A multi-scenario modal management method and system of a controller

Abstract

This invention provides a multi-scenario modal management method and system for a controller, relating to the field of smart home management. The method includes: collecting user characteristics from several high-frequency users and constructing several toilet seat temperature adaptation tables based on multiple room temperature ranges; predicting toilet usage probability to obtain multiple usage probabilities within a preset time zone; and adaptively adjusting the toilet seat temperature based on the several toilet seat temperature adaptation tables and multiple usage probabilities, combined with the indoor temperature within the preset time zone. This invention aims to solve the technical problem that traditional toilet seat temperature control methods often rely on fixed temperature settings and cannot adaptively adjust according to user preferences, health conditions, and environmental factors, resulting in insufficient comfort and energy efficiency. By utilizing the Internet of Things (IoT), combined with user behavior analysis and real-time data prediction, personalized temperature adjustment services can be provided to users, improving user comfort and effectively saving energy, achieving the technical effect of efficient toilet management.

Description

Technical Field

[0001] This invention relates to the field of smart home management, and in particular to a method and system for managing multi-scenario modalities of a controller. Background Technology

[0002] Currently, many smart toilet seat temperature control systems use fixed temperature settings or simple timer controls. These methods often ignore individual user needs and environmental changes, making it difficult for users to achieve optimal comfort. For example, in cold winters, the seat temperature may be too low to provide sufficient warmth; while in warmer seasons, the seat temperature may be too high, causing discomfort. Furthermore, existing temperature control systems typically do not consider changes in the user's health condition (such as body temperature and age) or environmental factors (such as indoor temperature and humidity), further impacting the user experience.

[0003] More importantly, traditional temperature control methods do not fully consider energy conservation. In most cases, the seat heating will continue to work, even when the user is not using the toilet, resulting in a lot of energy waste. This practice not only increases household energy costs, but also does not meet the energy conservation and environmental protection requirements of modern smart homes. Summary of the Invention

[0004] The purpose of this invention is to provide a multi-scenario modal management method and system for a controller, to solve the technical problem that traditional toilet seat temperature control methods often rely on fixed temperature settings and cannot adaptively adjust according to user preferences, health conditions, and environmental factors, resulting in insufficient comfort and energy efficiency. The invention includes:

[0005] In a first aspect, the present invention provides a multi-scenario modal management method for a controller, comprising: reading the usage records of a toilet seat within a preset historical time range via the Internet of Things, analyzing and identifying several high-frequency users; collecting user characteristics of the several high-frequency users, and constructing several high-frequency user-seat temperature adaptation tables in combination with multiple room temperature ranges; constructing a usage probability predictor based on the usage records to predict the toilet usage probability, and obtaining multiple usage probabilities within a preset time zone; and performing adaptive temperature adjustment of the toilet seat based on the several high-frequency user-seat temperature adaptation tables and the multiple usage probabilities, combined with the indoor temperature within the preset time zone.

[0006] Preferably, the multi-scenario modal management method of the controller further includes: reading the usage records of the toilet seat within a preset historical time range through the Internet of Things to obtain a usage record dataset, wherein each usage record data includes weight data; configuring a weight fluctuation range, and according to the usage record dataset, counting the usage frequency where the weight deviation is within the same weight fluctuation range to obtain multiple usage frequencies; setting users whose usage frequency is greater than a preset frequency threshold as high-frequency users, and determining several high-frequency users, wherein each high-frequency user is identified by a user weight range.

[0007] Preferably, the multi-scenario modal management method of the controller further includes: querying family user health management data based on the user weight range to obtain several high-frequency user characteristics; obtaining room temperature records within a preset historical time range to determine the room temperature fluctuation range; dividing the room temperature fluctuation range according to a preset temperature step size to determine multiple room temperature intervals; and performing a big data retrieval of daily life data based on the several high-frequency user characteristics and multiple room temperature intervals to construct several high-frequency user-seat temperature adaptation tables.

[0008] Preferably, the multi-scenario modal management method of the controller further includes: randomly selecting a first high-frequency user feature and a first room temperature range to construct a first comparison condition; using the first comparison condition as a constraint, performing a big data retrieval of daily life data to obtain a first sample seat temperature set, and calculating the mean of the first sample temperature; using the mean of the first sample temperature as a benchmark, calculating the deviation of multiple first sample seat temperatures respectively, and setting the first sample seat temperature with a temperature deviation less than a preset temperature error as the first qualified seat temperature, calculating the mean of multiple first qualified seat temperatures to obtain a first suitable seat temperature; sequentially analyzing and obtaining multiple suitable seat temperatures of the first high-frequency user feature, constructing a first high-frequency user-seat temperature adaptation table by combining multiple room temperature range mappings, and adding it to several high-frequency user-seat temperature adaptation tables.

[0009] Preferably, the multi-scenario modal management method for a controller further includes: dividing a preset time period according to a preset time step to obtain multiple time periods, wherein the preset time period is 24 hours; based on the usage records, statistically analyzing the usage frequency of several high-frequency users in each time period within a preset historical time range, setting it as the usage probability, and obtaining multiple time periods and multiple usage probabilities for each high-frequency user; establishing a mapping relationship between high-frequency users, multiple time periods, and multiple usage probabilities, and constructing a usage probability predictor based on the mapping relationship and several high-frequency users.

[0010] Preferably, the multi-scenario modal management method of the controller further includes: obtaining lock opening and closing records within a preset time zone through the smart door lock; determining whether the plurality of high-frequency users are at home based on the lock opening and closing records, and identifying multiple high-frequency users at home.

[0011] Preferably, the multi-scenario modal management method of the controller further includes: using the usage probability predictor to predict the toilet usage probability within a preset time zone for the multiple high-frequency users at home, and outputting multiple usage probabilities.

[0012] Preferably, the multi-scenario modal management method for a controller further includes: acquiring the indoor temperature within a preset time zone through IoT monitoring; using the plurality of high-frequency user-seat temperature adaptation tables, acquiring multiple adapted seat temperatures based on the indoor temperature and multiple high-frequency users at home; judging the plurality of usage probabilities according to a first probability threshold; if the plurality of usage probabilities are all less than or equal to the first probability threshold, then setting the toilet seat to an unheated state within the preset time zone, wherein the first probability threshold is less than or equal to 10%; if the number of usage probabilities greater than the first probability threshold is not 0, judging whether the number of usage probabilities greater than a second probability threshold is 0; if not 0, The toilet seat is set to be in a heated state within the preset time zone, and the heating temperature is the appropriate seat temperature for high-frequency users at home with the highest probability of use. Here, the second probability threshold is greater than or equal to 60%, and the heating state is periodic heating, including a preset heating time interval. If the number of users with a usage probability greater than the first probability threshold is not 0, and the number of users with a usage probability greater than the second probability threshold is 0, then the appropriate seat temperature for high-frequency users at home with a usage probability greater than the first probability threshold and less than or equal to the second probability threshold is obtained, resulting in multiple appropriate seat temperatures. The average appropriate temperature is calculated, and the toilet seat is set to be in a heated state within the preset time zone, with the heating temperature being the average appropriate temperature.

[0013] Preferably, the multi-scenario modal management method of the controller further includes: obtaining the number of users at home based on the lock and unlock records in a preset time zone, wherein the users at home include high-frequency users and non-high-frequency users; setting the reciprocal of the ratio of the number of users at home to the number of high-frequency users as an adjustment coefficient, multiplying it by the initial heating time interval to obtain the preset heating time interval.

[0014] Secondly, the present invention also provides a multi-scenario modal management system for a controller, used to execute a multi-scenario modal management method for a controller as described in the first aspect, comprising: a high-frequency user identification module, used to read the usage records of the toilet seat within a preset historical time range via the Internet of Things, and analyze and identify several high-frequency users; a temperature adaptation table construction module, used to collect user characteristics of the several high-frequency users, and construct several high-frequency user-seat temperature adaptation tables in combination with multiple room temperature ranges; a usage probability prediction module, used to construct a usage probability predictor based on the usage records to predict the toilet usage probability and obtain multiple usage probabilities within a preset time zone; and a temperature adjustment module, used to perform adaptive temperature adjustment of the toilet seat based on the several high-frequency user-seat temperature adaptation tables and multiple usage probabilities, combined with the indoor temperature within the preset time zone.

[0015] The embodiments of the present invention have the following advantages:

[0016] By reading toilet seat usage records within a preset historical time range, several high-frequency users are identified. Next, user characteristics of these high-frequency users are collected, and a series of user-seat temperature adaptation tables are constructed based on multiple room temperature ranges. Furthermore, a usage probability predictor is built based on the usage records to predict toilet usage probabilities, obtaining multiple usage probabilities within a preset time zone. Then, based on the high-frequency user-seat temperature adaptation tables and multiple usage probabilities, combined with the indoor temperature within the preset time zone, the toilet seat temperature is adaptively adjusted. In other words, by utilizing the Internet of Things, combined with user behavior analysis and real-time data prediction, personalized temperature adjustment services can be provided to users based on user preferences, health conditions, and environmental factors. This adapts to different environmental conditions, improves user comfort, and effectively saves energy, achieving the technical effect of efficient toilet management. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the steps of a multi-scenario modal management method for a controller according to the present invention.

[0018] Figure 2 This is a schematic diagram of the structure of a multi-scenario modal management system for a controller according to the present invention.

[0019] Explanation of reference numerals in the attached figures:

[0020] Module 11 for determining high-frequency users, module 12 for constructing temperature adaptation tables, module 13 for predicting usage probability, and module 14 for temperature adjustment. Detailed Implementation

[0021] This invention provides a multi-scenario modal management method and system for a controller, solving the technical problem that traditional toilet seat temperature control methods often rely on fixed temperature settings and cannot adaptively adjust according to user preferences, health conditions, and environmental factors, resulting in insufficient comfort and energy efficiency. By utilizing the Internet of Things (IoT), combined with user behavior analysis and real-time data prediction, personalized temperature adjustment services can be provided to users based on user preferences, health conditions, and environmental factors. This adapts to different environmental conditions, improves user comfort, and effectively saves energy, achieving the technical effect of efficient toilet management.

[0022] The technical solutions of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. It should be understood that the present invention is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention. It should also be noted that, for ease of description, only the parts related to the present invention are shown in the accompanying drawings, not all of them.

[0023] Example 1, please refer to the appendix. Figure 1 This invention provides a multi-scenario modal management method for a controller, applied to a multi-scenario modal management system for a controller, specifically including the following steps:

[0024] S10: Uses the Internet of Things to read the usage records of toilet seats within a preset historical time range, and analyzes and identifies several high-frequency users.

[0025] Furthermore, step S10 of the present invention further includes:

[0026] S11: Read the usage records of the toilet seat within a preset historical time range through the Internet of Things to obtain a usage record dataset, wherein each usage record data includes weight data; S12: Configure weight fluctuation ranges, and based on the usage record dataset, count the usage frequency when the weight deviation is within the same weight fluctuation range to obtain multiple usage frequencies; S13: Set users whose usage frequency is greater than a preset frequency threshold as high-frequency users, and determine several high-frequency users, wherein each high-frequency user is identified by a user weight range.

[0027] Specifically, the Internet of Things (IoT) reads the usage records of the toilet seat within a preset historical time range (such as the most recent month, which can be set according to the needs of the scenario). The IoT is used to collect various data of users using the toilet in real time. This data includes, but is not limited to, usage time, number of uses, weight information, etc. Each usage record includes weight data. Every time a user uses the toilet, sensors (such as pressure sensors) record the user's weight data and upload it to the cloud for storage through the IoT platform.

[0028] Next, configure the weight fluctuation range to analyze the user's weight fluctuations. This can be set according to the actual scenario. For example, setting the weight fluctuation range to ±2 kg means that weight fluctuations within this range are considered the same fluctuation range. Then, based on the usage record dataset, count the usage frequency when the weight deviation falls within the same weight fluctuation range. That is, calculate the usage frequency when the user's weight data fluctuations within this range and obtain multiple usage frequencies. For example, if the user's weight records are: weight 70 kg, usage frequency 1 time; weight 71 kg, usage frequency 1 time; weight 72 kg, usage frequency 1 time; then the user's weight range is identified as 70 to 72 kg, and the usage frequency is 3 times.

[0029] Then, a preset frequency threshold is configured. The preset frequency threshold can be set according to the scenario. If a user's usage frequency is greater than the frequency threshold, then the user is identified as a high-frequency user. Several high-frequency users are identified. Each high-frequency user has a weight range related to their weight fluctuations. This range is determined based on the range of changes in the user's weight. For example, assuming the preset frequency threshold is 2, if user A's usage frequency is 3 times, which is greater than the threshold, then user A is a high-frequency user with a weight range of 70kg to 72kg. User B's usage frequency is 1 time, which is less than the threshold. Therefore, user B is not a high-frequency user and there is no need to identify their weight range.

[0030] Through the above process, based on users' weight data and usage frequency, it is possible to analyze which users are high-frequency users and provide them with personalized temperature control services. This method can accurately identify users with high requirements for temperature regulation, providing effective support for subsequent intelligent seat temperature regulation.

[0031] S20: Collect user characteristics of the aforementioned high-frequency users and construct several high-frequency user-seat temperature adaptation tables by combining multiple room temperature ranges.

[0032] Furthermore, step S20 of the present invention also includes:

[0033] S21: Based on the user's weight range, query the family user health management data to obtain several high-frequency user characteristics; S22: Obtain room temperature records within a preset historical time range to determine the room temperature fluctuation range; S23: Divide the room temperature fluctuation range according to a preset temperature step size to determine multiple room temperature intervals.

[0034] Specifically, based on the user's weight range, the system queries family user health management data. This involves combining family user health management data to obtain more precise user characteristics. This health management data includes information such as the user's health status, age, body temperature, activity level, and lifestyle habits. This characteristic information helps to understand the user's physiological needs. Especially in the design of toilet seat temperature control systems, considering the user's health status can effectively improve the accuracy of personalized adjustment. For example, older users may prefer higher temperatures. Several high-frequency user characteristics (such as health status, age, body temperature, activity level, and lifestyle habits) are obtained. Through these health characteristics, a temperature control solution that better meets the needs of each high-frequency user can be designed.

[0035] Next, room temperature records within a preset historical time range (e.g., the most recent month) are obtained. Room temperature can be collected using IoT devices (e.g., smart thermostats or sensors). Changes in indoor temperature are closely related to user comfort, especially when adjusting toilet seat temperature, where the impact of indoor temperature needs to be considered. Then, the lowest and highest room temperatures within the preset historical time range are selected to determine the room temperature fluctuation range. Next, a preset temperature step size is obtained. The temperature step size refers to the temperature difference between each small interval within the temperature range. For example, with a step size of 1°C, the temperature range is divided into several 1°C intervals. Then, the room temperature fluctuation range is divided according to the preset temperature step size (e.g., 1°C) to determine multiple room temperature intervals. Each interval represents the adaptation logic of the toilet seat temperature control system within that temperature interval. When the indoor temperature fluctuates between different intervals, the seat temperature can be adaptively adjusted based on the user's weight characteristics, health status, etc.

[0036] S24: Using the aforementioned high-frequency user characteristics and multiple room temperature ranges as constraints, perform a large-scale life data retrieval to construct several high-frequency user-seat temperature adaptation tables.

[0037] Furthermore, step S24 of the present invention also includes:

[0038] S241: Randomly select a first high-frequency user feature and a first room temperature range to construct a first comparison condition; S242: Using the first comparison condition as a constraint, perform a big data retrieval of daily life data to obtain a first sample seat temperature set, and calculate the mean of the first sample temperature; S243: Using the mean of the first sample temperature as a benchmark, calculate the deviation of multiple first sample seat temperatures, and set the first sample seat temperature with a temperature deviation less than a preset temperature error as the first qualified seat temperature. Calculate the mean of multiple first qualified seat temperatures to obtain the first suitable seat temperature; S244: Sequentially analyze and obtain multiple suitable seat temperatures of the first high-frequency user feature, combine multiple room temperature range mappings to construct a first high-frequency user-seat temperature matching table, and add it to several high-frequency user-seat temperature matching tables.

[0039] Specifically, from the aforementioned high-frequency user features, any one high-frequency user feature is randomly selected as the first high-frequency user feature, and any one room temperature range is randomly selected from multiple room temperature ranges as the first room temperature range. The first high-frequency user feature and the first room temperature range are combined to obtain the first comparison condition, which serves as a constraint when retrieving historical data. This ensures that the selected temperature dataset matches the user's needs and the ambient temperature. Next, using the first comparison condition as a constraint, a big data retrieval is performed, that is, using big data (such as historical user data of similar toilets) to retrieve a set of sample seat temperatures related to the selected conditions (the first high-frequency user feature and the first room temperature range). The mean of the obtained sample seat temperature data is calculated to obtain an average value representing the seat temperature under that condition, which serves as a benchmark value and provides a reference for subsequent deviation calculations, thus obtaining the first sample temperature mean.

[0040] Then, using the average temperature of the first sample as a benchmark, the deviation of the seat ring temperature of multiple first samples is calculated to obtain multiple temperature differences; next, a temperature error threshold is set, for example, ±0.5°C, and all temperatures with deviations less than this error are considered to be qualified seat ring temperatures, and the first sample seat ring temperature with a temperature deviation less than the preset temperature error is set as the first qualified seat ring temperature, thus obtaining multiple first qualified seat ring temperatures; finally, the average of the multiple first qualified seat ring temperatures is calculated to obtain the first suitable seat ring temperature.

[0041] Next, the system sequentially analyzes and obtains multiple suitable seat temperatures for the first high-frequency user characteristics across multiple room temperature ranges. Then, based on the mapping relationship between room temperature ranges and suitable seat temperatures, a first high-frequency user-seat temperature adaptation table is constructed. Using the same method, several high-frequency user-seat temperature adaptation tables are then constructed for several high-frequency user characteristics. By combining user weight characteristics, indoor temperature fluctuations, and historical temperature data, a personalized toilet seat temperature is accurately calculated, and an adaptation temperature table is established for each high-frequency user. This process ensures that the adaptive adjustment of the seat temperature can meet the comfort needs of different users under different environmental conditions, improving the personalized adjustment effect of the smart toilet.

[0042] S30: Construct a usage probability predictor based on the usage records to predict the toilet usage probability and obtain multiple usage probabilities within a preset time zone.

[0043] Furthermore, step S30 of the present invention also includes:

[0044] S31: Divide the preset time period according to the preset time step to obtain multiple time periods, wherein the preset time period is 24 hours; S32: According to the usage records, count the usage frequency of several high-frequency users in each time period within the preset historical time range, set as the usage probability, and obtain multiple time periods and multiple usage probabilities for each high-frequency user; S33: Establish a mapping relationship between high-frequency users, multiple time periods and multiple usage probabilities, and construct a usage probability predictor based on the mapping relationship and several high-frequency users.

[0045] Specifically, a preset time period is divided according to a preset time step. The preset time period is 24 hours, from the beginning to the end of the day. Based on the set time step, the 24 hours are divided into multiple time periods. For example, assuming a time step of 1 hour, a day will be divided into 24 time periods (00:00 to 01:00, 01:00 to 02:00, ..., 23:00 to 24:00). Next, based on the usage records, the usage frequency of several high-frequency users within each time period of the preset historical time range is counted. That is, the number of times each high-frequency user uses the toilet during that time period. The ratio of toilet usage frequency to the total number of time periods is set as the usage probability. The usage probability represents the likelihood that a user will use the toilet during a certain time period, resulting in multiple time periods and multiple usage probabilities for each high-frequency user.

[0046] Then, the relationship between high-frequency users, time periods, and usage probabilities is organized and modeled. For example, user A has an 80% probability of using the toilet during the 7:00-8:00 time period, while user B has a 30% probability of using it during the same time period. By organizing the usage probabilities of each high-frequency user in different time periods, a usage probability predictor is constructed. This predictor can predict the user's usage probability in a future time period based on the user's characteristics, thus providing a precise basis for adjusting the toilet seat temperature control. This process realizes intelligent temperature control based on user behavior prediction, improves the personalized adjustment effect, and ensures that users can enjoy the most comfortable temperature experience during specific time periods.

[0047] Furthermore, step S30 of the present invention also includes:

[0048] S34: Obtain lock / unlock records within a preset time zone using the smart lock; S35: Based on the lock / unlock records, determine whether the plurality of high-frequency users are at home, and identify multiple high-frequency users at home.

[0049] Specifically, smart locks connected via the Internet of Things (IoT) acquire lock / unlock records. These records reflect the door's open / closed status and typically include timestamps for each unlocking and closing. The lock / unlock times recorded by the smart lock can be used to determine whether a user has entered or left the home. Next, the smart lock acquires lock / unlock records within a preset time zone (e.g., 7:00 AM to 8:00 AM). User entry and exit behaviors can be inferred from these records. Then, based on these lock / unlock records, it is determined whether several high-frequency users are home. For example, if a user does not unlock or lock within a preset time period, it is inferred that the user is not home; if a user unlocks and enters the home during a certain time period, it is determined that the user is home, thus identifying multiple high-frequency users at home. By combining the lock / unlock records acquired by the smart lock with the behavioral data of high-frequency users, it is possible to more accurately determine whether each high-frequency user is home. This information not only helps improve the accuracy of the toilet seat's adaptive temperature control but also ensures that the temperature adjustment matches the user's actual needs.

[0050] Furthermore, step S30 of the present invention also includes:

[0051] S36: Using the usage probability predictor, predict the toilet usage probability within a preset time zone for the multiple high-frequency users at home, and output multiple usage probabilities.

[0052] Specifically, the usage probability predictor is then used to predict the toilet usage probability within a preset time zone for the multiple high-frequency users at home, that is, to match and obtain the toilet usage probability of multiple high-frequency users at home within the preset time zone, thus obtaining multiple usage probabilities.

[0053] S40: Based on the aforementioned high-frequency user-seat temperature adaptation tables and multiple usage probabilities, combined with the indoor temperature within a preset time zone, the toilet seat temperature is adaptively adjusted.

[0054] Furthermore, step S40 of the present invention further includes:

[0055] S41: Obtain the indoor temperature within a preset time zone through IoT monitoring; S42: Utilize the aforementioned high-frequency user-seat temperature matching tables to obtain multiple matching seat temperatures based on the indoor temperature and multiple high-frequency users at home; S43: Judge the multiple usage probabilities according to a first probability threshold. If all of the multiple usage probabilities are less than or equal to the first probability threshold, then set the toilet seat to an unheated state within the preset time zone, wherein the first probability threshold is less than or equal to 10%.

[0056] Specifically, the system monitors and obtains the indoor temperature within a preset time zone using the Internet of Things (such as temperature sensors). For example, the temperature in a user's home is 21°C between 7:00 and 9:00 AM and 23°C between 7:00 and 9:00 PM. Next, using a set of high-frequency user-seat temperature matching tables, multiple compatible seat temperatures are obtained based on the indoor temperature and the data from multiple high-frequency users at home. Then, a first probability threshold is obtained, which is less than or equal to 10%, for example, 8%. The multiple usage probabilities are judged according to the first probability threshold. If all multiple usage probabilities are less than or equal to the first probability threshold, it indicates insufficient usage demand during this period. In this case, the toilet seat is set to an unheated state within the preset time zone to achieve energy saving. That is, if the predicted usage probability is very low during certain periods, the system automatically switches to energy-saving mode, not only saving energy for the user but also improving the system's intelligent management.

[0057] S44: If the number of users with a usage probability greater than the first probability threshold is not 0, determine whether the number of users with a usage probability greater than the second probability threshold is 0. If it is not 0, set the toilet seat to be in a heating state within the preset time zone, and the heating temperature is the appropriate seat temperature for high-frequency users at home with the highest usage probability. The second probability threshold is greater than or equal to 60%, and the heating state is periodic heating, including a preset heating time interval.

[0058] Furthermore, step S44 of the present invention also includes:

[0059] S441: Obtain the number of users at home based on the lock / unlock records within the preset time zone, where users at home include high-frequency users and low-frequency users; S442: Set the reciprocal of the ratio of the number of users at home to the number of high-frequency users as an adjustment coefficient, multiply it by the initial heating time interval to obtain the preset heating time interval.

[0060] Specifically, the system first checks if the number of high-frequency users with a usage probability greater than a first probability threshold (e.g., 10%) within a preset time zone is zero. If at least one high-frequency user has a usage probability greater than 10% during that time period, the system proceeds to the next step. Next, it checks if the number of users with a usage probability greater than a second probability threshold (≥60%, e.g., 65%) is zero. If not, the toilet seat is set to a heated state within the preset time zone. The seat temperature is set based on the optimal seat temperature for the most frequent user at home during that time period. Specifically, the system selects the user with the highest usage probability (and who is at home) and matches their optimal seat temperature as the heating temperature. The heating is periodic, meaning the seat will heat periodically at a preset time interval, for example, every 30 minutes, to ensure users can consistently enjoy a comfortable temperature during high-frequency usage periods. This intelligent adjustment automatically activates the heating function based on user needs, ensuring users can enjoy a comfortable seat temperature when needed while avoiding energy waste.

[0061] The method for setting the preset heating time interval is as follows: First, based on the lock / unlock records within the preset time zone, the number of users at home is obtained. These users include high-frequency users and low-frequency users (such as guests). Next, the reciprocal of the ratio of the number of users at home to the number of high-frequency users is set as an adjustment coefficient. For example, if the number of users at home is 6 and the number of high-frequency users is 3, indicating a large number of people at home at the current stage, then the adjustment coefficient is the reciprocal of 6 / 3, which equals 1 / 2. Then, the adjustment coefficient is multiplied by the initial heating time interval, and the product is used as the preset heating time interval. This method allows for flexible adjustment of the heating frequency based on the proportion of high-frequency users among those at home, ensuring that the heating system can operate effectively according to actual needs.

[0062] S45: If the number of users with a usage probability greater than the first probability threshold is not 0 and the number of users with a usage probability greater than the second probability threshold is 0, then obtain the suitable seat temperature for high-frequency users at home whose usage probability is greater than the first probability threshold and less than or equal to the second probability threshold, obtain multiple suitable seat temperatures, calculate the average suitable temperature, and set the toilet seat to be in a heating state within the preset time zone, with the heating temperature being the average suitable temperature.

[0063] Specifically, if the number of users with a usage probability greater than the first probability threshold is not zero, and the number of users with a usage probability greater than the second probability threshold is zero, then the suitable seat temperature for high-frequency users at home whose usage probability is greater than the first probability threshold and less than or equal to the second probability threshold is obtained, resulting in multiple suitable seat temperatures; further, the average of the multiple suitable seat temperatures is calculated to obtain the average suitable temperature, and the toilet seat is set to be in a heated state within the preset time zone, with the heating temperature being the average suitable temperature. That is, if there are multiple high-frequency users who meet the conditions within the preset time zone, then the seat heating is activated according to their average suitable temperature, and the heating temperature is ensured to meet the temperature control needs of these users.

[0064] In summary, the multi-scenario modal management method for controllers provided by this invention has the following technical effects:

[0065] By reading toilet seat usage records within a preset historical time range, several high-frequency users are identified. Next, user characteristics of these high-frequency users are collected, and a series of user-seat temperature adaptation tables are constructed based on multiple room temperature ranges. Furthermore, a usage probability predictor is built based on the usage records to predict toilet usage probabilities, obtaining multiple usage probabilities within a preset time zone. Then, based on the high-frequency user-seat temperature adaptation tables and multiple usage probabilities, combined with the indoor temperature within the preset time zone, the toilet seat temperature is adaptively adjusted. In other words, by utilizing the Internet of Things, combined with user behavior analysis and real-time data prediction, personalized temperature adjustment services can be provided to users based on user preferences, health conditions, and environmental factors. This adapts to different environmental conditions, improves user comfort, and effectively saves energy, achieving the technical effect of efficient toilet management.

[0066] Example 2: Based on the same inventive concept as the multi-scenario modal management method for a controller in the foregoing examples, this invention also provides a multi-scenario modal management system for a controller. Please refer to the appendix. Figure 2 The system includes: a high-frequency user identification module 11, used to read toilet seat usage records within a preset historical time range via the Internet of Things and analyze and identify several high-frequency users; a temperature adaptation table construction module 12, used to collect user characteristics of the several high-frequency users and construct several high-frequency user-seat temperature adaptation tables in combination with multiple room temperature ranges; a usage probability prediction module 13, used to construct a usage probability predictor based on the usage records to predict toilet usage probability and obtain multiple usage probabilities within a preset time zone; and a temperature adjustment module 14, used to adaptively adjust the toilet seat temperature based on the several high-frequency user-seat temperature adaptation tables and multiple usage probabilities, combined with the indoor temperature within the preset time zone.

[0067] Furthermore, the multi-scenario modal management system of the controller is also used to: read the usage records of the toilet seat within a preset historical time range through the Internet of Things to obtain a usage record dataset, wherein each usage record data includes weight data; configure weight fluctuation ranges, and according to the usage record dataset, count the usage frequency when the weight deviation is within the same weight fluctuation range to obtain multiple usage frequencies; set users whose usage frequency is greater than a preset frequency threshold as high-frequency users, and determine several high-frequency users, wherein each high-frequency user is identified by a user weight range.

[0068] Furthermore, the multi-scenario modal management system of the controller is also used to: query family user health management data based on the user's weight range to obtain several high-frequency user characteristics; obtain room temperature records within a preset historical time range to determine the room temperature fluctuation range; divide the room temperature fluctuation range according to a preset temperature step size to determine multiple room temperature intervals; and perform big data retrieval of daily life data based on the several high-frequency user characteristics and multiple room temperature intervals to construct several high-frequency user-seat temperature adaptation tables.

[0069] Furthermore, the multi-scenario modal management system of the controller is also used for: randomly selecting a first high-frequency user feature and a first room temperature range to construct a first comparison condition; using the first comparison condition as a constraint, performing a big data retrieval of daily life to obtain a first sample seat temperature set, and calculating the mean of the first sample temperature; using the mean of the first sample temperature as a benchmark, calculating the deviation of multiple first sample seat temperatures respectively, and setting the first sample seat temperature with a temperature deviation less than a preset temperature error as the first qualified seat temperature, calculating the mean of multiple first qualified seat temperatures to obtain a first suitable seat temperature; sequentially analyzing and obtaining multiple suitable seat temperatures of the first high-frequency user feature, combining multiple room temperature range mappings to construct a first high-frequency user-seat temperature adaptation table, and adding it to several high-frequency user-seat temperature adaptation tables.

[0070] Furthermore, the multi-scenario modal management system of the controller is also used for: dividing a preset time period according to a preset time step to obtain multiple time periods, wherein the preset time period is 24 hours; according to the usage records, counting the usage frequency of several high-frequency users in each time period within a preset historical time range, setting it as the usage probability, and obtaining multiple time periods and multiple usage probabilities for each high-frequency user; establishing a mapping relationship between high-frequency users, multiple time periods, and multiple usage probabilities, and constructing a usage probability predictor based on the mapping relationship and several high-frequency users.

[0071] Furthermore, the multi-scenario modal management system of the controller is also used to: obtain lock opening and closing records within a preset time zone through the smart door lock; and determine whether the plurality of high-frequency users are at home based on the lock opening and closing records, thereby identifying multiple high-frequency users at home.

[0072] Furthermore, the multi-scenario modal management system of the controller is also used to: use the usage probability predictor to predict the toilet usage probability within a preset time zone for the multiple high-frequency users at home, and output multiple usage probabilities.

[0073] Furthermore, the multi-scenario modal management system of the controller is also used for: acquiring the indoor temperature within a preset time zone through IoT monitoring; using the several high-frequency user-seat temperature adaptation tables, acquiring multiple adapted seat temperatures based on the indoor temperature and multiple high-frequency users at home; judging the multiple usage probabilities according to a first probability threshold; if the multiple usage probabilities are all less than or equal to the first probability threshold, then setting the toilet seat to an unheated state within the preset time zone, wherein the first probability threshold is less than or equal to 10%; if the number of usage probabilities greater than the first probability threshold is not 0, judging whether the number of usage probabilities greater than a second probability threshold is 0; if not 0... The toilet seat is set to be in a heated state within the preset time zone, and the heating temperature is the appropriate seat temperature for high-frequency users at home with the highest probability of use. Here, the second probability threshold is greater than or equal to 60%, and the heating state is periodic heating, including a preset heating time interval. If the number of users with a usage probability greater than the first probability threshold is not 0, and the number of users with a usage probability greater than the second probability threshold is 0, then the appropriate seat temperature for high-frequency users at home with a usage probability greater than the first probability threshold and less than or equal to the second probability threshold is obtained, resulting in multiple appropriate seat temperatures. The average appropriate temperature is calculated, and the toilet seat is set to be in a heated state within the preset time zone, with the heating temperature being the average appropriate temperature.

[0074] Furthermore, the multi-scenario modal management system of the controller is also used to: obtain the number of users at home based on the lock and unlock records in a preset time zone, wherein the users at home include high-frequency users and non-high-frequency users; and set the reciprocal of the ratio of the number of users at home to the number of high-frequency users as an adjustment coefficient, multiply it by the initial heating time interval to obtain the preset heating time interval.

[0075] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. The multi-scenario modal management method and specific examples of a controller in the foregoing embodiment one are also applicable to the multi-scenario modal management system of a controller in this embodiment. Through the foregoing detailed description of the multi-scenario modal management method of a controller, those skilled in the art can clearly understand the multi-scenario modal management system of a controller in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here. As for the system disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple, and relevant parts can be referred to in the method section.

[0076] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0077] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of this invention and its equivalents, this invention also intends to include these modifications and variations.

Claims

1. A multi-scenario modal management method for a controller, characterized in that, The methods include: By reading the usage records of toilet seats within a preset historical time range through the Internet of Things, several high-frequency users can be identified through analysis. Collect user characteristics of several high-frequency users and construct several high-frequency user-seat temperature adaptation tables by combining multiple room temperature ranges; Based on the usage records, a usage probability predictor is constructed to predict the toilet usage probability and obtain multiple usage probabilities within a preset time zone. Based on the aforementioned high-frequency user-seat temperature adaptation tables and multiple usage probabilities, combined with the indoor temperature in the preset time zone, the toilet seat temperature is adaptively adjusted. Constructing a usage probability predictor based on the usage records includes: The preset time period is divided according to a preset time step to obtain multiple time periods, wherein the preset time period is 24 hours. Based on the usage records, the usage frequency of several high-frequency users in each time period within a preset historical time range is counted and set as the usage probability, thus obtaining multiple time periods and multiple usage probabilities for each high-frequency user; Establish a mapping relationship between high-frequency users, multiple time periods, and multiple usage probabilities. Based on the mapping relationship, construct a usage probability predictor by combining several high-frequency users. Based on the aforementioned high-frequency user-seat temperature adaptation tables and multiple usage probabilities, combined with the indoor temperature within a preset time zone, the toilet seat temperature is adaptively adjusted, including: Indoor temperature within a preset time zone is obtained through IoT monitoring; Using the aforementioned high-frequency user-seat temperature matching tables, multiple matching seat temperatures are obtained based on the indoor temperature and multiple high-frequency users at home. The multiple usage probabilities are judged according to a first probability threshold. If all of the multiple usage probabilities are less than or equal to the first probability threshold, the toilet seat is set to be in an unheated state in the preset time zone, wherein the first probability threshold is less than or equal to 10%. If the number of users with a probability greater than the first probability threshold is not 0, determine whether the number of users with a probability greater than the second probability threshold is 0. If it is not 0, set the toilet seat to be in a heated state within the preset time zone, and the heating temperature is the seat temperature suitable for high-frequency users at home with the highest probability of use. The second probability threshold is greater than or equal to 60%, and the heating state is periodic heating, including a preset heating time interval. If the number of users with a usage probability greater than the first probability threshold is not 0, and the number of users with a usage probability greater than the second probability threshold is 0, then obtain the suitable seat temperature for high-frequency users at home whose usage probability is greater than the first probability threshold and less than or equal to the second probability threshold, obtain multiple suitable seat temperatures, calculate the average suitable temperature, and set the toilet seat to be in a heating state within the preset time zone, with the heating temperature being the average suitable temperature.

2. The multi-scenario modal management method for a controller according to claim 1, characterized in that, By reading toilet seat usage records within a preset historical time range using the Internet of Things (IoT), several high-frequency users are identified, including: By reading the usage records of the toilet seat within a preset historical time range through the Internet of Things, a usage record dataset is obtained, in which each usage record includes weight data; Configure weight fluctuation ranges, and based on the usage record dataset, count the frequency of use where the weight deviation is within the same weight fluctuation range to obtain multiple usage frequencies; Users whose usage frequency exceeds a preset frequency threshold are designated as high-frequency users. Several high-frequency users are identified, and each high-frequency user is identified by a weight range.

3. The multi-scenario modal management method for a controller according to claim 2, characterized in that, The user characteristics of several high-frequency users are collected, and several high-frequency user-seat ring temperature adaptation tables are constructed by combining multiple room temperature ranges, including: Based on the user's weight range, query the family user health management data to obtain several high-frequency user characteristics; Obtain room temperature records within a preset historical time range to determine the range of room temperature fluctuations; The room temperature fluctuation range is divided according to a preset temperature step size to determine multiple room temperature intervals; Using the aforementioned high-frequency user characteristics and multiple room temperature ranges as constraints, a large-scale life data retrieval was conducted to construct several high-frequency user-seat temperature adaptation tables.

4. The multi-scenario modal management method for a controller according to claim 3, characterized in that, Based on the aforementioned high-frequency user characteristics and multiple room temperature ranges as constraints, a large-scale life data retrieval is performed to construct several high-frequency user-seat temperature adaptation tables, including: The first high-frequency user feature and the first room temperature range are randomly selected to construct the first comparison conditions; Using the first comparison condition as a constraint, a big data search of daily life is performed to obtain the first sample seat temperature set, and the mean value of the first sample temperature is calculated. Based on the average temperature of the first sample, the deviation of the seat ring temperature of multiple first samples is calculated, and the first sample seat ring temperature with a temperature deviation less than the preset temperature error is set as the first qualified seat ring temperature. The average of multiple first qualified seat ring temperatures is calculated to obtain the first suitable seat ring temperature. The first high-frequency user characteristics are analyzed sequentially to obtain multiple matching seat ring temperatures. The first high-frequency user-seat ring temperature matching table is constructed by combining multiple room temperature range mappings and added to several high-frequency user-seat ring temperature matching tables.

5. The multi-scenario modal management method for a controller according to claim 1, characterized in that, Toilet usage probability prediction previously included: The smart door lock retrieves lock / unlock records within a preset time zone. Based on the lock and switch records, it is determined whether the several high-frequency users are at home, and multiple high-frequency users at home are identified.

6. The multi-scenario modal management method for a controller according to claim 5, characterized in that, Using the usage probability predictor, the toilet usage probability is predicted for the multiple high-frequency users at home within a preset time zone, and multiple usage probabilities are output.

7. The multi-scenario modal management method for a controller according to claim 1, characterized in that, The method for setting the preset heating time interval includes: Based on the lock opening and closing records within the preset time zone, the number of users at home is obtained, including high-frequency users and low-frequency users. The reciprocal of the ratio of the number of users at home to the number of high-frequency users is set as an adjustment coefficient, which is then multiplied by the initial heating time interval to obtain the preset heating time interval.

8. A multi-scenario modal management system for a controller, characterized in that, The steps for implementing a multi-scenario modal management method for a controller according to any one of claims 1 to 7 include: The high-frequency user identification module is used to read the usage records of toilet seats within a preset historical time range through the Internet of Things, and analyze and identify several high-frequency users. The temperature adaptation table construction module is used to collect user characteristics of the several high-frequency users and construct several high-frequency user-seat ring temperature adaptation tables in combination with multiple room temperature ranges. The probability prediction module is used to construct a usage probability predictor based on the usage records to predict the toilet usage probability and obtain multiple usage probabilities within a preset time zone. The temperature adjustment module is used to adaptively adjust the toilet seat temperature based on the aforementioned high-frequency user-seat temperature adaptation tables and multiple usage probabilities, combined with the indoor temperature in a preset time zone.

Images