Intelligent cultural relic showcase information pushing method and system

Abstract

The invention discloses an intelligent pushing method and system for cultural relic showcase information, and relates to the technical field of intelligent pushing of cultural relic exhibition data. The system collects audience behavior data in real time through a camera and an interaction device and preprocesses the data; the interest analysis module calculates an audience preference degree coefficient Gzp and a showcase popularity degree coefficient Zpr, summarizes and calculates to obtain a showcase comprehensive preference coefficient ZRD, preliminarily compares and evaluates the showcase comprehensive preference coefficient ZRD with a preset preference threshold value Z, and judges whether to trigger information push according to the evaluation result of the audience's preference to the showcase; the comprehensive pushing module dynamically calculates a path weight coefficient Ljq and a comprehensive pushing index ZLT, and compares and evaluates the path weight coefficient Ljq and the comprehensive pushing index ZLT with a preset comprehensive pushing threshold value M; the personalized pushing module pushes the optimized visiting route through an APP of the audience to guide the audience to visit and provide real-time adjustment suggestions, so that the visiting experience is improved; the system realizes intelligent and personalized recommendation of cultural relic showcase information through dynamic analysis and real-time pushing.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent push of cultural relic exhibition data, and in particular to an intelligent push method and system for cultural relic display cabinet information. Background Art

[0002] With the digital transformation of museums and exhibition halls, the application of intelligent technology in exhibition experience is becoming more and more extensive. The traditional display of information in display cabinets often cannot meet the personalized and diversified information needs of the audience, and it is difficult to provide an immersive visiting experience. With the rapid development of the Internet of Things, big data and artificial intelligence technologies, the intelligent push system can accurately and timely deliver display cabinet information to the audience, which can significantly improve the interactivity and sense of participation of the visit, provide the audience with richer cultural background and historical interpretation, and promote the in-depth integration of cultural relics protection and cultural inheritance.

[0003] At present, most cultural relics exhibition halls only provide fixed tour routes, or rely on visitors to explore the exhibition halls on their own. Although this method is simple, it lacks response to individual needs and can easily cause visitors to miss the showcases they are really interested in. In addition, the traditional tour mode cannot dynamically adjust the route, resulting in excessive crowds around some showcases during peak hours, which not only affects the exhibition experience but also may cause safety hazards. Traditional systems cannot perceive changes in audience interests in real time, making it difficult to provide personalized push services. Visitors often have to rely on themselves to find information, which is particularly inconvenient in exhibitions with a large amount of information. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a method and system for intelligently pushing information of cultural relics display cabinets, which solves the problems mentioned in the background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: an intelligent push method and system for cultural relics display cabinet information, including an audience behavior collection module, an interest analysis module, a display cabinet heat evaluation module, a comprehensive push module and a personalized push module; The audience behavior collection module is used to design a connection port with the camera and interactive equipment in the cultural relics exhibition hall, to integrate and connect to collect audience behavior data and display cabinet data in real time, and to generate audience behavior data group and exhibition space data group after pre-processing the collected audience behavior data and display cabinet data; The interest analysis module is used to summarize and calculate the pre-processed audience behavior data to obtain the audience preference coefficient Gzp and the showcase popularity coefficient Zpr; The showcase heat evaluation module is used to aggregate and calculate the acquired audience preference coefficient Gzp and showcase popularity coefficient Zpr to obtain the showcase comprehensive preference coefficient ZRD, and preset the preference threshold Z for preliminary comparative evaluation, and determine whether to trigger information push according to the evaluation result, and choose to start the secondary comparative evaluation; The comprehensive push module obtains the audience fatigue pl through exponential decay function calculation and combines it with the obtained exhibition space data group to obtain the path weight coefficient Ljq, and associates it with the obtained audience preference coefficient Gzp and the display cabinet popularity coefficient Zpr to obtain the comprehensive push index ZLT, and performs a secondary comparative evaluation with the preset comprehensive push threshold M, and optimizes the visiting route according to the corresponding evaluation results; The personalized push module is used to push the generated route to the audience app through the terminal device using the message push technology, and guide the audience to visit along the recommended route through the audience app interface.

[0006] Preferably, the audience behavior collection module includes an audience behavior collection unit and a data preprocessing unit; The audience behavior acquisition unit is used for the camera monitoring system installed in the cultural relics exhibition hall. Through the monitoring camera with a resolution lens greater than or equal to 2k and a 360-degree wide-angle coverage, the behavior and movement trajectory of the audience are captured in real time. The built-in AI video analysis function of the camera and the connected analysis software process the video stream to identify the behavior information of the audience. The monitoring camera monitors the flow of people in the exhibition area and obtains the crowd distribution information by using thermal imaging and motion detection technology. The monitoring camera processes the complex light in the exhibition hall through low-light performance and wide dynamic range WDR; The interactive device collects the audience's operation data in real time through the built-in infrared touch sensor, and records each click and touch of the audience on the screen to record each audience interaction operation information; The data preprocessing unit is used to perform missing value processing, outlier detection, data type conversion and data filtering preprocessing on the crowd distribution information, audience behavior information and audience interactive operation information collected by the audience behavior collection unit, so as to obtain audience behavior data and exhibition space data; The audience behavior data includes audience preference data and showcase attention data; The audience preference data includes the exhibition browsing time ls, the number of exhibition screen clicks dc, the number of shares fc and the distance between the audience and the display cabinet jl; The display cabinet attention data includes the number of views lc, the exhibit stay time zt and the number of interactions hc; The exhibition space data includes the crowd density rm and the distance jl between two display cabinets; The pre-processed data is transmitted to the central control center via a connection port, and the connection port is a wireless communication port.

[0007] Preferably, the interest analysis module includes a preference calculation unit and a showcase popularity calculation unit; The preference calculation unit is used to perform dimensionless processing on the obtained audience preference data, and then summarize and calculate to obtain the audience preference coefficient Gzp; The audience preference coefficient Gzp is obtained by the following formula: ; In the formula, Represents a logarithmic function, smoothing the time differences between different viewers and avoiding excessive preference caused by extremely long time; represents a logarithmic function, smoothing the impact of sharing behavior on preference and preventing a small number of viewers with extremely high sharing times fc from causing excessive inflation of preference; represents an exponential decay function, which is used to describe the combined effect of distance jl and browsing time ls on the audience's preference; The showcase popularity calculation unit is used to perform dimensionless processing on the acquired showcase attention data, and then summarize and calculate to obtain the showcase popularity coefficient Zpr; The showcase popularity coefficient Zpr is obtained by the following formula: ; In the formula, represents the adjustment factor of the number of views lc, and >1, represents a logarithmic function, which performs logarithmic scaling on the exhibit residence time zt; represents the adjustment factor for the number of interactions hc, and ≥1.

[0008] Preferably, the showcase heat evaluation module includes a showcase comprehensive heat calculation unit and a showcase comprehensive heat evaluation unit; The display cabinet comprehensive heat calculation unit is used to perform dimensionless processing on the obtained audience preference coefficient Gzp and the display cabinet heat coefficient Zpr, and then summarize and calculate to obtain the display cabinet comprehensive preference coefficient ZRD; The display cabinet comprehensive preference coefficient ZRD is calculated by the following formula: ; In the formula, represents the comprehensive preference of the kth showcase, represents the preference of the kth showcase, represents the popularity of the kth showcase, w1 and w2 represent the weight coefficients of the audience preference coefficient Gzp and the showcase popularity coefficient Zpr respectively, w1+w2=1, and 0<w1<0.47, 0<w2<0.53.

[0009] Preferably, the showcase comprehensive heat evaluation unit is used to preset the preference threshold Z through the average preference of historical audiences, and to make an initial comparative evaluation with the audience preference coefficient Gzp obtained by the preference calculation unit, and to generate optimization information according to the evaluation result. The specific evaluation scheme is as follows; When the audience preference coefficient Gzp> the preset preference threshold Z, it means that the audience is interested in this showcase. At this time, the push mechanism is triggered to push the cultural relics information of this showcase to the audience through the app, and the second evaluation module is triggered; When the audience preference coefficient Gzp ≤ the preset preference threshold Z, it means that the audience is not interested in this showcase. At this time, this showcase is marked as a non-visited showcase and the push mechanism is not triggered.

[0010] Preferably, the comprehensive push module includes a data calculation unit and a comprehensive calculation unit; The data calculation unit includes an audience fatigue calculation unit and a path weight calculation unit; The audience fatigue calculation unit is used to monitor the total visit time of the audience entering the exhibition hall through the camera monitoring system, and construct the audience fatigue pl formula through the inverse function of the exponential decay function; The audience fatigue degree pl is obtained by the following formula: ; In the formula, is the fatigue growth rate, t means the audience’s fatigue level increases with time t, represents an exponential function, indicating that the remaining non-fatigued portion will gradually decrease as time increases; The path weight calculation unit is used to perform dimensionless processing on the acquired exhibition space data and the audience fatigue pl acquired by the audience fatigue calculation unit, and then summarize and calculate to acquire the path weight coefficient Ljq; The path weight coefficient Ljq is obtained by the following formula: ; In the formula, represents the path weight from showcase i to showcase j, represents the distance between the i-th showcase and the j-th showcase, and log represents the logarithmic function.

[0011] Preferably, the comprehensive calculation unit includes a comprehensive push calculation unit and a push evaluation unit; The comprehensive push calculation unit is used to perform dimensionless processing on the obtained audience preference coefficient Gzp, showcase heat coefficient Zpr, path weight coefficient Ljq and the crowd density rm extracted from the exhibition space data, and then summarize and calculate to obtain the comprehensive push index ZLT; The comprehensive push index ZLT is obtained by the following formula: ; In the formula, R represents the route chosen by the visitor, i represents an exhibit in route R, represents the path from exhibit i to exhibit j in route R, Indicates the audience's preference for exhibit i, ranging from [0,1]. The closer it is to 1, the more interested the audience is in exhibit i. Indicates the popularity of exhibit i, ranging from [0,1], reflecting the current popularity of exhibit i. The higher the popularity, the greater the current appeal of exhibit i. It is expressed as the maximum allowed crowd density in the venue; It represents the dynamic path weight after adding the effect of fatigue. Represents the adjustment coefficient.

[0012] Preferably, the push evaluation unit is used to start the second evaluation mechanism when the audience is interested in the exhibits on this route. The second evaluation mechanism performs a secondary comparative evaluation by using the preset comprehensive push threshold M and the comprehensive push index ZLT obtained by the comprehensive calculation unit, and generates corresponding optimization information according to the relevant evaluation results. The specific evaluation scheme is as follows; When the comprehensive push index ZLT> the preset comprehensive push threshold M, it means that the visitors are satisfied with the pushed tour route. At this time, continuous monitoring is maintained and the cultural relic information of each display cabinet is pushed in real time according to the visitor's location information; When the comprehensive push index ZLT ≤ the preset comprehensive push threshold M, it means that the audience is not satisfied with the pushed tour route. At this time, an optimization signal is generated. The system transmits the optimization signal to the showcase heat evaluation module to perform iterative optimization based on the audience's preference for the showcase, and transmits the optimization results and the brief introduction information of the showcases along the way to the audience app through the wireless communication port for the audience's reference until the audience is satisfied.

[0013] Preferably, the personalized push module generates a tour route by integrating audience behavior data and exhibition space data. The generated route is pushed to the audience through mobile applications or other terminal devices. The system uses notification and message push technology to enable the audience to receive the recommended route in a timely manner. The user interaction design guides the audience to visit along the recommended route and provides real-time navigation and adjustment suggestions.

[0014] A method for intelligently pushing information of cultural relics display cabinets comprises the following steps: S1. Design connection ports with cameras and interactive devices in the cultural relics exhibition hall to integrate and connect to collect audience behavior data and display cabinet data in real time, and pre-process the collected audience behavior data and display cabinet data to generate audience behavior data groups and exhibition space data groups; S2. Summarize and calculate the pre-processed audience behavior data to obtain the audience preference coefficient Gzp and the showcase popularity coefficient Zpr; S3, summarizing and calculating the obtained audience preference coefficient Gzp and showcase popularity coefficient Zpr to obtain the showcase comprehensive preference coefficient ZRD, and presetting the preference threshold Z for preliminary comparative evaluation, and judging whether to trigger information push according to the evaluation results, and choosing to start secondary comparative evaluation; S4. Obtain the audience fatigue pl through exponential decay function calculation and combine it with the obtained exhibition space data group to obtain the path weight coefficient Ljq, and associate it with the obtained audience preference coefficient Gzp and showcase popularity coefficient Zpr to obtain the comprehensive push index ZLT, and perform secondary comparative evaluation with the preset comprehensive push threshold M, and optimize the tour route according to the corresponding evaluation results; S5. Push the generated route to the audience app through the terminal device using the message push technology, and guide the audience to visit along the recommended route through the audience app interface.

[0015] The present invention provides a method and system for intelligently pushing information of cultural relics display cabinets. It has the following beneficial effects: (1) The system collects the audience's behavior data and exhibition space data in real time through camera monitoring and interactive devices, and generates the audience preference coefficient Gzp and the showcase popularity coefficient Zpr after preprocessing. Through in-depth analysis of these data, the system can accurately identify the audience's points of interest, and dynamically push the showcase information that matches the audience's interests based on the audience preference coefficient Gzp and the showcase popularity coefficient Zpr. This function realizes intelligent exhibition guidance and enhances the audience's exhibition experience.

[0016] (2) The system calculates the comprehensive preference coefficient ZRD of the showcase through the showcase heat evaluation module, and uses the path weight coefficient Ljq and the audience fatigue factor pl to comprehensively evaluate the comprehensive push index ZLT. This optimized push mechanism not only improves the audience's satisfaction in the exhibition, but also optimizes the streamline design of the exhibition visit. In particular, the audience's fatigue is scientifically calculated through the exponential decay function, and the visit route is generated by combining the crowd density rm and the path weight coefficient Ljq in the exhibition space. The route is pushed to the audience in real time through the personalized push module. This path optimization design avoids the fatigue and ineffective movement that the audience may experience in the exhibition, making the exhibition more efficient.

[0017] (3) The system achieves dynamic and personalized exhibition information push through a data-driven approach. Visitors can not only obtain information about exhibits of interest in a timely manner, but also browse the entire exhibition efficiently through optimized tour routes. This innovative improvement not only improves the efficiency of exhibition visits, but also enhances the audience's understanding and participation in the exhibition content, greatly improving the overall exhibition experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a schematic diagram of the process of an intelligent push system for cultural relics display cabinet information of the present invention; Figure 2 The present invention is a schematic diagram of the steps of a method for intelligently pushing information of cultural relics display cabinets. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] Example 1 See also Figure 1 , the present invention provides an injection mold human-computer interaction intelligent design optimization system. To achieve the above purpose, the present invention is implemented through the following technical solutions: including audience behavior collection module, interest analysis module, showcase heat evaluation module, comprehensive push module and personalized push module; The audience behavior collection module is used to design a connection port with the camera and interactive equipment in the cultural relics exhibition hall, to integrate and connect to collect audience behavior data and display cabinet data in real time, and to generate audience behavior data group and exhibition space data group after pre-processing the collected audience behavior data and display cabinet data; The interest analysis module is used to summarize and calculate the pre-processed audience behavior data to obtain the audience preference coefficient Gzp and the showcase popularity coefficient Zpr; The showcase heat evaluation module is used to aggregate and calculate the acquired audience preference coefficient Gzp and showcase popularity coefficient Zpr to obtain the showcase comprehensive preference coefficient ZRD, and preset the preference threshold Z for preliminary comparative evaluation, and determine whether to trigger information push according to the evaluation result, and choose to start the secondary comparative evaluation; The comprehensive push module obtains the audience fatigue pl through exponential decay function calculation and combines it with the obtained exhibition space data group to obtain the path weight coefficient Ljq, and associates it with the obtained audience preference coefficient Gzp and the display cabinet popularity coefficient Zpr to obtain the comprehensive push index ZLT, and performs a secondary comparative evaluation with the preset comprehensive push threshold M, and optimizes the visiting route according to the corresponding evaluation results; The personalized push module is used to push the generated route to the audience app through the terminal device using the message push technology, and guide the audience to visit along the recommended route through the audience app interface.

[0021] In this embodiment, the audience behavior collection module is closely integrated with the camera and interactive equipment in the exhibition hall to collect audience behavior data and display cabinet data in real time. The audience behavior data group and the exhibition space data group are generated by the pre-processed data, which lays the foundation for subsequent analysis. This module not only realizes efficient data collection, but also reduces data noise through pre-processing, improves the accuracy and utilization efficiency of the data. Through the interest analysis module and the display cabinet heat evaluation module, the audience's preference and the popularity of the display cabinet are deeply analyzed. The interest analysis module calculates the audience preference coefficient Gzp and the display cabinet popularity coefficient Zpr, and the display cabinet heat evaluation module summarizes these coefficients to generate the display cabinet comprehensive preference coefficient ZRD. A preliminary evaluation is performed by presetting the preference threshold Z, and judging whether to trigger information push according to the evaluation results, and then a secondary comparative evaluation is performed based on the results to ensure the accurate judgment of the display cabinet heat. Compared with the traditional simple statistical analysis method, this multi-level evaluation method greatly improves the accuracy of the analysis results and helps museums better understand audience interests. The comprehensive push module combines the visitor's fatigue and exhibition space data, calculates the path weight coefficient Ljq, and associates it with the visitor's interest data to finally obtain the comprehensive push index ZLT, and further optimizes it according to the preset comprehensive push threshold M. Through the personalized push module, the system pushes the optimized tour route directly to the visitor's terminal device, and guides the visitor to visit in combination with the App interface, making the visit process smoother and more personalized.

[0022] Example 2 This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the audience behavior collection module includes an audience behavior collection unit and a data preprocessing unit; The audience behavior acquisition unit is used for the camera monitoring system installed in the cultural relics exhibition hall. Through the monitoring camera with a resolution lens greater than or equal to 2k and a 360-degree wide-angle coverage, the behavior and movement trajectory of the audience are captured in real time. The built-in AI video analysis function of the camera and the connected analysis software process the video stream to identify the behavior information of the audience. The monitoring camera monitors the flow of people in the exhibition area and obtains the crowd distribution information by using thermal imaging and motion detection technology. The monitoring camera processes the complex light in the exhibition hall through low-light performance and wide dynamic range WDR; The interactive device collects the audience's operation data in real time through the built-in infrared touch sensor, and records each click and touch of the audience on the screen to record each audience interaction operation information; The data preprocessing unit is used to perform missing value processing, outlier detection, data type conversion and data filtering preprocessing on the crowd distribution information, audience behavior information and audience interactive operation information collected by the audience behavior collection unit, so as to obtain audience behavior data and exhibition space data; The audience behavior data includes audience preference data and showcase attention data; The audience preference data includes the exhibition browsing time ls, the number of exhibition screen clicks dc, the number of shares fc and the distance between the audience and the display cabinet jl; The display cabinet attention data includes the number of views lc, the exhibit stay time zt and the number of interactions hc; The exhibition space data includes the crowd density rm and the distance jl between two display cabinets; The pre-processed data is transmitted to the central control center via a connection port, and the connection port is a wireless communication port.

[0023] In this embodiment, the audience behavior collection module uses a high-resolution camera and built-in AI video analysis function to accurately capture audience behavior and movement trajectories, and combines thermal imaging and motion detection technology to obtain crowd distribution information. At the same time, the interactive device records every interactive operation of the audience through an infrared touch sensor. The data preprocessing unit further processes these data and ensures the integrity and accuracy of the data through missing value processing, outlier detection, data type conversion and data filtering methods. This module not only improves the collection accuracy of audience behavior data, but also provides reliable data support for subsequent interest analysis and intelligent push, significantly improving the intelligence and personalization level of the exhibition.

[0024] Example 3 This embodiment is explained in Example 2. Please refer to Figure 1 ,Specifically: the interest analysis module includes a preference calculation unit and a showcase popularity calculation unit; The preference calculation unit is used to perform dimensionless processing on the obtained audience preference data, and then summarize and calculate to obtain the audience preference coefficient Gzp; The audience preference coefficient Gzp is obtained by the following formula: ; In the formula, represents the logarithmic function; represents the logarithmic function; represents an exponential decay function; The showcase popularity calculation unit is used to perform dimensionless processing on the acquired showcase attention data, and then summarize and calculate to obtain the showcase popularity coefficient Zpr; The showcase popularity coefficient Zpr is obtained by the following formula: ; In the formula, represents the adjustment factor of the number of views lc, and >1, represents a logarithmic function, which performs logarithmic scaling on the exhibit residence time zt; represents the adjustment factor for the number of interactions hc, and ≥1.

[0025] In this embodiment, by calculating the audience's preference coefficient and the display cabinet popularity coefficient, it helps museums or exhibition curators optimize the exhibit layout and exhibition content. The preference calculation unit calculates the audience preference coefficient Gzp by dimensionless processing the audience's preference data, which reflects the audience's overall interest in the exhibits. The display cabinet popularity calculation unit calculates the display cabinet popularity coefficient Zpr by dimensionless processing the display cabinet attention data, which combines the audience's browsing times lc, the exhibit stay time zt and the number of interactions hc to help evaluate the attractiveness of the display cabinet. Through the analysis of these coefficients, curators can accurately understand the audience's preferences, so as to make exhibition decisions that are more in line with the audience's interests, and improve the overall efficiency of the exhibition and the audience's satisfaction.

[0026] Example 4 This embodiment is explained in Example 3, please refer to Figure 1 ,Specifically: the showcase heat evaluation module includes a showcase comprehensive heat calculation unit and a showcase comprehensive heat evaluation unit; The display cabinet comprehensive heat calculation unit is used to perform dimensionless processing on the obtained audience preference coefficient Gzp and the display cabinet heat coefficient Zpr, and then summarize and calculate to obtain the display cabinet comprehensive preference coefficient ZRD; The display cabinet comprehensive preference coefficient ZRD is calculated by the following formula: ; In the formula, represents the comprehensive preference of the kth showcase, represents the preference of the kth showcase, represents the popularity of the kth showcase, w1 and w2 represent the weight coefficients of the audience preference coefficient Gzp and the showcase popularity coefficient Zpr respectively, and w1+w2=1.

[0027] The display cabinet comprehensive heat evaluation unit is used to preset the preference threshold Z through the average preference of historical audiences, and to make an initial comparative evaluation with the audience preference coefficient Gzp obtained by the preference calculation unit, and to generate optimization information according to the evaluation results. The specific evaluation scheme is as follows; When the audience preference coefficient Gzp> the preset preference threshold Z, it means that the audience is interested in this showcase. At this time, the push mechanism is triggered to push the cultural relics information of this showcase to the audience through the app, and the second evaluation module is triggered; When the audience preference coefficient Gzp ≤ the preset preference threshold Z, it means that the audience is not interested in this showcase. At this time, this showcase is marked as a non-visited showcase and the push mechanism is not triggered.

[0028] In this embodiment, personalized information push is achieved by intelligently evaluating the comprehensive popularity of the showcase, effectively improving the interaction between the audience and the showcase. By comparing the audience preference coefficient Gzp with the average preference in the historical data with the preset preference threshold Z, the audience's points of interest can be accurately identified, and relevant cultural relics information can be pushed in time when the audience is interested, increasing the audience's sense of participation and satisfaction. In addition, the optimized evaluation scheme further improves the system's push accuracy and reduces unnecessary push operations, thereby improving the system's efficiency and audience experience. This intelligent push mechanism can not only enhance the audience's exhibition experience, but also provide museums with more effective audience behavior data analysis, which is helpful for the optimization and management of exhibition content.

[0029] Example 5 This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the comprehensive push module includes a data calculation unit and a comprehensive calculation unit; The data calculation unit includes an audience fatigue calculation unit and a path weight calculation unit; The audience fatigue calculation unit is used to calculate the total visit time of the audience entering the exhibition hall monitored by the camera monitoring system, and construct the audience fatigue pl formula through the inverse function of the exponential decay function; The audience fatigue degree pl is obtained by the following formula: ; In the formula, is the fatigue growth rate, t means the audience’s fatigue level increases with time t, represents the exponential function, which indicates the remaining unfatigued portion; The path weight calculation unit is used to perform dimensionless processing on the acquired exhibition space data and the audience fatigue pl acquired by the audience fatigue calculation unit, and then summarize and calculate to acquire the path weight coefficient Ljq; The path weight coefficient Ljq is obtained by the following formula: ; In the formula, represents the path weight from showcase i to showcase j, represents the distance between the i-th showcase and the j-th showcase, and log represents the logarithmic function.

[0030] The comprehensive calculation unit includes a comprehensive push calculation unit and a push evaluation unit; The comprehensive push calculation unit is used to perform dimensionless processing on the obtained audience preference coefficient Gzp, showcase heat coefficient Zpr, path weight coefficient Ljq and the crowd density rm extracted from the exhibition space data, and then summarize and calculate to obtain the comprehensive push index ZLT; The comprehensive push index ZLT is obtained by the following formula: ; In the formula, R represents the route chosen by the visitor, i represents an exhibit in route R, represents the path from exhibit i to exhibit j in route R, represents the audience's preference for exhibit i, ranging from [0,1]; Indicates the popularity of exhibit i, ranging from [0,1]; It is expressed as the maximum allowed crowd density in the venue; It represents the dynamic path weight after adding the effect of fatigue. Represents the adjustment coefficient.

[0031] The push evaluation unit is used to start the second evaluation mechanism when the audience is interested in the exhibits on this route. The second evaluation mechanism performs a secondary comparative evaluation by presetting the comprehensive push threshold M and the comprehensive push index ZLT obtained by the comprehensive calculation unit, and generates corresponding optimization information according to the relevant evaluation results. The specific evaluation scheme is as follows; When the comprehensive push index ZLT> the preset comprehensive push threshold M, it means that the visitors are satisfied with the pushed tour route. At this time, continuous monitoring is maintained and the cultural relic information of each display cabinet is pushed in real time according to the visitor's location information; When the comprehensive push index ZLT ≤ the preset comprehensive push threshold M, it means that the audience is not satisfied with the pushed tour route. At this time, an optimization signal is generated. The system transmits the optimization signal to the showcase heat evaluation module to perform iterative optimization based on the audience's preference for the showcase, and transmits the optimization results and the brief introduction information of the showcases along the way to the audience app through the wireless communication port for the audience's reference until the audience is satisfied.

[0032] In this embodiment, by real-time monitoring of the visitor's visiting behavior and fatigue, the comprehensive push index ZLT is calculated using the multi-dimensional data of the path weight coefficient Ljq, the visitor preference coefficient Gzp and the showcase popularity coefficient Zpr. By comparing the comprehensive push index ZLT with the preset comprehensive push threshold M, the system can effectively evaluate the visitor's satisfaction with the current visiting route. When the visitor is satisfied, the system continues to push relevant showcase information; when the visitor is dissatisfied, the system starts the iterative optimization mechanism, adjusts the exhibition route and pushes the profile information of the optimized showcase. This dynamic push and optimization mechanism can enhance the visitor's visiting experience and increase the attractiveness and participation of the exhibition.

[0033] Example 6 This embodiment is explained in Example 1, please refer to Figure 1 Specifically: the personalized push module generates a tour route by integrating audience behavior data and exhibition space data. The generated route is pushed to the audience through mobile applications or other terminal devices. The system uses notification and message push technology to enable the audience to receive the recommended route in time. The user interaction design guides the audience to visit along the recommended route and provides real-time navigation and adjustment suggestions.

[0034] In this embodiment, the best tour route is generated by integrating the audience behavior data and the exhibition space data, and is pushed to the audience through mobile applications or other terminal devices. The beneficial benefit of this module is that it can provide personalized tour suggestions for the audience based on the interests of each audience and the dynamic changes of the exhibition space. The audience can receive a more targeted exhibition experience by receiving the recommended route. The system guides the audience to visit along the recommended route through message push and user interaction design, and provides real-time navigation and adjustment suggestions, thereby improving the efficiency of the visit and enhancing the audience's satisfaction and sense of participation.

[0035] Example 7 See also Figure 2 , a method for intelligently pushing information of cultural relics display cabinets, comprising the following steps: S1. Design connection ports with cameras and interactive devices in the cultural relics exhibition hall to integrate and connect to collect audience behavior data and display cabinet data in real time, and pre-process the collected audience behavior data and display cabinet data to generate audience behavior data groups and exhibition space data groups; S2. Summarize and calculate the pre-processed audience behavior data to obtain the audience preference coefficient Gzp and the showcase popularity coefficient Zpr; S3, summarizing and calculating the obtained audience preference coefficient Gzp and showcase popularity coefficient Zpr to obtain the showcase comprehensive preference coefficient ZRD, and presetting the preference threshold Z for preliminary comparative evaluation, and judging whether to trigger information push according to the evaluation results, and choosing to start secondary comparative evaluation; S4. Obtain the audience fatigue pl through exponential decay function calculation and combine it with the obtained exhibition space data group to obtain the path weight coefficient Ljq, and associate it with the obtained audience preference coefficient Gzp and showcase popularity coefficient Zpr to obtain the comprehensive push index ZLT, and perform secondary comparative evaluation with the preset comprehensive push threshold M, and optimize the tour route according to the corresponding evaluation results; S5. Push the generated route to the audience app through the terminal device using the message push technology, and guide the audience to visit along the recommended route through the audience app interface.

[0036] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent push system for cultural relics display cabinet information, characterized in that: It includes audience behavior collection module, interest analysis module, showcase popularity evaluation module, comprehensive push module and personalized push module; The audience behavior collection module is used to design a connection port with the camera and interactive equipment in the cultural relics exhibition hall, to integrate and connect to collect audience behavior data and display cabinet data in real time, and to generate audience behavior data group and exhibition space data group after pre-processing the collected audience behavior data and display cabinet data; The interest analysis module is used to summarize and calculate the pre-processed audience behavior data to obtain the audience preference coefficient Gzp and the showcase popularity coefficient Zpr; The showcase heat evaluation module is used to aggregate and calculate the acquired audience preference coefficient Gzp and showcase popularity coefficient Zpr to obtain the showcase comprehensive preference coefficient ZRD, and preset the preference threshold Z for preliminary comparative evaluation, and determine whether to trigger information push according to the evaluation result, and choose to start the secondary comparative evaluation; The comprehensive push module obtains the audience fatigue pl through exponential decay function calculation and combines it with the obtained exhibition space data group to obtain the path weight coefficient Ljq, and associates it with the obtained audience preference coefficient Gzp and the display cabinet popularity coefficient Zpr to obtain the comprehensive push index ZLT, and performs a secondary comparative evaluation with the preset comprehensive push threshold M, and optimizes the visiting route according to the corresponding evaluation results; The personalized push module is used to push the generated route to the audience app through the terminal device using the message push technology, and guide the audience to visit along the recommended route through the audience app interface.

2. The intelligent push system for cultural relics display cabinet information according to claim 1 is characterized by: The audience behavior collection module includes an audience behavior collection unit and a data preprocessing unit; The audience behavior acquisition unit is used for the camera monitoring system installed in the cultural relics exhibition hall. Through the monitoring camera with a resolution lens greater than or equal to 2k and a 360-degree wide-angle coverage, the behavior and movement trajectory of the audience are captured in real time. The built-in AI video analysis function of the camera and the connected analysis software process the video stream to identify the behavior information of the audience. The monitoring camera monitors the flow of people in the exhibition area and obtains the crowd distribution information by using thermal imaging and motion detection technology; The surveillance camera handles the complex light in the exhibition hall through low-light performance and wide dynamic range WDR; The interactive device collects the audience's operation data in real time through the built-in infrared touch sensor, and records each click and touch of the audience on the screen to record each audience interaction operation information; The data preprocessing unit is used to perform missing value processing, outlier detection, data type conversion and data filtering preprocessing on the crowd distribution information, audience behavior information and audience interactive operation information collected by the audience behavior collection unit, so as to obtain audience behavior data and exhibition space data; The audience behavior data includes audience preference data and showcase attention data; The audience preference data includes the exhibition browsing time ls, the number of exhibition screen clicks dc, the number of shares fc and the distance between the audience and the display cabinet jl; The display cabinet attention data includes the number of views lc, the exhibit stay time zt and the number of interactions hc; The exhibition space data includes the crowd density rm and the distance jl between two display cabinets; The pre-processed data is transmitted to the central control center via a connection port, and the connection port is a wireless communication port.

3. The intelligent push system for cultural relics display cabinet information according to claim 2 is characterized in that: The interest analysis module includes a preference calculation unit and a showcase popularity calculation unit; The preference calculation unit is used to perform dimensionless processing on the obtained audience preference data, and then summarize and calculate to obtain the audience preference coefficient Gzp; The audience preference coefficient Gzp is obtained by the following formula: ； In the formula, represents the logarithmic function; represents the logarithmic function; represents an exponential decay function; The showcase popularity calculation unit is used to perform dimensionless processing on the acquired showcase attention data, and then summarize and calculate to obtain the showcase popularity coefficient Zpr; The showcase popularity coefficient Zpr is obtained by the following formula: ； In the formula, represents the adjustment factor of the number of views lc, and >1, represents a logarithmic function, which performs logarithmic scaling on the exhibit residence time zt; represents the adjustment factor for the number of interactions hc, and ≥1.

4. The intelligent push system for cultural relics display cabinet information according to claim 3 is characterized by: The showcase heat evaluation module includes a showcase comprehensive heat calculation unit and a showcase comprehensive heat evaluation unit; The display cabinet comprehensive heat calculation unit is used to perform dimensionless processing on the obtained audience preference coefficient Gzp and the display cabinet heat coefficient Zpr, and then summarize and calculate to obtain the display cabinet comprehensive preference coefficient ZRD; The display cabinet comprehensive preference coefficient ZRD is calculated by the following formula: ； In the formula, represents the comprehensive preference of the kth showcase, represents the preference of the kth showcase, represents the popularity of the kth showcase, w1 and w2 represent the weight coefficients of the audience preference coefficient Gzp and the showcase popularity coefficient Zpr respectively, and w1+w2=1.

5. The intelligent push system for cultural relics display cabinet information according to claim 4 is characterized in that: The display cabinet comprehensive heat evaluation unit is used to preset the preference threshold Z through the average preference of historical audiences, and to make an initial comparative evaluation with the audience preference coefficient Gzp obtained by the preference calculation unit, and to generate optimization information according to the evaluation results. The specific evaluation scheme is as follows; When the audience preference coefficient Gzp> the preset preference threshold Z, it means that the audience is interested in this showcase. At this time, the push mechanism is triggered to push the cultural relics information of this showcase to the audience through the app, and the second evaluation module is triggered; When the audience preference coefficient Gzp≤the preset preference threshold Z, it means that the audience is not interested in this showcase. At this time, this showcase is marked as a not-visited showcase and the push mechanism is not triggered.

6. The intelligent push system for cultural relics display cabinet information according to claim 1 is characterized by: The comprehensive push module includes a data calculation unit and a comprehensive calculation unit; The data calculation unit includes an audience fatigue calculation unit and a path weight calculation unit; The audience fatigue calculation unit is used to calculate the total visit time of the audience entering the exhibition hall monitored by the camera monitoring system, and construct the audience fatigue pl formula through the inverse function of the exponential decay function; The audience fatigue degree pl is obtained by the following formula: ； In the formula, is the fatigue growth rate, t means the audience’s fatigue level increases with time t, represents the exponential function, which indicates the remaining unfatigued portion; The path weight calculation unit is used to perform dimensionless processing on the acquired exhibition space data and the audience fatigue pl acquired by the audience fatigue calculation unit, and then summarize and calculate to acquire the path weight coefficient Ljq; The path weight coefficient Ljq is obtained by the following formula: In the formula, represents the path weight from showcase i to showcase j, represents the distance between the i-th showcase and the j-th showcase, and log represents the logarithmic function.

7. The intelligent push system for cultural relics display cabinet information according to claim 6, characterized in that: The comprehensive calculation unit includes a comprehensive push calculation unit and a push evaluation unit; The comprehensive push calculation unit is used to perform dimensionless processing on the obtained audience preference coefficient Gzp, showcase heat coefficient Zpr, path weight coefficient Ljq and crowd density rm extracted from the exhibition space data, and then summarize and calculate to obtain the comprehensive push index ZLT; The comprehensive push index ZLT is obtained by the following formula: In the formula, R represents the route chosen by the visitor, i represents an exhibit in route R, represents the path from exhibit i to exhibit j in route R, represents the audience's preference for exhibit i, ranging from [0,1]; Indicates the popularity of exhibit i, ranging from [0,1]; It is expressed as the maximum allowed crowd density in the venue; It represents the dynamic path weight after adding the effect of fatigue. Represents the adjustment coefficient.

8. The intelligent push system for cultural relics display cabinet information according to claim 7, characterized in that: The push evaluation unit is used to start the second evaluation mechanism when the audience is interested in the exhibits on this route. The second evaluation mechanism performs a secondary comparative evaluation by presetting the comprehensive push threshold M and the comprehensive push index ZLT obtained by the comprehensive calculation unit, and generates corresponding optimization information according to the relevant evaluation results. The specific evaluation scheme is as follows; When the comprehensive push index ZLT> the preset comprehensive push threshold M, it means that the visitors are satisfied with the pushed tour route. At this time, continuous monitoring is maintained and the cultural relic information of each display cabinet is pushed in real time according to the visitor's location information; When the comprehensive push index ZLT ≤ the preset comprehensive push threshold M, it means that the audience is not satisfied with the pushed tour route. At this time, an optimization signal is generated. The system transmits the optimization signal to the showcase heat evaluation module to perform iterative optimization based on the audience's preference for the showcase, and transmits the optimization results and the brief introduction information of the showcases along the way to the audience app through the wireless communication port for the audience's reference until the audience is satisfied.

9. The intelligent push system for cultural relics display cabinet information according to claim 1, characterized in that: The personalized push module generates a tour route by integrating audience behavior data and exhibition space data. The generated route is pushed to the audience through mobile applications or other terminal devices. The system uses notification and message push technology to enable the audience to receive the recommended route in a timely manner. The user interaction design guides the audience to visit along the recommended route and provides real-time navigation and adjustment suggestions.

10. A method for intelligently pushing information of cultural relics display cabinets, comprising the intelligent pushing system for cultural relics display cabinet information according to any one of claims 1 to 9, characterized in that: The following steps are involved: S1. Design connection ports with cameras and interactive devices in the cultural relics exhibition hall to integrate and connect to collect audience behavior data and display cabinet data in real time, and pre-process the collected audience behavior data and display cabinet data to generate audience behavior data groups and exhibition space data groups; S2. Summarize and calculate the pre-processed audience behavior data to obtain the audience preference coefficient Gzp and the showcase popularity coefficient Zpr; S3, summarizing and calculating the obtained audience preference coefficient Gzp and showcase popularity coefficient Zpr to obtain the showcase comprehensive preference coefficient ZRD, and presetting the preference threshold Z for preliminary comparative evaluation, and judging whether to trigger information push according to the evaluation results, and choosing to start secondary comparative evaluation; S4. Obtain the audience fatigue pl through exponential decay function calculation and combine it with the obtained exhibition space data group to obtain the path weight coefficient Ljq, and associate it with the obtained audience preference coefficient Gzp and showcase popularity coefficient Zpr to obtain the comprehensive push index ZLT, and perform secondary comparative evaluation with the preset comprehensive push threshold M, and optimize the tour route according to the corresponding evaluation results; S5. Push the generated route to the audience app through the terminal device using the message push technology, and guide the audience to visit along the recommended route through the audience app interface.

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