Cultural and artistic space browsing interaction method, device and equipment

Abstract

The invention provides a browsing interaction method, device and equipment for a culture and art space, and relates to the technical field of computer networks. According to the method, historical behavior data and image data of the visitor are deeply mined, personal characteristics and behavior characteristics such as gender, age, wearing and body posture are accurately extracted, and detailed construction of the visitor portrait is achieved. In combination with an artificial intelligence recognition model, personal preferences of target visitors are accurately predicted, and then a personalized interaction scheme of the target visitors is obtained through matching based on data such as actual scenes of culture and art space, VR and AR. And finally, the current interaction instruction is responded based on the personalized interaction scheme, so that the immersion and interactivity of visitors browsing in the culture and art space are remarkably improved, the personalization and differentiation of service experience are ensured, personalized services of different visitors are realized, the experience of the visitors browsing in the culture and art space is improved, and the user experience is improved. And intelligent upgrading and experience optimization of the culture and art space service mode are successfully realized.

Description

Technical Field

[0001] The present invention relates to the field of computer network technology, and in particular to a browsing interaction method, device and equipment for a cultural and artistic space. Background Art

[0002] Museums, art exhibitions, and artistic performances all need to be held in carefully designed spaces for visitors to savor and explore. However, the traditional mode of visiting, whether it is individual free tours or group tours, has significant limitations: the lack of interactivity and the transmission of in-depth information makes it difficult for visitors to touch the essence of cultural and artistic works, thus affecting their overall experience.

[0003] Although there are currently ways to visit using augmented reality (AR) or virtual reality (VR) technology, visitors can obtain more detailed cultural and artistic details by wearing corresponding equipment. However, this technology is still limited to the dissemination of preset content and is difficult to flexibly adjust according to the personalized needs of visitors. Therefore, its user experience still needs to be improved.

[0004] Take museums or art exhibitions as an example. When facing the same exhibit, different visitors often have different questions and curiosities. However, existing AR or VR devices are difficult to give appropriate responses to each visitor's personalized questions, which undoubtedly limits the possibility of visitors' in-depth exploration and understanding. Summary of the invention

[0005] The present invention provides a browsing interaction method, device and equipment for a cultural and artistic space, which can realize personalized services for different visitors and improve the browsing experience of visitors in the cultural and artistic space.

[0006] In a first aspect, the present invention provides a browsing and interaction method for a cultural and artistic space, the method comprising: obtaining current interaction instructions of a target visitor in the cultural and artistic space, as well as behavioral data and image data in a historical period; the behavioral data comprising the target visitor's operation data on a mixed reality device; extracting personal characteristics and behavioral characteristics of the target visitor based on the behavioral data and image data in the historical period, the personal characteristics comprising gender, age, wearing characteristics and body posture characteristics; determining the personal preferences of the target visitor based on the personal characteristics and behavioral characteristics of the target visitor, and a preset artificial intelligence recognition model; matching a personalized interaction plan for the target visitor based on the personal preferences and the mixed reality data of the cultural and artistic space; the mixed reality data comprising actual scene data, VR data and AR data; responding to the current interaction instructions of the target visitor based on the personalized interaction plan of the target visitor.

[0007] In one possible implementation, personal characteristics and behavioral characteristics of target visitors are extracted based on behavioral data and image data from historical periods, including: performing frame processing based on image data from historical periods to obtain multiple frames of images of target visitors; performing image recognition based on multiple frames of images of target visitors to determine personal characteristics of target visitors; and performing feature extraction based on behavioral data from historical periods to determine behavioral characteristics of target visitors.

[0008] In a possible implementation, image recognition is performed based on multiple frames of images of the target visitor to determine the personal characteristics of the target visitor, including: performing face recognition based on multiple frames of images of the target visitor to determine the gender and age of the target visitor; performing image recognition based on multiple frames of images of the target visitor to determine the wearing characteristics of the target visitor, which include clothing color, style and accessories; performing image recognition based on multiple frames of images of the target visitor to determine the body posture of each frame; drawing the body movement trajectory of the target visitor based on the body posture of each frame; determining body posture characteristics based on the body posture of each frame and the body movement trajectory; body posture characteristics include standing posture, sitting posture and walking gait; walking posture includes stride size, walking speed, footsteps and body coordination rate.

[0009] In a possible implementation, feature extraction is performed based on behavioral data from historical periods to determine behavioral characteristics of target visitors, including: analyzing the behavioral patterns of target visitors when operating mixed reality devices based on behavioral data from historical periods, the behavioral patterns including the operating time, operating frequency, operating path, and dwell time of each mixed reality device; wherein the operating time of a mixed reality device is the duration between the target visitor entering the cultural and art space and operating the mixed reality device; calculating statistical characteristics of each mixed reality device based on the behavioral pattern, the statistical characteristics including the mean, standard deviation, kurtosis, and skewness; determining the behavioral characteristics of the target visitor based on the statistical characteristics of each mixed reality device and the cultural and art type of each mixed device; the behavioral characteristics include the statistical characteristics of each cultural and art type.

[0010] In one possible implementation, the personal preferences of the target visitors are determined based on the personal characteristics and behavioral characteristics of the target visitors and a preset artificial intelligence recognition model, including: constructing a feature vector based on the personal characteristics and behavioral characteristics of the target visitors; inputting the feature vector into the artificial intelligence recognition model to obtain a model output result, the model output result includes the preference probabilities of various artistic styles, the preference probabilities of various interactive experiences, the sensitivity of various cultural elements, the preference probabilities of various clothing styles, and the preference probabilities of various behavioral patterns; based on the model output results, the personal preferences of the target visitors are determined.

[0011] In a possible implementation, personalized interaction plans for target visitors are matched based on personal preferences and mixed reality data of cultural and artistic spaces, including: classifying the mixed reality data of cultural and artistic spaces into types, and determining type labels for each mixed reality data, the type labels including location information, artistic style attributes, interactive experience attributes, cultural element attributes, wearing style attributes, and recommended behavior pattern attributes; determining multiple matching plans based on personal preferences and the type labels for each mixed reality data; calculating the matching degrees of multiple matching plans based on the weight of each preference in the personal preferences; determining alternative matching plans based on the matching degrees of multiple matching plans; and determining personalized interaction plans for target visitors based on the alternative matching plans, the personalized interaction plans including display content, display method, display timing, and display duration.

[0012] In a possible implementation, based on the personalized interaction plan of the target visitor, responding to the current interaction instruction of the target visitor includes: determining the behavioral intention of the target visitor based on the current interaction instruction and the location information of the target visitor; matching the behavioral intention based on the behavioral intention and the personalized interaction plan to obtain a matching result, the matching result including a consistent match or an inconsistent match; if the match is consistent, displaying mixed reality data to the target user based on the personalized interaction plan; if the match is inconsistent, updating the personalized interaction plan based on the current interaction instruction; and displaying the mixed reality data to the target user based on the updated personalized interaction plan.

[0013] In a possible implementation, based on the personalized interaction scheme of the target visitor, responding to the current interaction instruction of the target visitor also includes: recording feedback information of the target visitor when browsing the mixed reality data, the feedback information including facial image, heart rate, voice data and body image; based on the feedback information, performing feature extraction to determine the feedback characteristics of the target visitor in multiple consecutive time periods; based on the feedback characteristics of the target visitor in multiple consecutive time periods, determining the preference coefficient of the target visitor when browsing the mixed reality data with the personalized interaction scheme in multiple consecutive time periods; based on the preference coefficient in multiple consecutive time periods, determining the update direction of the personalized interaction scheme; based on the update direction of the personalized interaction scheme, updating the personalized interaction scheme.

[0014] In a second aspect, an embodiment of the present invention provides a browsing and interactive device for a cultural and artistic space, the device comprising: a communication module and a processing module, the communication module being used to obtain the current interaction instructions of a target visitor in the cultural and artistic space, as well as behavioral data and image data in a historical period; the behavioral data comprising the target visitor's operation data on a mixed reality device; the processing module being used to extract personal characteristics and behavioral characteristics of the target visitor based on the behavioral data and image data in the historical period, the personal characteristics comprising gender, age, wearing characteristics and body posture characteristics; determining the personal preferences of the target visitor based on the personal characteristics and behavioral characteristics of the target visitor and a preset artificial intelligence recognition model; matching the target visitor's personalized interaction plan based on the personal preferences and the mixed reality data of the cultural and artistic space; the mixed reality data comprising actual scene data, VR data and AR data; responding to the target visitor's current interaction instructions based on the target visitor's personalized interaction plan.

[0015] In a third aspect, an embodiment of the present invention provides an electronic device, comprising a memory and a processor, the memory storing a computer program, the processor being used to call and run the computer program stored in the memory to perform the steps of the method described in the first aspect and any possible implementation method of the first aspect.

[0016] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and wherein when the computer program is executed by a processor, the steps of the method described in the first aspect and any possible implementation method of the first aspect are implemented.

[0017] The present invention provides a browsing interaction method, device and equipment for a cultural and artistic space. The present invention deeply mines the historical behavior data and image data of target visitors, accurately extracts personal characteristics and behavior characteristics such as gender, age, clothing and body posture of target visitors, and realizes the detailed construction of visitor portraits. Combined with the artificial intelligence recognition model, the personal preferences of target visitors are accurately predicted, and then based on the actual scenes of the cultural and artistic space, VR and AR data, the personalized interaction plan of the target visitors is matched. Finally, the current interaction instruction is responded to based on the personalized interaction plan, which can significantly improve the immersion and interactivity of visitors browsing in the cultural and artistic space, and ensure the high personalization and differentiation of the service experience, realize personalized services and dynamic communication for different visitors, improve the browsing experience of visitors in the cultural and artistic space, and successfully realize the intelligent upgrade and experience optimization of the service model of the cultural and artistic space. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0019] Figure 1 It is a flowchart of a browsing and interactive method of a cultural and artistic space provided by an embodiment of the present invention;

[0020] Figure 2 It is a structural schematic diagram of a browsing interaction device for a cultural and artistic space provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0022] In the description of the present invention, unless otherwise specified, " / " means "or", for example, A / B can mean A or B. "And / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, "at least one" and "plurality" refer to two or more. The words "first", "second", etc. do not limit the quantity and execution order, and the words "first", "second", etc. do not limit them to be different.

[0023] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a concrete way for easy understanding.

[0024] In addition, the terms "including" and "having" and any variations thereof mentioned in the description of this application are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or modules is not limited to the listed steps or modules, but may optionally include other steps or modules that are not listed, or may optionally include other steps or modules that are inherent to these processes, methods, products or devices.

[0025] In order to make the purpose, technical solutions and advantages of the present invention more clear, the following will be described through specific embodiments in conjunction with the accompanying drawings of the present invention.

[0026] like Figure 1 As shown, an embodiment of the present invention provides a browsing and interactive method for a cultural and artistic space, and the method includes steps S101-S105.

[0027] S101. Obtain current interaction instructions of target visitors in the cultural and art space, as well as behavioral data and image data in a historical period.

[0028] In the embodiment of the present application, the behavior data includes the target visitor's operation data on the mixed reality device.

[0029] In some embodiments, the behavioral data includes device operation data, navigation browsing paths and / or interactive behavior records.

[0030] For example, the embodiments of the present invention can record in detail all operations of visitors on mixed reality devices (such as AR glasses and VR helmets) during previous visits, including button clicks, handle movement trajectories, eye tracking data, etc. Track the movement trajectories of visitors in the cultural and art space, including the time spent in each exhibition area, frequently visited areas, etc. Record the specific behaviors of visitors interacting with exhibits, such as clicking on virtual information boards, participating in interactive games, using virtual tools, etc.

[0031] In some embodiments, embodiments of the present invention can use facial recognition technology to capture the facial features of visitors, analyze their expression changes, and understand the emotional reactions of visitors in different situations. Through depth cameras or motion sensors, the body postures and movements of visitors during browsing, such as standing, sitting, bending, waving, etc., are captured. The behavioral reactions of visitors under different environmental conditions such as light, sound, and temperature are recorded to evaluate their adaptability and preferences to the environment.

[0032] In some embodiments, the current interaction instructions include voice instructions, gesture instructions, body part movement instructions, etc.

[0033] For example, the embodiment of the present invention can capture the content of the target visitor's interaction using voice commands through voice recognition technology. Gesture recognition technology is used to detect specific gestures made by visitors in front of the mixed reality device and identify them as interaction commands. Through the touch screen or touchpad, the visitor's clicks, slides and other touch operations are recorded as interaction commands.

[0034] As another example, the embodiment of the present invention can analyze the micro-movements of visitors in the mixed reality environment, such as head rotation and eye movement, to infer their possible focus or interest direction as implicit interaction instructions. Monitoring visitors' navigation behavior in the virtual space, such as choosing to enter a certain exhibition area or approach a certain exhibit, is regarded as an interaction intention for the content.

[0035] S102: Extracting personal characteristics and behavioral characteristics of target visitors based on the behavioral data and image data of the historical period.

[0036] In the embodiment of the present application, personal characteristics include gender, age, wearing characteristics and body posture characteristics.

[0037] As a possible implementation manner, step S102 may be specifically implemented as steps S1021 - S1023 .

[0038] S1021. Perform frame processing based on the image data of the historical period to obtain multiple frames of images of the target visitor.

[0039] For example, the embodiment of the present invention can pre-process the image data before the frame processing, including denoising, contrast enhancement, resolution adjustment, etc., to improve the accuracy of subsequent image recognition. According to the original frame rate of the image data or according to actual needs, a suitable frame rate is selected for frame processing to balance computing resources and the integrity of image information. Timestamp information is added to each frame of the image so that the behavior changes of visitors at different time points can be tracked in subsequent analysis.

[0040] S1022: Perform image recognition based on the multiple frames of images of the target visitor to determine personal characteristics of the target visitor.

[0041] Exemplarily, step S1022 may be specifically implemented as steps A1-A5.

[0042] A1. Perform face recognition based on multiple frames of images of the target visitor to determine the gender and age of the target visitor.

[0043] Exemplarily, the embodiments of the present invention can use deep learning algorithms (such as convolutional neural networks) to perform face detection and extract face regions from multiple frames of images. Feature extraction is performed on the face region, including the location information of key points such as facial contours, eyes, nose, and mouth. The extracted features are input into a pre-trained gender and age recognition model to obtain gender and age prediction results.

[0044] A2. Based on the multi-frame images of the target visitor, image recognition is performed to determine the wearing characteristics of the target visitor.

[0045] In some embodiments, wear characteristics include clothing color, style, and accessories.

[0046] For example, the embodiment of the present invention can use color space conversion and clustering algorithms to identify the main colors of clothing and accessories. Through shape analysis and texture recognition, the style of clothing (such as T-shirts, shirts, skirts, etc.) and the type of accessories (such as hats, scarves, necklaces, etc.) can be determined. If the image contains a clear brand logo, image retrieval or deep learning algorithms can be used for brand recognition.

[0047] A3. Perform image recognition based on multiple frames of images of the target visitor to determine the body posture of each frame.

[0048] A4. Draw the body movement trajectory of the target visitor based on the body posture of each frame image.

[0049] A5. Determine the body posture features based on the body posture of each frame image and the body motion trajectory.

[0050] In some embodiments, body posture features include standing posture, sitting posture and walking gait; walking posture includes stride size, step speed, step weight and body coordination rate.

[0051] Exemplarily, embodiments of the present invention can use posture estimation algorithms (such as OpenPose, DeepLabCut, etc.) to extract the position information of key points of the body from multiple frames of images. According to the position changes of key points in consecutive frames, the motion trajectory of each part of the body is drawn. Based on the motion trajectory and key point position information, body posture features such as standing posture, sitting posture, and walking gait are extracted. The calculation of walking posture features can include quantitative analysis of parameters such as stride size, pace speed, weight of footsteps, and body coordination rate.

[0052] S1023. Based on the behavior data of the historical period, feature extraction is performed to determine the behavior characteristics of the target visitors.

[0053] Exemplarily, step S1023 may be specifically implemented as steps B1-B3.

[0054] B1. Analyze the behavior patterns of target visitors when operating mixed reality devices based on historical behavior data.

[0055] In some embodiments, the behavior pattern includes the operation time, operation frequency, operation path and dwell time of each mixed reality device.

[0056] Among them, the operating time of a mixed reality device is the time between the target visitor entering the cultural and art space and operating the mixed reality device.

[0057] For example, the embodiment of the present invention can organize the behavior data into behavior logs, including operation time, operation frequency, operation path, residence time, etc. The behavior patterns of target visitors can be mined from the behavior logs using data mining algorithms (such as association rule mining, cluster analysis, etc.).

[0058] B2. Calculate the statistical characteristics of each mixed reality device based on the behavior pattern.

[0059] In some embodiments, the statistical features include mean, standard deviation, kurtosis, and skewness;

[0060] For example, the embodiment of the present invention can select statistical features that are highly descriptive of the target visitor's behavioral characteristics for calculation based on the analysis results of the behavioral pattern, and use statistical methods (such as mean, standard deviation, kurtosis, and skewness) to calculate the statistical feature values ​​of each mixed reality device.

[0061] B3. Determine the behavioral characteristics of target visitors based on the statistical characteristics of each mixed reality device and the cultural and artistic types of each mixed reality device.

[0062] In some embodiments, the behavioral characteristics include statistical characteristics of each cultural art type.

[0063] For example, the embodiment of the present invention can classify the cultural and artistic content displayed by the mixed reality device into different cultural and artistic types (such as painting, sculpture, music, dance, etc.). The statistical features of each mixed reality device are integrated according to the cultural and artistic type to obtain the behavioral feature description of the target visitor in different cultural and artistic types. Combined with the actual application scenario and background knowledge, the extracted behavioral features are explained and interpreted so as to be used in the subsequent formulation of personalized interaction plans.

[0064] S103: Determine the personal preferences of the target visitor based on the personal characteristics and behavioral characteristics of the target visitor and a preset artificial intelligence recognition model.

[0065] In some embodiments, the artificial intelligence recognition model is used to identify the personal preferences of visitors, and is obtained by training a neural network based on the behavioral data and influence data of multiple visitors with known preferences over a historical period.

[0066] As a possible implementation manner, step S103 may be specifically implemented as steps S1031 - S1033 .

[0067] S1031. Construct a feature vector based on the personal characteristics and behavioral characteristics of the target visitor.

[0068] Exemplarily, embodiments of the present invention can select features that have an important influence on determining personal preferences from personal features (such as gender, age, wearing features, body posture features) and behavioral features (such as behavioral patterns of operating mixed reality devices, statistical features, etc.) of target visitors. Encode the selected features and convert non-numerical features (such as gender, wearing style, etc.) into numerical features for subsequent mathematical processing. Standardize or normalize the numerical features to eliminate dimensional differences between different features and improve the training efficiency and accuracy of the model. Combine the processed features into a feature vector in a certain order as the input of the artificial intelligence recognition model.

[0069] S1032. Input the feature vector into the artificial intelligence recognition model to obtain the model output result.

[0070] In some embodiments, the model output results include preference probabilities for various artistic styles, preference probabilities for various interactive experiences, sensitivity to various cultural elements, preference probabilities for various clothing styles, and preference probabilities for various behavioral patterns.

[0071] Exemplarily, the embodiment of the present invention can load a pre-trained artificial intelligence recognition model from a storage medium. The model should have been trained with a neural network based on a large number of historical behavior data and image data of visitors with known preferences. Input the constructed feature vector into the input layer of the model to ensure that the format of the input data is consistent with the input format required by the model. After receiving the input feature vector, the model performs forward propagation calculations and finally outputs the prediction results of the target visitor's personal preferences through processing by a multi-layer neural network. The model output result is usually a multi-dimensional probability distribution, which represents the probability of the target visitor's preference for different artistic styles, interactive experiences, cultural elements, dressing styles, and behavioral patterns.

[0072] S1033. Determine the personal preferences of the target visitors based on the model output results.

[0073] Exemplarily, the embodiments of the present invention can set one or more thresholds according to actual application scenarios and needs to convert the probability distribution of the model output into specific preference judgments. According to the set threshold, the probability distribution of the model output is judged to determine the personal preferences of the target visitor in different dimensions. For example, if the preference probability of a certain art style exceeds the set threshold, it is considered that the target visitor has a preference for the art style. The personal preferences of the target visitor in different dimensions are integrated to form a comprehensive description of personal preferences. This helps to provide personalized interaction solutions for the target visitor later. As the behavioral data and image data of the target visitor in the cultural and artistic space continue to accumulate, the feature vector can be updated regularly or in real time and re-input into the artificial intelligence recognition model to update and refine the personal preference description of the target visitor.

[0074] S104. Based on the mixed reality data of personal preferences and cultural and art spaces, a personalized interaction plan for the target visitor is matched.

[0075] In the embodiment of the present application, the mixed reality data includes actual scene data, VR data and AR data.

[0076] As a possible implementation manner, step S104 may be specifically implemented as steps S1041 - S1043 .

[0077] S1041. Classify the mixed reality data of the cultural and art space into different types and determine the type labels of each mixed reality data.

[0078] In some embodiments, the type tag includes location information, artistic style attributes, interactive experience attributes, cultural element attributes, wearing style attributes, and suggested behavior pattern attributes.

[0079] Exemplarily, embodiments of the present invention can collect mixed reality data from various data sources (such as sensors, cameras, VR / AR devices, etc.) of the cultural and art space. The collected data is preprocessed, including denoising, format conversion, time synchronization, etc., to ensure the accuracy and consistency of the data. According to the characteristics and application scenarios of the data, the mixed reality data is divided into different types, such as location information (such as the location coordinates and movement trajectory of visitors), artistic style attributes (such as the style and color matching of paintings), interactive experience attributes (such as the type of interactive objects and the interaction method), cultural element attributes (such as representative elements of historical periods and regional cultural characteristics), wearing style attributes (such as recommended clothing matching and accessories selection) and recommended behavior pattern attributes (such as recommended visiting routes and stay time). Generate corresponding type labels for each mixed reality data item, which will be used in the subsequent matching process.

[0080] S1042: Determine multiple matching schemes based on personal preferences and type labels of each mixed reality data.

[0081] Exemplarily, the embodiments of the present invention can extract preference information related to the target visitor from the personal preference data, such as artistic style preference, interactive experience preference, cultural element preference, etc. According to the preset matching rules or algorithms, the personal preferences are matched with the type labels of the mixed reality data to generate multiple possible matching schemes. The matching rules can be based on similarity calculation, logical reasoning, rule engine, etc. The matching results are organized into multiple specific matching schemes, each of which contains a series of mixed reality data items that match the preferences of the target visitor.

[0082] S1043. Calculate the matching degree of multiple matching solutions based on the weight of each preference in the personal preference.

[0083] For example, the embodiment of the present invention can assign a weight value to each preference according to the importance or priority of the personal preference. The weight value can be determined by questionnaire survey, user feedback, expert evaluation, etc. For each matching scheme, the matching degree between it and the personal preference of the target visitor is calculated. The matching degree can be calculated by weighted summation, weighted average, similarity scoring, etc. According to the size of the matching degree, multiple matching schemes are sorted so as to select the best scheme later.

[0084] S1044: Determine an alternative matching solution based on the matching degrees of multiple matching solutions.

[0085] Exemplarily, the embodiment of the present invention can set a matching degree threshold according to actual application scenarios and requirements. Only solutions with matching degrees exceeding the threshold can be selected as alternative solutions. According to the set threshold, alternative solutions are screened out from the sorted matching solutions. The number of alternative solutions can be adjusted according to actual requirements.

[0086] S1045. Determine a personalized interaction plan for the target visitor based on the alternative matching plans.

[0087] In some embodiments, the personalized interaction plan includes display content, display method, display timing and display duration.

[0088] For example, the embodiments of the present invention can further evaluate the alternatives, taking into account factors such as feasibility, cost-effectiveness, and user experience. Based on the evaluation results, the alternatives are optimized and adjusted to improve their quality and applicability. An optimal solution is selected from the optimized alternatives as the personalized interaction solution for the target visitor. The finalized personalized interaction solution is refined into detailed information such as specific display content, display method, display timing, and display duration. This information will be used to guide the subsequent interactive design and implementation of cultural and artistic spaces.

[0089] S105: responding to the target visitor's current interaction instruction based on the target visitor's personalized interaction solution.

[0090] As a possible implementation manner, step S105 may be specifically implemented as steps S1051 - S1055 .

[0091] S1051. Determine the behavior intention of the target visitor based on the current interaction instruction and the location information of the target visitor.

[0092] Exemplarily, an embodiment of the present invention can parse the current interaction instructions of the target visitor, which are usually captured by sensors or input devices (such as handles, gesture recognition, etc.) of mixed reality devices (such as VR helmets, AR glasses, etc.). At the same time, the location information of the target visitor is obtained through the positioning system of the cultural and art space (such as GPS, Bluetooth beacon, indoor positioning system, etc.). Combined with the current interaction instructions and location information, the behavioral intention of the target visitor is inferred using a preset algorithm or model. For example, if a visitor issues a "view details" command when approaching a work of art, it can be inferred that he wants to learn more about the work.

[0093] S1052: Based on the behavioral intention and the personalized interaction plan, behavioral intention matching is performed to obtain a matching result.

[0094] In some embodiments, the matching result includes a consistent match or an inconsistent match.

[0095] Exemplarily, an embodiment of the present invention may preset a set of matching rules for matching the inferred behavioral intention with the content in the personalized interaction plan. The behavioral intention is compared with each element in the personalized interaction plan (such as display content, display method, etc.) one by one to determine whether there are consistent or similar parts. Based on the comparison results, determine whether the match is consistent. Consistency indicates that the personalized interaction plan can meet the current behavioral intention; inconsistency indicates that the personalized interaction plan needs to be adjusted or updated.

[0096] S1053. If the match is consistent, the mixed reality data is displayed to the target user based on the personalized interaction solution.

[0097] Exemplarily, the embodiments of the present invention can select content that matches the current behavioral intention from the personalized interaction scheme and prepare corresponding mixed reality data. According to the preset display method (such as holographic projection, AR overlay, etc.) in the personalized interaction scheme, select a display method suitable for the current environment and device. The prepared data is displayed to the target visitor in the selected manner through the mixed reality device.

[0098] S1054: If the match is inconsistent, update the personalized interaction plan based on the current interaction instruction.

[0099] For example, the embodiment of the present invention can analyze the reasons why the current interaction instructions do not match the personalized interaction plan, and understand the new needs or preference changes of the target visitors. According to the results of the demand analysis, the personalized interaction plan is adjusted or updated. This may include modifying the display content, changing the display method, adjusting the display timing, etc. Before the adjusted personalized interaction plan is implemented, necessary verification and testing are carried out to ensure that it meets the expectations and needs of the target visitors.

[0100] S1055. Based on the updated personalized interaction solution, display the mixed reality data to the target user.

[0101] Exemplarily, the embodiment of the present invention can prepare corresponding mixed reality data according to the updated personalized interaction scheme. Set the display parameters of the mixed reality data, such as viewing angle, brightness, contrast, etc., to optimize the display effect. Present the updated mixed reality data to the target visitor in an appropriate display manner, and monitor the display effect and the feedback of the target visitor in real time.

[0102] As a possible implementation manner, step S105 can be specifically implemented as steps C1-C5.

[0103] C1. Record the feedback information of target visitors when browsing mixed reality data.

[0104] In some embodiments, the feedback information includes facial images, heart rate, voice data, and body images.

[0105] Exemplarily, embodiments of the present invention may be configured with a facial recognition camera, a heart rate monitor (such as a wearable device), a voice recognition system, and a body motion capture device to capture in real time the feedback information of the target visitor when browsing the mixed reality data. Ensure that all collected feedback information (facial images, heart rate, voice data, and body images) can be synchronized in real time and securely stored in a designated database for subsequent analysis. During the data collection process, strictly abide by privacy protection laws and regulations to ensure that the privacy rights and interests of the target visitors are not violated. Encrypt sensitive information and obtain the visitor's consent when necessary.

[0106] C2. Based on the feedback information, feature extraction is performed to determine the feedback characteristics of the target visitors in multiple consecutive time periods.

[0107] Exemplarily, the embodiments of the present invention can pre-process the collected feedback information, including denoising, format conversion, timestamp alignment, etc., to improve the accuracy of feature extraction. Apply machine learning or deep learning algorithms, such as convolutional neural networks (CNN) for feature extraction of facial images and body images, natural language processing (NLP) technology for sentiment analysis of voice data, and heart rate variability analysis for evaluating emotional states. Divide the browsing process into multiple consecutive time periods, each of which can be a fixed time length or dynamically adjusted according to the interactive behavior of the target visitor. After feature extraction of the feedback information in each time period, summarize and form a feedback feature set for that time period.

[0108] C3. Based on the feedback characteristics of the target visitors in multiple consecutive time periods, determine the preference coefficients of the target visitors when browsing the mixed reality data with personalized interaction solutions in multiple consecutive time periods.

[0109] Exemplarily, the embodiments of the present invention can construct a preference model of target visitors based on historical data and prior knowledge, which can reflect the visitor's preferences for different types of mixed reality data, display methods, interactive experiences, etc. The preference coefficient of the target visitor for the personalized interactive scheme in each time period is calculated using the feedback feature set and the preference model. This can be achieved by comparing the actual feedback features with the ideal features in the preference model, using similarity calculation, regression analysis and other methods. The changing trend of the preference coefficient in multiple consecutive time periods is analyzed to identify the dynamic changes in the preferences of the target visitors.

[0110] C4. Determine the update direction of the personalized interaction plan based on the preference coefficients in multiple consecutive time periods.

[0111] Exemplarily, the embodiment of the present invention may set one or more thresholds of preference coefficients to determine whether the personalized interaction scheme needs to be updated. For example, when the preference coefficient is lower than a certain threshold, it may mean that the current scheme no longer meets the preferences of the target visitor. According to the changing trend of the preference coefficient and the threshold judgment result, the update direction of the personalized interaction scheme is determined. This may include adjusting the display content, changing the interaction method, optimizing the display timing, etc. When determining the update direction, direct feedback from the target visitor (such as questionnaires, verbal feedback, etc.) can also be considered to more fully understand the needs and expectations of the visitors.

[0112] C5. Update the personalized interaction plan based on the update direction of the personalized interaction plan.

[0113] Exemplarily, the embodiment of the present invention can make specific adjustments and optimizations to the personalized interaction scheme according to the determined update direction. This may involve multiple aspects such as content editing, technical implementation, and interaction design. Before the implementation of the updated personalized interaction scheme, necessary testing and verification are carried out to ensure that it meets the preferences and needs of the target visitors, while ensuring the feasibility and stability of the technology. The updated personalized interaction scheme is put into actual use, and feedback from the target visitors is continuously collected for subsequent iterative optimization and improvement.

[0114] The present invention provides a browsing and interaction method for a cultural and artistic space. By deeply mining the historical behavior data and image data of target visitors, the personal characteristics and behavior characteristics of the target visitors, such as gender, age, clothing and body posture, are accurately extracted, thereby realizing the detailed construction of visitor portraits. Combined with an artificial intelligence recognition model, the personal preferences of target visitors are accurately predicted, and then personalized interaction plans for target visitors are matched based on actual scenes, VR and AR data of the cultural and artistic space. Finally, the current interaction instructions are responded to based on the personalized interaction plan, which can significantly enhance the immersion and interactivity of visitors browsing in the cultural and artistic space, and ensure the high personalization and differentiation of the service experience, realize personalized services and dynamic communication for different visitors, improve the browsing experience of visitors in the cultural and artistic space, and successfully realize the intelligent upgrade and experience optimization of the service model of the cultural and artistic space.

[0115] Optionally, the method for browsing and interacting with a cultural and artistic space provided by an embodiment of the present invention further includes steps S201 to S205 before step S103.

[0116] S201. Obtaining behavior data and influence data of multiple visitors with known preferences in a historical period, as well as personal preferences of the multiple visitors.

[0117] S202: Based on the behavior data and influence data of multiple visitors with known preferences in a historical period, feature extraction is performed to determine personal features and behavior features of the multiple visitors with known preferences.

[0118] Exemplarily, the embodiments of the present invention can clean, denoise and standardize the collected behavior data and influence data to ensure the consistency and accuracy of the data. Based on domain knowledge and machine learning algorithms, select features that have an important impact on visitor preference prediction, such as behavior frequency, behavior type, emotional response intensity, social interaction type, etc. Statistical methods, machine learning algorithms or deep learning models are used to extract high-dimensional vectors that can represent visitor personal characteristics and behavior characteristics from the preprocessed data.

[0119] S203: Generate feature vectors of multiple visitors based on personal features and behavioral features of multiple visitors with known preferences.

[0120] For example, the embodiment of the present invention can fuse the extracted personal features and behavioral features to form a comprehensive feature vector for each visitor. This can be achieved by methods such as feature concatenation, weighted summation, or feature dimension reduction. The generated feature vectors are standardized to ensure that the feature vectors of different visitors are on the same scale, which is convenient for subsequent calculation and analysis.

[0121] S204: Taking the feature vector of each visitor as input and the personal preference of each visitor as output, a plurality of training samples are generated.

[0122] Exemplarily, the embodiment of the present invention can pair the feature vector of each visitor with its corresponding personal preference to form a training sample. Each training sample is a tuple containing an input (feature vector) and an output (personal preference). The visitor's personal preference is encoded, such as using One-Hot Encoding to convert the preference type into a binary vector so that the machine learning model can understand and process it.

[0123] S205: Based on multiple training samples, neural network training is performed to obtain an artificial intelligence recognition model.

[0124] Exemplarily, the embodiments of the present invention can select a suitable neural network model, such as a multi-layer perceptron (MLP), a convolutional neural network (CNN), or a recurrent neural network (RNN), etc., according to the complexity of the problem and the characteristics of the data. The training samples are input into the neural network, and the weights and bias parameters of the model are continuously adjusted through optimization methods such as back propagation algorithm and gradient descent to minimize the prediction error. During the training process, the model is verified using a validation set to evaluate the generalization ability and prediction accuracy of the model. Based on the verification results, the model can be fine-tuned or a more suitable model structure can be selected.

[0125] In this way, the embodiment of the present invention can train an artificial intelligence recognition model based on historical data of known preferences. The model can automatically identify and predict visitors' personal preferences for mixed reality content based on their behavior data and influence data. This helps provide visitors with a more personalized and accurate mixed reality experience, improving user satisfaction and engagement.

[0126] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0127] The following is an embodiment of the device of the present invention. For details not described in detail therein, reference may be made to the corresponding method embodiment described above.

[0128] Figure 2The structure diagram of a browsing and interactive device for a cultural and artistic space provided by an embodiment of the present invention is shown. The browsing and interactive device 300 includes a communication module 301 and a processing module 302 .

[0129] The communication module 301 is used to obtain the current interaction instructions of the target visitor in the cultural and art space, as well as the behavior data and image data in the historical period; the behavior data includes the operation data of the target visitor on the mixed reality device.

[0130] Processing module 302 is used to extract personal characteristics and behavioral characteristics of target visitors based on behavioral data and image data of historical periods, and personal characteristics include gender, age, clothing characteristics and body posture characteristics; determine the personal preferences of target visitors based on the personal characteristics and behavioral characteristics of target visitors and a preset artificial intelligence recognition model; match the personalized interaction plan of the target visitors based on the personal preferences and the mixed reality data of the cultural and art space; the mixed reality data includes actual scene data, VR data and AR data; respond to the current interaction instructions of the target visitors based on the personalized interaction plan of the target visitors.

[0131] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A browsing and interactive method for a cultural and artistic space, characterized in that: include: Obtaining current interaction instructions of target visitors in the cultural and art space, as well as behavior data and image data in historical periods; the behavior data includes operation data of target visitors on mixed reality devices; Extracting personal characteristics and behavioral characteristics of target visitors based on the behavioral data and image data of the historical period, wherein the personal characteristics include gender, age, clothing characteristics, and body posture characteristics; Determine the personal preferences of the target visitor based on the personal characteristics and behavioral characteristics of the target visitor and the preset artificial intelligence recognition model; Based on the personal preferences and the mixed reality data of the cultural and art space, a personalized interaction plan for the target visitor is matched; the mixed reality data includes actual scene data, VR data and AR data; Based on the personalized interaction scheme of the target visitor, respond to the current interaction instruction of the target visitor.

2. The method for browsing and interacting with a cultural and artistic space according to claim 1, characterized in that: The personal characteristics and behavioral characteristics of the target visitor are extracted based on the behavioral data and image data of the historical period, including: Based on the image data of the historical period, frame processing is performed to obtain multiple frame images of the target visitor; Based on multiple frames of images of target visitors, image recognition is performed to determine the personal characteristics of the target visitors; Based on the behavioral data of the historical period, feature extraction is performed to determine the behavioral characteristics of the target visitors.

3. The method for browsing and interacting with a cultural and artistic space according to claim 2, characterized in that: The image recognition is performed based on multiple frames of images of the target visitor to determine the personal characteristics of the target visitor, including: Based on the multiple frames of images of the target visitor, face recognition is performed to determine the gender and age of the target visitor; Based on the multiple frames of images of the target visitor, image recognition is performed to determine the wearing characteristics of the target visitor, wherein the wearing characteristics include clothing color, style and accessories; Based on the multiple frames of images of the target visitor, image recognition is performed to determine the body posture of each frame of the image; Draw the target visitor’s body movement trajectory based on the body posture of each frame image; The body posture features are determined based on the body posture of each frame of the image and the body movement trajectory; the body posture features include standing posture, sitting posture and walking gait; the walking posture includes stride size, walking speed, footstep weight and body coordination rate.

4. The method for browsing and interacting with a cultural and artistic space according to claim 2, characterized in that: The feature extraction based on the behavior data of the historical period to determine the behavior characteristics of the target visitor includes: Based on the behavior data of the historical period, analyzing the behavior pattern of the target visitor when operating the mixed reality device, the behavior pattern includes the operation time, operation frequency, operation path and residence time of each mixed reality device; wherein the operation time of a mixed reality device is the time between the target visitor entering the cultural and art space and operating the mixed reality device; Calculate statistical characteristics of each mixed reality device according to the behavior pattern, wherein the statistical characteristics include a mean value, a standard deviation, a kurtosis, and a skewness; Based on the statistical characteristics of each mixed reality device and the cultural and artistic types of each mixed device, the behavioral characteristics of the target visitor are determined; the behavioral characteristics include the statistical characteristics of each cultural and artistic type.

5. The method for browsing and interacting with a cultural and artistic space according to claim 1, characterized in that: The determining of the personal preferences of the target visitor based on the personal characteristics and behavioral characteristics of the target visitor and the preset artificial intelligence recognition model includes: Constructing a feature vector based on the personal characteristics and behavioral characteristics of the target visitor; Inputting the feature vector into the artificial intelligence recognition model to obtain a model output result, wherein the model output result includes a preference probability of various artistic styles, a preference probability of various interactive experiences, a sensitivity of various cultural elements, a preference probability of various wearing styles, and a preference probability of various behavioral patterns; Based on the model output results, the personal preferences of the target visitors are determined.

6. The method for browsing and interacting with a cultural and artistic space according to claim 1, characterized in that: The mixed reality data based on the personal preferences and the cultural and art space is matched to obtain a personalized interaction plan for the target visitor, including: Classify the mixed reality data of the cultural and artistic space into different types, and determine a type label for each mixed reality data, wherein the type label includes location information, artistic style attributes, interactive experience attributes, cultural element attributes, wearing style attributes, and recommended behavior pattern attributes; Determining multiple matching schemes based on the personal preference and the type labels of each mixed reality data; Calculating the matching degree of multiple matching solutions based on the weight of each preference in the personal preferences; Determining an alternative matching scheme based on the matching degrees of the multiple matching schemes; Based on the alternative matching solutions, a personalized interaction solution for the target visitor is determined, wherein the personalized interaction solution includes display content, display method, display timing and display duration.

7. The method for browsing and interacting with a cultural and artistic space according to claim 1, characterized in that: The personalized interaction scheme based on the target visitor, responding to the current interaction instruction of the target visitor, includes: Determine the behavior intention of the target visitor based on the current interaction instruction and the location information of the target visitor; Based on the behavior intention and the personalized interaction scheme, a behavior intention matching is performed to obtain a matching result, wherein the matching result includes a consistent match or an inconsistent match; If the match is consistent, then based on the personalized interaction solution, the mixed reality data is displayed to the target user; If the match is inconsistent, updating the personalized interaction plan based on the current interaction instruction; Based on the updated personalized interaction solution, the mixed reality data is displayed to the target user.

8. The method for browsing and interacting with a cultural and artistic space according to claim 7, characterized in that: The personalized interaction scheme based on the target visitor, in response to the current interaction instruction of the target visitor, further includes: Recording feedback information of the target visitor when browsing the mixed reality data, wherein the feedback information includes facial image, heart rate, voice data and body image; Based on the feedback information, feature extraction is performed to determine the feedback features of the target visitor in multiple consecutive time periods; Determining, based on the feedback characteristics of the target visitor in a plurality of consecutive time periods, a preference coefficient of the target visitor when browsing the mixed reality data with a personalized interaction scheme in the plurality of consecutive time periods; Determining an update direction of the personalized interaction scheme based on the preference coefficients in the plurality of consecutive time periods; Based on the update direction of the personalized interaction plan, the personalized interaction plan is updated.

9. A browsing interactive device for a cultural and artistic space, characterized in that: include: A communication module, used to obtain the current interaction instructions of the target visitor in the cultural and art space, as well as the behavior data and image data in the historical period; the behavior data includes the operation data of the target visitor on the mixed reality device; A processing module, for extracting personal characteristics and behavioral characteristics of target visitors based on the behavioral data and image data of the historical period, wherein the personal characteristics include gender, age, clothing characteristics and body posture characteristics; Based on the personal characteristics and behavioral characteristics of the target visitor and a preset artificial intelligence recognition model, the personal preferences of the target visitor are determined; based on the personal preferences and the mixed reality data of the cultural and art space, the personalized interaction plan of the target visitor is matched; the mixed reality data includes actual scene data, VR data and AR data; based on the personalized interaction plan of the target visitor, the current interaction instruction of the target visitor is responded to.

10. An electronic device; the electronic device comprises a memory and a processor, the memory stores a computer program, and the processor is used to call and run the computer program stored in the memory to execute the method as claimed in any one of claims 1 to 8.

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