Hotel guest room intelligent room selection auxiliary method based on augmented reality technology

Abstract

The invention discloses a hotel guest room intelligent room selection auxiliary method based on an augmented reality technology. According to the method, multi-dimensional data, including a 3D model, a panoramic image, environment data and a facility state, of a guest room are collected, a dynamically updated digital twin model is constructed, and model data are optimized by adopting a lightweight algorithm. Through an indoor positioning technology, the digital twinborn model is accurately superposed in a real scene, and immersive house selection experience is provided for a user. The system supports various interaction modes such as gesture recognition and voice instructions, information display is dynamically adjusted according to user behaviors, user preferences are analyzed in combination with a machine learning algorithm, and a personalized recommendation result is generated. According to the method, the problems of non-visual information display, poor interactivity and insufficient personalized recommendation of a traditional house selection mode are solved, and an efficient and intelligent house selection auxiliary tool is provided for the user.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to an intelligent hotel room selection auxiliary method based on augmented reality technology. Background Art

[0002] In the field of hotel room reservation, the traditional room selection method mainly relies on users to obtain room information through the hotel's official website, third-party platforms or offline channels. Such information is usually based on static pictures, text descriptions and user reviews, supplemented by simple filtering functions (such as price, room type, bed type). However, this traditional room selection method has many problems: the information display dimension is single and not intuitive, and it is difficult for users to truly understand the spatial layout, actual size, facility details (such as bathroom configuration, furniture placement) and dynamic environment (such as window view, light changes) of the room, which is easy to produce "image and text do not match" cognitive bias; lack of interactivity and immersion, users passively receive information, lack of interactive means to actively explore the room environment, the room selection process is boring, and it is difficult to stimulate user interest; personalized recommendation capabilities are weak, and existing recommendation systems are mostly based on user historical booking records or simple labels (such as "family room" and "business room"), lack of in-depth mining of user preferences (such as landscape preferences, facility priorities, and space sensitivity), and the accuracy of recommendation results is limited.

[0003] In recent years, with the development of virtual reality (VR) and augmented reality (AR) technology, some hotels have tried to introduce technical means to improve the room selection experience. However, existing technologies still have key problems and limitations: VR technology equipment is highly dependent and costly, has a low penetration rate, and is prone to dizziness when worn for a long time, and cannot truly reflect the stay experience; existing AR applications mostly focus on the display of 3D models of guest rooms, with low information integration, lack of integrated presentation of guest room facilities, real-time environmental data and surrounding services, insufficient dynamic interaction capabilities, limited intelligence, and cannot dynamically adjust information display according to user real-time location and changes in perspective, nor can they actively provide personalized suggestions.

[0004] To sum up, the core problems of existing house selection technology are insufficient dimensions of information presentation, low user participation, and extensive recommendation logic, which makes it difficult to meet users' growing demand for personalized and immersive house selection. Summary of the invention

[0005] Based on this, an embodiment of the present application provides a hotel room intelligent room selection assistance method based on augmented reality technology, which can solve the problems existing in the existing room selection technology.

[0006] In a first aspect, a hotel room intelligent room selection assistance method based on augmented reality technology is provided, the method comprising:

[0007] Collect multi-dimensional data of each guest room, including obtaining 3D models of guest rooms through 3D scanning equipment, obtaining panoramic images and video data through high-definition cameras, obtaining real-time environmental data through environmental sensors, and obtaining guest room facility status data through the hotel management system;

[0008] Process the collected multi-dimensional data, build a dynamically updated digital twin model, and use lightweight algorithms to optimize the model data;

[0009] Superimpose the digital twin model onto the real scene, and use indoor positioning technology to accurately superimpose virtual information, so as to display the 3D model, real-time environmental data and facility status to users;

[0010] Obtain user behavior data and dynamically adjust the information display in the AR scene based on user behavior; user behavior includes gesture recognition, voice commands, and touch screen operations;

[0011] Analyze user behavior data through machine learning algorithms, dynamically adjust recommendation strategies based on real-time environmental data, generate personalized intelligent room selection results, and optimize in real time based on user feedback.

[0012] Optionally, the collecting of multi-dimensional data of each guest room also includes recording user preference data through a user behavior analysis module, wherein the user preference data includes click hotspots, length of stay, and operation records of guest room facilities during the room selection process.

[0013] Optionally, when processing the collected multi-dimensional data, hierarchical loading (LOD) technology is used to dynamically adjust the level of detail of the digital twin model according to the user's perspective and interaction needs to optimize the display performance of the model on mobile devices.

[0014] Optionally, when superimposing the digital twin model into the real scene, combined with indoor positioning technology, the superimposed position and angle of the virtual information can be dynamically adjusted according to the user's real-time position and viewing angle to ensure accurate alignment of the virtual information with the real scene.

[0015] Optionally, the acquiring of user behavior data and dynamically adjusting information display in the AR scene according to the user behavior specifically includes:

[0016] When the user triggers the virtual tour function through gestures or voice commands, the perspective and location information in the AR scene are updated in real time;

[0017] When the user approaches a specific area in the model, detailed information of the area is automatically loaded and displayed; wherein the specific area includes a bed and a window, and the detailed information includes the hardness of the mattress and the view outside the window.

[0018] Optionally, when analyzing user behavior data through machine learning algorithms, a collaborative filtering algorithm is combined with a deep learning model to analyze the user's historical behavior data and real-time interaction data, generate personalized recommendation weights, and dynamically adjust the recommendation strategy based on the user's real-time feedback.

[0019] In a second aspect, a hotel room intelligent room selection assistance system based on augmented reality technology is provided, the system comprising:

[0020] The data acquisition module is used to collect multi-dimensional data of each guest room, including obtaining the 3D model of the guest room through a 3D scanning device, obtaining panoramic images and video data through a high-definition camera, obtaining real-time environmental data through environmental sensors, and obtaining guest room facility status data through a hotel management system;

[0021] The data processing module is used to process the collected multi-dimensional data, build a dynamically updated digital twin model, and use lightweight algorithms to optimize the model data;

[0022] AR display module, which is used to superimpose the digital twin model onto the real scene and realize the accurate superposition of virtual information through indoor positioning technology, so as to display the 3D model, real-time environmental data and facility status to users;

[0023] The user interaction module is used to obtain user behavior data and dynamically adjust the information display in the AR scene according to user behavior; user behavior includes gesture recognition, voice commands and touch screen operation;

[0024] The intelligent recommendation module is used to analyze user behavior data through machine learning algorithms, dynamically adjust recommendation strategies based on real-time environmental data, generate personalized intelligent room selection results, and optimize in real time based on user feedback.

[0025] In a third aspect, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the hotel room intelligent room selection assistance method described in any one of the first aspects is implemented.

[0026] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the hotel room intelligent room selection assistance method described in any one of the first aspects is implemented.

[0027] In a fifth aspect, a computer program product is provided, including a computer program / instruction, which, when executed by a processor, implements the hotel room intelligent selection assistance method described in any one of the first aspects above.

[0028] The beneficial effects brought by the technical solution provided by the embodiment of the present application include at least:

[0029] (1) Through augmented reality technology and multimodal information display, users can intuitively view the actual layout, facilities, and view outside the window of the guest room. Combined with dynamic perspective switching and virtual roaming functions, it provides an immersive room selection experience, solving the problem of non-intuitive information display in traditional room selection methods.

[0030] (2) By leveraging machine learning algorithms and real-time environmental data, the system can generate personalized room recommendations based on user preferences, historical behaviors, and real-time needs, and dynamically optimize recommendation strategies based on user feedback, significantly improving the accuracy of recommendations and user satisfaction.

[0031] (3) It supports multiple interaction methods such as gesture recognition and voice commands. Users can adjust room selection parameters in real time and obtain dynamic feedback. At the same time, the system ensures that the information obtained by users is accurate through real-time data updates, thereby enhancing user participation and room selection efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0033] Figure 1 A flowchart of a method for intelligent hotel room selection assistance based on augmented reality technology provided in an embodiment of the present application;

[0034] Figure 2 A flowchart of an implementation of an intelligent hotel room selection assistance system based on augmented reality technology provided in an embodiment of the present application;

[0035] Figure 3 A schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0036] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0037] In the description of the present invention, the terms "comprises", "has" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may also include other steps or units that are not explicitly listed but are inherent to these processes, methods, products or apparatuses, or steps or units added based on further optimization schemes conceived by the present invention.

[0038] The present invention specifically relates to the cross-application of augmented reality (AR) technology, computer vision technology, indoor positioning technology and hotel management system, and aims to provide users with a hotel room intelligent room selection assistance system and method based on augmented reality technology.

[0039] The traditional hotel room selection method mainly relies on information such as pictures, text descriptions and user reviews provided by the hotel’s official website or third-party platforms. It is difficult for users to intuitively understand the actual layout, facility configuration and surrounding environment of the guest rooms, which can easily lead to room selection deviations and affect the stay experience.

[0040] In the field of hotel room reservation, the traditional room selection method mainly relies on users to obtain room information through the hotel's official website, third-party platforms (such as Ctrip, Booking.com) or offline channels. This type of information is usually based on static pictures, text descriptions and user reviews, supplemented by simple filtering functions (such as price, room type, bed type). In recent years, with the development of virtual reality (VR) and augmented reality (AR) technology, some hotels have tried to introduce technical means to improve the room selection experience. However, the existing technology still has the following key problems and limitations:

[0041] 1. Problems with the traditional way of choosing a house.

[0042] The information display dimension is single and not intuitive: static pictures and text descriptions cannot truly reflect the room space layout, actual size, facility details (such as bathroom configuration, furniture placement) and dynamic environment (such as window view, lighting changes), making it difficult for users to accurately evaluate the actual room experience and easily generating cognitive bias of "inconsistency between pictures and text".

[0043] Lack of interactivity and immersion: Users passively receive information and lack interactive means to actively explore the room environment (such as perspective switching, virtual roaming, and facility operation simulation). The room selection process is boring and difficult to arouse user interest.

[0044] Weak personalized recommendation capabilities: Existing recommendation systems are mostly based on users’ historical booking records or simple labels (such as “family room” and “business room”), lacking in-depth mining of user preferences (such as landscape preferences, facility priorities, and space sensitivity), and the accuracy of recommendation results is limited.

[0045] 2. Limitations of existing technology applications (VR / AR).

[0046] The limitations of virtual reality (VR) technology include:

[0047] Some hotels try to use VR technology to build virtual scenes in guest rooms, and users need to wear head-mounted displays for an immersive experience. The limitations are:

[0048] Equipment dependence and high cost: VR equipment has a low penetration rate, is complex to operate, and requires users to purchase or rent additional hardware, resulting in high promotion costs.

[0049] User experience defects: Wearing it for a long time can easily cause dizziness, and the virtual environment is separated from the real physical environment of the guest room (such as sound insulation, temperature and humidity), which cannot truly reflect the stay experience.

[0050] The limitations of augmented reality (AR) technology include:

[0051] Existing AR applications mostly focus on displaying 3D models of guest rooms (such as generating virtual rooms by scanning floor plans with mobile phones), but they still have the following shortcomings:

[0052] Low information integration: Only the space model is displayed, lacking the integrated presentation of guest room facilities (such as smart device status, sanitary conditions), real-time environmental data (such as noise, lighting) and surrounding services (such as restaurant and gym locations).

[0053] Insufficient dynamic interaction capabilities: Unable to dynamically adjust information display based on the user's real-time location and changes in viewing angle (such as automatically displaying information about the scenery outside the window when approaching a window), the interaction method is rigid, and the user experience is discontinuous.

[0054] The level of intelligence is limited: It does not combine user behavior data (such as length of stay, click hotspots) with environmental data for real-time analysis, and cannot proactively provide personalized suggestions (such as recommending quiet rooms to noise-sensitive users).

[0055] 3. Summary of technical pain points.

[0056] The core problems of existing house selection technologies (whether traditional or VR / AR applications) can be summarized as follows:

[0057] Insufficient dimensions of information presentation: unable to restore the real state of the guest room from multiple angles and modalities (visual, auditory, and environmental data).

[0058] Low user engagement: Due to the lack of natural interaction methods, it is difficult for users to deeply participate in the housing selection decision-making process.

[0059] The recommendation logic is crude: user preference data and real-time environment data are not fully utilized, and the recommendation results are poorly matched with user needs.

[0060] In recent years, with the rapid development of virtual reality (VR) and augmented reality (AR) technologies, some hotels have begun to try to apply these technologies to room display and reservation. For example:

[0061] VR virtual room viewing: VR technology is used to build a virtual scene of the hotel room, and users can immerse themselves in the room environment by wearing VR equipment. However, VR equipment is expensive, has limited popularity, and is prone to dizziness and other discomfort during the user experience.

[0062] AR augmented reality house viewing: Using AR technology to overlay virtual room information onto real scenes, users can view the room's 3D model, facility information, etc. through mobile devices such as mobile phones. However, existing AR hotel applications still have the following shortcomings:

[0063] Single information display: Only 3D model display of the guest room is provided, lacking in-depth integration and interaction of information such as guest room facilities and surrounding environment.

[0064] Low level of intelligence: Unable to dynamically adjust display content based on user’s real-time location, perspective changes and other information, and the user experience is not smooth and natural enough.

[0065] Lack of personalized recommendations: It is impossible to make intelligent room recommendations based on the user’s personal preferences, historical check-in records, and other information.

[0066] In summary, the existing hotel room selection methods have problems such as non-intuitive information display, poor interactivity, and insufficient personalized recommendations, which are difficult to meet the growing personalized and immersive room selection needs of users. Therefore, the development of an intelligent hotel room selection auxiliary system and method based on augmented reality technology has important practical significance and application value.

[0067] The purpose of the invention is to overcome the shortcomings of the prior art and provide a hotel room intelligent room selection auxiliary system and method based on augmented reality technology, which can:

[0068] Provide a more intuitive and realistic display of room information: Use AR technology to superimpose virtual room information onto real scenes. Users can use mobile devices such as mobile phones to intuitively view the room’s 3D model, facility configuration, window views and other information, giving them an immersive room selection experience.

[0069] Achieve more natural and smooth user interaction: Utilize computer vision and indoor positioning technology to track the user's position and perspective changes in real time, dynamically adjust AR display content, and provide a more natural and smooth user interaction experience.

[0070] Provide more accurate and personalized room recommendations: Utilize big data analysis and machine learning technology to deeply explore users' personal preferences, historical check-in records and other information to recommend more accurate and personalized room selection options to users.

[0071] Please refer to Figure 1 , which shows a flow chart of a hotel room intelligent room selection assistance method based on augmented reality technology provided by an embodiment of the present application, which may include the following steps:

[0072] S1, collect multi-dimensional data of each guest room.

[0073] This includes obtaining the 3D model of the guest room through 3D scanning equipment, obtaining panoramic images and video data through high-definition cameras, obtaining real-time environmental data through environmental sensors, and obtaining guest room facility status data through the hotel management system.

[0074] In this step, 3D models, panoramic images, and video data of the guest room are collected through 3D scanning equipment and high-definition cameras to provide basic spatial and visual information for building a digital twin model of the guest room.

[0075] Use environmental sensors (such as temperature and humidity sensors, noise sensors) to collect guest room environmental data in real time and obtain the real-time environmental status of the guest room, such as temperature, humidity, noise level, etc.

[0076] Get the guest room facility status from the hotel management system, including equipment availability, cleaning status and other information, to understand the real-time situation of guest room facilities.

[0077] The user behavior analysis module records user preference data, such as click hot spots, length of stay, etc., for subsequent personalized recommendations. At the same time, it supports dynamic data updates and real-time updates of room status (such as facility failures and cleaning progress) to ensure the timeliness of data and provide an accurate data basis for subsequent processing and display.

[0078] S2 processes the collected multi-dimensional data, builds a dynamically updated digital twin model, and uses lightweight algorithms to optimize the model data.

[0079] In this step, 3D modeling technology is used to integrate the collected guest room space, facilities and environmental data into a high-precision digital twin model to build a virtual guest room model for subsequent display and interaction.

[0080] It supports dynamic updating of model data, such as real-time environmental data, facility status, etc., so that the digital twin model can reflect the actual situation of the guest room in real time. At the same time, a lightweight algorithm is used to optimize the model data to ensure that the model can run smoothly on mobile devices and improve the user experience.

[0081] Supports hierarchical loading (LOD technology), dynamically adjusts model details according to the user's perspective, further improves the performance and efficiency of the system, and provides optimized data support for subsequent AR display.

[0082] S3 superimposes the digital twin model onto the real scene and uses indoor positioning technology to accurately superimpose virtual information, presenting the 3D model, real-time environmental data and facility status to users.

[0083] In this step, the constructed digital twin model is superimposed on the real scene. Through multimodal information display, it supports the integrated display of 3D models, real-time environmental data (such as noise decibels, temperature and humidity), and facility status (such as equipment availability), providing users with more comprehensive and intuitive guest room information.

[0084] It provides a dynamic perspective switching function, where users can adjust the perspective (such as looking down, looking straight, and surrounding) through gestures or voice commands to view the guest room from different angles, enhancing the user's interactive experience.

[0085] Combined with indoor positioning technology, virtual information can be accurately superimposed on the real scene, such as rendering the outside view into the virtual window in real time, realizing the seamless integration of virtual and reality. At the same time, it supports dynamic lighting simulation, adjusts the brightness of the virtual scene according to the real environment lighting, further enhances the sense of immersion, and provides users with an immersive room selection experience.

[0086] S4, obtains user behavior data and dynamically adjusts the information display in the AR scene based on user behavior.

[0087] Among them, user behaviors include gesture recognition, voice commands and touch screen operations.

[0088] In this step, a natural and smooth interaction method is provided, supporting multiple interaction methods such as gesture recognition, voice commands, touch screen operation, etc. to meet the needs of different users.

[0089] It provides a virtual tour function, where users can move freely in the guest room through gesture swiping or voice commands, as if they were in the guest room, thus improving user engagement.

[0090] Dynamically adjust information display based on user behavior (such as clicking on hot spots, length of stay), such as displaying mattress hardness information when approaching a bed, to provide users with more targeted information. At the same time, provide a virtual check-in experience function, users can simulate the operation of room facilities (such as turning on and off lights, adjusting air conditioning temperature), further enhance the user experience, so that users can more realistically feel the environment and facilities of the room, and provide a more sufficient basis for subsequent decision-making.

[0091] S5 analyzes user behavior data through machine learning algorithms, dynamically adjusts recommendation strategies based on real-time environmental data, generates personalized smart room selection results, and optimizes in real time based on user feedback.

[0092] In this step, the user's historical behavior data (such as preferred room type, facility priority, and landscape preference) is analyzed through machine learning algorithms, and the recommendation strategy is dynamically adjusted in combination with real-time environmental data (such as noise and light) to provide users with room recommendations that meet their preferences.

[0093] It uses collaborative filtering algorithms and deep learning models to comprehensively consider multiple factors and recommend rooms that best suit users' preferences. It also supports multi-objective optimization (such as price, environment, and facilities) and provides multiple recommendation options for users to choose from to meet the needs of different users.

[0094] The recommendation model is optimized in real time based on user feedback on the recommendation results (such as clicks, bookings), and the accuracy of recommendations is continuously improved to provide users with better recommendation services and help users find satisfactory rooms faster.

[0095] The embodiment of the present application also provides a block diagram of a hotel room intelligent room selection assistance system based on augmented reality technology. The system may include:

[0096] The data acquisition module is used to collect multi-dimensional data of each guest room, including obtaining the 3D model of the guest room through a 3D scanning device, obtaining panoramic images and video data through a high-definition camera, obtaining real-time environmental data through environmental sensors, and obtaining guest room facility status data through a hotel management system;

[0097] The data processing module is used to process the collected multi-dimensional data, build a dynamically updated digital twin model, and use lightweight algorithms to optimize the model data;

[0098] AR display module, which is used to superimpose the digital twin model onto the real scene and realize the accurate superposition of virtual information through indoor positioning technology, so as to display the 3D model, real-time environmental data and facility status to users;

[0099] The user interaction module is used to obtain user behavior data and dynamically adjust the information display in the AR scene according to user behavior; user behavior includes gesture recognition, voice commands and touch screen operation;

[0100] The intelligent recommendation module is used to analyze user behavior data through machine learning algorithms, dynamically adjust recommendation strategies based on real-time environmental data, generate personalized intelligent room selection results, and optimize in real time based on user feedback.

[0101] The system consists of the following core modules: data acquisition module, data processing module, AR display module, user interaction module, and intelligent recommendation module. Each module works together to achieve a complete closed loop from data acquisition to user interaction.

[0102] 1. Data acquisition module includes:

[0103] Function: Collect multi-dimensional data to provide a basis for building a digital twin model of the guest room.

[0104] Key points of the invention:

[0105] Multi-source data fusion:

[0106] The 3D model, panoramic images and video data of the guest room are collected through 3D scanning equipment and high-definition cameras.

[0107] Collect guest room environment data in real time through environmental sensors (such as temperature and humidity sensors, noise sensors).

[0108] Obtain guest room facility status (such as equipment availability, cleaning status) through the hotel management system.

[0109] User preference data (such as click hotspots and dwell time) is recorded through the user behavior analysis module.

[0110] Dynamic data update:

[0111] Supports real-time updates of room status (such as facility failures, cleaning progress) to ensure data timeliness.

[0112] 2. Data processing module includes:

[0113] Function: Process and analyze the collected data and build a dynamic digital twin model.

[0114] Key points of the invention:

[0115] Digital twin model construction:

[0116] Use 3D modeling technology to integrate guest room space, facilities and environmental data into a high-precision digital twin model.

[0117] Supports dynamic updating of model data (such as real-time environmental data, facility status).

[0118] Data optimization and compression:

[0119] A lightweight algorithm is used to optimize model data to ensure smooth operation on mobile devices.

[0120] Supports hierarchical loading (LOD technology) and dynamically adjusts model details according to the user's perspective.

[0121] 3. AR display module includes:

[0122] Function: Overlay the digital twin model onto the real scene to provide an immersive house selection experience.

[0123] Key points of the invention:

[0124] Multimodal information display:

[0125] Supports multi-modal fusion display of 3D models, real-time environmental data (such as noise decibels, temperature and humidity), and facility status (such as equipment availability).

[0126] It provides a dynamic perspective switching function, and users can adjust the perspective (such as looking down, looking straight, and surrounding) through gestures or voice commands.

[0127] Real-time environment overlay:

[0128] Combined with indoor positioning technology, virtual information can be accurately superimposed on the real scene (such as rendering the scenery outside the window into the virtual window in real time).

[0129] Supports dynamic lighting simulation and adjusts the brightness of the virtual scene according to the real environment lighting.

[0130] 4. User interaction module includes:

[0131] Function: Provide a natural and smooth interaction method to enhance user engagement.

[0132] Key points of the invention:

[0133] Multi-channel interaction design:

[0134] Supports multiple interaction methods such as gesture recognition, voice commands, and touch screen operation.

[0135] It provides a virtual tour function, allowing users to move freely in the guest room through gesture swiping or voice commands.

[0136] Real-time feedback mechanism:

[0137] Dynamically adjust information display based on user behavior (such as clicking on hot spots and length of stay) (such as displaying mattress hardness information when approaching a bed).

[0138] Provides a virtual check-in experience function, where users can simulate the operation of room facilities (such as turning on and off lights, adjusting air-conditioning temperature).

[0139] 5. Intelligent recommendation module includes:

[0140] Function: Provide personalized room recommendations based on user preferences and real-time data.

[0141] Key points of the invention:

[0142] User preference analysis:

[0143] Analyze user historical behavior data (such as preferred room type, facility priority, and landscape preference) through machine learning algorithms.

[0144] Dynamically adjust the recommendation strategy based on real-time environmental data (such as noise and light).

[0145] Intelligent recommendation algorithm:

[0146] Collaborative filtering algorithm and deep learning model are used to recommend rooms that best suit users' preferences.

[0147] Supports multi-objective optimization (such as price, environment, facilities), and provides multiple recommended solutions for users to choose from.

[0148] Real-time feedback optimization:

[0149] The recommendation model is optimized in real time based on user feedback on the recommendation results (such as clicks, bookings) to improve the accuracy of recommendations.

[0150] Technical advantages include:

[0151] Comprehensive and intuitive information display: Through multimodal data fusion and dynamic digital twin models, it provides real and comprehensive room information display.

[0152] The interactive experience is natural and smooth: Combining multi-channel interactive design with real-time feedback mechanism to enhance user engagement and immersion.

[0153] Accurate and personalized recommendation results: Through intelligent recommendation algorithms and real-time data optimization, users are provided with highly matched room selection options.

[0154] The system performance is efficient and stable: lightweight algorithms and hierarchical loading technology are used to ensure smooth operation of the system on mobile devices.

[0155] From the above, it can be seen that the beneficial effects of this application include:

[0156] 1. Immersive house selection experience.

[0157] Advantages: Users can use augmented reality technology to visually view the actual layout, facilities and views of the guest rooms, providing an immersive room selection experience.

[0158] Technical means:

[0159] Using AR space modeling technology, the SLAM (Simultaneous Localization and Mapping) algorithm builds a three-dimensional space model of the guest room in real time, which is different from traditional technology that only relies on static pictures or videos.

[0160] Ambient lighting simulation technology is introduced to dynamically adjust the lighting effects of virtual scenes according to the time period selected by the user (such as morning, evening, and night), while existing technologies usually only provide fixed lighting displays.

[0161] Use multi-sensor fusion technology (such as cameras, gyroscopes, accelerometers) to ensure seamless integration of virtual content with the real environment, avoiding jitter or offset problems common in traditional AR technology.

[0162] 2. Intelligent recommendation.

[0163] Advantages: The system can intelligently recommend the most suitable guest room based on user preferences, historical behavior and real-time needs.

[0164] Technical means:

[0165] Multi-dimensional user profiling technology is used to combine users' historical check-in records, evaluation feedback, and real-time search behavior (such as their attention to landscape, floor, and facilities) to generate a dynamic recommendation model. Existing technologies are usually based only on simple screening conditions (such as price and room type).

[0166] A real-time dynamic weight adjustment algorithm is introduced to dynamically adjust the recommendation priority based on the user's current operation behavior (such as viewing a certain type of room for a long time), which is different from the fixed weight recommendation mechanism in traditional technology.

[0167] A hybrid model of collaborative filtering and content recommendation is used to combine the choices of similar users and the characteristics of the room itself (such as view and noise level) to provide more accurate recommendation results.

[0168] 3. Real-time interaction and feedback.

[0169] Advantages: Users can interact with the system through natural interaction methods such as gestures and voice, adjust room selection parameters in real time and obtain feedback.

[0170] Technical means:

[0171] The system uses a fusion of gesture recognition and voice recognition technology to support users to interact with the system through gestures (such as sliding, zooming) and voice commands (such as "switch to a high-rise room"), while existing technologies usually only support touch screen operations.

[0172] The introduction of real-time rendering optimization technology, through GPU acceleration and dynamic LOD (level of detail) technology, ensures that users get smooth visual feedback during the interaction process, avoiding the common freezes or delays in traditional AR technology.

[0173] Using a multimodal feedback mechanism, combining vision (AR display), hearing (voice prompts) and touch (vibration feedback) provides a multi-dimensional interactive experience, while existing technologies usually only rely on a single feedback method.

[0174] 4. Dynamic data update.

[0175] Advantages: The system can update the room status (such as whether it has been booked, cleaning status) in real time to ensure that the information obtained by the user is accurate.

[0176] Technical means:

[0177] Real-time data synchronization technology is used to connect with the hotel management system through WebSocket to obtain room status information in real time, while existing technologies usually rely on scheduled updates or manual refreshes.

[0178] Dynamic labeling technology is introduced to display the status changes of guest rooms (such as "booked" and "cleaning") in real time in the AR scene, and intuitively prompt users through color coding (such as red means not available).

[0179] Use distributed caching technology to ensure that user requests can be responded to quickly in high-concurrency scenarios, avoiding room selection conflicts caused by data delays in traditional technologies.

[0180] 5. Personalized customization.

[0181] Advantages: Users can customize room layout or facilities according to personal needs and view the effects in real time.

[0182] Technical means:

[0183] Using configurable AR object library technology, a variety of room layouts and facility options (such as bed types, table and chair configurations) are provided. Users can adjust the position and combination of virtual objects by dragging and dropping, while existing technologies usually only support fixed layout displays.

[0184] The introduction of physical engine technology ensures that virtual objects adjusted by users comply with real-world physical rules (such as a bed cannot be suspended in the air), and provides collision detection and automatic alignment functions.

[0185] Using user preference memory technology, the user's customized plan is automatically saved, and similar configurations are recommended first when choosing a house next time. However, existing technologies usually do not provide storage functions for personalized plans.

[0186] 6. Multi-terminal collaboration.

[0187] Advantages: Supports users to seamlessly switch between multiple terminal devices such as mobile phones, tablets, AR glasses, etc.

[0188] Technical means:

[0189] Cross-platform rendering technology is used to achieve unified rendering of AR content on different devices based on the OpenXR standard, while existing technologies are usually only optimized for a single device.

[0190] Introducing cloud synchronization technology to store users' room selection records and preference settings in the cloud, ensuring that users have a consistent experience when using the app on different devices.

[0191] Use adaptive interface technology to dynamically adjust the interface layout and interaction logic according to the device screen size and interaction method (such as touch screen, gesture, voice).

[0192] Specifically, Figure 2 The flowchart of the hotel room intelligent room selection auxiliary system shown in the figure specifically includes:

[0193] Closed-loop data flow:

[0194] Data collection → Data processing → AR display → User interaction → Intelligent recommendation → (Return) Data processing / AR display.

[0195] The user interaction module provides real-time feedback to the data acquisition module.

[0196] Module function mapping:

[0197] Yellow module: data acquisition (system input).

[0198] Blue module: data processing (core computing layer).

[0199] Green module: AR display (visualization layer).

[0200] Pink modules: user interactions (user touchpoints).

[0201] Purple module: intelligent recommendation (decision-making layer).

[0202] Core interaction paths:

[0203] Data flow: raw data → structured processing → visual presentation → user operation → intelligent decision making.

[0204] Feedback flow: User behavior data is fed back to the system and recommendation algorithm in real time.

[0205] Features:

[0206] Two-way feedback mechanism: User interaction affects both data collection and recommendation systems.

[0207] Recommendation engine dual output: optimize data processing and display content simultaneously.

[0208] Real-time closed loop: User behavior instantly affects the system operation status.

[0209] For the specific definition of the hotel room intelligent room selection assistance system based on augmented reality technology, please refer to the definition of the hotel room intelligent room selection assistance method based on augmented reality technology above, which will not be repeated here. Each module in the above-mentioned hotel room intelligent room selection assistance system based on augmented reality technology can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0210] In one embodiment, an electronic device is provided. The electronic device may be a computer, and its internal structure diagram may be as follows: Figure 3 As shown. The electronic device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the device is used to provide computing and control capabilities. The memory of the device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used for hotel room intelligent room selection auxiliary data based on augmented reality technology. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a hotel room intelligent room selection auxiliary method based on augmented reality technology is implemented.

[0211] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0212] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which involves all or part of the processes in the above-mentioned embodiment method.

[0213] In one embodiment, a computer program product is also provided, including a computer program / instructions, which involves all or part of the processes in the above-mentioned embodiment method.

[0214] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in M ​​forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (SyMchliMk) DRAM (SLDRAM), memory bus (RaMbus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0215] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0216] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be construed as limiting the scope of the patent application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent application shall be subject to the attached claims.

Claims

1. A hotel room intelligent room selection auxiliary method based on augmented reality technology, characterized in that: The method comprises: Collect multi-dimensional data of each guest room, including obtaining 3D models of guest rooms through 3D scanning equipment, obtaining panoramic images and video data through high-definition cameras, obtaining real-time environmental data through environmental sensors, and obtaining guest room facility status data through the hotel management system; Process the collected multi-dimensional data, build a dynamically updated digital twin model, and use lightweight algorithms to optimize the model data; Superimpose the digital twin model onto the real scene, and use indoor positioning technology to accurately superimpose virtual information, so as to display the 3D model, real-time environmental data and facility status to users; Obtain user behavior data and dynamically adjust the information display in the AR scene based on user behavior; user behavior includes gesture recognition, voice commands, and touch screen operations; Analyze user behavior data through machine learning algorithms, dynamically adjust recommendation strategies based on real-time environmental data, generate personalized intelligent room selection results, and optimize in real time based on user feedback.

2. The hotel room intelligent room selection auxiliary method according to claim 1, characterized in that: The collecting of multi-dimensional data of each guest room also includes recording user preference data through a user behavior analysis module, wherein the user preference data includes the user's click hotspots, length of stay, and operation records of guest room facilities during the room selection process.

3. The hotel room intelligent room selection auxiliary method based on augmented reality technology according to claim 1 is characterized in that: When processing the collected multi-dimensional data, hierarchical loading (LOD) technology is used to dynamically adjust the detail level of the digital twin model according to the user's perspective and interaction needs to optimize the display performance of the model on mobile devices.

4. The hotel room intelligent room selection auxiliary method based on augmented reality technology according to claim 1 is characterized in that: When superimposing the digital twin model onto the real scene, combined with indoor positioning technology, the superimposed position and angle of the virtual information can be dynamically adjusted according to the user's real-time position and perspective to ensure accurate alignment of the virtual information with the real scene.

5. The hotel room intelligent room selection auxiliary method based on augmented reality technology according to claim 1 is characterized in that: The obtaining of user behavior data and dynamically adjusting the information display in the AR scene according to the user behavior specifically includes: When the user triggers the virtual tour function through gestures or voice commands, the perspective and location information in the AR scene are updated in real time; When the user approaches a specific area in the model, detailed information of the area is automatically loaded and displayed; wherein the specific area includes a bed and a window, and the detailed information includes the hardness of the mattress and the view outside the window.

6. The hotel room intelligent room selection auxiliary method based on augmented reality technology according to claim 1 is characterized in that: When analyzing user behavior data through machine learning algorithms, a collaborative filtering algorithm is combined with a deep learning model to analyze users' historical behavior data and real-time interaction data, generate personalized recommendation weights, and dynamically adjust recommendation strategies based on users' real-time feedback.

7. A hotel room intelligent room selection assistance system based on augmented reality technology, characterized in that: The system comprises: The data acquisition module is used to collect multi-dimensional data of each guest room, including obtaining the 3D model of the guest room through a 3D scanning device, obtaining panoramic images and video data through a high-definition camera, obtaining real-time environmental data through environmental sensors, and obtaining guest room facility status data through a hotel management system; The data processing module is used to process the collected multi-dimensional data, build a dynamically updated digital twin model, and use lightweight algorithms to optimize the model data; AR display module, which is used to superimpose the digital twin model onto the real scene and realize the accurate superposition of virtual information through indoor positioning technology, so as to display the 3D model, real-time environmental data and facility status to users; The user interaction module is used to obtain user behavior data and dynamically adjust the information display in the AR scene according to user behavior; user behavior includes gesture recognition, voice commands and touch screen operation; The intelligent recommendation module is used to analyze user behavior data through machine learning algorithms, dynamically adjust recommendation strategies based on real-time environmental data, generate personalized intelligent room selection results, and optimize in real time based on user feedback.

8. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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