A hotel room management method and a room management device based on the Internet of Things

Abstract

This application discloses a hotel room management method based on the Internet of Things (IoT), relating to the fields of smart hotels and human-computer interaction technology. It provides an interface adaptation method based on user cultural background and real-time interactive behavior. This method quantifies the operational difficulty score reflecting cultural cognitive conflict by monitoring users' micro-interaction behaviors such as repeated and invalid touches in real time. When the score exceeds a threshold, the system proactively prompts the user and obtains their consent. Then, based on the user's nationality and other cultural background information, it intelligently matches and switches to the most suitable interface from a pre-set diversified interface template library. Finally, the system verifies the validity of the new interface and binds the successful matching result to the user's profile. Implementing the technical solution provided in this application effectively solves the problems of user discomfort and low interaction efficiency caused by ignoring cultural differences in existing technologies, thereby reducing the frequency of guest misoperations and improving the fluency and efficiency of human-computer interaction.

Description

Technical Field

[0001] This application belongs to the field of smart hotel and human-computer interaction technology, and in particular relates to a hotel room management method and room management equipment based on the Internet of Things. Background Technology

[0002] With the rapid development of the tourism industry and the continuous improvement of people's living standards, the hotel industry has ushered in unprecedented development opportunities. However, traditional hotel room management mainly relies on manual operation, which has problems such as low management efficiency, high labor costs, and unstable service quality. In particular, room equipment maintenance, energy consumption management, and guest demand response often require a large amount of manpower and are prone to oversights, making it difficult to meet the requirements of modern hotel's refined management.

[0003] Currently, the industry has developed intelligent hotel management systems based on cloud computing and IoT technologies. These systems feature intelligent control panels in guest rooms, allowing for centralized control of in-room equipment such as air conditioning, lighting, and curtains via a touch interface. The systems employ a unified graphical interface design and are equipped with multilingual switching capabilities, providing basic intelligent control services for guests of different nationalities. This system enables intelligent control of guest room equipment, enhancing the guest's stay experience.

[0004] However, the above system only adapts to the needs of different guests through simple language conversion, ignoring the profound impact of cultural differences on the user interaction experience. Even if the system provides a multilingual interface, due to the different operating habits and cognitive patterns of different cultural groups, some guests will still repeatedly click or misoperate during the operation, thereby reducing the efficiency of customers interacting with smart devices in the guest room. Summary of the Invention

[0005] This application provides a hotel room management method and room management equipment based on the Internet of Things, which reduces the frequency of repeated clicks or misoperations during operation, thereby improving the efficiency of customer interaction with smart devices in the room.

[0006] Firstly, this application provides a hotel room management method based on the Internet of Things (IoT). The method includes: recording in real-time interaction data of guests operating the room's smart control panel, including touch location coordinates, duration of a single touch, and time interval between two adjacent touches; defining the number of touches of control buttons that are continuously touched within a unit time period with an interval less than a first time threshold as repeated touches; defining the number of touches of non-responsive areas within a unit time period with a single touch duration exceeding a second time threshold as invalid touches; weighting the repeated touches and invalid touches to obtain an operation difficulty score; when the operation difficulty score is determined to be greater than a preset benchmark score, controlling the smart control panel to generate and display an interface switching prompt; and upon determining that an interface switching prompt has been received... When prompted with a corresponding switch confirmation command, the system matches the corresponding target interface template set from the preset interface template database based on the pre-entered customer information. This target interface template set contains several target interface templates and their corresponding usage frequencies. The system updates the display interface of the smart control panel according to the target interface template with the highest usage frequency in the target interface template set. After deleting the target interface template with the highest usage frequency from the target interface template set, the system executes a step to record the guest's interaction data with the room's smart control panel in real time. If the operation difficulty score is determined to be no greater than the preset benchmark score, the target interface template corresponding to the current display interface of the smart control panel is used as the guest's final interface template, and the final interface template is added to the pre-entered customer information.

[0007] In the above embodiments, by adopting the aforementioned technical solution, the system first quantifies the operational difficulty score caused by users' cultural differences by recording interaction data in real time and distinguishing between repetitive and invalid touches. This scoring mechanism enables the system to objectively and sensitively capture potential operational confusions of users. When the difficulty exceeds a threshold, the system does not forcibly change the interface but instead generates and displays a user-friendly interface switching prompt, respecting the user's right to choose. After the user agrees, the system's core function of matching target interface template sets based on customer information begins to function. It utilizes big data analysis to match the interface most likely to suit the user's habits from a pre-set template library containing different cultural design concepts. Finally, the interface is updated using the target template, and the effect is continuously monitored until the score decreases, at which point the successful template is added to the customer information. This process ensures that every interface adjustment is data-driven, individualized, and self-optimizing, thereby effectively reducing misoperations caused by cultural differences and improving the efficiency and experience of customer interaction with smart devices.

[0008] In conjunction with some embodiments of the first aspect, in some embodiments, after adding the final interface template to the pre-entered customer information, the method further includes: determining the distribution positions of the control panels of each smart device in the room on the display interface corresponding to the final interface template, and the order in which guests click on the smart devices; constructing dynamic trajectory data of guests based on interaction data, the dynamic trajectory data including the movement path and dwell points of guests when completing device control; calculating the movement cost of guests when completing device control based on the dynamic trajectory data; establishing the spatial association strength between the control panels of each smart device based on the click operation order and movement cost; integrating control panels with a spatial association strength greater than a preset association strength value into a shared control panel; and controlling the smart control panel to display the shared control panel when it is detected that a guest triggers the control of any smart device in the shared control panel.

[0009] In the above embodiments, by adopting the aforementioned technical solution, after matching the optimal final interface template for the guest, the system further analyzes the guest's physical movement within the room by constructing dynamic trajectory data. The core lies in the innovative calculation of movement costs, which directly correlates the guest's physical movement with device control behavior, quantifying the convenience of the layout for the first time. Based on this, by combining the click operation sequence with movement costs, the spatial correlation strength between each control panel is established. This strength value profoundly reveals which functions are continuous in the guest's mental model but are irrationally separated in physical space. Finally, the system integrates these highly correlated control panels into a shared control panel, which can be actively displayed when the guest triggers any of the controls. This series of operations not only solves the cultural differences at the interface perception level but also further optimizes the efficiency at the physical interaction level. It intelligently aggregates sequential tasks that originally required multiple operations from different locations into one place, reducing the guest's movement burden and achieving an improvement from interface adaptation to spatial interaction efficiency.

[0010] In conjunction with some embodiments of the first aspect, in some embodiments, the calculation of the guest's movement cost when completing device control based on dynamic trajectory data specifically includes: calculating the movement distance between two adjacent stops based on the movement path; summing all movement distances of the guest within a preset time period to obtain the total length of the movement path; obtaining the total number of device control operations within the preset time period; and dividing the total length of the movement path by the total number of device control operations to obtain the movement cost.

[0011] In the above embodiments, by employing the aforementioned technical solution, the total length of the movement path is obtained by calculating and summing the movement distances between two adjacent stops, ensuring accurate measurement of the actual amount of guest activity. Simultaneously, by acquiring the total number of device control operations, a clear business anchor point is found for movement behavior. This enables the system to evaluate the convenience of the layout in a standardized manner, across time, users, and room types, improving the accuracy and effectiveness of the analysis.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, establishing the spatial association strength between the control panels of each smart device based on the click operation sequence and movement cost specifically includes: extracting the control panel combination corresponding to two adjacent device control operations from the click operation sequence; counting the frequency of each control panel combination within a preset time period; obtaining the corresponding movement cost for each control panel combination; and multiplying the frequency of the control panel combination by the corresponding movement cost to obtain the spatial association strength between the control panels in the control panel combination.

[0013] In the above embodiments, by employing the aforementioned technical solution, and by extracting the control panel combinations corresponding to two adjacent device control operations and statistically analyzing their frequency, the system accurately captures the continuity of user function usage. Higher frequency indicates stronger functional correlation. Simultaneously, the system combines user intent correlation (frequency) with physical implementation difficulty (mobility cost). This allows the spatial correlation strength indicator to not only identify which functions are frequently and continuously used, but also to highlight the most commonly used but most inconvenient pain point combinations. This weighted calculation method makes the system's optimization objectives more focused, accurately identifying the layout problems that have the greatest impact on user experience and are most urgently needed to be addressed, thus making subsequent operations more targeted.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, the weighted calculation of repeated touch counts and invalid touch counts to obtain an operation difficulty score specifically includes: counting the total number of touch counts per unit time; calculating the distribution density of repeated touch counts and invalid touch counts on the time axis; identifying consecutive intervals of difficult operations based on the distribution density; calculating the ratio of repeated touch counts to invalid touch counts within each consecutive interval; when the ratio is greater than a first preset ratio, using the repeated touch counts of the corresponding consecutive interval as the interval score; when the ratio is less than a second preset ratio, using the invalid touch counts of the corresponding consecutive interval as the interval score; when the ratio is between the first and second preset ratios, using the average of the repeated touch counts and invalid touch counts of the corresponding consecutive interval as the interval score; determining the time decay coefficient of the corresponding consecutive interval based on the time interval between each consecutive interval and the current time; multiplying the interval score corresponding to the consecutive interval by the corresponding time decay coefficient to obtain the sub-operation difficulty score of the consecutive interval; and summing the sub-operation difficulty scores corresponding to all consecutive intervals to obtain the operation difficulty score.

[0015] In the above embodiments, by employing the aforementioned technical solution, and by calculating the distribution density of the two touch behaviors on the time axis and identifying consecutive occurrence intervals, the system can capture the concentrated outbreak period of user's difficult emotions, rather than simply counting the total number. Within the interval, the proportion of repeated touches and invalid touches is calculated, and different scoring strategies are adopted accordingly. This allows the scoring to intelligently distinguish whether the user is in a state of rapid adjustment or confused accidental touch, improving the scenario interpretation ability of the scoring. The introduction of a time decay coefficient ensures that the scoring can reflect the immediacy of the problem, that is, the most recently occurring difficulty has a higher weight than the previously occurring difficulty. Finally, by adding up the sub-operation difficulty scores of all intervals, the system obtains a comprehensive score that considers both the intensity and type of the difficult behavior and its timeliness. This makes the assessment of operational difficulty extremely accurate and sensitive, avoiding misjudgment.

[0016] In conjunction with some embodiments of the first aspect, in some embodiments, after the intelligent control panel generates and displays the interface switching prompt, the method further includes: if it is determined that no switching confirmation instruction corresponding to the interface switching prompt has been received, confirming target similar customer information in a preset database based on pre-entered customer information; based on the target similar customer information, determining a number of control panels whose usage frequency is greater than a preset usage frequency threshold; generating control panel introduction information corresponding to the number of control panels, and a control recommendation page, the control recommendation page including controls corresponding to the number of control panels and control panel introduction information corresponding to the number of control panels; and controlling the display interface of the intelligent control panel to display the control recommendation page.

[0017] In the above embodiments, by employing the aforementioned technical solution, the system cleverly utilizes the concept of collaborative filtering—identifying what people similar to you like—by filtering target similar customer information from the database based on pre-entered customer information. Next, by analyzing the usage frequency of these similar customers, the system can identify several popular control panels that have undergone group validation. Finally, a control recommendation page containing controls and introductory information is generated and displayed. While respecting the user's initial choice, it provides more persuasive alternatives based on big data insights, improving the success rate of problem-solving and avoiding user experience interruptions caused by initial recommendation failures.

[0018] In conjunction with some embodiments of the first aspect, in some embodiments, the process of identifying target similar customer information in a preset database based on pre-recorded customer information specifically includes: extracting customer feature parameters from the pre-recorded customer information, the customer feature parameters including nationality, age group, and check-in frequency; performing a first screening of the customer information in the preset database based on nationality to obtain a first candidate customer information set; performing a second screening of the first candidate customer information set based on age group to obtain a second candidate customer information set; and selecting customer information in the second candidate customer information set whose check-in frequency is greater than a preset check-in frequency threshold as target similar customer information.

[0019] In the above embodiments, by employing the aforementioned technical solution and extracting nationality, age group, and check-in frequency as key feature parameters, the user group is refined, resulting in a more accurate profile. Using check-in frequency as the final screening threshold ensures that the samples selected as similar customers are frequent guests with in-depth experiences of the hotel environment, and their behavioral data is more representative and stable. This further enables the rapid and accurate identification of the most valuable user group, providing a high-quality data source for subsequent control panel recommendations, thereby ensuring the effectiveness and reliability of the recommendation results.

[0020] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0021] 1. By employing a dynamic interface adaptive technology that quantifies operational difficulty in real-time based on user micro-interaction behaviors (such as repeated touches and invalid touches) and combines this with user cultural background information (such as nationality) for intelligent matching and closed-loop learning from a preset template library, this solution can accurately identify and quantify operational obstacles caused by cultural differences. When the system detects operational difficulties, it does not blindly adjust but intelligently recommends and, with user confirmation, switches to an interface template that better suits the user's cognitive habits. After confirmation, this preference is bound to the user profile. This process effectively solves the technical problem in existing technologies that ignore cultural differences by only using language conversion, leading to frequent user errors due to different operating habits. This results in reduced operational errors and improved human-computer interaction efficiency.

[0022] 2. By employing a technology that integrates guest's indoor physical trajectory with equipment control timing, quantifies layout convenience by calculating movement costs, and further establishes a spatial correlation strength model, this solution dynamically aggregates highly correlated control items in a software-defined manner. Therefore, this solution can deeply correlate guests' physical movement burden with their functional usage habits at the data level. Through spatial correlation strength indicators, it accurately locates frequently used but inconvenient function combinations and intelligently generates a temporary shared control panel at the location where the user needs it most. This process effectively solves the technical problem in existing technologies where the layout of intelligent device control points is fixed, ignoring the actual behavioral flow of guests, leading to unnecessary movement when completing continuous tasks. Thus, it achieves the technical effect of optimizing the efficiency of physical interaction in guest rooms from a data-driven perspective, reducing guests' movement costs, and providing a context-aware, on-demand, and convenient control experience.

[0023] 3. By employing a refined operational difficulty scoring technique that combines time distribution density analysis and behavior type ratio judgment, and introduces a time decay coefficient, this solution can identify continuous occurrence intervals of difficult behaviors and analyze their internal composition. The system can distinguish between different user states, such as anxious exploration or accidental touches due to confusion. Simultaneously, time decay ensures that the score instantly reflects the user's current true feelings. This process effectively solves the technical problem in existing technologies where the judgment of user operational difficulties is too coarse, failing to capture the severity and immediacy of the problem, leading to inappropriate intervention timing. It achieves a highly accurate, sensitive, and scenario-interpretive quantitative assessment of the degree of user operational difficulty, ensuring that subsequent adaptive interface adjustments are triggered at the right time, avoiding ineffective interference or delayed assistance. Attached Figure Description

[0024] Figure 1 This is a flowchart illustrating a hotel room management method based on the Internet of Things (IoT) in an embodiment of this application.

[0025] Figure 2 This is another flowchart illustrating a hotel room management method based on the Internet of Things in this application embodiment;

[0026] Figure 3 This is a schematic diagram of an exemplary hardware structure of the guest room management device in this application embodiment. Detailed Implementation

[0027] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to and includes any or all possible combinations of one or more of the listed items.

[0028] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0029] The following example is used in conjunction with Figure 1 The present application describes a hotel room management method based on the Internet of Things (IoT) in its embodiments:

[0030] Please see Figure 1 This is a flowchart illustrating a hotel room management method based on the Internet of Things (IoT) in an embodiment of this application.

[0031] S101. Records real-time interaction data of guests operating the smart control panel in the room.

[0032] "Interactive data" refers to the structured information flow generated by the guest's specific actions. It typically includes a series of detailed data points such as operation timestamp, device identifier, operation type (e.g., switch, adjustment), parameter values ​​(e.g., brightness value, temperature setting value), and operator-related information (e.g., room number).

[0033] This step is executed throughout the guest's entire check-in and check-out cycle. It is triggered whenever the guest is in the room and interacts with the smart devices via the smart control panel. When the guest performs an action on the smart control panel, such as touching the "turn on reading light" button on the screen, the panel's client program captures this event. This event is encapsulated into a data packet containing detailed information, including not only the aforementioned timestamp, room number, target device, and action, but also potentially more detailed information such as the device's status before the operation and the source of the operation (whether it's the bedside panel or the door panel). Subsequently, this data packet is sent to the device's designated API interface via the hotel's internal LAN (usually Wi-Fi or a wired network) using a connection protocol. Upon receiving the data packet, the device's monitoring service first performs data verification, checking the data's integrity, format correctness, and source legitimacy (e.g., verifying if the request originated from a registered room). After successful verification, the device parses the data, extracts the values ​​of each field, and inserts this operation record as a new entry into the interaction log database according to a pre-defined database table structure. This process is continuous. Whether it's adjusting the air conditioner temperature, opening the curtains, or switching TV modes, each operation generates an independent record. In this way, the device builds a time-series data stream about the usage of the equipment in the room.

[0034] In some embodiments, each received interaction data can be written to a memory caching system, such as Redis. For example, upon receiving a data message about adjusting the air conditioning temperature in room 1208, the room management device quickly serializes it and stores it in a List or Stream structure in Redis. Simultaneously, a separate, timed background task is started (e.g., executed once per second or triggered when the amount of data in the cache reaches a certain threshold, such as 1000 records). This background task retrieves all pending interaction data from Redis in batches, then processes and formats this data in memory, and finally, through a single database connection, uses batch insert commands to write hundreds or thousands of data entries at once into a time-series database (such as InfluxDB or TimescaleDB) specifically designed for storing this type of data.

[0035] S102. The number of times a control button is continuously touched within a unit of time and the interval between adjacent touches is less than the first time threshold is taken as the number of repeated touches.

[0036] Among them, "continuous touch" refers to the multiple, uninterrupted operations performed by a guest on the same control button within a short period of time, without operating any other control buttons during this period; "adjacent touch interval" refers to the difference between the timestamps when the same control button is touched twice consecutively; "control button" is used to represent virtual or physical buttons on the smart control panel that represent specific functions, such as "light brightness +", "air conditioning temperature -", etc., and each button has a unique identifier.

[0037] The guest room management system maintains an independent, real-time updated analysis state machine for each control button in each room. When a new touch data arrives, its room number, control button identifier, and operation timestamp are first extracted. Next, the previous touch record for that button is queried. If a previous record exists, and it represents a consecutive touch (i.e., no other button operations occurred in between), the adjacent touch interval between the two touches is calculated. This interval is then compared to a preset "first time threshold" (e.g., 500ms). If the interval is less than this threshold, it is considered a duplicate touch, and a temporary counter associated with that button is incremented. This process continues as long as subsequent touches still meet the conditions of being consecutive and having intervals less than the threshold. The counting of this sequence terminates if any of the following occurs: 1) the new touch interval exceeds the first time threshold; 2) the guest operates a different control button; or 3) no new consecutive touches occur within a set "unit time" (e.g., 10 seconds). At this point, the final count value will be officially recorded as a "repeated touch count" event, and it will be associated with and stored in relation to the corresponding time period, room and control button. Then the state machine will be reset to prepare to identify the next repeated touch sequence.

[0038] S103. The number of times a non-response area is touched within a unit of time and the duration of a single touch exceeds the second time threshold is taken as the number of invalid touches.

[0039] Among them, "touch non-responsive area" refers to the area on the smart control panel or touch screen interface that is not defined as any control button, slider, icon or other interactive component, such as the blank space between buttons, the border of the screen or a pure background display area; "single touch duration" is used to represent the length of time a user's single touch operation lasts from the moment the finger touches the touch surface (Touch Down event) to the moment the finger leaves the surface (Touch Up event).

[0040] When the room management system's front-end sensors or software detect a "Touch Down" event, it immediately records the screen coordinates (x, y) and start timestamp of the event. It then queries its internal UI (user interface) layout map, which defines the precise locations and boundaries of all interactive components. The coordinates of the touch event are compared to all response areas in the map. If the coordinates do not fall within any defined response area, the touch is initially marked as "occurring in a non-response area." At this point, processing does not end immediately but continues to wait for the corresponding "Touch Up" event. When the user's finger leaves the screen and a "Touch Up" event is captured, the current timestamp is subtracted from the previously recorded start timestamp to calculate the accurate "single touch duration." This duration is then compared to a preset "second time threshold" (e.g., 500 milliseconds). If the calculated duration exceeds 500 milliseconds, the prolonged press on the non-response area is determined to be a valid invalid touch, and an "invalid touch counter" associated with the current "unit time" window is incremented. Conversely, if the duration is less than or equal to 500 milliseconds, it is considered to be an unintentional, brief swipe or accidental touch, and is not counted as an invalid touch. This process is repeated for all touch events in each "unit time" cycle, and the final counter value is the output of this step.

[0041] It should be added that the definition of non-responsive areas can be dynamic. For example, when a button is disabled due to specific conditions (becomes grayed out and unclickable), its original area can be temporarily classified as a non-responsive area during the disabling period, thereby more accurately identifying invalid user operations.

[0042] In some embodiments, the guest room management device counts the total number of touches per unit time and records the exact time of each repeated touch and invalid touch, thereby calculating the distribution density of these two behaviors on the time axis. Based on the density of this distribution, the guest room management device can automatically identify the time periods in which difficult user operations are concentrated, i.e., "continuous occurrence intervals". For each identified difficult interval, its internal composition is further analyzed, and the ratio of repeated touches to invalid touches is calculated. When this ratio is greater than a preset high ratio value, it indicates that the user is mainly making rapid adjustments, and the number of repeated touches in that interval is used as the "interval score"; when the ratio is less than a preset low ratio value, it indicates that the user is highly confused, and the number of invalid touches is used as the "interval score"; if the ratio is between the two, the average of the two numbers is taken as the "interval score". Subsequently, a "time decay coefficient" is assigned to each difficult interval based on its proximity to the current time. The score for each interval is multiplied by its corresponding time decay coefficient to obtain the sub-operational difficulty score for each interval. All these sub-scores are then summed up to obtain a comprehensive and dynamic final operational difficulty score that reflects the intensity, type, and timeliness of the difficult behavior.

[0043] In some embodiments, multiple baseline parameter configuration files are first established for different types of users (e.g., children, adults, elderly) and different application scenarios (e.g., daily lighting, cinema mode, reading mode). When a user or scenario switch is detected, the corresponding parameter configuration is automatically loaded. Furthermore, dynamic fine-tuning based on user history is introduced. The room management device continuously records each user's operational difficulty score and the user's acceptance rate after that score triggers subsequent assistance (e.g., a pop-up tutorial). If it is found that for a certain user, even when the score is high, the user consistently ignores or rejects assistance, this may mean that the current parameter settings are too sensitive for that user. In this case, a negative feedback adjustment algorithm can be activated, and in the next calculation, the parameter that caused the high score (e.g., W_invalid or a preset ratio) will be slightly adjusted towards a more lenient direction. Conversely, if the user frequently accepts assistance, it indicates that the score accurately captures their difficulty, and the sensitivity of the parameter can be maintained or slightly increased.

[0044] S104. The number of repeated touches and the number of invalid touches are weighted and calculated to obtain the operation difficulty score.

[0045] The room management device has two preset weighting coefficients: repeated touch weight and invalid touch weight. Typically, the "invalid touch weight" is higher than the "repeated touch weight." The difficulty score is calculated by weighting these coefficients with the corresponding number of repeated and invalid touches.

[0046] In some embodiments, the function of weighting the number of repeated touches and invalid touches to obtain an operation difficulty score can be implemented in several ways: Optionally, the system can set up a dedicated "user experience analysis module" that periodically polls (e.g., every "unit of time") to count the counters for repeated touches and invalid touches. Once these two values ​​are obtained, the module reads the preset static weight values ​​from the system configuration file, performs multiplication and addition operations, stores the final score result in the user session log, or directly triggers the corresponding system response. Optionally, the system can also adopt a more dynamic, rule engine-based implementation. The system inputs "repeated touches" and "invalid touches" as facts into the rule engine. The engine defines a series of rules, such as "when the number of invalid touches is greater than 3, temporarily increase the invalid touch weight to 3.0" or "when a repeated touch occurs on the light adjustment slider, reduce the repeated touch weight to 0.2". The engine dynamically selects and applies the most appropriate weight for calculation based on the specific input value and context, making the score more consistent with the actual scenario. It is understandable that other methods can be used to implement the logic of this step, such as training a simple linear regression model, using the two types of touch counts as feature inputs, and the model directly outputs an operation difficulty score; no limitation is made here.

[0047] It should be noted that the initial weights were determined by an expert panel comprised of multiple human-computer interaction (HCI) experts, hotel management experts, and cross-cultural studies scholars. Anonymous scoring (e.g., 1-10) was used to assess the degree of difficulty users faced in both behaviors. After collecting all expert scores, a set of industry-consensus-based initial weight coefficients was obtained by calculating the average or weighted average (based on the experts' qualifications).

[0048] S105. If the difficulty score of operation is determined to be greater than the preset benchmark score, control the intelligent control panel to generate and display the interface switching prompt.

[0049] The room management equipment has a continuously running monitoring service that obtains the latest "operation difficulty score" in real time. Whenever a new score is generated, it is compared with a "preset benchmark score." If the score is less than or equal to the benchmark score, the customer's operation is considered to be within the normal range of their cultural habits, and no action is taken. However, if the equipment determines that the operation difficulty score is greater than the preset benchmark score, it judges that the current interface layout or interaction logic may conflict with the customer's cultural cognitive model. In this case, an instruction is sent to the interface management module of the smart control panel. This instruction not only includes the signal to trigger the prompt but may also carry a cultural background tag based on customer information (such as nationality or language selection). After receiving the instruction, the interface module "generates" a culturally targeted interface switching prompt. For example, for users from Eastern cultures who are accustomed to contextualized and holistic control, the prompt may suggest switching to "one-click scene mode"; while for users from Western cultures who are accustomed to precise and independent control, it may recommend a more centralized "advanced control" panel. After generation, the prompt is "displayed" on the current screen, awaiting the user's selection.

[0050] In some embodiments, the function of controlling the intelligent control panel to generate and display interface switching prompts when the difficulty score is determined to be greater than a preset benchmark score can be implemented in various ways, and further personalized assistance can be provided when the user does not accept the switching suggestion: Optionally, a publish-subscribe pattern based on event listening can be adopted. After each new score is calculated, a "score update" event containing that score is broadcast. The interface management module, as a subscriber, pre-subscribes to this event. When a new event is detected, its callback function is triggered, and the function executes comparison logic. If the score is greater than the benchmark score, the UI rendering function is called to draw a prompt box on the screen. Optionally, the system can also adopt a more intelligent, multi-layered assistance strategy. After the system displays the interface switching prompt, a timer is started to wait for the user's "switching confirmation instruction". If the instruction is not received within a preset time (i.e., the user ignores or rejects the suggestion), the system will initiate a deeper recommendation process. First, the system will extract customer characteristic parameters such as nationality, age group, and check-in frequency based on the pre-entered current customer information. Then, the system performs multiple rounds of filtering in the background customer information database based on these parameters: first, it filters the first batch of candidate customers by nationality; then, it filters the second batch of candidate customers by age group; finally, it selects frequent guests from the second batch whose check-in frequency exceeds a certain threshold, identifying them as "target similar customers" with highly similar characteristics to the current user. Next, the system analyzes the historical interaction data of these target similar customers to identify several control panel interfaces that they use most frequently and whose usage frequency exceeds a preset threshold. Based on these findings, the system dynamically generates a brand-new "control panel recommendation page," which not only includes shortcut controls for these frequently used control panels but also provides brief descriptions of their functions. Finally, the system controls the smart control panel to replace the current confusing interface with this newly generated, highly personalized control panel recommendation page, thus providing the user with an interface layout based on the usage habits of similar groups and more likely to meet their needs. It is understandable that other methods can be used to implement the logic of this step, such as using an independent rule engine, taking the score as the input fact, and executing a preset display prompt when the rule "score > benchmark score" is met. This is not limited here.

[0051] It should be noted that the preset benchmark score does not have to be a fixed value. It can be dynamically adjusted based on the customer's cultural background tags. For example, for certain cultural groups with known differences in operating habits, the benchmark score can be appropriately lowered in order to provide assistance earlier.

[0052] S106. If it is confirmed that a switching confirmation instruction corresponding to the interface switching prompt has been received, the corresponding target interface template set is obtained by matching the pre-entered customer information from the preset interface template database.

[0053] Among them, "pre-entered customer information" refers to the personal information that customers register when checking into the hotel, which is highly relevant to their cultural background, such as nationality, place of residence, preferred language, and age group. This information is the basis for personalized matching. "Preset interface template database" refers to a structured collection that stores a variety of interface designs. These interfaces are not designed randomly, but are carefully classified and labeled according to the cognitive habits and operational preferences (such as layout, icon style, and control logic) of different cultural groups. "Target interface template set" is the final output of this matching process. It is a collection of one or more interface templates that are determined to be most suitable for the current customer's cultural habits.

[0054] When the front-end interface of the room management device detects the user clicking the "Confirm Switch" command, it immediately sends the command along with the current user's unique identifier to the device's backend. Upon receiving the request, the backend service first retrieves the customer's "pre-entered customer information" from the hotel's customer management device based on the user identifier. The room management device specifically extracts key cultural tags such as nationality and language. Then, using these tags as query conditions, it performs a matching operation in the "preset interface template database." Each template in this database is pre-labeled with a series of attributes, such as "Applicable Culture: East Asia," "Interaction Logic: Scene Priority," "Icon Style: Pictographic," or "Applicable Culture: North America," "Interaction Logic: Functional Tiling," "Icon Style: Text." The matching algorithm searches for the template with the highest overlap with the customer's cultural tags. Finally, one or a set of the most matching templates are determined as the "target interface template set," and this result is passed to the subsequent interface rendering step.

[0055] In some embodiments, the function of matching the corresponding target interface template set from a preset interface template database based on pre-entered customer information can be implemented in several ways when a switching confirmation instruction corresponding to the interface switching prompt is received. Optionally, a matching mechanism based on tag weight scoring can be used. First, different weights are set for the cultural attribute tags of each interface template in the database. Second, after obtaining customer information, a set of cultural tags is generated for the customer. Then, all templates in the database are traversed, and the customer tags are compared with the template tags. Whenever a tag matches, the weight value corresponding to that tag is accumulated, and finally a total matching score is obtained. Finally, the template with the highest score is selected as the final target interface template. Optionally, a recommendation method based on user profile clustering can also be used. In advance, through big data analysis, all customers in history are clustered into several typical "culture-behavioral profiles" based on their cultural background and actual interaction behavior (e.g., which cultural group T users are more likely to use slider adjustment rather than buttons). Each interface template in the database is associated with one or more profiles that best suit it. Upon receiving a new customer's confirmation instruction, the system first determines which predefined profile the customer belongs to based on their customer information. Then, it directly recommends the set of interface templates most relevant to that profile to the customer. It's understandable that other methods could be used to implement this logic, such as collaborative filtering algorithms to find and recommend interface templates most frequently used by other customers with similar cultural backgrounds to the current customer; this is not a limitation here.

[0056] It should be noted that the target interface template set can be a combination of a main interface and multiple optional widgets, allowing users to make certain fine-tuning adjustments within the overall framework recommended by the device.

[0057] In some embodiments, when a customer confirms the switch for the first time, an initial match is performed based on pre-recorded information such as their nationality, and the user is switched to the recommended interface. The system then continuously and silently monitors the customer's difficulty rating on the new interface. If the rating decreases and remains low, it indicates a successful initial match, and this successful "template-customer" pair is recorded to strengthen the association between the customer and this type of interface. However, if the customer's difficulty rating remains high after the switch, a secondary recommendation logic is triggered: the system searches the database for another template with the greatest cultural difference from the previously failed template and recommends it to the user, or directly presents the user with a selection page containing 2-3 interface thumbnails with distinct styles (e.g., a minimalist scenario-based template and a function list-based template), allowing the user to make a secondary selection based on their true preferences. This second selection is recorded by the device with higher priority and serves as a basis for future adjustments to the user's profile.

[0058] S107. Update the display interface of the smart control panel according to the target interface template with the highest frequency of use in the target interface template set, and after deleting the target interface template with the highest frequency of use in the target interface template set, perform the step of recording the interaction data of guests operating the smart control panel in real time.

[0059] Once the room management device identifies the "target interface template set," it queries the metadata associated with this set, which includes historical "usage frequency" statistics for each template. By comparing the frequencies of all templates within the set, it identifies the one with the highest frequency and designates it as the "target interface template" for this update. Subsequently, it sends a command to the UI rendering engine of the smart control panel, containing all layout files, icon resources, and interaction logic scripts for this target template. Upon receiving the command, the UI engine clears the current screen and redraws the entire display interface according to the definition of the new template. Almost simultaneously with the user seeing the new interface, the device's background processes perform a database operation, marking the recently applied template as "used" or removing it from the current session's "target interface template set." Immediately afterward, the device's data logging module, activated for the new interface, begins comprehensively and in real-time capturing all user interaction events, including the location of each click, the trajectory of each swipe, the frequency of each function's call, and the number of repeated and invalid touches under the new interface. This data is timestamped and stored in a log for subsequent continuous optimization.

[0060] In some embodiments, the display interface of the smart control panel can be updated according to the most frequently used target interface template in the target interface template set, and then real-time recording can be performed. Optionally, the device can adopt a version control and dynamic loading mechanism. Each interface template is like an independent software package in the database, with its own version number and resource list. When the most frequently used target template is determined, the version number of the template is sent to the control panel. The client program of the control panel checks the local cache. If the template of that version already exists, it is loaded directly; if it does not exist or the version is too old, it requests to download the latest template package from the server. After the download is complete, the client program will execute the interface switching animation effect to smoothly transition to the new interface. After the interface is successfully loaded, the client will automatically trigger an "interface updated" event. After the data recording module listens for this event, it will start recording all subsequent user interaction data with the new interface ID as the tag. Optionally, the device can also be implemented in a server-driven UI (Server-Driven UI) manner. In this architecture, the display content of the smart control panel is entirely defined by the configuration issued by the server. When an interface update is needed, the server generates a JSON or XML data structure describing the new interface layout and behavior based on the selected target template and pushes it to the control panel. The control panel client is simply a renderer; it parses this data and renders it into a user-visible interface. Simultaneously with pushing the new interface, the server starts a logging session associated with the new interface for that user session. All interaction events reported from the client are automatically included in this new session for analysis. It's understandable that this logic could be implemented in other ways, such as using web technologies to treat each interface template as an independent webpage, where updating the interface simply loads a new URL in the browser and records interaction data using an embedded analysis script. This is not a limitation here.

[0061] In some embodiments, after the device matches a "target interface template set" based on cultural background, if it finds that two or three templates have very similar "usage frequency," the device may not directly select the one with the highest frequency. Instead, it may randomly assign the current customer to a test group. For example, customers in group A see the most frequently used template A, while customers in group B see the second most frequently used template B. After switching interfaces, the device not only records interaction data in real time but also pays special attention to a core metric—"task completion efficiency" (e.g., the average time from entering the interface to successfully adjusting the light brightness). The room management device continuously compares the task completion efficiency of templates A and B among customers in this cultural group. If, after a period of data analysis (e.g., 24 hours), it finds that the average task completion efficiency of template B is higher than that of template A, it automatically updates its backend database, increases the weight of template B's "usage frequency," and may even set it as the preferred default template for this cultural group. Simultaneously, for customers assigned to template A and whose interaction data is poor, the device can prioritize recommending the proven superior template B the next time it detects operational difficulties.

[0062] S108. If the difficulty score of operation is determined to be no greater than the preset benchmark score, the target interface template corresponding to the current display interface of the intelligent control panel shall be used as the final interface template for the customer, and the final interface template shall be added to the pre-entered customer information.

[0063] After the interface is updated in step S107, the room management device will continuously and silently execute steps S101 to S104 in the background, that is, continuously calculate the user's "operation difficulty score" under the new interface. It waits for this score to stabilize. When the device determines that the operation difficulty score remains below the "preset benchmark score" for a representative period of time (e.g., 5 consecutive minutes or after several complete device control interactions), the device considers the interface optimization adjustment successful. At this point, a confirmation process is triggered, officially marking the unique identifier (ID) of the "target interface template" corresponding to the interface currently displayed on the smart control panel as the customer's "final interface template". Subsequently, the customer information management interface is called to write this template ID into the customer's personal profile database field, storing it alongside their nationality, language, and other information.

[0064] In some embodiments, the function of using the target interface template corresponding to the currently displayed interface as the final interface template and updating customer information can be implemented in several ways when the operation difficulty score is determined to be no greater than a preset benchmark score. Optionally, the device can set up a background scheduled task for "experience evaluation." This task periodically (e.g., every minute) checks the latest operation difficulty scores of all customers who have recently switched interfaces. Once it is found that a customer's score is lower than the benchmark score multiple times consecutively, the task will trigger a "preference confirmation" workflow. This workflow will lock the interface template ID currently being used by the customer and call the database update statement to insert it into the "preferred interface template" field of the customer information table. Optionally, the device can also adopt an event-driven approach based on a state machine. Each customer's interaction session can be regarded as a state machine, including states such as "exploring," "adaptation successful," and "adaptation failed." When the interface is switched, the session enters the "exploring" state. In this state, the device continuously listens for the operation difficulty score. Once the score is consistently lower than the benchmark score, an "operational smoothness" event is triggered, which drives the state machine to transition to the "adaptation successful" state. The action of entering the "adaptation successful" state inherently includes the logic of writing the current template ID into the customer information. Understandably, this step can also be implemented in other ways, such as through a machine learning model. When the model predicts with high confidence that the user has adapted to the current interface based on a series of consecutive low-rated data, it triggers an information update operation; this is not limited to this approach. Furthermore, the customer information written can include not only the template ID but also more granular preference data, such as the most frequently used function layouts under this template and custom shortcuts, thus forming a richer user profile.

[0065] In some embodiments, the device uses an operational difficulty score and preset auxiliary indicators, such as "feature exploration breadth" (whether the user only uses 1-2 functions or tries multiple functions on the interface), "task completion time" (whether the time required for the user to complete a standard task, such as adjusting the air conditioner temperature, has shortened and stabilized), and "proactive customization behavior" (whether the user has begun to personalize the interface). An adaptability index is obtained, which is a weighted average of the above indicators. Only when this comprehensive index remains at a healthy level for a relatively long period (e.g., spanning different activity times of the day, morning, noon, and evening), will the device finally confirm that the customer has fully adapted and add the final interface template to the pre-entered customer information.

[0066] In this embodiment, a dynamic interface matching and adaptive learning method based on user cultural background and real-time interactive behavior feedback is adopted. Therefore, the system no longer relies solely on language switching but quantifies the difficulty of operation by monitoring users' repeated and invalid touch behaviors in real time. When the score exceeds a preset benchmark, the system identifies that the current interface may not conform to the user's cultural cognitive model. After obtaining the user's consent, based on their nationality and other cultural information, the system intelligently matches and pushes the most likely acceptable interface from a pre-designed template database according to cultural dimensions. Through this process, this application effectively solves the technical problem in existing technologies that only rely on language conversion and ignore the deep impact of cultural differences on the interactive experience, leading to frequent repeated clicks or misoperations by users from different cultural groups due to differences in operating habits and cognitive patterns. This improves the interaction efficiency between customers and smart devices in guest rooms.

[0067] In the above embodiments, the room management system can solve the operational difficulties caused by cultural differences by using a dynamic interface matching and adaptive learning method based on user cultural background and real-time interactive behavior feedback, thereby improving interaction efficiency and customer satisfaction. In practical applications, although the above method can match a more comfortable interface "style" or "type" for customers in a macro sense, this matching is still based on a preset template. The layout of specific functional controls within the template is fixed, making it difficult to adapt to the personalized and continuous operation flow of customers when completing specific tasks in later use. As a result, when customers control multiple related devices (for example, they need to close the curtains, ceiling light, and turn on the bedside lamp at the same time before going to bed), they still need to perform multiple cross-area visual searches and finger movements, thus limiting the efficiency of scenario-based operation.

[0068] Please see Figure 2 This is an example of an embodiment in this application. A Hotel Room Management Method Based on the Internet of Things Another flowchart.

[0069] S201. Records real-time interaction data of guests operating the smart control panel in the room.

[0070] S202. The number of times a control button is continuously touched within a unit of time and the interval between adjacent touches is less than the first time threshold is taken as the number of repeated touches.

[0071] S203. The number of times a non-responsive area is touched within a unit of time and the duration of a single touch exceeds the second time threshold is taken as the number of invalid touches.

[0072] S204. The number of repeated touches and the number of invalid touches are weighted and calculated to obtain the operation difficulty score.

[0073] S205. If the difficulty score of operation is determined to be greater than the preset benchmark score, control the intelligent control panel to generate and display the interface switching prompt.

[0074] S206. If it is confirmed that a switching confirmation instruction corresponding to the interface switching prompt has been received, the corresponding target interface template set is obtained by matching the pre-entered customer information from the preset interface template database.

[0075] S207. Update the display interface of the smart control panel according to the target interface template with the highest frequency of use in the target interface template set, and after deleting the target interface template with the highest frequency of use in the target interface template set, perform the step of recording the interaction data of guests operating the smart control panel in real time.

[0076] S208. If the difficulty score of operation is determined to be no greater than the preset benchmark score, the target interface template corresponding to the current display interface of the intelligent control panel shall be used as the final interface template for the customer, and the final interface template shall be added to the pre-entered customer information.

[0077] Steps S201~S208 and Figure 1 Steps S101 to S108 in the illustrated embodiment are similar and will not be repeated here.

[0078] S209. Determine the distribution of the control panels of each smart device in the room on the display interface corresponding to the final interface template, as well as the order in which guests click on the smart devices.

[0079] Among them, "control panels for various smart devices in the room" refers to virtual control areas or controls on the display interface that represent different physical devices (such as lights, air conditioners, curtains, and televisions), such as a light brightness slider, an air conditioner temperature regulator, or a curtain opening / closing button; "the display interface corresponding to the final interface template" refers to the interface layout that has been successfully matched and solidified for the customer in Example 1 and conforms to their macro-cultural habits; "distribution location" refers to the specific coordinates and spatial layout of these control panels on the display interface.

[0080] The room management device processes the massive amount of interaction data collected in S201. The system first segments the data stream by timestamp, identifying sequences of user actions involving multiple devices within a short period. This typically represents a complete "task flow" (e.g., a series of actions before going to sleep). For each identified task flow, the system performs a two-dimensional analysis: First, it parses the control panels of all smart devices involved in the task flow and retrieves their precise "distribution locations" on the screen from the layout definition file of the "final interface template." Second, the system meticulously reconstructs the customer's "click operation sequence" according to the timestamps of the interaction events, forming an ordered list of device operations.

[0081] In some embodiments, the function of determining the distribution of control panels of various smart devices in the room on the display interface corresponding to the final interface template, as well as the order of guest clicks on the smart devices, can be implemented in several ways: Optionally, the system can adopt a session-based log parsing method. The system records each user interaction as a log with a time sequence and control ID. When analysis is needed, a background parsing service pulls log data within a specific time period. This service first divides different operation sessions according to a time interval threshold (e.g., two operations with an interval of less than 5 seconds are considered consecutive operations). Within each session, the service arranges all operated control IDs in chronological order, which constitutes the "click operation order". At the same time, the service queries a control layout mapping table, which stores the screen coordinates of each control ID in different interface templates, thereby obtaining the "distribution position" of these controls. Optionally, the system can also adopt a front-end event stream real-time processing method. When the user clicks, the front-end application of the smart control panel not only records the clicked control ID, but also instantly obtains the position coordinates of the control on the current screen, and sends this pair of data [control ID, coordinates, timestamp] as a complete event to the background stream processing platform. The stream processing platform maintains a sliding time-series window for each user, aggregating events within the window into an ordered sequence of operations in real time, and directly outputting structured data containing distribution locations and operation orders. It's understandable that other methods could be used to implement this logic, such as using image recognition technology to analyze screen recordings to deduce the user's operation order and control positions; this is not limited here.

[0082] S210. Construct dynamic trajectory data of guests based on interactive data.

[0083] "Interactive data" refers to the raw dataset collected and preliminarily processed by the preceding steps (such as S201 and S209), which contains information such as user operation sequences and control screen positions.

[0084] The room management system uses the "click operation sequence" determined in the previous step as a blueprint, treating each control panel in the sequence as a node. Then, the system sequentially connects adjacent nodes in this sequence, forming a directed path. For each segment of the path (e.g., from operating the "main light" to operating the "curtains"), the system performs quantitative calculations: First, based on the coordinates of the "distribution location" of the two control panels, it calculates the Euclidean distance between them, reflecting the physical distance the user's finger or line of sight needs to move. Second, based on the timestamps of these two operation events, it calculates the time interval between them, reflecting the time spent by the user in finding and executing the next operation. Finally, the system integrates these nodes, connecting paths, and information such as spatial distances and time consumption attached to the paths into a complete data structure, namely "dynamic trajectory data."

[0085] In some embodiments, the function of constructing dynamic trajectory data of guests based on interaction data can be implemented in several ways: Optionally, the system can adopt a post-processing data batch processing method. First, the system stores the operation sequence and location information analyzed in S209 in a log file in JSON format. Then, a periodic background analysis task reads these logs and parses out the operation sequence. Next, the task iterates through each operation step in the sequence (starting from the second step). For each step, it obtains the control coordinates and timestamps of the current operation and the previous operation, and calls mathematical functions to calculate the distance and time difference between the two points. Finally, it encapsulates these calculation results together with the operation steps into a "trajectory segment" object, and organizes all "trajectory segment" objects into an array in sequence to form the final dynamic trajectory data, which is then stored in the analysis database. Optionally, the system can also adopt a real-time map data stream construction method. The system sends each user interaction event (including control ID, location, and timestamp) to a graph database in real time. In this database, each control panel is a node. When a new interaction event occurs, the system locates the node corresponding to the previous interaction event and creates a new, directed "edge" between the two nodes. This "edge" is immediately assigned two core attributes: distance (calculated from the coordinates of the two nodes) and duration (calculated from the timestamps of the two events). In this way, the user's dynamic trajectory data is constructed and stored in real time in the form of a continuously growing graph. It is understandable that other methods can be used to implement this logic, such as modeling the trajectory data as a time-series vector and inputting it into a machine learning model for processing; this is not limited here.

[0086] In some embodiments, when recording interaction data, not only the final click event is recorded, but also micro-interaction patterns such as "hovering," "swiping," and "hesitating" of the cursor or finger on the screen before the click. If a long-duration segment is accompanied by a large number of aimless swipes or brief hovers over multiple non-target controls, it can be determined that this is highly likely due to "difficulty in finding" the target. Simultaneously, environmental sensor data within the room, such as microphone arrays or indoor cameras, can be correlated (analyzing only ambient sound or human posture while protecting privacy). If, at the same time as the long-duration segment, the sensors detect continuous human conversation or the user getting up and leaving, the system can mark this long-duration segment as an "external event interruption" and reduce its weight or exclude it entirely in subsequent optimization analysis.

[0087] S211. Calculate the movement cost of guests when completing equipment control based on dynamic trajectory data.

[0088] The room management equipment uses the "dynamic trajectory data" generated by S210 as input. This data is essentially a series of "path segments" connecting different device controls. For each path segment (e.g., from operating "room service" to operating "curtains"), the system extracts two core quantities: spatial distance (the number of pixels the finger needs to move on the screen) and time cost (the total number of "device control operations" performed by the guest, i.e., the "total number of device control operations"). The system calculates the cost of each path segment using a weighted formula; for example, the segment cost is the calculated "total length of the movement path" divided by the "total number of device control operations". Finally, the system sums the costs of all path segments in a complete task flow to obtain the total "movement cost" of the task flow.

[0089] In some embodiments, a cost function more closely aligned with human factors engineering can be employed, such as one incorporating Fitts' Law. In this approach, the cost of each trajectory segment is related not only to the distance traveled (D) but also to the size of the target control (W). The cost function might be designed as segment cost = log2(D / W+1) + (time cost × weight). Finally, the total movement cost is obtained by summing all segment costs. It is understood that other methods can be used to implement this step's logic, which are not limited here.

[0090] In some embodiments, while calculating the total movement cost, the cost of each individual "path segment" in the task flow is also recorded and analyzed by setting an independent "peak cost threshold," which is much higher than the average segment cost. During the calculation, once the cost of any path segment exceeds this peak threshold, the system immediately marks that path segment as a "critical bottleneck," regardless of the final total movement cost. Furthermore, the variance or standard deviation of the costs of all segments within the task flow is calculated simultaneously. A high variance value indicates a highly uneven cost distribution, also suggesting a potential bottleneck. For example, the total cost of a task flow is 100, below the optimization threshold of 120. However, its segment cost distribution is [5, 5, 80, 5, 5]. The peak cost detection mechanism will immediately identify and mark the segment cost "80," which far exceeds the threshold (e.g., 50). Thus, in subsequent optimization steps, the system can prioritize addressing this identified bottleneck (e.g., by placing the controls for steps three and four in adjacent layouts on the interface) for local optimization.

[0091] S212. Based on the click operation sequence and movement cost, establish the spatial association strength between the control panels of each smart device.

[0092] The room management system takes "click operation sequence" and "movement cost" as input. A complete task flow (e.g., [main light -> curtains -> air conditioner]) is broken down into a series of consecutive operation pairs: (main light, curtains) and (curtains, air conditioner). For each operation pair, the system extracts its corresponding "single-segment movement cost." The internal algorithm works as follows: if two control panels are frequently and consecutively operated, and the movement cost between them is high, then their "spatial association strength" should be high. Therefore, a strength value is calculated for each operation pair using a function, such as spatial association strength = f(operation frequency, movement cost). This function could be a simple product or a more complex weighted formula. By performing this calculation on all high-frequency task flows for guests, the room management system ultimately generates an "association strength map" covering all control panels, where the weight of the connection between any two control panel nodes represents the urgency with which they should be placed together.

[0093] In some embodiments, the room management device can establish the spatial association strength between control panels of various smart devices based on the click operation sequence and movement cost in several ways: Optionally, the room management device can adopt an accumulation method based on a weighted adjacency matrix. First, the device maintains an N×N matrix in memory (N is the total number of control panels), with all initial values ​​being 0. When the device analyzes a pair of consecutive operations (control i, control j) in a task flow, it obtains the single-segment movement cost Cost(i,j) of this pair of operations. Then, it updates the corresponding elements in the matrix, for example, Matrix[i][j] = Matrix[i][j] + Cost(i,j). By continuously accumulating the costs of all task flows, the element with the larger value in the matrix represents the higher the spatial association strength between its corresponding two control panels. Optionally, the room management device can also adopt a dynamic simulation method based on a gravity model. The device treats each control panel as a physical particle, and the "spatial association strength" is simulated as the "gravity" between them. When the device identifies a high-cost consecutive operation (A,B), it adds a gravity constant between particle A and particle B. A physics simulation engine runs in the background, and these particles naturally gravitate towards each other under the influence of gravity. Eventually, when the system reaches a stable state, the clusters of particles that have gathered in space visually demonstrate which control panels have the strongest spatial correlation. Understandably, room management devices could implement this logic in other ways, which are not limited here.

[0094] It should be added that the spatial association strength can be undirected or directional. For example, the strength of (turning on the light -> closing the curtains) may be much higher than that of (closing the curtains -> turning on the light). This helps to consider the logical flow of operations when optimizing the layout.

[0095] In some embodiments, when analyzing the "click operation sequence," the room management device not only looks at adjacent pairs but also uses algorithms (e.g., analyzing the end point of the sequence or identifying special buttons representing scene transitions) to identify the "anchor controls" of each task flow. In the example above, "Main Power" would be identified as the anchor point of the "Leave Room" task flow. Then, when establishing spatial association strength, an additional calculation step is performed: for each non-anchor control in the task flow, the device calculates its "centralized association strength" with the anchor control. This strength value is based on their co-occurrence relationship and movement cost in the task flow. Finally, the final association strength between two controls will be the weighted sum of their "linear adjacency association strength" and their respective "centralized association strength." In this way, the room management device can not only understand that "the air conditioner is usually turned off behind the TV," but also deeply understand that "turning off the TV and turning off the air conditioner are both part of the core task of 'leaving the room.'" Therefore, in subsequent interface optimization, it is more likely to intelligently aggregate these functional controls around the core anchor point "Main Power," forming a logically clear and operationally efficient functional cluster.

[0096] S213. Integrate control panels with spatial association strength greater than the preset association strength value into the shared control panel.

[0097] The "control panel" refers to the virtual area on the interface that represents the control functions of a single smart device.

[0098] The room management device iterates through the "association strength map" or "association strength matrix" generated in S212. For each pair of control panels, it obtains the "spatial association strength" value between them and compares it with the "preset association strength value". If the strength value is greater than the threshold, the room management device marks the pair of control panels as "to be integrated". This process continues, searching for all control panels that meet the criteria, and using community detection algorithms in graph theory (such as the Louvain algorithm) or simple clustering algorithms, these control panels that are strongly associated with each other are divided into one or more "functional clusters". For each identified functional cluster, the room management device dynamically creates a new interface layout in memory - the "shared control panel". In this new panel, the core controls of the previously scattered multiple control panels (such as light switches, curtain sliders, and air conditioning temperature settings) are intelligently and compactly arranged together to form a one-stop "task center".

[0099] In some embodiments, the room management device can integrate control panels with spatial association strength greater than a preset association strength value into a shared control panel in several ways: Optionally, the room management device can employ a static generation method based on threshold filtering and template splicing. First, the association strength matrix is ​​traversed to filter out all control panel pairs with strength values ​​greater than the threshold. Then, the device treats these panel pairs as edges of a graph and constructs an undirected graph. Next, the device searches for all connected components on the graph, each connected component representing a functional cluster to be integrated. Finally, for each functional cluster, from a preset "shared panel layout template library," the most suitable template (e.g., a two-control template, a three-control template) is selected based on the number and type of controls within the cluster, and the controls within the cluster are filled in to generate the final shared control panel. Optionally, the room management device can also employ a dynamic generation method based on a force-oriented layout algorithm. The device treats each control panel as a node, and the "spatial association strength" between them is transformed into "spring force" between nodes (the greater the strength, the greater the spring constant, and the shorter the desired length). The device initiates a physical simulation process, under which all nodes move freely under the action of these spring forces. After a period of iteration, the system will naturally reach a stable state with minimum energy. At this point, nodes with strong correlations will automatically cluster together. The device only needs to draw a "frame" around these naturally formed clusters and perform appropriate aesthetic enhancements and alignment to dynamically and organically generate a reasonably laid-out shared control panel. It is understandable that room management devices can also implement this logic in other ways, which are not limited here.

[0100] In some embodiments, once the device identifies a cluster of functions to be integrated, it first analyzes the task flow context corresponding to that cluster (e.g., "bedtime mode"). Based on this context, it intelligently selects the "most frequent" or "most essential" function for each control in that scenario (e.g., for lights, "on / off" instead of "color temperature adjustment"; for air conditioning, "temperature" instead of "fan speed"). The resulting "shared control panel" initially displays only these core functions in a minimalist manner. If guests require more precise adjustments, they can click on the icon of a control, at which point the control's full function panel will be revealed progressively without interfering with other controls, either as a "floating" or "expanded" layer. Through this layered design of "aggregating the core first, then expanding as needed," the room management device can address the mobility costs of cross-regional operations through integration, while avoiding information overload through context awareness and progressive reveal.

[0101] S214. If a guest triggers a control on any smart device in the shared control panel, control the smart control panel to display the shared control panel.

[0102] The room management device continuously monitors all user input events on the smart control panel. Simultaneously, the device maintains an "integration mapping table" generated by S2t13 in memory, recording which original, independent controls have been integrated into which "shared control panel." When the device detects a click event, such as a guest clicking the "main light" switch, it immediately queries this mapping table. If it finds that the "main light" control belongs to an existing "shared control panel" (e.g., a "bedtime mode" panel that includes the main light, curtains, and air conditioning), the device interrupts the original interaction flow that only responds to individual controls. Instead, it immediately sends a command to the UI rendering engine, requesting it to hide or minimize the current main interface and fully display the pre-designed "shared control panel" as a pop-up, overlay, or full-screen page.

[0103] In some embodiments, the room management device can implement this function in several ways: Optionally, the room management device can adopt a front-end implementation based on event interception and redirection. In the front-end application, each independent control, in addition to binding its original click event listener, is also additionally bound to an "integration check" interceptor. When a guest clicks a control, this interceptor is triggered first. It queries the locally stored "integration mapping table" to determine whether the control belongs to a shared panel. If it does not belong, it allows the process and executes the original single-device control logic. If it does belong, the interceptor prevents the event from propagating further and instead triggers a global "display shared panel" event, passing the shared panel's ID as a parameter. A dedicated UI manager listens to this global event and is responsible for rendering the corresponding shared control panel. Optionally, the room management device can also adopt a server-driven dynamic UI update mechanism. When a guest clicks any control, the operation event is sent to the room management device's back-end server. After receiving the event, the server queries the "shared control panel" configuration associated with the guest. If the clicked control is found to be a trigger for a shared panel, the server will not return a state update for that individual control. Instead, it will directly return a completely new UI data structure describing the layout and state of the entire shared control panel. The front-end smart control panel, acting as a "thin client," only needs to receive and render this new interface dynamically delivered by the server. Understandably, room management devices could also implement this logic in other ways, such as through gesture recognition, directly invoking the corresponding shared control panel when a guest makes a specific gesture associated with a particular task flow; this is not a limitation here.

[0104] In this embodiment, a dynamic reorganization and personalized recommendation technology for intelligent control panels based on spatial correlation strength and guest behavior patterns is employed. This allows for the generation of a temporary, personalized "virtual control set" on a core control panel (such as a bedside panel) that is closest to the user's current location or that the user uses most frequently. This set aggregates functionally related but previously scattered control items (such as "turn off the main light" and "draw the curtains"), providing a "one-stop" interface for the user through software-defined hardware. Through this process, this application effectively solves the technical problem in existing intelligent control panels where the functional layout is fixed, making it difficult to adapt to the personalized needs and dynamically changing behavioral scenarios of different guests. This results in guests needing to perform multiple independent operations at multiple physical locations when completing a complete living scenario, thus improving operational efficiency in complex scenarios.

[0105] The following describes an exemplary guest room management device 300 provided in an embodiment of this application. Figure 3 This is an exemplary hardware structure diagram of the guest room management device 300 provided in this application embodiment.

[0106] In some embodiments, the guest room management device 300 is a computer device or includes a computer device. The computer device includes a processor, memory, and a network interface connected via a system bus. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the computer device stores data. The network interface of the computer device is used to communicate with other external terminals or servers via a network connection. In some embodiments, the network interface can be a wired network interface; in some embodiments, the network interface can also be a wireless network interface. When the computer program is executed by the processor, it implements the methods in the embodiments of this application.

[0107] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0108] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0109] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as meaning "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as meaning "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0110] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive), etc.

[0111] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A hotel room management method based on the Internet of Things, characterized in that, The method includes: Real-time recording of guest interaction data on the room's smart control panel, including touch position coordinates, duration of a single touch, and time interval between two adjacent touches; The number of times a control button is continuously touched within a unit of time and the interval between adjacent touches is less than the first time threshold is taken as the number of repeated touches. The number of times a non-responsive area is touched within a unit of time and the duration of a single touch exceeds the second time threshold is defined as the number of invalid touches. The difficulty score of operation is obtained by weighting the number of repeated touches and the number of invalid touches. If the difficulty score of the operation is determined to be greater than the preset benchmark score, the intelligent control panel is controlled to generate and display an interface switching prompt. Upon receiving the switching confirmation instruction corresponding to the interface switching prompt, a corresponding target interface template set is obtained from the preset interface template database based on the pre-entered customer information. The target interface template set includes several target interface templates and usage frequencies that correspond one-to-one with the several target interface templates. The customer information includes nationality. Update the display interface of the smart control panel according to the most frequently used target interface template in the target interface template set, and after deleting the most frequently used target interface template from the target interface template set, execute the step of recording the interaction data of guests operating the room smart control panel in real time; If the difficulty score of the operation is determined to be no greater than the preset benchmark score, the target interface template corresponding to the current display interface of the smart control panel is used as the final interface template for the customer, and the final interface template is added to the pre-entered customer information.

2. The method according to claim 1, characterized in that, After adding the final interface template to the pre-entered customer information, the method further includes: Determine the distribution location of the control panels of each smart device in the room on the display interface corresponding to the final interface template, as well as the order in which the guest clicks on the smart devices; Based on the interaction data, dynamic trajectory data of the guest is constructed, which includes the guest's movement path and stop points when completing device control. Calculate the guest's movement cost when completing device control based on the dynamic trajectory data; Based on the click operation sequence and the movement cost, establish the spatial association strength between the control panels of each smart device; Integrate the control panels with spatial association strength greater than a preset association strength value into a shared control panel; If the guest triggers a control on any smart device in the shared control panel, the smart control panel is controlled to display the shared control panel.

3. The method according to claim 2, characterized in that, The calculation of the guest's movement cost when completing device control based on the dynamic trajectory data specifically includes: Based on the movement path, calculate the distance the guest moves between two adjacent stops; The total length of the travel path is obtained by summing all the travel distances of the guest within a preset time period; Obtain the total number of device control operations within the preset time period; The movement cost is obtained by dividing the total length of the movement path by the total number of device control operations.

4. The method according to claim 2, characterized in that, The step of establishing the spatial association strength between the control panels of each smart device based on the click operation sequence and the movement cost specifically includes: Extract the control panel combination corresponding to two adjacent guest room management equipment control operations from the click operation sequence; Statistically count the frequency of each control panel combination within the preset time period; For each of the aforementioned control panel combinations, obtain the corresponding mobility cost; Multiplying the frequency of the control panel combination by the corresponding mobility cost yields the spatial association strength between the control panels in the control panel combination.

5. The method according to claim 1, characterized in that, The step of weighting the number of repeated touches and the number of invalid touches to obtain an operation difficulty score specifically includes: Count the total number of touches per unit time. Calculate the distribution density of the repeated touch counts and the invalid touch counts on the time axis; Identify the intervals in which difficult operations occur consecutively based on the distribution density; Calculate the ratio of repeated touches to invalid touches within each consecutive occurrence interval; When the ratio is greater than the first preset ratio, the number of repeated touches in the corresponding continuous interval will be used as the interval score. When the ratio is less than the second preset ratio, the number of invalid touches in the corresponding consecutive interval will be used as the interval score. When the ratio is between the first preset ratio and the second preset ratio, the average of the number of repeated touches and the number of invalid touches in the corresponding continuous interval is used as the interval score. Based on the time interval between each of the consecutive occurrence intervals and the current time, determine the time decay coefficient of the corresponding consecutive occurrence interval; Multiply the interval score corresponding to the continuous occurrence interval by the corresponding time decay coefficient to obtain the sub-operation difficulty score of the continuous occurrence interval; The operation difficulty score is obtained by summing the sub-operation difficulty scores corresponding to all the consecutive occurrence intervals.

6. The method according to claim 1, characterized in that, After the intelligent control panel generates and displays the interface switching prompt, the method further includes: If it is determined that no switching confirmation instruction corresponding to the interface switching prompt has been received, the target similar customer information is confirmed in the preset database based on the pre-entered customer information; Based on the target similar customer information, identify a number of control panels whose usage frequency is greater than a preset usage frequency threshold; Generate control panel description information corresponding to the plurality of control panels, and a control recommendation page, wherein the control recommendation page includes controls corresponding to the plurality of control panels and control panel description information corresponding to the plurality of control panels; The control panel's display interface displays the control recommendation page.

7. The method according to claim 6, characterized in that, The step of confirming target similar customer information in a preset database based on the pre-entered customer information specifically includes: Customer characteristic parameters are extracted from the pre-entered customer information, including nationality, age group, and frequency of stay. Based on the nationality, the customer information in the preset database is first filtered to obtain a first candidate customer information set; The first candidate customer information set is filtered a second time based on the age group to obtain the second candidate customer information set. The customer information in the second candidate customer information set whose check-in frequency is greater than a preset check-in frequency threshold is used as the target similar customer information.

8. A guest room management device, characterized in that, The device includes: One or more processors and a memory; the memory is coupled to the one or more processors, the memory being used to store computer program code, the computer program code including computer instructions, the one or more processors invoking the computer instructions to cause the guest room management device to perform the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that, When the instruction is executed on the guest room management device, the guest room management device performs the method as described in any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product is run on the guest room management device, the guest room management device performs the method as described in any one of claims 1-7.

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