Hotel room control method and system based on ai model driving

Abstract

The embodiment of the application provides a hotel room control method and system based on an AI model, the method comprising: a server receiving environment data collected by a sensor, calling an AI model to analyze and understand the environment data to obtain an analysis result; the AI model generating a control instruction corresponding to the analysis result according to the analysis result; the server querying a device corresponding to the control instruction according to the control instruction, and sending the control instruction to the device to realize intelligent control of a hotel customer or a smart home.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of smart home and hotel room intelligent control, and particularly relates to a hotel room control method and system based on AI model driving. BACKGROUND

[0002] Hotels (also known as inns, guesthouses, hotels, hotels, business travel, guesthouses, inns, etc.) are commercial institutions that provide safe, comfortable, and short-term rest or sleep spaces for users. With the development of the domestic tourism industry, the services provided in hotel rooms are becoming more and more diverse, and the requirements of users are also increasing. With the continuous development of technology, the demand for intelligentization of smart homes and hotel rooms is increasing. Traditional control methods have problems such as inconvenient operation and low intelligence, and cannot meet people's pursuit of convenience, efficiency, and intelligent life. And the current intelligent control does not have the ability of intention understanding, scene understanding and reasoning, and can only realize simple logic control. In this case, it cannot meet the personalized needs of users and reduces the user's experience of staying. SUMMARY

[0003] The application provides a hotel room control method and system based on AI model driving, which can automatically control the hotel room and improve the user's housing experience.

[0004] In a first aspect, the application provides a hotel room control method based on AI model driving, characterized in that the method comprises the following steps:

[0005] The server receives the environment data collected by the sensor, calls the AI model to analyze and understand the environment data to obtain an analysis result;

[0006] The AI model generates a control instruction corresponding to the analysis result according to the analysis result;

[0007] The server queries the device corresponding to the control instruction according to the control instruction, sends the control instruction to the device to realize intelligent control of the hotel customer or smart home.

[0008] In a second aspect, a hotel room control system based on AI model driving is provided, and the system comprises:

[0009] A sensor unit for collecting environment data;

[0010] An AI model unit for analyzing and understanding the environment data to obtain an analysis result, and generating a control instruction corresponding to the analysis result according to the analysis result;

[0011] The control execution unit is used to query the device corresponding to the control command based on the control command, and send the control command to the device to realize intelligent control of hotel customers or smart home.

[0012] Thirdly, this application provides a computer storage medium storing a computer program for electronic data interchange, wherein the computer program causes a computer to perform some or all of the steps described in the first aspect of this application.

[0013] The embodiments of this application have the following beneficial effects:

[0014] The technical solution provided in this application offers a method and system for intelligent control of hotel rooms and integrated control of smart homes based on an AI-driven large-scale model. By utilizing the AI ​​large-scale model to analyze and make decisions on environmental data, intelligent control of hotel rooms and smart homes is achieved, improving the accuracy and convenience of control and providing users with a more comfortable and intelligent living experience. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application;

[0017] Figure 2 This is a flowchart illustrating a hotel room control method based on an AI model, as provided in Embodiment 1 of this application.

[0018] Figure 3 This is a schematic diagram of a hotel room control system based on an AI model, provided in an embodiment of this application. Detailed Implementation

[0019] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0020] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, systems, products, or apparatuses.

[0021] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0022] The following is a schematic diagram of the structure of the electronic device involved in the embodiments of this application.

[0023] This application also provides a hotel control system, such as Figure 1 As shown, the hotel control system includes a server and multiple sensors connected to the server. These sensors include, but are not limited to, door card sensors, cameras, etc. The sensors are communicatively connected to the server, and this communication connection can be wired or wireless. The server includes at least one processor 11 and memory 12, and may also include a communication interface 14, a display screen 16, and a bus 13. The processor 11, memory 12, display screen 16, and communication interface 14 can communicate with each other via the bus 13. The communication interface 14 can transmit information and may have wireless communication capabilities, such as short-range or long-range wireless communication (e.g., LTE or NR). The processor 11 can call logical instructions in the memory 12 to execute or support the methods described in this embodiment.

[0024] The memory 12, as a computer-readable storage medium, can be configured to store software programs, computer-executable programs, such as program instructions or modules corresponding to the methods in the embodiments of this disclosure. The processor 11 executes functional applications and data processing by running the software programs, instructions, or modules stored in the memory 12, that is, implements or supports the methods in the embodiments of this application.

[0025] The main technical application scenarios of the embodiments of this application are described below.

[0026] Hotels primarily offer a private stay environment, and some guests also require personalized services. For example, air conditioning, television, charging, and shopping have become standard features in hotels today. However, existing hotels cannot collect the corresponding data and lack the analytical processing capabilities of large AI models, making it impossible to achieve intelligent control of hotel rooms based on the hotel's specific needs.

[0027] The specific methods will be described in detail below.

[0028] Example 1

[0029] Embodiment 1 of this application provides a hotel room control method based on an AI model. The above method, in the case of... Figure 1 The hotel control system shown is executed. Figure 2 This is a flowchart illustrating an AI model-driven hotel room control method provided in Embodiment 1 of this application. Figure 2 As shown, the above method includes the following steps:

[0030] Step S201: The server receives environmental data collected by the sensor, calls the AI ​​model to analyze and understand the environmental data to obtain analysis results;

[0031] Step S202: The AI ​​model generates control commands corresponding to the analysis results based on the analysis results;

[0032] Step S203: The server queries the device corresponding to the control command based on the control command, and sends the control command to the device to realize intelligent control of hotel customers or smart home.

[0033] Optionally, the step of calling the AI ​​model to analyze and understand the environmental data to obtain analysis results specifically includes:

[0034] The AI ​​model generates input data based on the environmental data, inputs the input data into the AI ​​model for computation to obtain the computation result, and obtains the analysis result based on the computation result.

[0035] The technical solution provided in this application offers a method and system for intelligent control of hotel rooms and integrated control of smart homes based on an AI-driven large-scale model. By utilizing the AI ​​large-scale model to analyze and make decisions on environmental data, intelligent control of hotel rooms and smart homes is achieved, improving the accuracy and convenience of control and providing users with a more comfortable and intelligent living experience.

[0036] For example, the above control commands can be control commands for a socket, specifically:

[0037] The server detects the status of the room key card slots in the hotel room. If the card slot is in the inserted state, it periodically collects the power status of all power sockets. Each time it collects data, it generates a status vector for a user socket. When the socket is in the power supply state, the corresponding element in the status vector is 1, and otherwise it is 0. If the card slot is not in the inserted state, the server extracts the first status vector corresponding to the previous sampling period, extracts the socket identifier with an element value of 1 in the first status vector, and determines the location corresponding to the socket identifier based on the mapping relationship between the socket identifier and the location. If the location is within the user's socket usage range, the server controls the socket corresponding to the socket identifier to maintain power supply.

[0038] The following example uses four plugs, in the following order: bedside socket 1, bedside socket 2, TV socket, and sideboard socket. If the power usage status is: powered, not powered, not powered, powered, the corresponding state vector is 1001. If the power usage status in the next data collection cycle is: not powered, powered, powered, powered, powered, the corresponding state vector is 0111. The above data collection cycle can be set by the hotel itself, and generally does not need to be very frequent, for example, once every 5 or 10 minutes is sufficient.

[0039] The above methods may also include:

[0040] Multiple state vectors are combined into a state matrix in the order of collection time; the server detects the state matrix and extracts the row number or column number where each row or column element value is 1, and extracts the socket corresponding to the row number or column number according to the preset mapping relationship. If the socket is located outside the user's usage range, the server sends an alarm message to the hotel service system.

[0041] The following example uses the two vectors mentioned above. In other application scenarios, the number of vectors in the state matrix will be larger. The vectors 1001 and 0111 are used to form a state matrix.

[0042]

[0043] Then extract column number 4, where every element in each column is 1. The socket corresponding to column number 4 is the sideboard socket. If the sideboard socket is outside the user's usage area, an alarm message will be issued. If the sideboard socket is within the user's usage area (based on its location), no alarm will be issued.

[0044] The hotel can define the range of user-use sockets and the range of non-user-use sockets. For example, sockets near bedside tables can be considered user-use sockets, while TV sockets can be considered non-user-use sockets.

[0045] For rooms with illegally installed devices, there are two characteristics: firstly, the illegally installed device is always in a working state (i.e., powered) whenever the room card provides power; secondly, the power outlet is always a relatively concealed outlet (i.e., an outlet that users would not use, such as a TV outlet). Based on these characteristics, the above technical solution extracts the outlets that are always powered from the state matrix. To avoid false alarms, some commonly used outlets are identified as having normal power supply. This effectively alerts users to illegally installed devices without generating too many false alarms, thus protecting user privacy.

[0046] The aforementioned hotel service system can be a server or a mobile terminal. For example, it can directly send alarm information to the terminal devices of hotel staff. The alarm information includes the location of the socket and the room number, which makes it convenient for hotel staff to check.

[0047] For example, the above methods may also include:

[0048] The server records the status and status parameters of all devices in the room corresponding to the room card, constructs a mapping relationship between the status and status parameters of all devices in the room and the user's identity information, collects the user's first facial data, and when the user's identity information is identified based on the first facial data, the server extracts the status and status parameters of all devices in the room according to the mapping relationship, and determines whether to adjust all devices in the room to the aforementioned status and status parameters based on the camera position of the collected user's first facial data.

[0049] For example, the determination of whether to adjust all devices in the room to the aforementioned state based on the camera position of the collected user's first facial data, and the specific state parameters, may include:

[0050] The server acquires the first position of the camera that collects the first facial data of the user, calculates the first distance between the first position and the room position, acquires the second position of the camera that collects the second facial data of the user, calculates the second distance between the second position and the room position, and if the second distance is less than the first distance, it executes the adjustment of all devices in the room to the state and state parameters; if the second distance is greater than the first distance, it does not execute the adjustment of all devices in the room to the state and state parameters; the camera that collects the second facial data is the camera that identifies the user's identity information.

[0051] The above technical solution determines whether to adjust the room's equipment to the user's desired state by recognizing the user's identity information. This equipment and its status parameters include, for example, television (whether it's in use, and the corresponding channel), air conditioning (temperature value), curtains (curtain opening distance), etc. If the distance decreases (corresponding to a second distance < first distance), it's determined that the user is moving towards the room, requiring the use of the room's equipment; conversely, if the distance increases, the equipment is not needed. It's important to note that the above distances are walking distances, not straight-line distances, and floor information needs to be considered. The specific distance calculation method can be found in indoor positioning technology solutions; improvements to indoor positioning solutions are not discussed here and will not be elaborated further. This method improves the user's hotel experience and facilitates a better user experience.

[0052] For example, the above methods may also include:

[0053] The server obtains the user's registered first mobile phone number and first room number, and receives a Wi-Fi login request sent by the mobile terminal. The Wi-Fi login request includes: all searched Wi-Fi names and the first mobile phone number. The server queries whether the above Wi-Fi names contain a first room number corresponding to the first mobile phone number. If the query finds that the above Wi-Fi names contain a first room number corresponding to the first mobile phone number, the server receives the Wi-Fi login request and allows the mobile terminal to log in to the Wi-Fi without a password.

[0054] For hotel Wi-Fi login, users typically log in using the username and password on their room key or room card. This method doesn't allow for seamless login. However, allowing direct login can lead to many users logging into the same Wi-Fi network, causing signal degradation and insufficient data channels. Therefore, it's necessary to differentiate between Wi-Fi networks with different room numbers and achieve seamless login. This is addressed by including all found Wi-Fi names and the user's primary phone number in the login request. By checking if the primary phone number matches the primary room number, the system can determine if the user (i.e., the mobile device) is near their matched room. If so, the mobile device is allowed to log in to the Wi-Fi without a password, thus achieving passwordless and seamless Wi-Fi login and improving the hotel experience.

[0055] For example, the above methods may also include:

[0056] The server extracts room occupancy and check-in time data from the hotel's historical time range. This data is then grouped by day to obtain multiple sets of data. Each set includes the room occupancy and check-in time for that day. Using these sets as the basic unit, a first function f(t) = (at) is fitted using the daily room occupancy and check-in time data. 2 +b1; where t is time in hours, and a and b1 are constant variables of the function f(t). The average of the values ​​of a in each function group is used to obtain a. 均 Calculate the average value of b1 for each set of functions to obtain b1. 均 Construct the prediction function f(t)' = (a 均 -t) 2 +b1 均 The system obtains the current time, estimates the room occupancy rate f(t1)' based on the current time t1 and the prediction function, extracts the cleaning room quantity and time data within the hotel's historical time interval, and uses the cleaning room quantity and time data to fit a second function f(x) = kx + b2; where x is the time in hours, k is the slope, and b2 is a constant variable. The system calculates the cleaning room quantity f(t1) for the current time t1 based on the current time. If f(t1)' > f(t1), a room check-in delay message is sent to the user.

[0057] The aforementioned delayed check-in message can carry the delayed check-in time, which is equal to [f(t1)'-f(t1)] / k rounded up. Of course, the delayed check-in time can also be directly estimated to a set time, such as 1 hour or 2 hours. In this case, when sending the message, the users are divided into multiple groups, and each group receives a different delayed check-in time.

[0058] The above method uses two functions to predict whether hotel guests will delay their check-in. In some application scenarios, such as a hotel corresponding to an amusement park, the number of guests staying in a room changes curvilinearly with the hour. For example, hotel check-in is generally after 1 PM, so the highest occupancy rate is usually around 2 PM or 3 PM. Therefore, historical data is needed to fit the specific occupancy curve, allowing for prediction of today's room occupancy by time. For room cleaning, the quantity changes linearly because the number of cleaning staff is fixed per day, and the number of rooms cleaned by one person per unit of time is almost constant, thus exhibiting a linear change. This allows for the estimation of the number of rooms to be cleaned (i.e., the number of rooms available for occupancy). If the estimated number of rooms to be occupied is greater than the number of rooms to be cleaned, then a message needs to be sent to users indicating a delayed check-in. Otherwise, users may be stranded in the hotel lobby. In simple terms, if a user arrives at a certain time but there are no rooms available, it greatly affects the user experience. Therefore, by notifying users in advance of a delayed check-in, they can arrange other activities and postpone their arrival at the hotel to avoid being stranded in the lobby. Thus, through the above-mentioned prediction, the hotel can effectively control its guest rooms and improve the user's hotel stay experience.

[0059] See Figure 3 , Figure 3 A schematic diagram of a hotel room control system based on an AI model is provided. The system includes:

[0060] Sensor unit 301 is used to collect environmental data;

[0061] AI model unit 302 is used to analyze and understand the environmental data to obtain analysis results; and generate control commands corresponding to the analysis results based on the analysis results.

[0062] The control execution unit 303 is used to query the device corresponding to the control command based on the control command, and send the control command to the device to realize intelligent control of hotel customers or smart home.

[0063] Optional,

[0064] AI model unit 302 specifically generates input data for the AI ​​model based on the environmental data, inputs the input data into the AI ​​model for calculation to obtain the calculation result, and obtains the analysis result based on the calculation result.

[0065] For example, the system described above may also include a processing unit.

[0066] The processing unit is used to record the status and status parameters of all devices in the room corresponding to the room card, construct a mapping relationship between the status and status parameters of all devices in the room and the user's identity information, collect the user's first face data, and when the user's identity information is identified based on the first face data, the status and status parameters of all devices in the room are extracted according to the mapping relationship, and the camera position of the collected user's first face data is used to determine whether to adjust all devices in the room to the aforementioned status and status parameters.

[0067] Example,

[0068] The processing unit is specifically used to acquire the first position of the camera that collects the first facial data of the user, calculate the first distance between the first position and the room position, acquire the second position of the camera that collects the second facial data of the user, calculate the second distance between the second position and the room position, and if the second distance is less than the first distance, execute the adjustment of all devices in the room to the state and state parameters; if the second distance is greater than the first distance, do not execute the adjustment of all devices in the room to the state and state parameters; the camera that collects the second facial data is a camera that can identify user identity information.

[0069] Example,

[0070] The processing unit is further configured to obtain the user's registered first mobile phone number and first room number, and receive a Wi-Fi login request sent by the mobile terminal. The Wi-Fi login request includes: all searched Wi-Fi names and the first mobile phone number. The server queries whether the above Wi-Fi names contain a first room number corresponding to the first mobile phone number. If the query shows that the above Wi-Fi names contain a first room number corresponding to the first mobile phone number, the server receives the Wi-Fi login request and allows the mobile terminal to log in to the Wi-Fi without a password.

[0071] For example, the processing unit described above is also used to extract room occupancy and check-in time data within the hotel's historical time intervals, group the room occupancy and check-in time data by day to obtain multiple sets of data, each set of data including the room occupancy and check-in time of the day, and use the set of data as the basic unit to fit the room occupancy and check-in time of the day to obtain the first function f(t) = (at). 2 +b1; where t is time in hours, and a and b1 are constant variables of the function f(t). The average of the values ​​of a in each function group is used to obtain a. 均 Calculate the average value of b1 for each set of functions to obtain b1. 均 Construct the prediction function f(t)' = (a 均 -t) 2 +b1 均The system obtains the current time, estimates the room occupancy rate f(t1)' based on the current time t1 and the prediction function, extracts the cleaning room quantity and time data within the hotel's historical time interval, and uses the cleaning room quantity and time data to fit a second function f(x) = kx + b2; where x is the time in hours, k is the slope, and b2 is a constant variable. The system calculates the cleaning room quantity f(t1) for the current time t1 based on the current time. If f(t1)' > f(t1), a room check-in delay message is sent to the user.

[0072] This application also provides a computer storage medium storing a computer program for electronic data interchange, which causes a computer to perform some or all of the steps of any of the methods described in the above method embodiments, wherein the computer includes an electronic device.

[0073] This application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any of the methods described in the above method embodiments. The computer program product may be a software installation package, and the computer may include an electronic device.

[0074] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0075] In the several embodiments provided in this application, it should be understood that the disclosed methods, apparatuses, and systems can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for example, the division of units is merely a logical functional division, and other division methods may exist in actual implementation; for example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0076] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0077] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can be physically comprised separately, or two or more units can be integrated into one unit. The integrated unit described above can be implemented in hardware or in the form of hardware plus software functional units.

[0078] The integrated units implemented as software functional units described above can be stored in a computer-readable storage medium. These software functional units, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute some steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, volatile memory, or non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM), etc., various media capable of storing program code.

[0079] While the present invention has been disclosed above, it is not limited thereto. Any person skilled in the art can easily conceive of variations or substitutions without departing from the spirit and scope of the present invention, and various modifications and alterations can be made, including combinations of the different functions and implementation steps described above, as well as software and hardware implementation methods, all of which are within the protection scope of the present invention.

Claims

1. A hotel room control method based on an AI model, characterized in that, The method includes the following steps: The server receives environmental data collected by sensors, calls an AI model to analyze and understand the environmental data, and obtains analysis results. The AI ​​model generates control commands corresponding to the analysis results based on the analysis results; The server queries the device corresponding to the control command based on the control command, and sends the control command to the device to realize intelligent control of hotel customers or smart home. The method further includes: The server extracts room occupancy and check-in time data from the hotel's historical time intervals. The room occupancy and check-in time data are grouped by day to obtain multiple sets of data. Each set of data includes the room occupancy and check-in time for that day. Using the set of data as the basic unit, the first function f(t) = (at) is fitted using the room occupancy and check-in time for that day. 2 +b1; where t is time in hours, and a and b1 are constant variables of the function f(t). The average of the a values ​​for each function group is used to obtain a. 均 Calculate the average value of b1 for each set of functions to obtain b1. 均 Construct the prediction function f(t)' = (a 均 -t) 2 +b1 均 The system obtains the current time, estimates the room occupancy rate f(t1)' based on the current time t1 and the prediction function, extracts the cleaning room quantity and time data within the hotel's historical time interval, and uses the cleaning room quantity and time data to fit a second function f(x) = kx + b2; where x is the time in hours, k is the slope, and b2 is a constant variable. The system calculates the cleaning room quantity f(t1) for the current time t1 based on the current time. If f(t1)' > f(t1), a room check-in delay message is sent to the user. The delayed check-in message carries the time of delayed check-in, and the check-in time is equal to [f(t1)'-f(t1)] / k rounded up.

2. The hotel room control method based on AI model driving according to claim 1, characterized in that, The specific steps involved in calling the AI ​​model to analyze and understand the environmental data to obtain analysis results include: The AI ​​model generates input data based on the environmental data, inputs the input data into the AI ​​model for computation to obtain the computation result, and obtains the analysis result based on the computation result.

3. The hotel room control method based on AI model driving according to claim 1, characterized in that, The method further includes: The server records the status and status parameters of all devices in the room corresponding to the room card, constructs a mapping relationship between the status and status parameters of all devices in the room and the user's identity information, collects the user's first facial data, and when the user's identity information is identified based on the first facial data, the server extracts the status and status parameters of all devices in the room according to the mapping relationship, and determines whether to adjust all devices in the room to the aforementioned status and status parameters based on the camera position of the collected user's first facial data.

4. The hotel room control method based on AI model driving according to claim 3, characterized in that, The determination of whether to adjust all room devices to the specified state based on the camera position of the collected user's first facial data, and the state parameters specifically include: The server acquires the first position of the camera that collects the first facial data of the user, calculates the first distance between the first position and the room position, acquires the second position of the camera that collects the second facial data of the user, calculates the second distance between the second position and the room position, and if the second distance is less than the first distance, it executes the adjustment of all devices in the room to the state and state parameters; if the second distance is greater than the first distance, it does not execute the adjustment of all devices in the room to the state and state parameters; the camera that collects the second facial data is the camera that identifies the user's identity information.

5. The hotel room control method based on AI model driving according to claim 1, characterized in that, The method further includes: The server obtains the user's registered first mobile phone number and first room number, and receives a Wi-Fi login request sent by the mobile terminal. The Wi-Fi login request includes: all searched Wi-Fi names and the first mobile phone number. The server queries whether the above Wi-Fi names contain a first room number corresponding to the first mobile phone number. If the query finds that the above Wi-Fi names contain a first room number corresponding to the first mobile phone number, the server receives the Wi-Fi login request and allows the mobile terminal to log in to the Wi-Fi without a password.

6. A hotel room control system based on an AI model, characterized in that, The system includes: Sensor unit, used to collect environmental data; The AI ​​model unit is used to analyze and understand the environmental data to obtain analysis results; and to generate control commands corresponding to the analysis results based on the analysis results. The control execution unit is used to query the device corresponding to the control command based on the control command, and send the control command to the device to realize intelligent control of hotel customers or smart home. The system is also used to extract room occupancy and check-in time data within a historical time interval of the hotel, and to group the room occupancy and check-in time data by day to obtain multiple sets of data. Each set of data includes the room occupancy and check-in time of the day. Using the set of data as the basic unit, the system fits the room occupancy and check-in time of the day to obtain the first function f(t) = (at). 2 +b1; where t is time in hours, and a and b1 are constant variables of the function f(t). The average of the a values ​​for each function group is used to obtain a. 均 Calculate the average value of b1 for each set of functions to obtain b1. 均 Construct the prediction function f(t)' = (a 均 -t) 2 +b1 均 The system obtains the current time, estimates the room occupancy rate f(t1)' based on the current time t1 and the prediction function, extracts the cleaning room quantity and time data within the hotel's historical time interval, and uses the cleaning room quantity and time data to fit a second function f(x) = kx + b2; where x is the time in hours, k is the slope, and b2 is a constant variable. The system calculates the cleaning room quantity f(t1) for the current time t1 based on the current time. If f(t1)' > f(t1), a room check-in delay message is sent to the user. The delayed check-in message carries the time of delayed check-in, and the check-in time is equal to [f(t1)'-f(t1)] / k rounded up.

7. The hotel room control system based on AI model driving according to claim 6, characterized in that, The AI ​​model unit specifically generates input data for the AI ​​model based on the environmental data, inputs the input data into the AI ​​model for calculation to obtain the calculation result, and obtains the analysis result based on the calculation result.

8. The hotel room control system based on AI model driving according to claim 6, characterized in that, The system also includes: a processing unit, The processing unit is also used to record the status and status parameters of all devices in the room corresponding to the room card, construct a mapping relationship between the status and status parameters of all devices in the room and the user's identity information, collect the user's first face data, and when the server identifies the user's identity information based on the first face data, it extracts the status and status parameters of all devices in the room based on the mapping relationship, and determines whether to adjust all devices in the room to the aforementioned status and status parameters based on the camera position of the collected user's first face data.

9. The hotel room control system based on an AI model as described in claim 8, characterized in that, The processing unit is specifically used to acquire the first position of the camera that collects the first facial data of the user, calculate the first distance between the first position and the room position, acquire the second position of the camera that collects the second facial data of the user, calculate the second distance between the second position and the room position, and if the second distance is less than the first distance, execute the adjustment of all devices in the room to the state and state parameters; if the second distance is greater than the first distance, do not execute the adjustment of all devices in the room to the state and state parameters; the camera that collects the second facial data is a camera that can identify user identity information.

10. The hotel room control system based on an AI model as described in claim 8, characterized in that, The system also includes: The processing unit is further configured to obtain the user's registered first mobile phone number and first room number, and receive a Wi-Fi login request sent by the mobile terminal. The Wi-Fi login request includes: all searched Wi-Fi names and the first mobile phone number. The server queries whether the above Wi-Fi names contain a first room number corresponding to the first mobile phone number. If the query shows that the above Wi-Fi names contain a first room number corresponding to the first mobile phone number, the server receives the Wi-Fi login request and allows the mobile terminal to log in to the Wi-Fi without a password.

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