Industrial heritage digitalization display system

Abstract

The application discloses an industrial heritage digital display system, which comprises a front-end display layer, a back-end data management layer and a technical support layer; the front-end display layer comprises a VR / AR experience module, an interactive touch screen display module, a mobile application function module, a deep culture interpretation module and a diversified form module; the back-end data management layer comprises a digital asset management platform module, an intelligent recommendation system module and a customized experience module; the digital asset management platform receives and stores a 3D model generated by a 3D scanning and modeling technology module; the intelligent recommendation system module is responsible for identifying the interests and preferences of users by using association rule mining and clustering analysis, and intelligently recommending relevant industrial heritage content; the intelligent recommendation system module generates personalized recommendation content by analyzing user data, and transmits the personalized recommendation content to the interactive touch screen display module through a data interface; and the customized experience module is responsible for customizing a visiting route according to the interests of users and providing differentiated experiences.

Description

Technical Field

[0001] This invention relates to the field of industrial heritage display technology, and in particular to a digital display system for industrial heritage. Background Technology

[0002] Industrial heritage is an important carrier of industrial civilization, witnessing the history of human industrialization. These heritages not only possess historical and cultural value but also contain rich social, economic, and technological information. However, with rapid industrialization and urbanization, many industrial heritage sites face the risk of demolition or abandonment, and their value and significance are often overlooked or forgotten. In recent years, digital technologies have developed rapidly, with a new generation of digital technologies, including big data, cloud computing, artificial intelligence, virtual reality (VR), and augmented reality (AR), constantly emerging, providing strong technical support for the digital display of industrial heritage. These technologies enable high-precision data collection, processing, display, and interaction with industrial heritage, allowing visitors to experience its charm and value firsthand.

[0003] In existing technologies, the amount of information that can be carried by existing carriers of industrial heritage cultural content is limited, the cultural characteristics and connotations are difficult to highlight, the cultural functions and cultural value attributes of product carriers are not clearly reflected, the interpretation and depth of specific industrial cultural content are insufficient, and the forms of expression of cultural content are monotonous, which is not conducive to the display and dissemination of industrial heritage. Therefore, a digital display system for industrial heritage is proposed. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies, such as insufficient interpretation and depth of specific industrial cultural content, limited information content of industrial heritage, and difficulty in displaying and disseminating industrial heritage. Therefore, this invention proposes a digital display system for industrial heritage.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A digital display system for industrial heritage includes a front-end display layer, a back-end data management layer, and a technical support layer;

[0007] The front-end presentation layer includes a VR / AR experience module, an interactive touchscreen display module, a mobile application function module, an in-depth cultural interpretation module, and a diversified presentation module. The VR / AR experience module is responsible for creating an immersive industrial heritage environment, realizing the construction of a virtual environment and user interaction. When users explore the industrial heritage environment through VR / AR devices, the VR / AR experience module calls data stored in the digital asset management platform. The interactive touchscreen display module is responsible for displaying industrial heritage through a large touchscreen, and users can obtain detailed information through touch operations. The mobile application function module is responsible for developing a dedicated APP, providing functions such as online tours, interactive Q&A, and virtual exhibitions. The mobile application function collects user feedback and behavioral data, which is transmitted to the data analysis and feedback module through the mobile application function module. The in-depth cultural interpretation module is responsible for telling the historical stories and biographies behind the industrial heritage through animation, micro-films, etc., to increase cultural depth, and inviting industry experts to record videos or audio interpretations to provide in-depth analysis from a professional perspective. The diversified presentation module is responsible for designing games based on industrial heritage knowledge and using holographic technology to display key scenes or products of industrial heritage.

[0008] The backend data management layer includes a digital asset management platform module, an intelligent recommendation system module, and a customized experience module. The digital asset management platform module is responsible for centrally storing and managing all digital content, including high-definition images, 3D models, video materials, audio narration, and text descriptions. The digital asset management platform receives and stores 3D models generated by the 3D scanning and modeling technology module. The intelligent recommendation system module is responsible for identifying user interests and preferences using association rule mining and cluster analysis, and intelligently recommending relevant industrial heritage content. The intelligent recommendation system module generates personalized recommendation content by analyzing user data and transmits it to the interactive touch screen display module through a data interface. The customized experience module is responsible for customizing the tour route according to user interests and providing a differentiated experience.

[0009] The technical support layer includes a 3D scanning and modeling technology module, a big data and cloud computing module, and an artificial intelligence and machine learning module. The 3D scanning and modeling technology module is responsible for scanning industrial heritage and generating 3D models. The big data and cloud computing module is responsible for using a big data processing framework and cloud computing platform to achieve distributed storage, parallel processing, and efficient access of data. The big data and cloud computing module provides data processing and storage capabilities for the intelligent recommendation system module. The artificial intelligence and machine learning module is used for content classification, intelligent question answering, and sentiment analysis. The artificial intelligence and machine learning module interacts with the data analysis and feedback module.

[0010] The above technical solution further includes:

[0011] Furthermore, the in-depth cultural interpretation module includes a content planning unit, a multimedia creation unit, an expert resource unit, a data management and storage unit, and a front-end display unit. The content planning unit is responsible for planning and formulating the content framework and theme of the in-depth cultural interpretation, ensuring the accuracy and depth of the content. The multimedia creation unit creates content using multimedia formats such as animation and micro-films according to the requirements of the content planning unit, ensuring the vividness and attractiveness of the content. The expert resource unit is responsible for inviting industry experts to record videos or audios, providing professional interpretations and analyses to enhance the authority and credibility of the content. The data management and storage unit is responsible for managing and storing all multimedia content and expert interpretations in the in-depth cultural interpretation module, ensuring the integrity and security of the data. The front-end display unit is responsible for displaying the content of the in-depth cultural interpretation module to users through the user interface, realizing user interaction with the content.

[0012] Furthermore, the digital asset management platform module includes a data storage unit, a content management unit, a data retrieval unit, a permission management unit, and a data analysis unit. The data storage unit is responsible for storing and backing up digital content, ensuring data reliability and security. This data storage unit utilizes a database management system or cloud storage technology. The content management unit provides management functions such as content uploading, downloading, editing, and deleting, serving as a bridge between users and digital content. The content management unit includes a user interface and a backend management interface. The data retrieval unit is responsible for efficient retrieval and rapid response of digital content, ensuring users can quickly obtain the resources they need. This data retrieval unit employs distributed storage and caching technologies to optimize performance. The permission management unit… The content management unit provides access control for users' access to digital content, ensuring data security and controllability. This access control unit includes user authentication, role assignment, and permission settings. The data analysis unit statistically analyzes the usage of digital content, providing data support for optimizing content management and improving user experience. When a user needs to access digital content, the data retrieval unit sends a request to the data storage unit to obtain the necessary resources. When a user attempts to access content, the content management unit sends a request to the access control unit to verify the user's permissions. The data analysis unit statistically analyzes the usage of digital content, generating reports and data visualization charts. These reports and charts are fed back to the content management unit for optimizing content management and improving user experience.

[0013] Furthermore, the intelligent recommendation system module includes a data collection unit, an interest analysis unit, a content recommendation unit, and an effect evaluation unit. The data collection unit is responsible for comprehensively collecting user behavior data, including system logs and user feedback. This unit needs to ensure the accuracy and completeness of the data to provide a reliable foundation for subsequent analysis. The interest analysis unit uses big data analysis and machine learning algorithms to deeply mine and analyze user behavior data, identifying user interests and preferences. The interest analysis unit continuously optimizes the algorithm model to improve the accuracy and efficiency of the analysis. The content recommendation unit intelligently recommends relevant industrial heritage content based on user interests and preferences. The content recommendation unit ensures the diversity and richness of recommended content and adjusts the recommendation strategy in real time based on user feedback. The effect evaluation unit evaluates and optimizes the recommendation effect in real time through user feedback, click-through rate, and conversion rate. This unit needs to analyze the evaluation results regularly and propose improvement suggestions to ensure the continuous optimization and improvement of the recommendation system. The data collection unit transmits the collected user behavior data to the interest analysis unit, and the interest analysis unit transmits the analyzed user interests and preferences to the content recommendation unit.

[0014] Furthermore, the customized experience module includes a route customization unit, an experience provision unit, and a cultural dissemination unit. The route customization unit generates personalized tour routes based on user interests and preferences, combined with the characteristics and display needs of industrial heritage. This ensures the route's rationality and feasibility while meeting users' individual needs. The experience provision unit provides differentiated experiences based on the customized tour routes, including content display, interactive exhibits, and game design. This ensures the quality and effectiveness of the experience, enhancing user satisfaction. The cultural dissemination unit, while providing personalized experiences, emphasizes the dissemination and popularization of industrial heritage culture, including providing cultural interpretations, historical background introductions, and interactive Q&A sessions to increase users' cultural identification. The interest analysis unit transmits the analyzed user interests and preferences to the route customization unit, which then transmits the generated tour routes to the experience provision unit. The experience provision unit, while providing personalized experiences, collaborates with the cultural dissemination unit to focus on the dissemination and popularization of industrial heritage culture.

[0015] Furthermore, the 3D scanning and modeling technology module includes a scanning equipment unit, a data processing unit, and a modeling software unit. The scanning equipment unit is responsible for scanning industrial heritage using 3D scanning equipment to capture its surface details and shape features. The scanning equipment unit needs to ensure the accuracy and completeness of the scan to provide a reliable data foundation for subsequent modeling. The data processing unit preprocesses and cleans the scanned data, removing noise and redundant information to improve data quality and usability. The data processing unit also needs to convert the processed data into a format recognizable by the modeling software for subsequent modeling operations. The modeling software unit performs 3D reconstruction on the processed data to generate a 3D model. The modeling software unit needs to support multiple modeling algorithms and tools to meet the modeling needs of industrial heritage with different shapes and structures.

[0016] Furthermore, the interest analysis unit combines association rule mining and cluster analysis to identify users' interests and preferences. Specific steps include:

[0017] Data preprocessing: Collect user behavior data, including browsing history, click count, purchase history, etc., clean the data, remove redundancy and outliers, and convert the data into a format suitable for association rule mining and cluster analysis;

[0018] Association rule mining: Apriori is used to extract frequent itemsets and association rules from user behavior data. Support and confidence thresholds are set to filter out meaningful association rules. The support represents the frequency of an itemset in the dataset, with the formula Support(A→B)=P(A∪B). The confidence represents the probability of itemset B appearing given that itemset A appears, with the formula Confidence(A→B)=P(B|A).

[0019] Cluster analysis: Users are grouped using K-means, with each group having similar interests and preferences. Based on the results of association rule mining, frequent itemsets and association rules are used as features and input into K-means, with the number of clusters K set.

[0020] Results Interpretation and Validation: Analyze the clustering results to interpret the interests and preferences of different user groups. Use metrics such as cross-validation and accuracy to evaluate the stability and accuracy of the clustering results. Adjust the parameters of association rule mining based on the clustering results to optimize model performance.

[0021] Furthermore, the content recommendation unit uses a combined recommendation algorithm to calculate which industrial heritage sites the user is interested in and generates a recommendation list. The specific steps are as follows:

[0022] Data collection: Obtaining user profiles and industrial heritage information;

[0023] Data cleaning: Identify and remove data that is irrelevant to industrial heritage recommendations, fill in missing values ​​in users' browsing history, ratings or reviews, and convert users' browsing behavior into a uniform scale;

[0024] Feature extraction: Extract textual features from the titles, summaries, content, or user comments of industrial heritage sites, convert them into numerical features, and construct user feature vectors based on user behavior;

[0025] Collaborative filtering:

[0026] Constructing a User-Industry Heritage Matrix: Based on users' browsing history and ratings, construct a user-industry heritage interaction matrix. Assuming there are m users and n industry heritages, the user-industry heritage matrix R can be represented as:

[0027]

[0028] Where, r ij This represents the rating or number of times user i has viewed industrial heritage site j. If a user has not viewed a certain industrial heritage site, the corresponding element can be set to 0 or left blank.

[0029] Calculate similarity: Calculate the similarity between users with shared browsing preferences or between industrial heritage sites with similar themes or content.

[0030]

[0031] Among them, I uv It is a collection of industrial heritage that both users u and v have interacted with, r ui It is a rating or some metric given by user u to industrial heritage site i, r vi It is the rating or metric given by user v to industrial heritage site i;

[0032] Generate a recommendation list: Based on the browsing history of similar users and the similarity of industrial heritage sites, generate a recommendation list for the current user;

[0033] Content-based recommendations:

[0034] Feature matching: Match the feature vectors of user browsing preferences with the feature vectors of industrial heritage sites, and calculate the similarity.

[0035]

[0036] in, It is a feature vector of user reading preferences. It is the feature vector of industrial heritage;

[0037] Rating prediction: Based on content similarity, predict users' potential interest or satisfaction with unread industrial heritage sites;

[0038] Mixed recommendations:

[0039] Weighting: Different weights are assigned to collaborative filtering and content-based recommendation based on the actual application scenario and data characteristics;

[0040] Overall score: The scores from collaborative filtering and content-based recommendation are weighted and summed to obtain the final recommendation score;

[0041] Generate recommendations: Based on the final score, select the industrial heritage site with the highest score as the recommendation result;

[0042] User feedback: Collect user feedback on the recommendation results;

[0043] Model tuning: Based on user feedback, dynamically adjust the parameters and weights of the recommendation model to optimize recommendation performance.

[0044] The present invention has the following beneficial effects:

[0045] In this invention, the cultural content of industrial heritage is presented through diverse forms of expression, making the presentation more vivid and interesting. By using association rule mining and cluster analysis to identify users' interests and preferences, relevant industrial heritage content is intelligently recommended. At the same time, tour routes are customized according to user interests to provide a differentiated experience, enabling users to gain a deeper understanding and experience of the cultural characteristics and connotations of specific industrial heritage sites. Attached Figure Description

[0046] Figure 1 This is a system block diagram of an industrial heritage digital display system proposed in this invention. Detailed Implementation

[0047] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0048] Please see Figure 1 As shown, the present invention is a digital display system for industrial heritage, including a front-end display layer, a back-end data management layer, and a technical support layer;

[0049] The front-end presentation layer includes a VR / AR experience module, an interactive touchscreen display module, a mobile application function module, an in-depth cultural interpretation module, and a diversified presentation module. The VR / AR experience module is responsible for creating an immersive industrial heritage environment, realizing the construction of a virtual environment and user interaction. When users explore the industrial heritage environment through VR / AR devices, the VR / AR experience module calls data stored in the digital asset management platform. The interactive touchscreen display module is responsible for displaying industrial heritage through a large touchscreen, and users can obtain detailed information through touch operation. The mobile application function module is responsible for developing a dedicated APP, providing functions such as online tours, interactive Q&A, and virtual exhibitions. The mobile application function collects user feedback and behavioral data, which is then transmitted to the data analysis and feedback module through the mobile application function module. The in-depth cultural interpretation module is responsible for telling the historical stories and biographies behind the industrial heritage through animation, micro-films, etc., to increase cultural depth, and inviting industry experts to record videos or audio interpretations to provide in-depth analysis from a professional perspective. The diversified presentation module is responsible for designing games based on industrial heritage knowledge and using holographic technology to display key scenes or products of industrial heritage.

[0050] The backend data management layer includes a digital asset management platform module, an intelligent recommendation system module, and a customized experience module. The digital asset management platform module is responsible for centrally storing and managing all digital content, including high-definition images, 3D models, video materials, audio narration, and text descriptions. The digital asset management platform receives and stores 3D models generated by the 3D scanning and modeling technology module. The intelligent recommendation system module is responsible for identifying user interests and preferences using association rule mining and cluster analysis, and intelligently recommending relevant industrial heritage content. The intelligent recommendation system module generates personalized recommendation content by analyzing user data and passes it to the interactive touch screen display module through a data interface. The customized experience module is responsible for customizing the tour route according to user interests and providing a differentiated experience.

[0051] The technical support layer includes a 3D scanning and modeling technology module, a big data and cloud computing module, and an artificial intelligence and machine learning module. The 3D scanning and modeling technology module is responsible for scanning industrial heritage and generating 3D models. The big data and cloud computing module is responsible for using big data processing frameworks and cloud computing platforms to achieve distributed storage, parallel processing, and efficient access of data. The big data and cloud computing module provides data processing and storage capabilities for the intelligent recommendation system module. The artificial intelligence and machine learning module is used for content classification, intelligent question answering, and sentiment analysis. The artificial intelligence and machine learning module interacts with the data analysis and feedback module.

[0052] In one embodiment, the aforementioned in-depth cultural interpretation module includes a content planning unit, a multimedia creation unit, an expert resource unit, a data management and storage unit, and a front-end display unit. The content planning unit is responsible for planning and formulating the content framework and theme of the in-depth cultural interpretation, ensuring the accuracy and depth of the content. The multimedia creation unit creates content using multimedia forms such as animation and micro-films according to the requirements of the content planning unit, ensuring the vividness and attractiveness of the content. The expert resource unit is responsible for inviting industry experts to record videos or audios, providing professional interpretations and analyses to enhance the authority and credibility of the content. The data management and storage unit is responsible for managing and storing all multimedia content and expert interpretations in the in-depth cultural interpretation module, ensuring the integrity and security of the data. The front-end display unit is responsible for displaying the content of the in-depth cultural interpretation module to users through the user interface, enabling user interaction with the content.

[0053] In one embodiment, the aforementioned digital asset management platform module includes a data storage unit, a content management unit, a data retrieval unit, a permission management unit, and a data analysis unit. The data storage unit is responsible for storing and backing up digital content, ensuring data reliability and security. The data storage unit utilizes a database management system or cloud storage technology. The content management unit provides management functions such as content uploading, downloading, editing, and deleting, serving as a bridge between users and digital content. The content management unit includes a user interface and a backend management interface. The data retrieval unit is responsible for the efficient retrieval and rapid response of digital content, ensuring users can quickly obtain the necessary resources when needed. The data retrieval unit employs distributed storage and caching technologies to optimize... The content management unit provides access control for users accessing digital content, ensuring data security and controllability. This includes user authentication, role assignment, and permission settings. The data analysis unit statistically analyzes the usage of digital content, providing data support for optimizing content management and improving user experience. When a user needs to access digital content, the data retrieval unit sends a request to the data storage unit to obtain the necessary resources. When a user attempts to access content, the content management unit sends a request to the access control unit to verify the user's permissions. The data analysis unit statistically analyzes the usage of digital content, generating reports and data visualization charts. These reports and charts are fed back to the content management unit for optimizing content management and improving user experience.

[0054] In one embodiment, the intelligent recommendation system module includes a data collection unit, an interest analysis unit, a content recommendation unit, and an effect evaluation unit. The data collection unit is responsible for comprehensively collecting user behavior data, including system logs and user feedback. This unit needs to ensure the accuracy and completeness of the data to provide a reliable foundation for subsequent analysis. The interest analysis unit uses big data analysis and machine learning algorithms to deeply mine and analyze user behavior data, identify user interests and preferences, and continuously optimize the algorithm model to improve the accuracy and efficiency of the analysis. The content recommendation unit intelligently recommends relevant content based on user interests and preferences, ensuring the diversity and richness of the recommended content and adjusting the recommendation strategy in real time based on user feedback. The effect evaluation unit evaluates and optimizes the recommendation effect in real time through user feedback, click-through rate, and conversion rate. This unit needs to analyze the evaluation results regularly and propose improvement suggestions to ensure the continuous optimization and improvement of the recommendation system. The data collection unit transmits the collected user behavior data to the interest analysis unit, and the interest analysis unit transmits the analyzed user interests and preferences to the content recommendation unit.

[0055] In one embodiment, the customized experience module includes a route customization unit, an experience provision unit, and a cultural dissemination unit. The route customization unit generates personalized tour routes based on user interests and preferences, combined with the characteristics and display needs of industrial heritage. The route customization unit ensures the rationality and feasibility of the routes while meeting the personalized needs of users. The experience provision unit provides differentiated experiences to users based on the customized tour routes, including content display, interactive project setup, and game design. The experience provision unit ensures the quality and effectiveness of the experiences and improves user satisfaction. The cultural dissemination unit, while providing personalized experiences, focuses on the dissemination and popularization of industrial heritage culture, including providing cultural interpretations, historical background introductions, and interactive Q&A sessions to increase users' cultural identification. The interest analysis unit transmits the analyzed user interests and preferences to the route customization unit, which then transmits the generated tour routes to the experience provision unit. The experience provision unit, while providing personalized experiences, collaborates with the cultural dissemination unit to focus on the dissemination and popularization of industrial heritage culture.

[0056] In one embodiment, the 3D scanning and modeling technology module includes a scanning equipment unit, a data processing unit, and a modeling software unit. The scanning equipment unit is responsible for scanning industrial heritage sites using 3D scanning equipment to capture their surface details and shape features. The scanning equipment unit needs to ensure the accuracy and completeness of the scans to provide a reliable data foundation for subsequent modeling. The data processing unit preprocesses and cleans the scanned data, removing noise and redundant information to improve data quality and usability. The data processing unit also needs to convert the processed data into a format recognizable by the modeling software for subsequent modeling operations. The modeling software unit performs 3D reconstruction of the processed data to generate a 3D model. The modeling software unit needs to support various modeling algorithms and tools to meet the modeling needs of industrial heritage sites with different shapes and structures.

[0057] In one embodiment, the interest analysis unit combines association rule mining and cluster analysis to identify user interests and preferences. The specific steps are as follows:

[0058] Data preprocessing: Collect user behavior data, including browsing history, click count, purchase history, etc., clean the data, remove redundancy and outliers, and convert the data into a format suitable for association rule mining and cluster analysis;

[0059] Association rule mining: Apriori is used to extract frequent itemsets and association rules from user behavior data. Support and confidence thresholds are set to filter out meaningful association rules. Support represents the frequency of an itemset in the dataset, and the formula is Support(A→B)=P(A∪B). Confidence represents the probability of itemset B appearing given that itemset A appears, and the formula is Confidence(A→B)=P(B|A).

[0060] Cluster analysis: Users are grouped using K-means, with each group having similar interests and preferences. Based on the results of association rule mining, frequent itemsets and association rules are used as features and input into K-means, with the number of clusters K set.

[0061] Results Interpretation and Validation: Analyze the clustering results to interpret the interests and preferences of different user groups. Use metrics such as cross-validation and accuracy to evaluate the stability and accuracy of the clustering results. Adjust the parameters of association rule mining based on the clustering results to optimize model performance.

[0062] Data preprocessing: Converting user behavior data into a format suitable for association rule mining and cluster analysis, such as a user-product matrix.

[0063] Association rule mining: The Apriori algorithm is used to extract frequent itemsets and association rules. For example, the association rule "80% of users who buy milk also buy bread" is found to have a support of 0.2 and a confidence of 0.8.

[0064] Cluster analysis: Users were grouped using the K-means algorithm. Based on the results of association rule mining, frequent itemsets and association rules were used as features input into the K-means algorithm. With the number of clusters K set to 3, three user groups were obtained: Group 1 prefers to buy milk and bread, Group 2 prefers to buy electronics and books, and Group 3 prefers to buy clothing and cosmetics.

[0065] Results Interpretation and Validation: Analysis of the clustering results revealed that users in Group 1 might be interested in food and daily necessities, users in Group 2 might be interested in technology and culture, and users in Group 3 might be interested in fashion and beauty. Cross-validation was used to evaluate the stability and accuracy of the clustering results, showing high accuracy. Based on the clustering results, the parameters for association rule mining were adjusted, such as increasing the support threshold to filter out infrequent association rules, further optimizing model performance.

[0066] In one embodiment, for the content recommendation unit described above, the content recommendation unit uses a combined recommendation algorithm to calculate which industrial heritage sites the user is interested in and generates a recommendation list. The specific steps are as follows:

[0067] Data collection: Obtaining user profiles and industrial heritage information;

[0068] Data cleaning: Identify and remove data that is irrelevant to industrial heritage recommendations, fill in missing values ​​in users' browsing history, ratings or reviews, and convert users' browsing behavior into a uniform scale;

[0069] Feature extraction: Extract textual features from the titles, summaries, content, or user comments of industrial heritage sites, convert them into numerical features, and construct user feature vectors based on user behavior;

[0070] Collaborative filtering:

[0071] Constructing a User-Industry Heritage Matrix: Based on users' browsing history and ratings, construct a user-industry heritage interaction matrix. Assuming there are m users and n industry heritages, the user-industry heritage matrix R can be represented as:

[0072]

[0073] Where, r ij This represents the rating or number of times user i has viewed industrial heritage site j. If a user has not viewed a certain industrial heritage site, the corresponding element can be set to 0 or left blank.

[0074] Calculate similarity: Calculate the similarity between users with shared browsing preferences or between industrial heritage sites with similar themes or content.

[0075]

[0076] Among them, I uv It is a collection of industrial heritage that both users u and v have interacted with, r ui It is a rating or some metric given by user u to industrial heritage site i, r vi It is the rating or metric given by user v to industrial heritage site i;

[0077] Generate a recommendation list: Based on the browsing history of similar users and the similarity of industrial heritage sites, generate a recommendation list for the current user;

[0078] Content-based recommendations:

[0079] Feature matching: Match the feature vectors of user browsing preferences with the feature vectors of industrial heritage sites, and calculate the similarity.

[0080]

[0081] in, It is a feature vector of user reading preferences. It is the feature vector of industrial heritage;

[0082] Rating prediction: Based on content similarity, predict users' potential interest or satisfaction with unread industrial heritage sites;

[0083] Mixed recommendations:

[0084] Weighting: Different weights are assigned to collaborative filtering and content-based recommendation based on the actual application scenario and data characteristics;

[0085] Overall score: The scores from collaborative filtering and content-based recommendation are weighted and summed to obtain the final recommendation score;

[0086] Generate recommendations: Based on the final score, select the industrial heritage site with the highest score as the recommendation result;

[0087] User feedback: Collect user feedback on the recommendation results;

[0088] Model tuning: Based on user feedback, dynamically adjust the parameters and weights of the recommendation model to optimize recommendation performance.

[0089] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An industrial heritage digitization exhibition system, characterized by, It comprises a front-end display layer, a back-end data management layer, and a technical support layer; The front-end display layer comprises a VR / AR experience module, an interactive touch screen display module, a mobile application function module, a deep cultural interpretation module, and a diversified form module. The VR / AR experience module is responsible for creating an immersive industrial heritage environment and realizing the construction of a virtual environment and user interaction. When a user explores an industrial heritage environment through a VR / AR device, the VR / AR experience module calls data stored in a digital asset management platform. The interactive touch screen display module is responsible for displaying industrial heritage through a large touch screen. The mobile application function module is responsible for developing a dedicated APP. The mobile application function collects user feedback and behavior data, which are transmitted to an intelligent recommendation system module through the mobile application function module. The deep cultural interpretation module is responsible for telling the historical stories and biographies behind industrial heritage. The diversified form module is responsible for designing games based on industrial heritage knowledge and utilizing holographic technology to display key scenes or products of industrial heritage. The back-end data management layer comprises a digital asset management platform module, an intelligent recommendation system module, and a customized experience module. The digital asset management platform module is responsible for centrally storing and managing all digitalized content. The digital asset management platform receives and stores 3D models generated by a 3D scanning and modeling technology module. The intelligent recommendation system module is responsible for identifying user interests and preferences through association rule mining and cluster analysis and recommending relevant industrial heritage content. The intelligent recommendation system module generates personalized recommendation content by analyzing user data and transmits it to the interactive touch screen display module through a data interface. The customized experience module is responsible for customizing visiting routes according to user interests and providing differentiated experiences. The technical support layer comprises a 3D scanning and modeling technology module, a big data and cloud computing module, and an artificial intelligence and machine learning module. The 3D scanning and modeling technology module is responsible for scanning industrial heritage and generating 3D models. The big data and cloud computing module is responsible for adopting a big data processing framework and a cloud computing platform. The big data and cloud computing module provides data processing and storage capabilities for the intelligent recommendation system module. The artificial intelligence and machine learning module is used for content classification, intelligent question answering, and sentiment analysis. The artificial intelligence and machine learning module interacts with the data analysis and feedback module.

2. The industrial heritage digitization display system of claim 1, wherein, The deep cultural interpretation module includes a content planning unit, a multimedia creation unit, an expert resource unit, a data management and storage unit, and a front-end display unit. The content planning unit is responsible for planning and formulating the content framework and theme of deep cultural interpretation. The multimedia creation unit creates content in multimedia form according to the requirements of the content planning unit. The expert resource unit invites industry experts to record videos or audios, providing professional perspectives for interpretation and analysis. The data management and storage unit manages and stores all multimedia content and expert interpretations in the deep cultural interpretation module. The front-end display unit displays the content of the deep cultural interpretation module to users through the user interface, realizing user interaction with the content.

3. The industrial heritage digitization display system of claim 1, wherein, The digital asset management platform module includes a data storage unit, a content management unit, a data calling unit, a permission management unit, and a data analysis unit. The data storage unit is responsible for the storage and backup of digital content. The content management unit provides content management functions. The content management unit includes a user interface and a background management interface. The data calling unit is responsible for the calling and response of digital content. The data calling unit uses distributed storage and caching technology. The permission management unit provides permission control for users accessing digital content. The permission management unit includes user identity verification, role allocation, and permission setting. The data analysis unit conducts statistics and analysis on the use of digital content. When a user needs to access digital content, the data calling unit initiates a request to the data storage unit to obtain the required resources. When a user attempts to access content, the content management unit initiates a request to the permission management unit to verify the user's permissions. The data analysis unit conducts statistics and analysis on the use of digital content, generates reports and data visualization charts, and feeds back the reports and charts to the content management unit for optimizing content management and improving user experience.

4. The industrial heritage digitization display system of claim 1, wherein, The intelligent recommendation system module includes a data collection unit, an interest analysis unit, a content recommendation unit, and an effect evaluation unit. The data collection unit is responsible for collecting user behavior data. The interest analysis unit uses big data analysis and machine learning algorithms to deeply mine and analyze user behavior data, identify user interests and preferences, and recommend relevant industrial heritage content based on user interests and preferences. The effect evaluation unit evaluates and optimizes the recommendation effect in real time through user feedback, click-through rate, and conversion rate. The data collection unit transmits the collected user behavior data to the interest analysis unit, and the interest analysis unit transmits the analyzed user interests and preferences to the content recommendation unit.

5. An industrial heritage digitalization presentation system according to claim 4, characterized in that, The customized experience module includes a route customization unit, an experience providing unit, and a culture spreading unit. The route customization unit generates personalized visiting routes based on the interests and preferences of users, combined with the characteristics and display requirements of industrial heritage. The experience providing unit provides differentiated experiences for users according to the customized visiting routes, including content display, interactive project setting, and game design. The culture spreading unit focuses on the dissemination and popularization of industrial heritage culture while providing personalized experiences. The interest analysis unit transmits the analyzed user interests and preferences to the route customization unit, which generates visiting routes and transmits them to the experience providing unit. The experience providing unit cooperates with the culture spreading unit while providing personalized experiences.

6. The industrial heritage digitization display system of claim 1, wherein, The 3D scanning and modeling technology module includes a scanning device unit, a data processing unit, and a modeling software unit. The scanning device unit is responsible for scanning industrial heritage using 3D scanning equipment to capture surface details and shape features. The data processing unit preprocesses and cleans the scanned data to remove noise and redundant information. The modeling software unit performs three-dimensional reconstruction on the processed data to generate a 3D model.

7. The industrial heritage digitization display system of claim 4, wherein, The interest analysis unit uses association rule mining combined with clustering analysis to identify user interests and preferences. The specific steps are as follows: Data preprocessing: Collect user behavior data, clean the data, remove redundancy and outliers, and convert the data into a format suitable for association rule mining and clustering analysis. Association rule mining: Use Apriori to extract frequent itemsets and association rules from user behavior data, set support and confidence thresholds, and filter out meaningful association rules. The support represents the frequency of itemsets appearing in the data set, and the formula is Support(A→B)=P(A∪B). The confidence represents the probability of itemset B appearing under the condition of itemset A, and the formula is Confidence(A→B)=P(B|A). Clustering analysis: Use K-means to group users, each group having similar interests and preferences. According to the results of association rule mining, input frequent itemsets and association rules into K-means as features, and set the number of clusters K. Result interpretation and verification: Analyze the clustering results, interpret the interests and preferences of different user groups, evaluate the stability and accuracy of the clustering results, adjust the parameters of association rule mining according to the clustering results, and optimize the model performance.

8. The industrial heritage digitization display system of claim 4, wherein, The content recommendation unit uses a combination recommendation algorithm to calculate which industrial heritage users are interested in and generates a recommendation list. The specific steps are as follows: Collect data: Obtain user profiles and industrial heritage information; Data cleaning: Identify and remove irrelevant data for industrial heritage recommendation, reasonably fill in missing values in user browsing records, ratings, or comments, and convert user browsing behavior to a unified scale; Feature extraction: Extract text features from industrial heritage titles, abstracts, content, or user comments and convert them to numerical features. Construct user feature vectors based on user behavior; Collaborative filtering: Constructing User-Industrial Heritage Matrix: Based on the user's browsing history and ratings, construct a user-industrial heritage interaction matrix, assuming there are m users and n industrial heritage, the user-industrial heritage matrix R can be represented as: wherein r ij represents the rating or the number of visits of user i to industrial heritage j, and if a certain user has not visited a certain industrial heritage, the corresponding element can be set to 0 or left blank; Calculate Similarity: Calculate the similarity between users with common browsing preferences or the similarity between industrial heritage with similar themes and content: where I uv is the set of industrial heritage that both user u and user v have interacted with, r ui is the rating or some measure value of industrial heritage i by user u, r vi is the rating or measure value of industrial heritage i by user v; Generate Recommendation List: Based on the browsing history of similar users and the similarity of industrial heritage, generate a recommendation list for the current user; Content-based Recommendation: Feature Matching: Match the feature vector of the user's browsing preferences with the feature vector of the industrial heritage, and calculate the similarity: wherein, is a feature vector of the user's reading preferences, is a feature vector of the industrial heritage; Score Prediction: Based on the content similarity, predict the user's potential interest or satisfaction for the industrial heritage not read; Hybrid Recommendation: Weight Assignment: According to the actual application scene and data characteristics, assign different weights to collaborative filtering and content-based recommendation; Comprehensive Score: Weighted sum the scores of collaborative filtering and content-based recommendation according to the weight, get the final recommendation score; Generate Recommendations: According to the final score, select the industrial heritage with the highest score as the recommendation result; User Feedback: Collect user feedback on the recommendation result; Model Adjustment: According to the user feedback, dynamically adjust the parameters and weights of the recommendation model, optimize the recommendation effect.

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