Intelligent display method and system for digital smart museum

Abstract

The invention provides an intelligent display method and system for a digital smart museum, and the method comprises the steps: obtaining multi-modal data of the interaction between multi-culture audiences and cultural relics, and constructing a cross-culture knowledge graph model; according to the cross-culture knowledge graph model, identifying the culture background and interaction characteristics of the audience; constructing an emotion prediction and interpretation model according to the identified interaction features; executing intelligent display dynamic adjustment according to a prediction and interpretation result of the emotion prediction and interpretation model; according to the method, cultural relic classification and culture explanation of different museums are integrated, data barriers are broken, a unified semantic network is constructed, audiences are enabled to consecutively understand generality and differences of different civiliations, and cross-museum resource integration and culture transmission efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of digital cultural heritage technology, and in particular to a digital smart museum intelligent display method and system. Background Art

[0002] The existing intelligent display technology of digital smart museums has the following core bottlenecks in terms of cultural communication and user experience:

[0003] First, the classification logic and cultural interpretation of artifacts differ significantly across museums, creating data silos. For example, the ritual symbols of Central Plains bronze artifacts lack semantic connection to the totemic symbols of Sanxingdui artifacts, making cross-museum resource integration difficult. Traditional knowledge graphs can only achieve surface-level label mapping and fail to capture the deeper metaphors of cultural symbols, such as the evolution of the religious meaning of the Taotie pattern across different dynasties. This makes it difficult for visitors to gain a coherent cross-cultural cognitive experience.

[0004] Second, existing systems rely on superficial behavioral data such as duration of visitor interaction and number of actions to assess visitor interest, lacking in-depth analysis of emotional states such as awe and confusion. For example, a visitor's prolonged lingering time at a particular artifact may stem from a crowded exhibition hall rather than from the compelling content, but existing technology cannot distinguish between these. Furthermore, analysis of neurofeedback data such as EEG and eye movement data remains at a basic statistical level, failing to establish connections with cultural meanings. This results in a lack of scientific basis for adjusting display strategies.

[0005] Third, the lack of semantic recognition of interactive gestures from ethnic minorities or marginalized cultures can easily lead to conflicts over cultural taboos. For example, specific gestures in Bimo rituals can be misinterpreted as common operations, potentially violating community cultural norms. Furthermore, neural data collection may inadvertently capture subconscious responses to religious symbols, making existing privacy protection mechanisms unable to address the ethical challenges of culturally specific data, such as the risk of misuse of thangka gaze data.

[0006] Fourth, traditional digital displays rely on fixed narrative paths and are unable to dynamically adjust content based on audience cultural background or real-time feedback, such as attention distraction. For example, international audiences have culturally diverse perceptions of the dragon, but the system cannot automatically switch to an appropriate semantic interpretation. For example, it may associate the dragon with a symbol of Western wisdom rather than a single symbol of imperial power, leading to a bottleneck in the experience, resulting in a uniform experience. To address this issue, we propose a smart display method and system for digital smart museums. Summary of the Invention

[0007] In view of this, the embodiments of the present invention hope to provide a digital smart museum intelligent display method and system to solve or alleviate the technical problems existing in the prior art and at least provide a beneficial option.

[0008] To solve the above technical problems, a technical solution adopted in this application is to provide a digital smart museum intelligent display method, which is characterized by comprising the following steps:

[0009] Acquire multimodal data on interactions between multicultural audiences and cultural relics, and construct a cross-cultural knowledge graph model;

[0010] Identify the audience’s cultural background and interaction characteristics based on the cross-cultural knowledge graph model;

[0011] Based on the identified interaction features, a sentiment prediction and interpretation model is constructed;

[0012] Perform intelligent display dynamic adjustment based on the prediction and interpretation results of the sentiment prediction and interpretation model;

[0013] Based on the full-process data, the cross-cultural knowledge graph model and the sentiment prediction and interpretation model are optimized, and finally an intelligent display closed-loop system is formed to realize the digital smart museum's precise display, explainable experience and cultural sensitivity protection for different cultural groups.

[0014] As a further preferred embodiment of the present technical solution, the multimodal data includes: action interaction data: detailed action videos of different cultural groups operating replicas of cultural relics; neurofeedback data: alpha wave power and beta wave synchronization rate collected by the EEG headband, and pupil diameter changes and gaze point trajectories collected by the eye tracker; cultural semantic data: cultural relics classification systems, intangible cultural heritage craft specifications and community taboo rules of various museums.

[0015] As a further preferred embodiment of the present technical solution: the construction of a cross-cultural knowledge graph model includes: extracting text features of cultural semantic data based on the BERTMHSA model, parsing the visual features of cultural relic images / models through a three-dimensional point cloud convolutional network, and constructing a triple knowledge graph including cultural relics, features, and culture; using a federated learning algorithm to train a cross-museum ontology alignment sub-model, identifying the semantic differences of different cultures for the same cultural relic features, and generating a globally shared cultural semantic anchor library, wherein each semantic anchor in the cultural semantic anchor library contains a multi-cultural mapping relationship.

[0016] As a further preferred embodiment of the present technical solution: the identification of the audience's cultural background and interaction characteristics based on the cross-cultural knowledge graph model includes: based on the cross-cultural knowledge graph model, identifying the audience's cultural background label and interaction feature vector, specifically including: parsing the action interaction data through the OpenPose key point detection algorithm, extracting the motion parameters of 21 joints, and generating quantitative detailed action index parameters; combining the cultural semantic anchor point library, matching the audience's actions with the standard action templates in the cultural knowledge library, and identifying the audience's cultural interaction normative characteristics; based on the neural feedback data, extracting features such as the alpha wave power change rate and pupil diameter fluctuation through the temporal convolutional network, and generating the audience's neural state vector.

[0017] As a further preferred embodiment of the present technical solution: constructing an emotion prediction and interpretation model based on the identified interaction features, including: using the LightGBM algorithm to train a prediction sub-model, taking the filtered interaction features as input, and outputting a probability value of the audience's understanding of the cultural connotation of the cultural relics; training an interpretation sub-model through the SHAP interpretability algorithm, calculating the feature contribution of each interaction feature, and generating an interpretable prediction basis report.

[0018] As a further preferred embodiment of the present technical solution: the intelligent display dynamic adjustment is performed based on the prediction and interpretation results of the emotion prediction and interpretation model, including: if the understanding probability value is less than a preset threshold value k1 and the visual jump frequency contribution is less than a preset threshold value k2, then automatically switching to the cultural scene narrative mode, calling the related stories in the knowledge graph (enhanced explanation); if it is detected that the β wave power of the non-local audience is greater than the baseline value preset threshold value k3, according to the taboo rules in the cultural semantic anchor point library, the dynamic display speed of the geometric pattern is reduced, and the voice explanation adapted to the non-local audience is matched; if the cultural interaction normative feature is lower than the preset threshold value k4, the force feedback glove is used to provide movement guidance resistance, and the voice prompt is triggered at the same time.

[0019] As a further preferred embodiment of this technical solution: the cross-cultural knowledge graph model and the sentiment prediction and interpretation model are optimized based on the full-process data, including: reversely optimizing the parameters of the cross-cultural knowledge graph model and the sentiment prediction model based on the user feedback data generated during the display process; at the same time, the authorization records of the cultural community for data collection and display are recorded through blockchain technology, and the homomorphic encryption algorithm is used to protect the privacy of the neural feedback data to ensure the ethical compliance of the cross-cultural display.

[0020] To solve the above technical problems, another technical solution adopted in this application is a digital smart museum intelligent display system, including: a multimodal data acquisition layer, used to obtain three types of data: cultural semantics, audience interaction, and neural feedback, to provide basic input for intelligent display; a cross-cultural knowledge graph layer, used to construct a structured representation of the cultural semantics of cultural relics and resolve cross-museum data semantic conflicts; an interactive feature analysis layer, used to parse audience interaction behavior and match cultural semantic knowledge; an emotional prediction and interpretation layer, used to predict the audience's cultural understanding level and provide explainable analysis; an intelligent display execution layer, used to dynamically adjust the display strategy according to the prediction and interpretation results; a data feedback and optimization layer, used to collect display data to iterate the model and ensure ethical compliance; the multimodal data acquisition layer, cross-cultural knowledge graph layer, interactive feature analysis layer, emotional prediction and interpretation layer, intelligent display execution layer, and data feedback and optimization layer realize real-time communication through standardized data interfaces.

[0021] To solve the above technical problems, another technical solution adopted in this application is: a computer device, which includes a processor and a memory coupled to the processor, wherein program instructions are stored in the memory, and when the program instructions are executed by the processor, the processor executes the steps of the digital smart museum intelligent display method and system as described above.

[0022] In order to solve the above technical problems, another technical solution adopted in this application is: a storage medium storing program instructions that can implement the digital smart museum intelligent display method and system as described above.

[0023] The embodiment of the present invention adopts the above technical solution, which has the following advantages:

[0024] Cross-cultural semantic integration: Integrate the cultural relics classification and cultural interpretation of different museums, break down data barriers, build a unified semantic network, enable the audience to understand the similarities and differences of different civilizations in a coherent manner, and enhance the effectiveness of cross-museum resource integration and cultural communication.

[0025] In-depth experience analysis: Through neural feedback and interpretable models, audience emotions are converted into intuitive feature contribution analysis, accurately locating the key factors affecting the experience, providing a scientific basis for display optimization, and enhancing the audience's understanding of the connotation of cultural relics.

[0026] Protecting cultural sensitivity: Identifying interactions with marginalized cultures such as those of ethnic minorities, avoiding taboo conflicts, combining data encryption with blockchain evidence storage, protecting cultural privacy and sovereignty, reducing ethical risks, and ensuring compliance and security of displays.

[0027] Dynamically adapt the experience: Based on the audience's cultural background and real-time feedback, the display content and interaction methods are dynamically adjusted, such as providing customized narratives for different groups and triggering interesting demonstrations for children, to increase audience engagement and length of stay.

[0028] Efficient cross-museum collaboration: Unify data standards and architecture, support low-cost joint exhibitions and resource sharing among museums, shorten the curatorial cycle, promote the universal dissemination of cultural resources in small and medium-sized museums and border areas, and enhance the coverage of cultural communication.

[0029] The above summary is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments and features described above, further aspects, embodiments and features of the present invention will be readily apparent by reference to the accompanying drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0031] Figure 1 is a flow chart of the method of the present invention;

[0032] Figure 2 Schematic diagram of the module of the system of the present invention

[0033] Figure 3 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0034] The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0035] It should be clear that the following embodiments of the present disclosure are described through specific concrete examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all the embodiments. The present disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that the following embodiments and features in the embodiments can be combined with each other in the absence of conflict. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present disclosure.

[0036] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein can be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on this disclosure, it should be understood by those skilled in the art that an aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects described herein can be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein can be used to implement this device and / or practice this method.

[0037] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present disclosure. The illustrations only show components related to the present disclosure and are not drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component can be changed at will, and the component layout type may also be more complicated.

[0038] Additionally, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, one skilled in the art will appreciate that aspects may be practiced without these specific details.

[0039] Figure 1 It is a flow chart of the digital smart museum intelligent display method and system according to an embodiment of the present invention. It should be noted that if there is substantially the same result, the method of this application is not based on Figure 1 The process sequence shown is limited. Figure 1 As shown: The digital smart museum intelligent display method includes the following steps:

[0040] S100. Acquire multimodal data on interactions between multicultural audiences and cultural relics, and construct a cross-cultural knowledge graph model; the multimodal data includes:

[0041] Motion interaction data: videos of detailed movements of different cultural groups manipulating replicas of cultural relics (e.g., videos of Han people bowing during sacrificial ceremonies, and videos of Miao people performing joint movements while wearing silver jewelry);

[0042] Neurofeedback data: alpha wave power and beta wave synchronization rate collected by the EEG headband, and pupil diameter changes and gaze trajectory collected by the eye tracker;

[0043] Cultural semantic data: museums’ cultural relic classification systems, intangible cultural heritage craft standards, and community taboos;

[0044] Specifically, step S100 may include:

[0045] S110: Extract text features of cultural semantic data based on the BERTMHSA model, analyze the visual features of cultural relic images / models through a 3D point cloud convolutional network, and construct a triplet knowledge graph containing cultural relics, features, and culture.

[0046] S120. Use a federated learning algorithm to train a cross-museum ontology alignment sub-model to identify the semantic differences between different cultures regarding the same cultural relic features (such as the difference between the "dragon" as a "symbol of imperial power" in Central Plains culture and a "mythical creature" in Western culture), and generate a globally shared cultural semantic anchor library, where each semantic anchor in the cultural semantic anchor library contains a multi-cultural mapping relationship.

[0047] S200, identifying the audience’s cultural background and interaction characteristics based on the cross-cultural knowledge graph model;

[0048] Specifically, step S200 may include: identifying the audience's cultural background labels and interaction feature vectors based on the cross-cultural knowledge graph model, specifically including:

[0049] S210: Analyze the action interaction data using the OpenPose key point detection algorithm, extract the motion parameters of 21 joints (such as wrist rotation angular velocity and knee flexion and extension angle), and generate quantitative detailed action index parameters;

[0050] S220: Combine the cultural semantic anchor point library to match the audience's actions with the standard action templates in the cultural knowledge library (e.g., comparing the standard hand trajectory of wearing Miao silver jewelry) and identify the audience's cultural interaction normative characteristics (e.g., "action standardization score");

[0051] S230. Based on the neural feedback data, the features such as the rate of change of alpha wave power and pupil diameter fluctuation are extracted through the temporal convolutional network (TCN) to generate the audience's neural state vector (such as "attention concentration" and "emotional arousal").

[0052] S300, constructing a sentiment prediction and interpretation model based on the identified interaction features;

[0053] Step S300 may include: constructing an emotion prediction and interpretation model based on the interaction feature vector (including the action normative feature and the neural state vector), specifically including:

[0054] The prediction sub-model is trained using the LightGBM algorithm. The input is the filtered interactive features (the TOP15 core indicators selected by the Boruta algorithm, such as "single-leg standing duration" and "frontal blood oxygen concentration"). The output is the probability value of the audience's understanding of the cultural connotation of the cultural relics (for example, the probability of "understanding the religious significance of the Liangzhu jade cong" is 0.81).

[0055] The explanation sub-model is trained through the SHAP interpretability algorithm to calculate the feature contribution of each interaction feature (such as the contribution of "hand grip strength stability" +0.38 and the contribution of "visual jump frequency" 0.25), and generate an explainable prediction basis report.

[0056] S400, performing intelligent display dynamic adjustment based on the prediction and interpretation results of the emotion prediction and interpretation model;

[0057] Step S400 may include: performing intelligent display dynamic adjustment based on the output of the emotion prediction and interpretation model, specifically including:

[0058] S410: If the probability of understanding is less than a preset threshold value k1 and the visual jump frequency contribution is less than a preset threshold value k2 (for example, if the probability of understanding is <0.6 and the visual jump frequency contribution is <0, indicating distraction), the system automatically switches to the cultural scene narrative mode and uses related stories in the knowledge graph to enhance the explanation (for example, the legend of the Liangzhu jade cong being associated with the "combination of divine power and royal power");

[0059] S420: If it is detected that the β wave power of the non-local audience is greater than the baseline value preset threshold k3, the dynamic display speed of the geometric pattern is reduced according to the taboo rules in the cultural semantic anchor point library, and the voice explanation is matched to suit the non-local audience;

[0060] S430: If the cultural interaction normative feature is lower than the preset threshold value k4, the force feedback glove is used to provide movement guidance resistance (such as limiting excessive rotation of the wrist joint when wearing Miao silver jewelry), and a voice prompt is triggered at the same time, such as please follow the standard gesture operation.

[0061] S500, based on full-process data, optimizes cross-cultural knowledge graph models and sentiment prediction and interpretation models, and ultimately forms an intelligent display closed-loop system to enable digital smart museums to provide precise display, explainable experience, and cultural sensitivity protection for different cultural groups.

[0062] The method of the present invention has the following benefits:

[0063] Cross-cultural semantic integration: Integrate the cultural relics classification and cultural interpretation of different museums, break down data barriers, build a unified semantic network, enable the audience to understand the similarities and differences of different civilizations in a coherent manner, and enhance the effectiveness of cross-museum resource integration and cultural communication.

[0064] In-depth experience analysis: Through neural feedback and interpretable models, audience emotions are converted into intuitive feature contribution analysis, accurately locating the key factors affecting the experience, providing a scientific basis for display optimization, and enhancing the audience's understanding of the connotation of cultural relics.

[0065] Protecting cultural sensitivity: Identifying interactions with marginalized cultures such as those of ethnic minorities, avoiding taboo conflicts, combining data encryption with blockchain evidence storage, protecting cultural privacy and sovereignty, reducing ethical risks, and ensuring compliance and security of displays.

[0066] Dynamically adapt the experience: Based on the audience's cultural background and real-time feedback, the display content and interaction methods are dynamically adjusted, such as providing customized narratives for different groups and triggering interesting demonstrations for children, to increase audience engagement and length of stay.

[0067] Efficient cross-museum collaboration: Unify data standards and architecture, support low-cost joint exhibitions and resource sharing among museums, shorten the curatorial cycle, promote the universal dissemination of cultural resources in small and medium-sized museums and border areas, and enhance the coverage of cultural communication.

[0068] Continuous technological evolution: Automatically optimize models and knowledge graphs based on audience feedback, update cultural data in real time, adapt to technological development and new demands, improve system response speed and scalability, extend the technology life cycle and reduce maintenance costs.

[0069] Figure 2 Schematic diagram of the functional modules of the digital smart museum intelligent display method and system according to the embodiment of the present application. Figure 2 As shown, the digital smart museum intelligent display system is characterized by including:

[0070] The multimodal data collection layer is used to obtain three types of data: cultural semantics, audience interaction, and neural feedback, providing basic input for intelligent display. Specifically, it includes:

[0071] Cultural semantic data module: This module uses high-precision scanners, web crawlers, and other equipment and tools to collect data such as text descriptions of cultural relics (such as the "ritual vessels and weapons" classification of the Forbidden City), 3D models (such as the Sanxingdui bronze sacred tree), and videos of intangible cultural heritage crafts (such as the forging of silver jewelry by the Miao ethnic group).

[0072] Audience interaction data module: This module uses VR controllers, data gloves, and depth cameras to collect the movement trajectories (e.g., gesture sequences for virtual casting of bronze artifacts) and joint motion parameters (e.g., wrist rotation angular velocity) of the audience manipulating replicas of cultural relics.

[0073] Neurofeedback data module: Through EEG headband, eye tracker, and electromyography bracelet, physiological indicators such as alpha wave power, gaze point trajectory, and hand grip strength changes are collected.

[0074] The cross-cultural knowledge graph layer is used to construct a structured representation of cultural semantics of cultural relics and resolve semantic conflicts in cross-library data. Specifically, it includes:

[0075] Multimodal feature extraction module: This module extracts text semantic features through the BERTMHSA model and uses a 3D point cloud convolutional network to analyze the visual features of cultural relic images / models to generate a "cultural relic feature culture" triplet.

[0076] Cross-museum ontology alignment module: This module uses a federated learning algorithm to train a cross-museum ontology alignment model, identifying semantic differences between different cultures regarding the same cultural relic features (e.g., the symbolic differences between the dragon in Central Plains and Western cultures), and generating a globally shared cultural semantic anchor library.

[0077] Dynamic knowledge update module: Through incremental learning and blockchain evidence storage, it automatically absorbs new archaeological discoveries or intangible cultural heritage data and expands the semantic associations of the knowledge graph (such as the potential connection between Liangzhu jade cong and "theocracy");

[0078] The interactive feature analysis layer is used to analyze audience interactive behaviors and match them with cultural semantic knowledge. Specifically, it includes:

[0079] Action feature engineering module: This module uses the OpenPose algorithm to analyze action videos, generate detailed action indicator parameters (such as the elbow flexion degree of the bowing action), and filter outliers based on the IQR method.

[0080] Cultural Background Recognition Module: This module performs cosine similarity matching between audience actions and predefined cultural action templates (e.g., the Han Chinese “three bows and three yields” ritual), and outputs a cultural background label and action standardization score.

[0081] The sentiment prediction and interpretation layer is used to predict the audience's cultural understanding and provide interpretable analysis, including:

[0082] Emotion prediction module: uses the LightGBM model to fuse action features and neural data to output the "probability of cultural understanding" (e.g., the probability of "understanding the significance of Liangzhu jade cong" = 0.82);

[0083] Interpretability module: Calculates feature contributions based on the SHAP algorithm (e.g., the contribution of "hand movement stability" + 0.38) and generates a feature importance ranking report;

[0084] The intelligent display execution layer is used to dynamically adjust the display strategy based on the prediction and interpretation results. Specifically, it includes:

[0085] Content generation module: Generates cross-cultural narrative text through the GPT4M model and uses the Unity engine to render personalized display scenes (such as generating "Bon religion ritual" AR animation for the audience);

[0086] Interactive Control Module: This module simulates the tactile feel of cultural relics (e.g., the rusty feel of bronze artifacts) through force feedback gloves, and dynamically adjusts VR scene parameters based on neural feedback (e.g., reducing the density of dynamic elements for viewers with high cognitive load).

[0087] Taboo avoidance module: uses computer vision to identify high-risk actions (such as touching prohibited virtual artifacts), triggering voice warnings and freezing operation permissions;

[0088] The data feedback and optimization layer is used to collect and display data to iterate models and ensure ethical compliance. Specifically, it includes:

[0089] Model optimization module: This module feeds audience feedback data (such as the improvement in the probability of understanding) back into the cross-cultural knowledge graph and sentiment prediction model, and updates parameters through reinforcement learning.

[0090] Ethical protection module: Use homomorphic encryption to protect the privacy of neural data, and use blockchain to store cultural community authorization records (for example, Bimo action data collection requires on-chain confirmation);

[0091] The multimodal data acquisition layer, cross-cultural knowledge graph layer, interactive feature analysis layer, sentiment prediction and interpretation layer, intelligent display execution layer, and data feedback and optimization layer achieve real-time communication through standardized data interfaces.

[0092] For other details about the technical solutions for implementing each module in the system of the above embodiment, please refer to the description of the digital smart museum intelligent display method and system in the above embodiment, which will not be repeated here.

[0093] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Similarities between the various embodiments can be referred to in conjunction with each other. For system-related embodiments, since they are generally similar to method-related embodiments, their description is relatively simple. For relevant details, refer to the description of the method-related embodiments.

[0094] An electronic device according to an embodiment of the present disclosure includes a memory and a processor. The memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may, for example, include a read-only memory (ROM), a hard disk, a flash memory, etc.

[0095] The processor may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment of the present disclosure, the processor is used to execute the computer-readable instructions stored in the memory, causing the electronic device to execute all or part of the steps of the digital smart museum intelligent display method described in the aforementioned embodiments of the present disclosure.

[0096] Those skilled in the art should understand that in order to solve the technical problem of how to obtain a good user experience, this embodiment may also include well-known structures such as a communication bus and an interface, and these well-known structures should also be included in the scope of protection of this disclosure.

[0097] like Figure 3 The present invention provides a schematic diagram of the structure of an electronic device according to an embodiment of the present invention, which is suitable for implementing the electronic device according to an embodiment of the present invention. Figure 3 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present disclosure.

[0098] like Figure 3As shown, the electronic device may include a processor (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) or a program loaded from a storage device into a random access memory (RAM). In the RAM, various programs and data required for the operation of the electronic device are also stored. The processor, ROM, and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0099] Typically, the following devices can be connected to the I / O interface: input devices such as sensors or visual information acquisition devices; output devices such as display screens; storage devices such as tapes and hard disks; and communication devices. The communication device allows the electronic device to communicate with other devices (such as edge computing devices) wirelessly or by wire to exchange data. Figure 3 The electronic device is shown with various devices, but it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed instead.

[0100] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through a communication device, or installed from a storage device, or installed from a ROM. When the computer program is executed by the processor, all or part of the steps of the digital smart museum intelligent display method of the embodiment of the present disclosure are executed.

[0101] For detailed description of this embodiment, please refer to the corresponding description in the aforementioned embodiments, which will not be repeated here.

[0102] According to an embodiment of the present disclosure, a computer-readable storage medium stores non-transitory computer-readable instructions. When the non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the digital smart museum intelligent display method described in the embodiments of the present disclosure are executed.

[0103] The above-mentioned computer-readable storage media include, but are not limited to, optical storage media (e.g., CD-ROMs and DVDs), magneto-optical storage media (e.g., MOs), magnetic storage media (e.g., magnetic tapes or mobile hard disks), media with built-in rewritable non-volatile memory (e.g., memory cards), and media with built-in ROM (e.g., ROM cartridges).

[0104] For detailed description of this embodiment, please refer to the corresponding description in the aforementioned embodiments, which will not be repeated here.

[0105] The basic principles of the present disclosure have been described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, and effects mentioned in this disclosure are merely illustrative and not restrictive, and should not be construed as necessarily possessed by each embodiment of the present disclosure. Furthermore, the specific details disclosed above are provided for illustrative purposes and to facilitate understanding, rather than as limitations. These details do not limit the present disclosure to necessarily being implemented using these specific details.

[0106] In the present disclosure, relational terms such as first and second, etc. are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. The block diagrams of the devices, devices, equipment, and systems involved in the present disclosure are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As will be appreciated by those skilled in the art, these devices, devices, equipment, and systems can be connected, arranged, or configured in any manner. Words such as "including," "comprising," "having," and the like are open-ended words, meaning "including but not limited to," and can be used interchangeably therewith. The words "or" and "and" used herein refer to the words "and / or" and can be used interchangeably therewith, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to," and can be used interchangeably therewith.

[0107] Additionally, as used herein, "or" used in a list of items beginning with "at least one" indicates a separate list, so that, for example, a list of "at least one of A, B, or C" means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the word "exemplary" does not mean that the example described is preferred or better than other examples.

[0108] It should also be noted that in the system and method of the present disclosure, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present disclosure.

[0109] Various changes, substitutions, and modifications may be made to the technology described herein without departing from the teachings defined by the appended claims. Moreover, the scope of the claims of this disclosure is not limited to the specific aspects of the processes, machines, manufactures, compositions of things, means, methods, and actions described above. Currently existing or later developed processes, machines, manufactures, compositions of things, means, methods, or actions that perform substantially the same function or achieve substantially the same results as the corresponding aspects described herein may be utilized. Accordingly, the appended claims include within their scope such processes, machines, manufactures, compositions of things, means, methods, or actions.

[0110] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the aspects shown herein, but rather to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0111] The above description has been provided for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present disclosure to the forms disclosed herein. Although a number of example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A digital smart museum intelligent display method, characterized in that: The following steps are involved: Acquire multimodal data on interactions between multicultural audiences and cultural relics, and construct a cross-cultural knowledge graph model; Identify the audience’s cultural background and interaction characteristics based on the cross-cultural knowledge graph model; Based on the identified interaction features, a sentiment prediction and interpretation model is constructed; Perform intelligent display dynamic adjustment based on the prediction and interpretation results of the sentiment prediction and interpretation model; Based on the full-process data, the cross-cultural knowledge graph model and the sentiment prediction and interpretation model are optimized, and finally an intelligent display closed-loop system is formed to realize the digital smart museum's precise display, explainable experience and cultural sensitivity protection for different cultural groups.

2. The digital smart museum intelligent display method according to claim 1, characterized in that: The multimodal data includes: Action interaction data: videos of detailed actions of different cultural groups operating replicas of cultural relics; Neurofeedback data: alpha wave power and beta wave synchronization rate collected by the EEG headband, and pupil diameter changes and gaze trajectory collected by the eye tracker; Cultural semantic data: the cultural relics classification system of each museum, intangible cultural heritage craft standards and community taboos.

3. The digital smart museum intelligent display method and system according to claim 2, characterized in that: The construction of the cross-cultural knowledge graph model includes: The BERTMHSA model is used to extract text features of cultural semantic data. The visual features of cultural relic images / models are analyzed through a 3D point cloud convolutional network to construct a triple knowledge graph containing cultural relics, features, and culture. A federated learning algorithm is used to train a cross-museum ontology alignment sub-model to identify the semantic differences of different cultures on the characteristics of the same cultural relic and generate a globally shared cultural semantic anchor library, where each semantic anchor in the cultural semantic anchor library contains a multi-cultural mapping relationship.

4. The digital smart museum intelligent display method according to claim 1, characterized in that: The identification of the audience's cultural background and interaction characteristics based on the cross-cultural knowledge graph model includes: Based on the cross-cultural knowledge graph model, the cultural background labels and interaction feature vectors of the audience are identified, specifically including: The OpenPose key point detection algorithm is used to analyze the action interaction data, extract the motion parameters of 21 joints, and generate quantitative detailed action index parameters; Combined with the cultural semantic anchor library, the audience's actions are matched with the standard action templates in the cultural knowledge library to identify the audience's cultural interaction normative characteristics; Based on the neural feedback data, features such as the alpha wave power change rate and pupil diameter fluctuation are extracted through a temporal convolutional network to generate the audience's neural state vector.

5. The digital smart museum intelligent display method according to claim 4, characterized in that: The sentiment prediction and interpretation model is constructed based on the identified interaction features, including: The prediction sub-model is trained using the LightGBM algorithm, with the filtered interaction features as input and the output being the probability value of the audience's understanding of the cultural connotation of the cultural relics; The explanation sub-model is trained through the SHAP interpretability algorithm, the feature contribution of each interactive feature is calculated, and an explainable prediction basis report is generated.

6. The digital smart museum intelligent display method according to claim 5, characterized in that: The intelligent display dynamic adjustment is performed based on the prediction and interpretation results of the emotion prediction and interpretation model, including: If the probability of understanding is less than the preset threshold k1 and the visual jump frequency contribution is less than the preset threshold k2, it will automatically switch to the cultural scene narrative mode and call the related stories in the knowledge graph to enhance the explanation; If the beta wave power of the non-local audience is detected to be greater than the preset threshold value k3 of the baseline value, the dynamic display speed of the geometric patterns will be reduced according to the taboo rules in the cultural semantic anchor point library, and the voice explanation will be matched to suit the non-local audience; If the normative characteristics of cultural interaction are lower than the preset threshold k4, the force feedback gloves provide movement guidance resistance and trigger voice prompts.

7. The digital smart museum intelligent display method according to claim 1, characterized in that: The cross-cultural knowledge graph model and sentiment prediction and interpretation model are optimized based on full-process data, including: Based on the user feedback data generated during the display process, the parameters of the cross-cultural knowledge graph model and the sentiment prediction model are reversely optimized; at the same time, blockchain technology is used to record the cultural community's authorization records for data collection and display, and a homomorphic encryption algorithm is used to protect the privacy of neural feedback data to ensure the ethical compliance of cross-cultural display.

8. The digital smart museum intelligent display system is characterized by: include: The multimodal data collection layer is used to obtain three types of data: cultural semantics, audience interaction, and neural feedback, providing basic input for intelligent display; The cross-cultural knowledge graph layer is used to construct a structured representation of cultural semantics of cultural relics and resolve semantic conflicts in cross-library data; Interaction feature analysis layer, used to analyze audience interaction behaviors and match them with cultural semantic knowledge; The sentiment prediction and interpretation layer is used to predict the audience's cultural understanding and provide interpretable analysis; Intelligent display execution layer, used to dynamically adjust display strategies based on prediction and interpretation results; Data feedback and optimization layer, used to collect and display data to iterate models and ensure ethical compliance; The multimodal data acquisition layer, cross-cultural knowledge graph layer, interactive feature analysis layer, emotion prediction and interpretation layer, intelligent display execution layer, and data feedback and optimization layer achieve real-time communication through standardized data interfaces.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the digital smart museum intelligent display method described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, which are used to enable a computer to execute the digital smart museum intelligent display method described in any one of claims 1-7.

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