A hotel intelligent check-in and personalized service recommendation system

Abstract

This invention discloses a smart hotel check-in and personalized service recommendation system, belonging to the field of smart hotel IoT intelligent control technology. It addresses issues such as low identity verification security, cumbersome check-in processes, rigid service recommendations, poor hardware connectivity, insufficient risk control and traceability, and weak scenario adaptability. The system includes a front-end interaction layer, a core processing layer, a data support layer, and a service execution layer. Each layer achieves bidirectional communication and linkage through the IoT and encrypted internet, forming a closed-loop data system throughout the entire process. Check-in efficiency is significantly improved, achieving contactless, unmanned, and seamless check-in. The check-in time for a single check-in is reduced to less than 60 seconds, completely solving the problem of queuing and congestion during peak hours. Peak hotel front desk staff can be reduced by 30% to 45%, significantly lowering routine operating costs. Identity verification is secure and compliant; a triple-encrypted linkage verification mechanism combined with full-process risk control alarms ensures a near 100% interception rate against identity theft and unauthorized unlocking risks.

Description

Technical Field

[0001] This invention relates to a smart hotel check-in and personalized service recommendation system, belonging to the field of smart hotel IoT intelligent control technology, specifically to the technical field of smart hotel check-in and personalized service recommendation systems. Background Technology

[0002] With the popularization of digitalization and IoT technologies in the cultural tourism industry, the hotel industry is accelerating its transformation and upgrading towards unmanned and intelligent operations. However, existing technical solutions still have many core defects and cannot simultaneously address check-in efficiency, service quality, operational safety, and industrial applicability. Specific issues are as follows:

[0003] Traditional manual check-in relies on the front desk to complete the entire process, which leads to prominent queuing and congestion problems during peak hours, high error rate of manual registration, inability to meet the demand for 24-hour unattended service, and high front desk manpower operating costs.

[0004] The existing self-service check-in equipment has limited functionality, only supporting simple ID card reading and room card issuance. It does not achieve the linkage verification of biometrics, physical documents, and credit data, which poses security vulnerabilities such as identity theft and information theft, and does not meet the requirements of public security real-name registration and traceability.

[0005] Smart door locks and guest room hardware are used in a fragmented manner, only realizing independent unlocking functions, without being linked to user reservation data and preference information, and unable to complete the guest room environment adjustment in advance, resulting in a disjointed user check-in experience;

[0006] The hotel service recommendation technology is rigid, relying solely on offline static tags such as age and room type, and using a single algorithm model. It does not integrate dynamic data such as check-in time, weather, and real-time user behavior, resulting in low recommendation accuracy, a "one-size-fits-all" approach, and poor conversion rate for value-added services.

[0007] The lack of a full-process security risk control system, the absence of standardized abnormal behavior identification and alarm mechanisms, and the lack of encrypted evidence storage for operational data make it impossible to effectively prevent risks such as unauthorized check-in and abnormal consumption. At the same time, it is difficult to meet the compliance requirements for personal privacy protection and financial settlement.

[0008] Existing technologies have not formed an integrated closed-loop technology system. Data from each stage of the check-in process, hardware control, service recommendation, and risk control settlement are isolated from each other, resulting in poor system adaptability and an inability to flexibly meet the personalized needs of different types of accommodation scenarios.

[0009] In summary, current technologies have not yet proposed a fully integrated solution that can achieve "reservation and pre-collection - seamless verification and check-in - hardware linkage and adaptation - dynamic and accurate recommendation - risk control and settlement with traceability". The industry urgently needs a smart hotel service system that combines efficiency, security, personalization and scenario adaptability. Summary of the Invention

[0010] The technical problem this invention aims to solve is to overcome existing deficiencies and provide a smart hotel check-in and personalized service recommendation system, thereby addressing issues such as low identity verification security, cumbersome check-in process, rigid service recommendations, poor hardware connectivity, insufficient risk control and traceability, and weak scenario adaptability.

[0011] To achieve the above objectives, the present invention provides the following technical solution:

[0012] A smart hotel check-in and personalized service recommendation system includes a front-end interaction layer, a core processing layer, a data support layer, and a service execution layer. Each layer achieves bidirectional communication and linkage through the Internet of Things and an encrypted Internet, forming a closed-loop data process. The front-end interaction layer is a data acquisition and instruction receiving terminal; the core processing layer is the system's computing and control center, integrating multiple modules to realize core functions; the data support layer is a distributed encrypted database, providing secure data source support; and the service execution layer is the instruction implementation unit, completing hardware control, service delivery, and fee settlement.

[0013] As a preferred technical solution of the present invention, the front-end interaction layer includes a hotel mobile APP / mini-program, an offline self-service check-in terminal, a guest room smart touch terminal, an encrypted smart door lock, and an elevator access control system. Each terminal device is equipped with an encrypted data transmission module to ensure the security of user information and interactive data transmission. The mobile APP / mini-program enables online reservations, credit authorization, facial feature collection, electronic key reception, service selection, and check-out applications. The self-service check-in terminal enables offline triple identity verification and check-in voucher printing. The guest room smart touch terminal enables guests to perform local service operations and control equipment.

[0014] As a preferred technical solution of the present invention, the core processing layer is deployed on a hotel cloud server or edge computing node, integrating an identity verification module, a check-in scheduling module, a personalized recommendation engine, a scene adaptation module, and a full-process risk control module; each module adopts a modular design, can be independently debugged and upgraded, and can work collaboratively through a unified data interface to ensure the stability and scalability of the system operation.

[0015] As a preferred technical solution of the present invention, the identity verification module adopts a triple encryption linkage verification mechanism of face encryption feature value + physical document verification + blockchain credit pre-authorization, and none of the three can be missing; the face feature value is stored after irreversible encryption processing, the original image is destroyed in real time, the credit pre-authorization data is connected to a compliant third-party credit reporting / payment platform, and blockchain is used to ensure that the data is tamper-proof, thereby eliminating the risk of identity theft and illegal check-in from the root.

[0016] As a preferred technical solution of the present invention, the personalized recommendation engine is a three-layer multi-model fusion algorithm architecture. The basic layer generates an initial recommendation list based on a collaborative filtering model. The dynamic layer analyzes the user's real-time operation behavior through an LSTM temporal neural network to correct preference tags. The scenario layer dynamically adjusts the recommendation weights based on factors such as check-in time, weather, and hotel type. User feedback data such as clicks, favorites, and rejections are written back to an encrypted guest profile database in real time, enabling the recommendation algorithm to achieve autonomous iterative optimization and truly realize "personalized" scenario-based accurate recommendations.

[0017] As a preferred technical solution of the present invention, the data support layer adopts a distributed encrypted database, including an encrypted guest profile database, a hotel room resource database, a scene context database, a historical behavior log database, and a risk control threshold standard database. All data is encrypted and stored and transmitted using national cryptographic algorithms. The database supports synchronous backup between the edge and the cloud to ensure data security and traceability, while providing comprehensive and real-time data source support for all operations of the core processing layer.

[0018] As a preferred technical solution of the present invention, the service execution layer includes smart guest room hardware, hotel value-added service module, and automatic settlement and electronic invoice system; the smart guest room hardware can receive pre-start instructions from the core processing layer, and automatically adjust the status of equipment such as air conditioner, lights, and curtains by combining user historical preferences with environmental parameters such as outdoor temperature, humidity, and light, so as to achieve "enjoy the appropriate environment as soon as you open the door"; the automatic settlement and electronic invoice system can automatically verify the status of guest room equipment and full-cycle consumption records, connect with credit pre-authorization to complete seamless deduction, and generate and push electronic invoices in real time, so as to achieve unmanned check-out settlement.

[0019] As a preferred technical solution of the present invention, the scenario adaptation module has a built-in parameter configuration library for three core scenarios: business hotels, resort hotels, and long-term rental apartments. It can automatically adjust the service recommendation weight, check-in process rules, and hardware control logic according to the scenario type. At the same time, it supports hotel operators to customize and add scenario parameters, modify risk control thresholds, and adjust recommendation priorities through the backend to meet the personalized operation needs of different accommodation scenarios.

[0020] Compared with the prior art, the beneficial effects of the present invention are:

[0021] Check-in efficiency is greatly improved, achieving a completely contactless, unmanned and seamless check-in process. The check-in time for a single check-in is reduced to less than 60 seconds, completely solving the problem of queuing and congestion during peak hours. The peak staffing at the hotel front desk can be reduced by 30% to 45%, significantly reducing routine operating costs.

[0022] Identity verification is secure and compliant. The triple-encryption linkage verification mechanism, combined with full-process risk control and alarms, has a near 100% interception rate for identity theft and illegal unlocking risks. Operation logs are encrypted and stored as evidence, fully meeting the compliance requirements of public security real-name registration traceability, personal privacy protection, and financial settlement.

[0023] The service recommendations are accurate and personalized. The recommendation engine, which integrates three layers and multiple models and combines real-time user feedback for iterative optimization, breaks through the limitations of traditional static recommendations. The click-through rate of value-added services is increased by more than 40%, and the service conversion rate is increased by 25% to 35%, effectively increasing the hotel's value-added service revenue.

[0024] The hardware integration experience has been upgraded, enabling deep integration between the check-in process and the smart hardware in the guest rooms. Based on user preferences and environmental parameters, the guest room environment can be adjusted in advance, solving the problem of the disconnect between the existing technology experience and significantly improving the guest's check-in experience and satisfaction.

[0025] With strong scene adaptability, modular design and customizable scene parameter configuration library, it can be directly adapted to various accommodation scenarios. The system has strong scalability and industry replicability, and does not require large-scale transformation of existing hotel IoT hardware, resulting in low implementation cost.

[0026] The system is self-iterative and optimized, relying on user interaction feedback and full-process behavior data to achieve continuous iteration of user profiles and recommendation algorithms. At the same time, each module can be upgraded independently to ensure the long-term technological advancement and service accuracy of the system.

[0027] Intelligent operation and management, with full-process data encryption and storage, real-time alarms for abnormal behavior, and automatic updates to room status, enables digital and intelligent control of hotel operations, reducing manual inspection and management costs, and improving operational efficiency and security levels. Attached Figure Description

[0028] Figure 1 This is a block diagram of the overall system architecture of the present invention;

[0029] Figure 2 This is a flowchart illustrating the identity verification and seamless check-in process of the present invention.

[0030] Figure 3 This is a flowchart illustrating the data flow of the personalized recommendation engine in this invention.

[0031] Figure 4 This is a flowchart of the entire process of risk control alarm logic of the present invention; Detailed Implementation

[0032] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] Please see Figure 1-4 The present invention provides a technical solution:

[0034] A smart hotel check-in and personalized service recommendation system comprises a front-end interaction layer, a core processing layer, a data support layer, and a service execution layer. Each layer achieves bidirectional communication and linkage through the Internet of Things (IoT) and an encrypted internet. The front-end interaction layer includes a mobile app / mini-program, self-service check-in terminals, smart touchscreen terminals in guest rooms, encrypted smart door locks, and an elevator access control system, used to collect user data and receive execution commands. The core processing layer, deployed on a cloud server, integrates an identity verification module, a check-in scheduling module, a personalized recommendation engine, a scenario adaptation module, and a full-process risk control module, serving as the system's computational control center. The data support layer is a distributed encrypted database, including an encrypted guest profile database, a hotel room resource database, a scenario context database, a historical behavior log database, and a risk control threshold standard database, providing secure data source support. The service execution layer includes smart guest room hardware, hotel value-added service modules, and an automatic settlement and electronic invoice system, completing command implementation and service delivery.

[0035] The overall operation process of this system is divided into four stages:

[0036] Pre-registration and data collection phase: Users complete the property reservation through mobile APP / mini-program, submit real-name information and activate blockchain credit pre-authorization. The system collects the user's facial image and generates irreversible encrypted feature values. All data is encrypted by national cryptographic algorithms and stored in the data support layer. The system pushes reservation confirmation information and on-site verification instructions to the user.

[0037] Upon arrival and check-in: After arriving at the hotel, users complete three verifications through the self-service check-in terminal: facial encryption feature value verification, physical document verification, and blockchain credit pre-authorization. The risk control module monitors the verification process in real time. If the verification is successful, the check-in scheduling module automatically matches the best room and simultaneously issues electronic keys and access permissions to each terminal. At the same time, it triggers the pre-start of the smart hardware in the room and adjusts the air conditioning, lighting, and other equipment according to user preferences and outdoor environmental parameters. Users can open the door by scanning their face through the encrypted smart door lock and complete the check-in seamlessly.

[0038] Dynamic service recommendation and execution phase: The personalized recommendation engine collects multi-source data such as user profiles, scene context, and hotel resources from the data support layer. After feature extraction and multi-model fusion calculation, it generates a personalized service recommendation list and pushes it. Users complete the service selection through mobile devices or in-room terminals. The hotel value-added service module of the service execution layer is implemented and delivered. At the same time, user clicks, rejections and other feedback data are written back to the encrypted guest profile database in real time to realize profile iteration and algorithm optimization.

[0039] Check-out settlement and risk control stage: Users submit check-out requests via mobile devices. The automatic settlement and electronic invoice system verifies the status of room equipment and the entire consumption record, connects with credit pre-authorization to complete seamless deduction and generate electronic invoices; the full-process risk control module detects abnormal behavior in real time throughout the system operation. After risk assessment, if the risk control threshold is triggered, the user's access permission is immediately frozen, a real-time alarm is pushed to the hotel backend, and the operation log is encrypted and retained. After manual review, the permission is restored or revoked.

[0040] In business hotel applications, the scenario adaptation module automatically increases the recommendation weight of meetings, offices, and business catering, while the check-in scheduling module prioritizes allocating quiet rooms on higher floors. Smart hardware in guest rooms pre-activates supplemental lighting in office areas, and the personalized recommendation engine pushes services such as meeting room reservations and business afternoon tea based on weekday scenarios, significantly improving the business traveler experience and meeting service conversion rates. In family-friendly resort hotel applications, the scenario adaptation module prioritizes services such as childcare and parent-child classes, while the check-in scheduling module allocates rooms on lower floors near children's playgrounds. The system can also dynamically adjust the recommendation list based on real-time weather, automatically blocking outdoor entertainment services and switching to indoor parent-child services on rainy days. In long-term rental apartment applications, the system focuses on pushing recurring living services such as monthly cleaning and appliance maintenance, while also integrating a water and electricity consumption monitoring module to achieve intelligent and convenient residential management for long-term renters.

[0041] All modules operate in accordance with the principles of data encryption and privacy protection. Sensitive data such as users' original facial images and real-name information are all irreversibly encrypted, and only encrypted features and anonymization identifiers are stored. All data transmission and storage comply with relevant national privacy protection regulations. At the same time, the entire process operation log is encrypted and stored as evidence, meeting the requirements of public security real-name registration traceability and hotel security compliance.

[0042] This system adopts a modular design, allowing each level and module to be independently debugged, upgraded, and expanded. It can be directly adapted to existing IoT hardware devices in hotels without large-scale modifications, resulting in low industry implementation costs and high replicability. At the same time, it relies on user feedback to achieve autonomous iteration and optimization of algorithms and profiles, ensuring long-term service accuracy and system technological advancement, and providing an integrated technical solution for the digital and unmanned transformation and upgrading of the hotel industry.

[0043] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A smart hotel check-in and personalized service recommendation system, characterized in that: It includes a front-end interaction layer, a core processing layer, a data support layer, and a service execution layer. Each layer achieves bidirectional communication and linkage through the Internet of Things and the encrypted Internet. The core processing layer is the system's computing and control center, which interacts with the front-end interaction layer, the data support layer, and the service execution layer to realize encrypted identity verification, check-in process scheduling, intelligent service recommendation, scenario parameter adaptation, and full-process risk control. The data support layer adopts a distributed encrypted database to provide secure and reliable data source support for the core processing layer.

2. The hotel intelligent check-in and personalized service recommendation system according to claim 1, characterized in that: The front-end interaction layer includes a hotel mobile APP / mini-program, offline self-service check-in terminals, guest room smart touch terminals, encrypted smart door locks, and an elevator access control system. It is used to collect user identity information, facial biometrics, check-in preferences, and real-time operation behavior data, receive control commands from the core processing layer and provide feedback on the execution results, and simultaneously display check-in information, service recommendation lists, and device status to the user.

3. The hotel intelligent check-in and personalized service recommendation system according to claim 1, characterized in that: The core processing layer is deployed on the hotel's cloud server or edge computing node, and integrates an identity verification module, a check-in scheduling module, a personalized recommendation engine, a scenario adaptation module, and a full-process risk control module. The modules work together to form a closed-loop technology system for the entire process of "verification-scheduling-recommendation-adaptation-risk control".

4. The hotel intelligent check-in and personalized service recommendation system according to claim 3, characterized in that: The identity verification module adopts a triple encryption linkage verification mechanism of face encryption feature value + physical document verification + blockchain credit pre-authorization. Only after all three verifications are passed can a temporary encrypted electronic key and access permission be generated. The face encryption feature value is the feature data after irreversible encryption processing. The system only stores the encrypted features and does not retain the original face image.

5. The hotel intelligent check-in and personalized service recommendation system according to claim 3, characterized in that: After receiving the identity verification signal, the check-in scheduling module automatically matches the best room based on the real-time room cleaning status, user reservation preferences, and the availability of rooms for the day. At the same time, it pushes the check-in voucher to the self-service terminal, issues an electronic key to the mobile terminal, synchronizes access permissions to the smart door lock and elevator system, and triggers the pre-start of the smart hardware in the room.

6. The hotel intelligent check-in and personalized service recommendation system according to claim 3, characterized in that: The personalized recommendation engine is a three-layer multi-model fusion algorithm architecture, including a base layer using a collaborative filtering model, a dynamic layer introducing an LSTM temporal neural network, and a scene layer equipped with an adaptive weight parameter tuning unit; user interaction feedback is written back to the data support layer in real time, forming an iterative optimization closed loop between the recommendation algorithm and user profile.

7. The hotel intelligent check-in and personalized service recommendation system according to claim 3, characterized in that: The full-process risk control module has a built-in standardized abnormal risk control threshold library. When it detects risky behaviors such as face comparison failure ≥3 times consecutively, door lock triggering outside of check-in time, high-frequency ordering of value-added services, or abnormal cross-floor access, it immediately freezes the user's electronic access permission, pushes real-time alarm information to the hotel backend, and encrypts and retains the full-process operation log with timestamp, device number, and anonymous user identifier.

8. The hotel intelligent check-in and personalized service recommendation system according to claim 1, characterized in that: The data support layer includes an encrypted guest profile database, a hotel room resource database, a scene context database, a historical behavior log database, and a risk control threshold standard database. All data is encrypted and stored using national cryptographic algorithms, covering encrypted user biometrics, basic information, historical consumption records, real-time room status, environmental meteorological parameters, and full-process operation logs.

9. The hotel intelligent check-in and personalized service recommendation system according to claim 1, characterized in that: The service execution layer includes smart guest room hardware, hotel value-added service modules, and an automatic settlement and electronic invoice system. The smart guest room hardware includes air conditioning, lighting, curtains, and bathroom equipment, which can be adaptively adjusted according to user preferences and outdoor environmental parameters. The automatic settlement and electronic invoice system can connect to credit pre-authorization data to complete seamless deduction and automatically generate and push electronic invoices.

10. The hotel intelligent check-in and personalized service recommendation system according to claim 1, characterized in that: The scenario adaptation module has a built-in multi-scenario parameter configuration library, which can directly adapt to three core accommodation scenarios: business hotels, resort hotels, and long-term rental apartments. It also supports hotel operators to customize and add scenario parameters, adjust service recommendation weights, and check-in process rules.

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