Intelligent hotel door lock data processing method, server, medium and program product

Abstract

The invention provides an intelligent hotel door lock data processing method, a server, a medium and a program product, and relates to the technical field of electric digital data processing. A door lock state, a weather condition and room environment parameter information including temperature, humidity and light intensity are collected through an intelligent door lock and an Internet of Things sensor. Then, a customer behavior preference model which is trained in advance through deep learning is utilized, the model is constructed based on historical data with labels, the multi-dimensional data can be analyzed and processed, and then the behavior preference characteristics of the customer in the aspects of room temperature and humidity and room entering and exiting are recognized. And finally, the server matches the most suitable check-in strategy from a personalized service strategy library according to the identified behavior preference information in combination with the room information, so that the intellectualization and personalization of the service are realized.

Description

Technical Field

[0001] The present application relates to the technical field of electronic digital data processing, and in particular to a smart hotel door lock data processing method, server, medium and program product. Background Art

[0002] With the widespread application of Internet of Things technology and big data, data collection and analysis of smart door lock servers have become an important means to improve user experience and service quality.

[0003] At present, the common practice is to use traditional statistical analysis methods to simply process isolated door lock switch data. Specifically, by recording the switch time, switch state changes and other basic data of the door lock, using simple statistical methods, such as calculating the switch frequency, counting the number of unlocking times in a specific time period, etc., to monitor the basic working status of the door lock, so as to determine whether the door lock is operating normally, whether there is any abnormal opening, etc.

[0004] However, traditional methods are unable to fully utilize the environmental information around the door lock, resulting in limited understanding of user behavior and difficulty in accurately identifying the differences in needs of different customer groups, thus limiting the improvement of personalized service levels. Summary of the invention

[0005] The present application provides a smart hotel door lock data processing method, server, medium and program product, which are used to realize the intelligence and personalization of hotel services based on the surrounding environment information of the door lock.

[0006] In a first aspect, the present application provides a hotel door lock data processing method based on the Internet of Things, which is applied to a server, and the method includes: obtaining door lock status data; obtaining weather condition information and room environmental parameter information through multiple Internet of Things sensors, and the room environmental parameter information at least includes room temperature, humidity and light intensity information; combining the door lock status data, weather condition information and room environmental parameter information, and determining the behavior preference information of the occupant through a preset customer behavior preference model, and the behavior preference information at least includes the room temperature and humidity information preferred by the occupant and the preference information of the occupant for entering and exiting the room, and the customer behavior preference model is constructed in advance based on multiple door lock status data, weather condition information and indoor and outdoor environmental parameter information sets marked with behavior preference information through deep learning; determining the room information of the occupant based on the behavior preference information; combining the behavior preference information and the room information, and determining a matching occupancy strategy based on a preset personalized service strategy library.

[0007] By adopting the above technical solutions, multi-dimensional data such as door lock status, weather conditions and room environment parameters are obtained in real time through IoT sensors, and these data are input into the pre-trained customer behavior preference model. The model uses deep learning methods to establish a mapping relationship between data features and customer behavior preferences, and can accurately identify customers' habitual characteristics in terms of temperature, humidity and entering and exiting the room. Based on this personalized behavior preference information, the server can automatically match the most suitable room and check-in strategy, thereby completing the preparation of personalized services before the customer checks in, effectively improving service efficiency and customer satisfaction.

[0008] In combination with some embodiments of the first aspect, in some embodiments, the step of obtaining the door lock status data specifically includes: identifying the identity of the person unlocking the door through the guest identity authentication server; if it is a guest staying in the room, recording the current unlocking time and door lock status change information; if it is not a guest staying in the room and not a hotel staff, recording the abnormal unlocking information and sending an abnormal prompt to the management end.

[0009] By adopting the above technical solution, when unlocking behavior is detected, the server first confirms the identity category of the unlocker through identity verification. For legitimate guests staying in the hotel, the server will record their unlocking time and status changes to build a customer behavior database; for illegal unlocking behavior, the server will immediately send an alarm to the management end. This hierarchical processing mechanism not only ensures hotel safety, but also avoids disturbing normal customers. At the same time, the collected data can also be used for subsequent personalized service optimization.

[0010] In combination with some embodiments of the first aspect, in some embodiments, the step of determining the room information of the staying customers in combination with the behavioral preference information specifically includes: determining the preference information of the staying customers for entering and exiting the room in combination with the behavioral preference information; if the room information is determined by the preference information and the room isolation strategy, the room isolation strategy includes if the customer's preference information is that he has a habit of going out within a set time at night, then isolating the corresponding room from the rooms corresponding to the customer group who does not have a habit of going out within the set time at night.

[0011] By adopting the above technical solution, intelligent room allocation is achieved based on the customer's room entry and exit preference information and the room isolation strategy. In particular, for customer groups with large differences in nighttime schedules, the server will reasonably isolate the rooms of customers who have the habit of going out at night from those who have regular schedules. This room allocation method based on behavioral characteristics effectively avoids mutual interference between customers with different schedules and significantly improves customers' living comfort and sleep quality.

[0012] In combination with some embodiments of the first aspect, in some embodiments, in combination with the behavioral preference information and the room information, the step of determining a matching check-in strategy according to a preset personalized service strategy library specifically includes: adjusting the temperature and humidity in the room according to the behavioral preference information within a set time before the check-in customer checks in; after the check-in customer checks in, providing a matching check-in service in combination with the current weather condition information and the behavioral preference information, the check-in service including sending a reminder message to the management end to provide rain gear if the weather condition information is a rainy day and the behavioral preference information is a preference to go out on rainy days.

[0013] By adopting the above technical solutions, before the customer checks in, the server will adjust the room temperature and humidity in advance according to their preferences to keep the environment in the best condition. During the stay, the server will also proactively provide personalized services according to weather changes, such as providing rain gear for customers who are used to going out on rainy days. This predictive service model not only improves the timeliness of the service, but also allows customers to feel the meticulous and thoughtful service of the hotel.

[0014] In combination with some embodiments of the first aspect, in some embodiments, after the step of combining the behavioral preference information and the room information and determining a matching check-in strategy according to a preset personalized service strategy library, it also includes: obtaining membership information of the checked-in customer, the membership information at least including the checked-in customer's sports hobby information and food hobby information; obtaining current time information; if the behavioral preference information is that the checked-in customer prefers to go out, and the weather condition information is sunny, then pushing recommended sports venue information to the checked-in customer's user terminal in combination with the membership information; if the behavioral preference information is that the checked-in customer prefers to go out, and the current time information is within the set dining time, then pushing dining recommendation information to the checked-in customer's user terminal in combination with the membership information.

[0015] By adopting the above technical solutions, the server deeply integrates the customer's behavioral preference information with the member information, and combines time and weather factors to achieve personalized recommendation services. When the server determines that the customer intends to go out, it will push the corresponding venue and restaurant information at the appropriate time based on their sports and food preferences. This intelligent recommendation mechanism that integrates multi-dimensional information not only meets the personalized needs of customers, but also improves the accuracy of recommendations and the intelligence level of services.

[0016] In combination with some embodiments of the first aspect, in some embodiments, after the step of determining a matching check-in strategy according to a preset personalized service strategy library in combination with the behavioral preference information, it also includes: controlling the environmental parameter adjustment device of the room corresponding to the checked-in customer to adjust the environment according to the check-in strategy; receiving feedback data on the check-in strategy from multiple customers; determining the customer's satisfaction with different services based on the feedback data; and for services with a satisfaction level higher than a set threshold, increasing the priority of the corresponding service when the current customer checks in.

[0017] By adopting the above technical solutions, the server automatically controls the room environment equipment according to the check-in strategy to achieve service automation. Secondly, by collecting and analyzing customer feedback data, the server can quantitatively evaluate the satisfaction of different services. For services with high satisfaction, the server will increase their priority in subsequent services, forming a dynamic adjustment process of continuous optimization and continuously improving service quality.

[0018] In combination with some embodiments of the first aspect, in some embodiments, after the step of determining a matching check-in strategy according to a preset personalized service policy library in combination with the behavioral preference information, it also includes: obtaining door lock status and room activity status information; determining whether the door lock status is closed and whether there is no activity in the room; if the judgment result is that the door lock is in a closed state and the duration of no activity in the room exceeds a preset time threshold, controlling the room lighting server to reduce the lighting brightness.

[0019] By adopting the above technical solution, the server can accurately determine the room usage status by real-time monitoring of the door lock status and room activity status. When it detects that the room has been unoccupied for a long time, the server will automatically lower the lighting brightness. This energy-saving control mechanism based on multiple state judgments not only ensures that the normal use of customers is not affected, but also realizes the efficient use of energy, achieving the dual goals of intelligent service and energy saving and environmental protection.

[0020] In a second aspect, the present application provides a server, comprising: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the server to execute the method described in the first aspect and any possible implementation of the first aspect.

[0021] In a third aspect, the present application provides a computer-readable storage medium comprising instructions, which, when executed on a server, causes the server to execute the method described in the first aspect and any possible implementation of the first aspect.

[0022] In a fourth aspect, the present application provides a computer program product, which, when executed on a server, enables the server to execute the method described in the first aspect and any possible implementation manner of the first aspect.

[0023] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. By adopting the above technical solution, due to the use of a customer behavior preference model based on deep learning, the model can integrate multi-dimensional data such as door lock status, weather conditions and room environmental parameters for feature extraction and behavior pattern recognition. Therefore, it effectively solves the technical problem that it is difficult to accurately capture and predict customers' personalized living habits in the existing technology, and thus achieves the technical effect of actively identifying customer behavior preference characteristics before the customer moves in, and automatically matching the optimal room and formulating personalized service strategies based on this.

[0024] 2. By adopting the above technical solution, since an intelligent room isolation strategy based on the customer's work and rest habits is adopted and the night activity patterns are used as the key basis for division, the technical problem in the existing technology that customers with different work and rest habits are prone to interfere with each other and affect the accommodation experience is effectively solved. Then, the technical effect of ensuring that the work and rest of different types of customers do not interfere with each other and significantly improving the living comfort is achieved through an intelligent room allocation mechanism.

[0025] 3. By adopting the above technical solution, since a recommendation mechanism is adopted that integrates customer behavior preference information, member information and real-time environmental factors (time, weather) in multiple dimensions, the technical problems of low matching between recommendation services and actual customer needs and inappropriate recommendation timing in the existing technology are effectively solved, thereby achieving the technical effect of providing accurate and personalized recommendation services at the most appropriate time and based on the customer's true intentions. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 This is a flow chart of a smart hotel door lock data processing method in an embodiment of the present application; Figure 2 This is another flow chart of the smart hotel door lock data processing method in the embodiment of the present application; Figure 3 It is a schematic diagram of a physical device structure of a server in an embodiment of the present application. DETAILED DESCRIPTION

[0027] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification and appended claims of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to and includes any or all possible combinations of one or more listed items.

[0028] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0029] For ease of understanding, the following is a description of the process of the method provided by this implementation. Figure 1 , which is a flow chart of the smart hotel door lock data processing method in the embodiment of the present application.

[0030] S101, obtaining door lock status data; The server first establishes a communication connection with the smart door lock and uses a standardized IoT communication protocol to receive door lock status data in real time. These status data mainly include: switch lock status (open / closed), switch lock timestamp, battery power, signal strength and other basic information.

[0031] In some embodiments, when a person unlocks the door, the identity of the person is distinguished through identity verification, and different processing methods are adopted, as follows: With the help of the guest identity verification system, the identity of the unlocking person is identified when the unlocking behavior is detected. The system can be implemented by a variety of technical means, such as reading room card information, identifying fingerprints, facial recognition or entering passwords, and comparing the acquired identity information with the guest information and staff information stored in the hotel system. When the identity verification system determines that the unlocking person is a guest, the hotel system will record the current unlocking time, accurate to the specific moment, and record the door lock state change information, such as from a closed state to an open state. These recorded data will be stored in the hotel's database for subsequent analysis of customer behavior habits. If the identity verification system determines that the unlocking person is neither a guest nor a hotel staff member, the system will determine that the unlocking behavior is an abnormal situation. The hotel system will record abnormal unlocking information, including detailed information such as unlocking time and door lock location. At the same time, the system will send an abnormal prompt to the management end, which can be a mobile phone, computer terminal or other device of the hotel manager. Abnormal prompts can be in the form of push messages, alarm sounds, etc., so that hotel managers can be aware of abnormal situations in a timely manner and take corresponding measures, such as checking surveillance and going to the site for confirmation, to ensure the safety of the hotel and its guests.

[0032] S102, obtaining weather condition information and indoor environmental parameter information through multiple IoT sensors, where the indoor environmental parameter information at least includes indoor temperature, humidity and light intensity information; For obtaining weather information, the server connects to the interface of professional meteorological data providers. For example, using the API provided by meteorological platforms such as China Weather Network, the server sends a request to it based on the hotel's geographical location information (such as longitude and latitude) to obtain real-time weather data, including temperature, humidity, weather conditions (sunny, rainy, cloudy, etc.), wind speed and direction, etc. At the same time, a small meteorological station is installed on the top of the hotel building or in an open area, equipped with temperature sensors, humidity sensors, air pressure sensors, rainfall sensors, light sensors, etc. These sensors transmit the collected raw data to the server through low-power wireless communication technology. The server integrates the data from the meteorological data interface and the local meteorological station, compares and analyzes the accuracy and consistency of the two data, and takes the data with higher credibility as the final weather information.

[0033] In terms of obtaining environmental parameter information in the room, temperature and humidity sensors, light sensors, etc. are installed in the room. These sensors send the collected environmental data to the intelligent control nodes in the room, and the intelligent control nodes then summarize these environmental parameter data and send them to the server via Wi-Fi or Bluetooth.

[0034] S103, combining the door lock state data, weather condition information and room environment parameter information, determining the behavior preference information of the occupant through a preset customer behavior preference model, the behavior preference information at least including the room temperature and humidity information preferred by the occupant and the preference information of the occupant entering and exiting the room, the customer behavior preference model is constructed in advance based on multiple sets of door lock state data, weather condition information and indoor and outdoor environment parameter information marked with behavior preference information through deep learning; The server will pre-process the multi-source data obtained. In the door lock status data, the information such as the switch lock status and timestamp may be noisy or incomplete. In some cases, such as when the door lock is in an unstable signal area, the switch lock status may be falsely reported, or the timestamp may be deviated due to clock synchronization problems. To solve this problem, the server uses a sliding window-based filtering algorithm to smooth the door lock status data and remove outliers. For weather condition information, data from the meteorological data interface and the local meteorological station may have accuracy differences and data missing. The server establishes a data quality assessment model and performs weighted fusion of the data based on factors such as data source reliability and historical data comparison.

[0035] After completing the data preprocessing, the server inputs the processed data into the preset customer behavior preference model, which is built based on the long short-term memory network (LSTM) in deep learning. The LSTM network can effectively process time series data and capture the long-term dependencies in the data. It is very suitable for analyzing the changes in door lock status and environmental parameters over time. During the training phase, the server collects a large amount of door lock status data, weather conditions information, and indoor and outdoor environmental parameter information sets with behavioral preference information. These labeled information include the room temperature and humidity range that the customer actually prefers, as well as the habitual patterns of entering and leaving the room (such as fixed time to go out, frequent entry and exit periods, etc.). Using this data, the server uses the back propagation algorithm to train the LSTM network, and continuously adjusts the weights and parameters of the network so that the model can accurately extract features related to customer behavior preferences from the input data.

[0036] In practical applications, the server can also introduce transfer learning technology to further optimize the customer behavior preference model. For newly opened hotels or hotels with less data, a general customer behavior preference model can be trained using a large amount of historical data from similar hotels. Then, based on a small amount of local data from the new hotel, the general model is fine-tuned to make it more suitable for the actual situation of the new hotel. This transfer learning method can quickly establish an effective customer behavior preference model with limited data, improving the generalization ability and accuracy of the model.

[0037] S104, determining the room information of the staying customer based on the behavior preference information; The server analyzes the customer's special needs for the room environment based on their behavioral preference information. For customers who prefer a specific temperature and humidity range, the server will screen out rooms equipped with a precise environmental control system. These rooms are equipped with intelligent temperature and humidity control equipment, which can be remotely controlled by the server. The server uses fuzzy control algorithms to automatically adjust the operating parameters of equipment such as air conditioners and humidifiers according to the customer's preferred temperature and humidity range to ensure that the temperature and humidity in the room are always maintained in a range that satisfies the customer. For example, if the customer's preferred temperature is 22-24°C and the humidity is 40%-50%, the server will monitor the room temperature and humidity data in real time. When the temperature is higher than 24°C, the air conditioner will be controlled to increase the cooling power; when the humidity is lower than 40%, the humidifier will be started for humidification.

[0038] Considering the preference information of customers entering and exiting the room, the server allocates rooms in combination with the room isolation strategy. If it is determined through the customer behavior preference model that a customer frequently unlocks and goes out during a specific time period (such as 22:00-24:00) on multiple nights, and the indoor environmental parameters remain unchanged for a long time, it will be determined that the customer has the habit of going out at a set time at night. At this time, in order to avoid interference to other customers by entering and exiting the room, the server will allocate such customers to rooms in different areas from customers with regular work and rest. According to whether the customer has the habit of going out at a set time at night, the customers are divided into two categories: one is customers who have the habit of going out at a set time at night; the other is customers who do not have the habit of going out during this time period and need a quiet rest. The hotel server will arrange customers who have the habit of going out at a set time at night to a specific room type area according to the room isolation strategy, and arrange customers who do not have the habit of going out during this time period to an isolated room type area. Specifically, the server can use geographic information system (GIS) technology to visually manage the room layout of the hotel, and classify and label the rooms in different areas according to attributes such as function and location. For example, rooms close to elevators, stairs, etc. that are prone to noise are classified into one category, and rooms far away from these areas and relatively quiet are classified into another category. For customers who are accustomed to going out at night, they will be given priority to be assigned rooms near elevators and stairs to facilitate their travel while reducing the impact on other customers.

[0039] S105: Determine a matching check-in strategy based on the behavior preference information and the room information according to a preset personalized service strategy library.

[0040] After determining the behavioral preference information and room information of the guests staying in the hotel, the server determines a matching check-in strategy based on the preset personalized service strategy library. This process covers a series of detailed service plans before, during and after check-in.

[0041] Before check-in, the server optimizes the room environment in advance with the help of the intelligent environment control system based on the room temperature and humidity information preferred by the customer. Taking the customer's preferred temperature of 22-24℃ and humidity of 40%-50% as an example, the server establishes a communication connection with the intelligent air conditioner, humidifier and other equipment in the room in advance, and starts the intelligent control program at a set time (such as 1 hour) before the customer's expected arrival. Using the fuzzy control algorithm, when it is detected that the current temperature of the room is higher than 24℃, the server sends a command to the air conditioner to gradually increase the cooling power; if the temperature is lower than 22℃, the air conditioner heating mode is adjusted. For humidity, if it is lower than 40%, the server controls the humidifier to increase the amount of water sprayed; if it is higher than 50%, the humidifier operating frequency is reduced to ensure that customers can enjoy a comfortable temperature and humidity environment when they enter the room.

[0042] During the customer's stay, the server provides dynamic services in real time by combining weather information with customer behavior preferences. If it rains and the customer prefers to go out, the server quickly sends a reminder to the management end to provide rain gear. After receiving the instruction, the management end arranges staff to place umbrellas, raincoats and other rain gear at the door of the customer's room or at a designated pick-up point. At the same time, if the customer prefers a quiet environment, and there are noise sources such as construction around the hotel on the day of check-in, the server uses the intelligent sound insulation equipment control system to adjust the room's sound insulation facilities (such as intelligent sound insulation windows, sound insulation curtains, etc.) to enhance the sound insulation effect and reduce external noise interference. If the customer has the habit of getting up early and prefers reading, the server automatically adjusts the light intensity in the room in the early morning to simulate natural morning light and create a comfortable reading environment for the customer.

[0043] After check-in, the server will continue to collect behavioral data of the customer during the stay, such as frequency of equipment use, room service needs, etc., to further improve the customer profile and provide a basis for subsequent service optimization. If the customer frequently uses the fitness facilities in the room, the server will record this information and associate it with the customer's sports preference tag, or directly obtain the customer's selected hobbies through the customer's user terminal. When the customer books a hotel next time, the server will give priority to recommending rooms with better fitness facilities, or push preferential information about fitness venues around the hotel.

[0044] In the above embodiment, the door lock status data, weather condition information and room environment parameter information are collected in real time through the Internet of Things sensor, and the customer behavior preference model based on deep learning training is used to analyze these multi-dimensional data. The check-in strategy is matched from the personalized service strategy library in combination with the room information to realize the intelligence and personalization of hotel services. This not only effectively solves the problems in the prior art that it is difficult to fully utilize the environmental information around the door lock, it is difficult to accurately identify the differences in the needs of different customer groups, and the level of personalized services is limited, but also improves service efficiency and customer satisfaction, and enhances the competitiveness of the hotel in the market.

[0045] In some embodiments, the hotel will control the environmental parameter adjustment equipment of the corresponding room of the occupant according to the determined check-in strategy, such as smart air conditioners, humidifiers, smart lighting equipment, etc. The smart control program is started before the customer checks in to ensure that the customer can enjoy a comfortable environment when entering the room. During and after the customer's stay, the hotel will collect feedback data from multiple customers on the check-in strategy. These feedback data can be collected in a variety of ways, such as inviting customers to fill out online questionnaires when checking out, setting up feedback boxes, or recording customer active feedback information through the hotel's customer service system. The feedback content covers the customer's evaluation and opinions on the room environment and various services (such as catering, cleaning, facilities, etc.). The hotel determines the customer's satisfaction with different services based on the collected feedback data. Satisfaction can be quantified by setting certain evaluation criteria and calculation methods. For services with satisfaction higher than the set threshold, the priority of the corresponding service is increased when the current customer checks in later, which means that when formulating check-in strategies for customers, the system will give priority to providing these high-satisfaction services.

[0046] In some embodiments, after step S105, a more comprehensive and personalized service experience can be provided to the guests. In combination with the above content, the method provided by this embodiment is further described in more detail below. Figure 2 , is another flow chart of the smart hotel door lock data processing method in the embodiment of the present application.

[0047] S201, obtaining member information of the occupant, where the member information at least includes the occupant's sports hobby information and food hobby information; The server obtains the member information of the guests staying in the hotel by establishing a secure data connection with the hotel's member management system. When the server receives the relevant information of the guests staying in the hotel, it sends a query request containing the customer's identity to the member management system. During the data acquisition process, the server will verify the obtained member information in real time. For example, a hash verification algorithm is used to verify the integrity of the data to ensure that the data has not been tampered with during the transmission process. If the data is found to be abnormal, the server will automatically re-request the data and record the abnormal situation for subsequent troubleshooting. The member information of the guests staying in the hotel includes at least the sports hobby information and food hobby information of the guests staying in the hotel. This information will be combined with the customer's behavioral preferences, weather conditions and current time information to provide customers with personalized service recommendations.

[0048] S202, obtaining current time information; The server mainly relies on the Network Time Protocol (NTP) to obtain the current time information. The server establishes connections with multiple reliable NTP servers, which are usually provided by professional time service agencies and have high-precision atomic clocks as time sources.

[0049] S203: If the behavior preference information indicates that the occupant prefers to go out, and the weather condition information indicates that it is sunny, push the recommended sports venue information to the occupant's user terminal in combination with the member information; When the server determines that the customer has a preference for going out and the weather is fine, the server will conduct an in-depth analysis of the sports hobbies in the member information, and use natural language processing technology to extract keywords and understand the semantics of the sports hobbies information. For example, if the member information mentions "likes to run and climb mountains outdoors", the server will extract keywords such as "running" and "climbing", and combine geographic information system (GIS) technology to screen out suitable places for running and climbing in the local sports venue database.

[0050] S204: If the behavior preference information indicates that the occupant prefers to go out, and the current time information is within the set dining time, then restaurant recommendation information is pushed to the occupant's user terminal in combination with the member information.

[0051] The server first determines whether the current time is within the set meal time. The set meal time can be flexibly set according to the cultural habits of the hotel's area and the meal time of most customers. When it is determined that it is meal time and the customer has a preference for going out, the server analyzes the food preferences in the member information and uses big data analysis technology to mine the member's previous dining records, browsing and evaluation data on online food platforms to understand the customer's taste preferences, consumption habits and other information, and make recommendations based on the customer's habits. For example, if a member often browses and likes Sichuan cuisine-related content on the food platform, the server will focus on screening restaurants that provide Sichuan cuisine.

[0052] In the embodiment of the present application, accurate personalized service recommendations are achieved by obtaining the membership information and current time information of the staying customers and combining the customer's behavioral preferences and weather conditions. This not only effectively solves the problem of mismatch between recommended services and actual customer needs and inappropriate timing in the prior art, but also significantly improves the customer's accommodation experience, enhances the attractiveness and competitiveness of hotel services, and further taps into the value of customer data and optimizes hotel resource allocation.

[0053] In some embodiments, the door lock status and room activity status information can also be obtained through specific devices. The door lock status information includes whether the door is open or closed. The room activity status information is collected through human infrared sensors, sound sensors and other devices installed in the room to determine whether there is anyone active in the room. These sensors transmit the collected data to the server in real time, and the server integrates and analyzes these data. After receiving the door lock status and room activity status information, the server will make a judgment. First, it is determined whether the door lock state is closed, and at the same time, it is determined whether there is no activity in the room. Only when the two conditions of the door lock being closed and no one in the room are met at the same time, the server will further determine whether the duration of the door lock being closed and no one in the room exceeds the preset time threshold. The preset time threshold is set according to the actual operating needs and energy-saving standards of the hotel, for example, it can be set to 15 minutes. If the duration does not exceed the preset time threshold, the lighting system maintains the current state; if it exceeds the preset time threshold, the next step is performed. When it is determined that the door lock is in a closed state and the duration of no one in the room exceeds the preset time threshold, the server will send an instruction to the room lighting system to control it to reduce the lighting brightness. This operation is aimed at achieving efficient use of energy. Without affecting the normal use of customers, it avoids wasting energy by continuously turning on lighting equipment at high brightness when no one is in the room, thereby achieving the goal of energy saving and environmental protection.

[0054] The following describes the server in the embodiment of the present invention from the perspective of hardware processing. Figure 3 , is a schematic diagram of a physical device structure of a server in an embodiment of the present application.

[0055] It should be noted that Figure 3 The structure of the server shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0056] like Figure 3 As shown, the server includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage part 308 to the random access memory (RAM) 303, such as executing the method described in the above embodiment. In RAM 303, various programs and data required for the operation of the server are also stored. CPU 301, ROM 302 and RAM 303 are connected to each other through bus 304. Input / output (I / O) interface 305 is also connected to bus 304.

[0057] The following components are connected to the I / O interface 305: an input section 306 including an audio input device, a button switch, etc.; an output section 307 including a liquid crystal display (LCD) and an audio output device, an indicator light, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read therefrom is installed into the storage section 308 as needed.

[0058] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 309, and / or installed from a removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, various functions defined in the present invention are performed.

[0059] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution server, device, or device.

[0060] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions and operations of the servers, methods and computer program products according to various embodiments of the present invention. Each box in the flowchart or block diagram may represent a module, a program segment, or a portion of a code, and the above module, program segment, or a portion of a code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the boxes may also occur in an order different from that marked in the accompanying drawings.

[0061] Specifically, the server of this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, the smart hotel door lock data processing method provided in the above embodiment is implemented.

[0062] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the server described in the above embodiment; or may exist independently without being assembled into the server. The above storage medium carries one or more computer programs, and when the above one or more computer programs are executed by a processor of the server, the server implements the smart hotel door lock data processing method provided in the above embodiment.

[0063] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0064] As used in the above embodiments, the term "when..." may be interpreted to mean "if..." or "after..." or "in response to determining..." or "in response to detecting...", depending on the context. Similarly, the phrases "upon determining..." or "if (the stated condition or event) is detected" may be interpreted to mean "if determining..." or "in response to determining..." or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)", depending on the context.

[0065] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

Claims

1. A smart hotel door lock data processing method, applied to a server, characterized in that: The method comprises: Get door lock status data; Acquire weather condition information and indoor environmental parameter information through multiple IoT sensors, wherein the indoor environmental parameter information at least includes indoor temperature, humidity and light intensity information; In combination with the door lock state data, weather condition information and room environment parameter information, the behavior preference information of the occupant is determined through a preset customer behavior preference model, wherein the behavior preference information at least includes the room temperature and humidity information preferred by the occupant and the preference information of the occupant entering and exiting the room, and the customer behavior preference model is constructed in advance through deep learning based on multiple sets of door lock state data, weather condition information and indoor and outdoor environment parameter information marked with behavior preference information; Determine the room information of the staying customer based on the behavior preference information; In combination with the behavior preference information and the room information, a matching check-in strategy is determined according to a preset personalized service strategy library.

2. The method according to claim 1, characterized in that The steps for obtaining door lock status data specifically include: The identity of the person who unlocks the door is identified through the customer identity verification server; If it is a guest staying in the room, the current unlocking time and door lock status change information will be recorded; If the person is not a guest staying in the hotel and is not a hotel staff, the abnormal unlocking information will be recorded and an abnormal prompt will be sent to the management end.

3. The method according to claim 1, characterized in that The step of determining the room information of the checked-in customer in combination with the behavior preference information specifically includes: Determine the guest's room entry and exit preference information based on the behavior preference information; If the preference information and the room isolation strategy determine the room information, the room isolation strategy includes if the customer's preference information is that they have the habit of going out within the set time at night, then the corresponding room will be isolated from the rooms corresponding to the customer group who do not have the habit of going out within the set time at night.

4. The method according to claim 1, characterized in that: The step of combining the behavior preference information and the room information and determining a matching check-in strategy according to a preset personalized service strategy library specifically includes: Adjust the temperature and humidity in the room according to the behavioral preference information within a set time before the guest checks in; After the guest checks in, matching check-in services are provided based on the current weather condition information and the behavior preference information. The check-in service includes sending a reminder message to the management end to provide rain gear if the weather condition information indicates that it is rainy and the behavior preference information indicates that the guest prefers to go out on rainy days.

5. The method according to claim 1, characterized in that After combining the behavior preference information and the room information and determining a matching check-in strategy according to a preset personalized service strategy library, the method further includes: Acquire the member information of the occupant, wherein the member information at least includes the sports hobby information and food hobby information of the occupant; Get current time information; If the behavior preference information indicates that the resident customer prefers to go out, and the weather condition information indicates that it is sunny, then the recommended sports venue information is pushed to the resident customer's user terminal in combination with the member information; If the behavior preference information indicates that the occupant prefers to go out, and the current time information is within the set dining time, the dining recommendation information is pushed to the occupant's user terminal in combination with the member information.

6. The method according to claim 1, characterized in that After the step of determining a matching check-in strategy according to a preset personalized service strategy library in combination with the behavior preference information, the method further includes: According to the check-in strategy, the environmental parameter adjustment device of the corresponding room of the check-in customer is controlled to adjust the environment; Receive feedback data from multiple customers on check-in strategies; determining customer satisfaction with different services based on the feedback data; For the services whose satisfaction level is higher than a set threshold, the priority of the corresponding service is increased when the current customer checks in.

7. The method according to claim 1, characterized in that After the step of determining a matching check-in strategy according to a preset personalized service strategy library in combination with the behavior preference information, the method further includes: Get door lock status and room activity status information; Determine whether the door lock is in a closed state and whether there is no activity in the room; If the result of the determination is that the door lock is in the closed state and the duration of no activity in the room exceeds the preset time threshold, the room lighting server is controlled to reduce the lighting brightness.

8. A server, characterized in that: The server includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the server to execute the method described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on a server, the server is caused to execute the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product is run on a server, the server is caused to execute the method according to any one of claims 1 to 7.

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