determining the allocation strategy for the preset number of interactive operations according to the allocation parameters, including the allocation number of interactive operations for each browsing behavior preference mode, determining the local display interactive operation recommendation schemes for the multiple browsing behavior preference modes under the local display period according to the preferred interactive operation characteristics and the allocation number of interactive operations under the browsing behavior preference mode, and generating the first interactive display strategy under the global display period according to the local display interactive operation recommendation schemes under the multiple local display periods; The content environment correlation features are extracted and a content environment analysis model is constructed. The second interactive display strategy for multiple display devices in the exhibition hall is generated through the content environment analysis model, including: Extract multiple groups of display environment features of each display device from the historical display environment data, extract the exhibit type heat data of each group of display environment features from the historical display interaction behavior data according to the time information of the display environment features, associate each group of display environment features with the corresponding exhibit type heat data to construct a sample data set, use the multiple groups of display environment features in the sample data set as the input of the content environment analysis model, use the exhibit type heat data corresponding to the multiple groups of display environment features in the sample data set as the training target of the content environment analysis model, and obtain the content environment analysis model through training with the sample data set; After acquiring target display environment data about multiple display devices, the target display environment data is input into a trained content environment analysis model to generate exhibit type heat distribution data about the multiple display devices, and a second interactive display strategy about the multiple display devices is generated according to the target heat distribution data; The first interactive display strategy and the second interactive display strategy are integrated to generate a target interactive display strategy, and interactive display optimization is performed on multiple display devices in the exhibition hall based on the target interactive display strategy, including: According to the heat distribution data of exhibit types about multiple display devices, multiple recommended exhibit types are set for each display device. For the target display device, after determining the target exhibit currently browsed by the user, according to the local display time period to which the current moment belongs, a local display interactive operation recommendation scheme for the target display device is determined, and according to the local display interactive operation recommendation scheme, multiple recommended display interactive operations for the target display device are set.

2. The data interactive display method of a digital exhibition hall according to claim 1 is characterized in that: Extract the behavioral synergistic influence features of each browsing behavior preference pattern and generate an interactive synergistic optimization strategy for each browsing behavior preference pattern, including: Segment multiple groups of candidate collaborative interaction data from real-time display interaction behavior data, perform time-series correlation screening on multiple groups of candidate collaborative interaction data to determine multiple groups of target collaborative interaction data, extract the interaction operation evolution path of each user in each group of target collaborative interaction data, perform collaborative impact analysis on multiple interaction operation evolution paths of each group of target collaborative interaction data, and calculate the behavioral collaborative impact parameters corresponding to multiple display interaction operations under each group of target collaborative interaction data; Browsing behavior preference pattern matching is performed on each interactive operation evolution path, pattern matching results are generated, and an operation synergy influence set under each browsing behavior preference pattern is constructed, including multiple behavior synergy influence parameters corresponding to each display interactive operation, multiple interactive synergy influence operations of the browsing behavior preference pattern are determined according to the operation synergy influence set, and an interactive synergy optimization strategy for the browsing behavior preference pattern is generated according to the multiple interactive synergy influence operations.

3. A data interactive display system for a digital exhibition hall, characterized in that: The system is used to implement the data interactive display method of a digital exhibition hall as described in any one of claims 1-2, including: A browsing behavior preference analysis module is used to collect historical display interaction behavior data of multiple display devices in the exhibition hall, extract browsing behavior preference features corresponding to multiple users from the historical display interaction behavior data, perform browsing behavior preference analysis on multiple users based on the browsing behavior preference features, and determine multiple browsing behavior preference patterns; A time period distribution analysis module is used to determine the browsing behavior label corresponding to each user according to the browsing behavior preference pattern to which the user belongs, extract browsing time period preference features of multiple browsing behavior preference patterns according to the browsing behavior label, perform browsing time period distribution analysis on multiple browsing behavior preference patterns according to the browsing time period preference features, and determine the time period distribution pattern of multiple browsing behavior preference patterns; A first interactive display optimization module is used to perform time period behavior association analysis on historical display interactive behavior data according to time period distribution patterns corresponding to multiple browsing behavior preference patterns, extract the preferred interactive operation features under each browsing behavior preference pattern, and determine a first interactive display strategy for multiple display devices in the exhibition hall according to the time period distribution patterns corresponding to the multiple browsing behavior preference patterns; A second interactive display optimization module is used to collect historical display environment data of multiple display devices, perform content environment association analysis on historical display interaction behavior data based on the historical display environment data, extract content environment association features and construct a content environment analysis model, and generate a second interactive display strategy for multiple display devices in the exhibition hall through the content environment analysis model; An interactive display management module, used to merge the first interactive display strategy and the second interactive display strategy to generate a target interactive display strategy, and optimize the interactive display of multiple display devices in the exhibition hall based on the target interactive display strategy; The interactive collaborative optimization module is used to obtain the real-time display interactive behavior data of each display device, perform user behavior collaborative impact analysis on the real-time display interactive behavior data, extract the behavioral collaborative impact characteristics of each browsing behavior preference pattern, generate an interactive collaborative optimization strategy for each browsing behavior preference pattern, and perform interactive display collaborative optimization on multiple display devices based on the interactive collaborative optimization strategy.

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