Multi-scene modal management method and system of controller

Abstract

The invention provides a multi-scene modal management method and system for a controller, and relates to the field of intelligent home management, and the method comprises the steps: collecting the user characteristics of a plurality of high-frequency users, and constructing a plurality of seat ring temperature adaptation tables through combining with a plurality of room temperature intervals; performing closestool use probability prediction to obtain a plurality of use probabilities in a preset time zone; and based on the plurality of seat ring temperature adaptation tables and the plurality of use probabilities, in combination with the indoor temperature in the preset time zone, carrying out temperature self-adaptive adjustment on the toilet seat. The objective of the invention is to solve the technical problem that a conventional toilet seat temperature control method often depends on fixed temperature setting and cannot perform adaptive adjustment according to user preferences, health conditions and environmental factors, resulting in insufficient comfort and energy saving performance. According to the invention, personalized temperature regulation service can be provided for the user, the experience comfort is improved, the energy consumption is effectively saved, and the technical effect of efficient closestool management is achieved.

Description

Technical Field

[0001] The present invention relates to the field of smart home management, and particularly to a multi-scenario mode management method and system for a controller. Background Art

[0002] At present, the temperature control systems of many intelligent toilet seats adopt fixed temperature settings or simple timing control methods. Such temperature control methods often ignore the personalized needs of users and environmental changes, resulting in users being difficult to obtain the best comfort experience during use. For example, in cold winters, the seat temperature may be too low to provide sufficient warmth; while in warm seasons, the seat temperature may be too high, causing discomfort; in addition, existing temperature control systems usually do not consider changes in the health status of users (such as body temperature, age, etc.) and environmental factors (such as indoor temperature, humidity, etc.), which further affects the use experience.

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

[0004] The purpose of the present invention is to provide a multi-scenario mode management method and system for a controller to solve the technical problems that traditional toilet seat temperature control methods often rely on fixed temperature settings and cannot be adaptively adjusted according to user preferences, health status, and environmental factors, resulting in insufficient comfort and energy conservation, including: In a first aspect, the present invention provides a multi-scenario mode management method for a controller, including: reading the usage records of the toilet seat within a preset historical time range through the Internet of Things, analyzing and determining a number of high-frequency users; collecting the user characteristics of the number of high-frequency users, and combining multiple room temperature ranges to construct a number of high-frequency user-seat temperature adaptation tables; 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; based on the number of high-frequency user-seat temperature adaptation tables and multiple usage probabilities, and combining the indoor temperature within the preset time zone, performing adaptive temperature adjustment of the toilet seat.

[0005] Preferably, the multi-scenario mode management method for 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 data set, where each usage record data includes weight data; configuring a weight fluctuation range, and according to the usage record data set, counting the usage frequencies within the same weight fluctuation range of the weight deviation to obtain multiple usage frequencies; setting users with usage frequencies greater than a preset frequency threshold as high-frequency users, and determining several high-frequency users, where each high-frequency user is marked with a user weight range.

[0006] Preferably, the multi-scenario mode management method for the controller further includes: querying the family user health management data based on the user weight range to obtain several high-frequency user characteristics; obtaining the 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; performing life big data retrieval with the several high-frequency user characteristics and multiple room temperature intervals as constraints to construct several high-frequency user-seat ring temperature adaptation tables.

[0007] Preferably, the multi-scenario mode management method for the controller further includes: randomly selecting a first high-frequency user characteristic and a first room temperature interval to construct a first comparison condition; performing life big data retrieval with the first comparison condition as a constraint to obtain a first sample seat ring temperature set, and calculating the mean value to obtain a first sample temperature mean; taking the first sample temperature mean as a benchmark, calculating the deviation of each of the multiple first sample seat ring temperatures respectively, and setting the first sample seat ring temperatures with temperature deviations less than a preset temperature error as first qualified seat ring temperatures, calculating the mean value of the multiple first qualified seat ring temperatures to obtain a first adapted seat ring temperature; sequentially analyzing the multiple adapted seat ring temperatures of the first high-frequency user characteristic, and constructing a first high-frequency user-seat ring temperature adaptation table in combination with the mapping of multiple room temperature intervals, and adding it to the several high-frequency user-seat ring temperature adaptation tables.

[0008] Preferably, the multi-scenario mode management method for the controller further includes: dividing a preset time period according to a preset time step size to obtain multiple time periods, where the preset time period is 24 hours; according to the usage records, counting the usage frequencies of several high-frequency users in each time period within a preset historical time range, set as usage probabilities, to obtain multiple time periods and multiple usage probabilities for each high-frequency user; establishing a mapping relationship among high-frequency users, multiple time periods and multiple usage probabilities, and constructing a usage probability predictor in combination with several high-frequency users according to the mapping relationship.

[0009] Preferably, the multi-scenario mode management method of the controller further includes: obtaining the lock unlocking records within a preset time zone through an intelligent door lock; judging whether the several high-frequency users are at home according to the lock unlocking records, and determining multiple high-frequency users at home.

[0010] Preferably, the multi-scenario mode management method of the controller further includes: using the usage probability predictor to predict the toilet usage probability of the multiple high-frequency users at home within a preset time zone, and outputting multiple usage probabilities.

[0011] Preferably, the multi-scenario mode management method of the controller further includes: obtaining the indoor temperature within a preset time zone through Internet of Things monitoring; using the several high-frequency user-seat ring temperature adaptation tables, matching multiple adapted seat ring temperatures according to the indoor temperature and the 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, the toilet seat ring is set to an unheated state within the preset time zone, where 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, judge whether the number of usage probabilities greater than a second probability threshold is 0. If not 0, the toilet seat ring is set to a heated state within the preset time zone, and the heating temperature is the adapted seat ring temperature of the high-frequency user with the maximum usage probability, where 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 usage probabilities greater than the first probability threshold is not 0, and the number of usage probabilities greater than the second probability threshold is 0, then obtain the adapted seat ring temperatures of the high-frequency users at home whose usage probabilities are greater than the first probability threshold and less than or equal to the second probability threshold, obtain multiple adapted seat ring temperatures, calculate the average adapted temperature, and set the toilet seat ring to a heated state within the preset time zone, and the heating temperature is the average adapted temperature.

[0012] Preferably, the multi-scenario mode management method of the controller further includes: obtaining the number of users at home according to the lock unlocking records within a preset time zone, where 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, and multiplying it by the initial heating time interval to obtain the preset heating time interval.

[0013] In a second aspect, the present invention further provides a multi-scenario mode management system for a controller, which is used to execute a multi-scenario mode management method for a controller as described in the first aspect, including: a high-frequency user determination module, configured to read the usage records of the toilet seat within a preset historical time range through the Internet of Things, and analyze and determine a plurality of high-frequency users; a temperature adaptation table construction module, configured to collect the user characteristics of the plurality of high-frequency users, and construct a plurality of high-frequency user-seat ring temperature adaptation tables in combination with multiple room temperature ranges; a usage probability prediction module, configured to construct a usage probability predictor based on the usage records to predict the toilet usage probability, and obtain a plurality of usage probabilities within a preset time zone; a temperature adjustment module, configured to perform temperature adaptive adjustment of the toilet seat ring based on the plurality of high-frequency user-seat ring temperature adaptation tables and the plurality of usage probabilities, in combination with the indoor temperature within the preset time zone.

[0014] The embodiments of the present invention have the following advantages: By reading the usage records of the toilet seat within a preset historical time range, a plurality of high-frequency users are analyzed and determined; then, the user characteristics of the plurality of high-frequency users are collected, and a plurality of high-frequency user-seat ring temperature adaptation tables are constructed in combination with multiple room temperature ranges; on the other hand, a usage probability predictor is constructed based on the usage records to predict the toilet usage probability, and a plurality of usage probabilities within a preset time zone are obtained; then, based on the plurality of high-frequency user-seat ring temperature adaptation tables and the plurality of usage probabilities, in combination with the indoor temperature within the preset time zone, temperature adaptive adjustment of the toilet seat ring is performed. That is to say, by using the Internet of Things, combining user behavior analysis and real-time data prediction, personalized temperature adjustment services can be provided for users according to user preferences, health conditions, and environmental factors, so as to adapt to different environmental conditions, improve the experience comfort, and effectively save energy consumption, achieving the technical effect of efficient management of the toilet. Description of the Drawings

[0015] Figure 1 It is a step flow chart of a multi-scenario mode management method for a controller of the present invention; Figure 2 It is a structural schematic diagram of a multi-scenario mode management system for a controller of the present invention.

[0016] Description of the Reference Numerals: High-frequency user determination module 11, temperature adaptation table construction module 12, usage probability prediction module 13, temperature adjustment module 14. Detailed Embodiments

[0017] The present invention provides a multi-scenario mode management method and system for a controller, which solves the technical problem that traditional toilet seat temperature control methods often rely on fixed temperature settings and cannot be adaptively adjusted according to user preferences, health conditions, and environmental factors, resulting in insufficient comfort and energy conservation. By utilizing the Internet of Things and combining user behavior analysis and real-time data prediction, personalized temperature adjustment services can be provided for users according to user preferences, health conditions, and environmental factors, thereby adapting to different environmental conditions, enhancing the comfort of the experience, and effectively saving energy consumption, achieving the technical effect of efficient management of the toilet.

[0018] Next, the technical solutions in the present invention will 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, rather than all the embodiments of the present invention. It should be understood that the present invention is not limited by the exemplary embodiments described herein. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention. Additionally, it should be noted that for the sake of description, only the parts related to the present invention are shown in the drawings rather than all of them.

[0019] Embodiment 1, please refer to the attached Figure 1 , the present invention provides a multi-scenario mode management method for a controller, which is applied to a multi-scenario mode management system for a controller, and specifically includes the following steps: S10: Read the usage records of the toilet seat within a preset historical time range through the Internet of Things, and analyze and determine a number of high-frequency users.

[0020] Furthermore, step S10 of the present invention further includes: 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 data set, where each usage record data includes weight data; S12: Configure a weight fluctuation range, and according to the usage record data set, count the usage frequencies of weight deviations within the same weight fluctuation range to obtain a plurality of usage frequencies; S13: Set the users with usage frequencies greater than a preset frequency threshold as high-frequency users, and determine a number of high-frequency users, where each high-frequency user is marked with a user weight range.

[0021] Specifically, read the usage records of the toilet seat within a preset historical time range (such as within the most recent month, which can be set according to the scenario requirements) through the Internet of Things. The Internet of Things is used to collect various data of users using the toilet in real time, and these data include but are not limited to usage time, usage times, weight information, etc. Among them, each usage record data includes weight data. Each time a user uses the toilet, a sensor (such as a pressure sensor) will record the user's weight data and upload it to the cloud for storage through the Internet of Things platform.

[0022] Next, configure the weight fluctuation range to analyze the weight fluctuation of the user, which can be set according to the actual scenario. For example, set the weight fluctuation range to ±2 kg, that is, the weights fluctuating within this range are regarded as the same fluctuation range. Then, according to the usage record dataset, count the usage frequencies of weight deviations within the same weight fluctuation range, that is, calculate the usage frequencies of the user's weight data fluctuations within this range, and obtain multiple usage frequencies. For example, 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 it is identified that the user's weight range is 70 to 72 kg, and the usage frequency is 3 times.

[0023] Then configure a preset frequency threshold, which can be set according to the scenario. If the usage frequency of a certain user is greater than the frequency threshold, then this user is identified as a high-frequency user. Determine several high-frequency users. Among them, each high-frequency user has a weight range related to their weight fluctuation, and this range is determined according to the change range of the user's weight. For example, assume that the preset frequency threshold is 2, and the usage frequency of user A is 3 times, which is greater than the threshold, then user A is a high-frequency user, and the weight range is 70 kg to 72 kg; the usage frequency of user B is 1 time, which is lower than the threshold, so user B is not a high-frequency user, and there is no need to identify its weight range.

[0024] Through the above process, it is possible to analyze which users are high-frequency users based on the user's weight data and usage frequencies, and provide personalized temperature control services for them. This method can accurately identify users with higher requirements for temperature adjustment, providing effective support for subsequent intelligent toilet seat temperature adjustment.

[0025] S20: Collect the user characteristics of the several high-frequency users, and combine multiple room temperature ranges to construct several high-frequency user - toilet seat temperature adaptation tables.

[0026] Furthermore, step S20 of the present invention further includes: S21: Based on the user's weight range, query the family user health management data to obtain several high-frequency user characteristics; S22: Obtain the room temperature records within a preset historical time range, and 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 ranges.

[0027] Specifically, based on the user weight range, query the health management data of household users, that is, combine the health management data of household users to obtain more accurate user characteristics. These health management data include information such as the user's health status, age, body temperature, activity level, and living habits. These characteristic information helps to understand the user's physiological needs. Especially in the design of the toilet seat temperature control system, considering the user's health status can effectively improve the accuracy of personalized adjustment. For example, older users may prefer higher temperatures, etc. Obtain several high-frequency user characteristics (such as health status, age, body temperature, activity level, living habits, etc.). Through these health characteristics, a temperature control scheme more in line with the needs of each high-frequency user can be designed.

[0028] Next, obtain the room temperature records within a preset historical time range (such as within the last month). The room temperature can be collected by using Internet of Things devices (such as smart thermostats or sensors). The change of indoor temperature is closely related to the user's comfort. Especially when adjusting the temperature of the toilet seat, the influence of indoor temperature needs to be considered; then select the lowest room temperature and the highest room temperature within the preset historical time range to determine the room temperature fluctuation range. Then obtain the preset temperature step. The temperature step refers to the temperature difference of each small interval divided within the temperature range. For example, when the step is 1°C, the temperature range is divided into several 1°C small intervals; then, divide the room temperature fluctuation range according to the preset temperature step (such as 1°C) to determine multiple room temperature intervals. Each interval represents the adaptation logic of the toilet seat temperature control system at that temperature interval. When the indoor temperature fluctuates in different intervals, the seat temperature can be adaptively adjusted according to the user's weight characteristics, health status, etc.

[0029] S24: Conduct a search of life big data with the several high-frequency user characteristics and multiple room temperature intervals as constraints, and construct several high-frequency user-seat temperature adaptation tables.

[0030] Furthermore, step S24 of the present invention further includes: S241: Randomly select the first high-frequency user characteristic and the first room temperature interval to construct the first comparison condition; S242: Conduct a search of life big data with the first comparison condition as a constraint to obtain the first sample seat temperature set, and calculate the mean value to obtain the first sample temperature mean value; S243: Based on the first sample temperature mean value, calculate the deviation of each of the multiple first sample seat temperatures respectively, and set the first sample seat temperatures with temperature deviations less than the preset temperature error as the first qualified seat temperatures, and calculate the mean value of the multiple first qualified seat temperatures to obtain the first adapted seat temperature; S244: Analyze the multiple adapted seat temperatures of the first high-frequency user characteristic in sequence, combine with the mapping of multiple room temperature intervals to construct the first high-frequency user-seat temperature adaptation table, and add it to the several high-frequency user-seat temperature adaptation tables.

[0031] Specifically, randomly select any one of the several high-frequency user characteristics as the first high-frequency user characteristic, and randomly select any one of multiple room temperature ranges as the first room temperature range, and combine the first high-frequency user characteristic and the first room temperature range to obtain the first comparison condition, which is used as a constraint condition when retrieving historical data. This can ensure that the selected temperature data set matches the user's demand conditions and the ambient temperature. Then, with the first comparison condition as a constraint, conduct a search for life big data, that is, utilize life big data (such as historical user data of similar toilets, etc.) to retrieve a set of sample seat ring temperature sets related to the selected conditions (the first high-frequency user characteristic and the first room temperature range), and calculate the mean value of the obtained sample seat ring temperature data to obtain an average value representing the seat ring temperature under this condition, which is used as a reference value for subsequent deviation calculation to obtain the first sample temperature mean value.

[0032] Then, taking the first sample temperature mean value as a reference, calculate the deviation of each of the multiple first sample seat ring temperatures respectively to obtain multiple temperature differences; then, set a temperature error threshold, such as ±0.5°C. All temperatures with deviations less than this error will be considered qualified seat ring temperatures, and the first sample seat ring temperatures with temperature deviations less than the preset temperature error will be set as the first qualified seat ring temperatures to obtain multiple first qualified seat ring temperatures; finally, calculate the mean value of the multiple first qualified seat ring temperatures to obtain the first adapted seat ring temperature.

[0033] Next, sequentially analyze and obtain the multiple adapted seat ring temperatures of the first high-frequency user characteristic in multiple room temperature ranges; then, based on the mapping relationship between the room temperature range and the adapted seat ring temperature, construct a first high-frequency user-seat ring temperature adaptation table according to the multiple room temperature ranges and the multiple adapted seat ring temperatures. Then, use the same method as constructing the first high-frequency user-seat ring temperature adaptation table to sequentially construct several high-frequency user-seat ring temperature adaptation tables for several high-frequency user characteristics. By combining the user's weight characteristics, indoor temperature fluctuations, and historical temperature data, accurately calculate the personalized toilet seat ring temperature and establish an adaptation temperature table for each high-frequency user. This process ensures that the adaptive adjustment of the seat ring temperature can meet the comfort requirements of different users under different environmental conditions and improves the personalized adjustment effect of the intelligent toilet.

[0034] S30: Construct a usage probability predictor based on the usage record to predict the toilet usage probability and obtain multiple usage probabilities within a preset time zone.

[0035] Furthermore, step S30 of the present invention further includes: S31: Divide a preset time period into multiple time segments according to a preset time step length, where the preset time period is 24 hours; S32: According to the usage records, count the usage frequencies of several high-frequency users in each time segment within a preset historical time range, set it as the usage probability, and obtain multiple time segments and multiple usage probabilities for each high-frequency user; S33: Establish a mapping relationship among high-frequency users, multiple time segments, and multiple usage probabilities, and construct a usage probability predictor by combining several high-frequency users according to the mapping relationship.

[0036] Specifically, divide a preset time period into multiple time segments according to a preset time step length, where the preset time period is 24 hours, that is, from the start to the end of a day. According to the set time step length, divide 24 hours into multiple time segments. For example, assuming the time step length is 1 hour, then a day will be divided into 24 time segments (00:00 to 01:00, 01:00 to 02:00,..., 23:00 to 24:00). Then, according to the usage records, count the usage frequencies of several high-frequency users in each time segment within a preset historical time range, that is, the number of times each high-frequency user uses the toilet in that time segment, and set the ratio of the toilet usage times to the total number of time segments as the usage probability. The usage probability represents the possibility of the user using the toilet in a certain time segment, and obtain multiple time segments and multiple usage probabilities for each high-frequency user.

[0037] Then, organize and model the relationship among high-frequency users, time segments, and usage probabilities. For example, the usage probability of user A in the time segment from 07:00 to 08:00 is 80%, while the usage probability of user B in this time segment is 30%. By organizing the usage probabilities of each high-frequency user in different time periods, construct a usage probability predictor. This predictor can predict the usage probability of a user in a future time segment based on the user's characteristics, thereby providing an accurate adjustment basis for the temperature control of the toilet seat ring. 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 in a specific time segment.

[0038] Furthermore, step S30 of the present invention further includes: S34: Obtain the lock opening and closing records within a preset time zone through an intelligent door lock; S35: According to the lock opening and closing records, determine whether the several high-frequency users are at home, and determine multiple high-frequency users who are at home.

[0039] Specifically, the intelligent door lock connected through the Internet of Things obtains the door opening and closing records, which reflect the opening and closing states of the door and usually include the timestamps of each unlocking and locking. The opening and closing times recorded by the intelligent door lock can be used as the basis for judging whether the user enters or leaves the home. Then, the door opening and closing records within a preset time zone (for example, from 7:00 to 8:00 in the morning) are obtained through the intelligent door lock, and the user's behavior of entering and leaving the home can be inferred from these records. Then, based on the door opening and closing records, it is judged whether the several high-frequency users are at home. For example, if a user does not unlock or lock the door within a preset period, it is inferred that the user is not at home; if the user unlocks and enters the home at a certain time, it is judged that the user is at home, and multiple high-frequency users at home are determined. By combining the switch records obtained through the intelligent door lock with the behavior data of high-frequency users, it is possible to more accurately judge whether each high-frequency user is at home. This information can not only help improve the adaptive temperature control accuracy of the toilet seat ring, but also ensure that the temperature adjustment matches the actual needs of the user.

[0040] Further, step S30 of the present invention further includes: S36: Using the usage probability predictor, predict the toilet usage probability of the multiple high-frequency users at home within a preset time zone, and output multiple usage probabilities.

[0041] Specifically, then, using the usage probability predictor, predict the toilet usage probability of the multiple high-frequency users at home within a preset time zone, that is, match and obtain the toilet usage probabilities of the multiple high-frequency users at home within the preset time zone to obtain multiple usage probabilities.

[0042] S40: Based on the several high-frequency user-seat ring temperature adaptation tables and the multiple usage probabilities, and in combination with the indoor temperature within a preset time zone, perform adaptive temperature adjustment of the toilet seat ring.

[0043] Further, step S40 of the present invention further includes: S41: Monitor and obtain the indoor temperature within a preset time zone through the Internet of Things; S42: Using the several high-frequency user-seat ring temperature adaptation tables, match and obtain multiple adapted seat ring temperatures according to the indoor temperature and the multiple high-frequency users at home; S43: Judge the multiple usage probabilities according to the first probability threshold. If the multiple usage probabilities are all less than or equal to the first probability threshold, set the toilet seat ring to the unheated state within the preset time zone, where the first probability threshold is less than or equal to 10%.

[0044] Specifically, the indoor temperature within a preset time zone is monitored and obtained through the Internet of Things (such as temperature sensors). For example, the temperature in the user's home is 21°C from 7:00 to 9:00 and 23°C from 19:00 to 21:00. Then, using the several high-frequency user-seat ring temperature adaptation tables, multiple adapted seat ring temperatures are obtained by matching the indoor temperature with multiple high-frequency users at home. Next, a first probability threshold is obtained, where the first probability threshold is less than or equal to 10%, such as 8%. The multiple usage probabilities are judged according to the first probability threshold. If all the multiple usage probabilities are less than or equal to the first probability threshold, it indicates that there is not enough usage demand during this time period, and the toilet seat ring is set to the unheated state within the preset time zone to achieve the effect of energy conservation. That is, if it is predicted that the usage probability during certain time periods is very low, the system automatically switches to the energy-saving mode, which not only saves energy for users but also improves the intelligent management of the system.

[0045] S44: If the number of usage probabilities greater than the first probability threshold is not 0, judge whether the number of usage probabilities greater than the second probability threshold is 0. If it is not 0, the toilet seat ring will be set to the heated state within the preset time zone, and the heating temperature will be the adapted seat ring temperature of the high-frequency user at home with the maximum usage probability. Among them, the second probability threshold is greater than or equal to 60%, and the heating state is regular heating, including a preset heating time interval.

[0046] Furthermore, step S44 of the present invention further includes: S441: Obtain the number of users at home according to the switch lock records within the preset time zone, where the users at home include high-frequency users and non-high-frequency users; S442: Set the reciprocal of the ratio of the number of users at home to the number of high-frequency users as the adjustment coefficient, and multiply it by the initial heating time interval to obtain the preset heating time interval.

[0047] Specifically, first, check whether the number of high-frequency users with a usage probability greater than the first probability threshold (e.g., 10%) in the preset time zone is 0. If there is at least one high-frequency user with a usage probability greater than 10% during this period, then continue to the next judgment; then, judge whether the number of users with a usage probability greater than the second probability threshold is 0, where the second probability threshold is greater than or equal to 60%, e.g., 65%. If it is not 0, set the toilet seat ring to the heating state in the preset time zone, and the heating temperature of the seat ring will be set according to the adapted seat ring temperature of the high-frequency user at home with the maximum usage probability, that is, select the user with the highest usage probability (and at home) during this period, and use the adapted seat ring temperature matched for him as the heating temperature; among them, the heating state is regular heating, that is, the seat ring will be heated regularly at a preset time interval. For example, set the heating time interval to heat once every 30 minutes to ensure that users can continuously enjoy a comfortable temperature during the high-frequency usage period. This intelligent adjustment can automatically activate the heating function according to user needs, ensuring that users can enjoy a comfortable seat ring temperature when needed, while avoiding wasting energy.

[0048] Among them, the method for setting the preset heating time interval is as follows. First, according to the switch lock records in the preset time zone, obtain the number of users at home, where the users at home include high-frequency users and non-high-frequency users (such as guests, etc.); then, set the reciprocal of the ratio of the number of users at home to the number of high-frequency users as the adjustment coefficient. For example, if the number of users at home is 6 and the number of high-frequency users is 3, indicating that the number of people at home is relatively large at the current stage, then the adjustment coefficient is the reciprocal of 6 / 3, which is equal to 1 / 2; then multiply the adjustment coefficient by the initial heating time interval, and use the product of the two as the preset heating time interval. This method can flexibly adjust the heating frequency according to the proportion of high-frequency users among the users at home, ensuring that the heating system can work effectively according to actual needs.

[0049] 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 adapted seat ring temperatures of the 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, obtain multiple adapted seat ring temperatures, calculate the average adapted temperature, and set the toilet seat ring to the heating state in the preset time zone, and the heating temperature is the average adapted temperature.

[0050] Specifically, if the number of usage probabilities greater than the first probability threshold is not zero, and the number of usage probabilities greater than the second probability threshold is zero, then obtain the adapted seat ring temperatures of the high-frequency users at home whose usage probabilities are greater than the first probability threshold and less than or equal to the second probability threshold, and obtain multiple adapted seat ring temperatures; further calculate the average value of the multiple adapted seat ring temperatures to obtain the average adapted temperature, and set the toilet seat ring to the heating state within the preset time period, and the heating temperature is the average adapted temperature, that is, if there are multiple eligible high-frequency users within the preset time period, then start the seat ring heating according to the average adapted temperature thereof, and ensure that the heating temperature meets the temperature control requirements of these users.

[0051] In summary, the multi-scenario mode management method of a controller provided by the present invention has the following technical effects: By reading the usage records of the toilet seat cushion within a preset historical time range, analyze and determine a number of high-frequency users; then collect the user characteristics of the number of high-frequency users, and combine multiple room temperature ranges to construct a number of high-frequency user-seat ring temperature adaptation tables; on the other hand, construct a usage probability predictor according to the usage records to predict the toilet usage probability, and obtain multiple usage probabilities within the preset time period; then based on the number of high-frequency user-seat ring temperature adaptation tables and multiple usage probabilities, combined with the indoor temperature within the preset time period, perform temperature adaptive adjustment of the toilet seat ring, that is to say, by using the Internet of Things, combining user behavior analysis and real-time data prediction, personalized temperature adjustment services can be provided for users according to user preferences, health conditions and environmental factors, so as to adapt to different environmental conditions, improve the experience comfort, and effectively save energy consumption, achieving the technical effect of efficient management of the toilet.

[0052] Embodiment 2, based on the same inventive concept as the multi-scenario mode management method of a controller in the foregoing embodiment, the present invention also provides a multi-scenario mode management system of a controller. Please refer to the attached Figure 2 , including: a high-frequency user determination module 11, configured to read the usage records of the toilet seat cushion within a preset historical time range through the Internet of Things, and analyze and determine a number of high-frequency users; a temperature adaptation table construction module 12, configured to collect the user characteristics of the number of high-frequency users, and combine multiple room temperature ranges to construct a number of high-frequency user-seat ring temperature adaptation tables; a usage probability prediction module 13, configured to construct a usage probability predictor according to the usage records to predict the toilet usage probability, and obtain multiple usage probabilities within the preset time period; a temperature adjustment module 14, configured to perform temperature adaptive adjustment of the toilet seat ring based on the number of high-frequency user-seat ring temperature adaptation tables and multiple usage probabilities, combined with the indoor temperature within the preset time period.

[0053] Further, the multi-scenario mode management system of the controller is also used for: reading the usage records of the toilet seat within a preset historical time range through the Internet of Things to obtain a usage record data set, where each usage record data includes weight data; configuring a weight fluctuation range, and according to the usage record data set, counting the usage frequencies of weight deviations within the same weight fluctuation range to obtain multiple usage frequencies; setting users with usage frequencies greater than a preset frequency threshold as high-frequency users, and determining a number of high-frequency users, where each high-frequency user is marked with a user weight range.

[0054] Further, the multi-scenario mode management system of the controller is also used for: querying the health management data of family users based on the user weight range to obtain a number of high-frequency user characteristics; obtaining the room temperature records within a preset historical time range and determining the room temperature fluctuation range; dividing the room temperature fluctuation range according to a preset temperature step size to determine multiple room temperature ranges; performing life big data retrieval with the number of high-frequency user characteristics and multiple room temperature ranges as constraints to construct a number of high-frequency user-seat ring temperature adaptation tables.

[0055] Further, the multi-scenario mode management system of the controller is also used for: randomly selecting a first high-frequency user characteristic and a first room temperature range to construct a first comparison condition; performing life big data retrieval with the first comparison condition as a constraint to obtain a first sample seat ring temperature set, and calculating the mean value to obtain a first sample temperature mean; taking the first sample temperature mean as a benchmark, calculating the deviation of each of the multiple first sample seat ring temperatures, and setting the first sample seat ring temperatures with temperature deviations less than a preset temperature error as first qualified seat ring temperatures, and calculating the mean value of the multiple first qualified seat ring temperatures to obtain a first adapted seat ring temperature; sequentially analyzing the multiple adapted seat ring temperatures of the first high-frequency user characteristic, and constructing a first high-frequency user-seat ring temperature adaptation table by combining with multiple room temperature ranges and adding it to the number of high-frequency user-seat ring temperature adaptation tables.

[0056] Further, the multi-scenario mode management system of the controller is also used for: dividing a preset time period according to a preset time step size to obtain multiple time periods, where the preset time period is 24 hours; according to the usage records, counting the usage frequencies of a number of high-frequency users in each time period within a preset historical time range, which are set as usage probabilities, to obtain multiple time periods and multiple usage probabilities for each high-frequency user; establishing a mapping relationship among high-frequency users, multiple time periods, and multiple usage probabilities, and constructing a usage probability predictor based on the mapping relationship and in combination with a number of high-frequency users.

[0057] Furthermore, the multi-scenario mode management system of the described controller is also used for: obtaining the lock-unlock records within a preset time zone through the intelligent door lock; judging whether the several high-frequency users are at home according to the lock-unlock records, and determining multiple high-frequency users at home.

[0058] Furthermore, the multi-scenario mode management system of the described controller is also used for: using the usage probability predictor to predict the toilet usage probability of the multiple high-frequency users at home within a preset time zone, and outputting multiple usage probabilities.

[0059] Furthermore, the multi-scenario mode management system of the described controller is also used for: obtaining the indoor temperature within a preset time zone through Internet of Things monitoring; using the several high-frequency user-seat ring temperature adaptation tables, matching multiple adapted seat ring temperatures according to the indoor temperature and the 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, the toilet seat ring is set to an unheated state within the preset time zone, where 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, judge whether the number of usage probabilities greater than a second probability threshold is 0. If not 0, the toilet seat ring is set to a heated state within the preset time zone, and the heating temperature is the adapted seat ring temperature of the high-frequency user with the maximum usage probability, where 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 usage probabilities greater than the first probability threshold is not 0, and the number of usage probabilities greater than the second probability threshold is 0, then obtain the adapted seat ring temperatures of the high-frequency users at home whose usage probabilities are greater than the first probability threshold and less than or equal to the second probability threshold, obtain multiple adapted seat ring temperatures, calculate the average adapted temperature, and set the toilet seat ring to a heated state within the preset time zone, and the heating temperature is the average adapted temperature.

[0060] Furthermore, the multi-scenario mode management system of the described controller is also used for: obtaining the number of users at home according to the lock-unlock records within a preset time zone, where 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, and multiplying it by the initial heating time interval to obtain the preset heating time interval.

[0061] In this specification, the various embodiments are described in a progressive manner. The key point of each embodiment is to illustrate the differences from other embodiments. The multi-scenario mode management method and specific examples of a controller in the foregoing Embodiment 1 are equally applicable to the multi-scenario mode management system of a controller in this embodiment. Through the detailed description of the multi-scenario mode management method of a controller above, those skilled in the art can clearly understand the multi-scenario mode management system of a controller in this embodiment. Therefore, for the sake of simplicity of the specification, it will not be elaborated here. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple. For the relevant parts, reference may be made to the description in the method section.

[0062] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0063] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the present invention and its equivalent technologies, the present invention is also intended to include these changes and modifications.

Claims

1. A multi-scenario mode management method for a controller, characterized in that The method includes: Reading the usage records of the toilet seat within a preset historical time range through the Internet of Things, and analyzing and determining a number of high-frequency users; Collecting the user characteristics of the number of high-frequency users, and constructing a number of 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; Based on the number of high-frequency user-seat temperature adaptation tables and multiple usage probabilities, and combining the indoor temperature within a preset time zone, perform temperature adaptive adjustment of the toilet seat.

2. The multi-scenario mode management method of a controller according to claim 1, wherein, Reading the usage records of the toilet seat within a preset historical time range through the Internet of Things, and analyzing and determining a number of high-frequency users, including: Reading the usage records of the toilet seat within a preset historical time range through the Internet of Things to obtain a usage record data set, where each usage record data includes weight data; Configuring a weight fluctuation range, and according to the usage record data set, counting the usage frequencies when the weight deviation is within the same weight fluctuation range to obtain multiple usage frequencies; Setting users with usage frequencies greater than a preset frequency threshold as high-frequency users, and determining a number of high-frequency users, where each high-frequency user is marked with a user weight range.

3. The multi-scenario mode management method of a controller according to claim 2, wherein Collecting the user characteristics of the number of high-frequency users, and constructing a number of high-frequency user-seat temperature adaptation tables in combination with multiple room temperature ranges, including: Based on the user weight range, querying the family user health management data to obtain a number of high-frequency user characteristics; Obtaining the room temperature records within a preset historical time range, and determining the room temperature fluctuation range; Dividing the room temperature fluctuation range according to a preset temperature step size to determine multiple room temperature ranges; Performing life big data retrieval with the number of high-frequency user characteristics and multiple room temperature ranges as constraints, and constructing a number of high-frequency user-seat temperature adaptation tables.

4. The multi-scenario mode management method of a controller according to claim 3, characterized in that Performing life big data retrieval with the number of high-frequency user characteristics and multiple room temperature ranges as constraints, and constructing a number of high-frequency user-seat temperature adaptation tables, including: Randomly selecting the first high-frequency user characteristic and the first room temperature range to construct the first comparison condition; Performing life big data retrieval with the first comparison condition as a constraint to obtain the first sample seat temperature set, and calculating the mean value to obtain the first sample temperature mean; Based on the first sample temperature mean, calculating the deviation of multiple first sample seat temperatures respectively, and setting the first sample seat temperatures with temperature deviations less than the preset temperature error as the first qualified seat temperatures, and calculating the mean value of multiple first qualified seat temperatures to obtain the first adapted seat temperature; Analyzing and obtaining the adapted seat temperatures of multiple first high-frequency user characteristics in sequence, mapping and constructing the first high-frequency user-seat temperature adaptation table in combination with multiple room temperature ranges, and adding it to the number of high-frequency user-seat temperature adaptation tables.

5. A multi-scenario mode management method for a controller according to claim 1, characterized in that, Constructing a usage probability predictor based on the usage records, including: Dividing a preset time period according to a preset time step size to obtain multiple time periods, where the preset time period is 24 hours; According to the usage record, count the usage frequencies of several high-frequency users in each period within a preset historical time range, which is set as the usage probability, and obtain multiple periods and multiple usage probabilities for each high-frequency user; Establish a mapping relationship among high-frequency users, multiple periods, and multiple usage probabilities, and construct a usage probability predictor based on the mapping relationship and several high-frequency users.

6. The multi-scenario mode management method of a controller according to claim 5, characterized in that, Before predicting the toilet usage probability, it further includes: Obtain the lock opening and closing records within a preset time zone through the smart door lock; According to the lock opening and closing records, determine whether the several high-frequency users are at home, and determine multiple high-frequency users at home.

7. The multi-scenario mode management method of a controller according to claim 6, characterized in that Use the usage probability predictor to predict the toilet usage probability of the multiple high-frequency users at home within the preset time zone, and output multiple usage probabilities.

8. The multi-scenario mode management method of a controller according to claim 6, characterized in that, Based on the several high-frequency user-seat ring temperature adaptation tables and multiple usage probabilities, combined with the indoor temperature within the preset time zone, perform temperature adaptive adjustment of the toilet seat ring, including: Obtain the indoor temperature within the preset time zone through Internet of Things monitoring; Use the several high-frequency user-seat ring temperature adaptation tables to match and obtain multiple adapted seat ring temperatures according to the indoor temperature and multiple high-frequency users at home; Judge the multiple usage probabilities according to the first probability threshold. If the multiple usage probabilities are all less than or equal to the first probability threshold, set the toilet seat ring to the unheated state within the preset time zone, where 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, judge whether the number of usage probabilities greater than the second probability threshold is 0. If it is not 0, set the toilet seat ring to the heated state within the preset time zone, and the heating temperature is the adapted seat ring temperature of the high-frequency user with the maximum usage probability, where the second probability threshold is greater than or equal to 60%, and the heating state is regular heating, including a preset heating time interval; If the number of usage probabilities greater than the first probability threshold is not 0, and the number of usage probabilities greater than the second probability threshold is 0, then obtain the adapted seat ring temperatures of the high-frequency users at home whose usage probabilities are greater than the first probability threshold and less than or equal to the second probability threshold, obtain multiple adapted seat ring temperatures, calculate the average adapted temperature, and set the toilet seat ring to the heated state within the preset time zone, and the heating temperature is the average adapted temperature.

9. The multi-scenario mode management method of a controller according to claim 8, characterized in that The setting method of the preset heating time interval includes: According to the lock opening and closing records within the preset time zone, obtain the number of users at home, where the users at home include high-frequency users and non-high-frequency users; Set the reciprocal of the ratio of the number of users at home to the number of high-frequency users as the adjustment coefficient, and multiply it by the initial heating time interval to obtain the preset heating time interval.

10. A multi-scenario modal management system for a controller, characterized in that, The steps for implementing the multi-scenario modal management method of a controller according to any one of claims 1 to 9 include: A high-frequency user determination module, configured to read the usage records of the toilet seat cushion within a preset historical time range through the Internet of Things, and analyze and determine several high-frequency users; A temperature adaptation table construction module, configured to collect the 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; A usage probability prediction module is used to construct a usage probability predictor based on the usage records for predicting the toilet usage probability and obtain multiple usage probabilities within a preset time zone; A temperature adjustment module is used to perform adaptive temperature adjustment of the toilet seat ring based on the plurality of high-frequency user-seat ring temperature adaptation tables and the multiple usage probabilities, in combination with the indoor temperature within a preset time zone.

Images