Mobile terminal-based intelligent wet wipe machine temperature and humidity cooperative regulation system

Abstract

This invention provides a mobile-based intelligent wet wipe dispenser temperature and humidity coordinated adjustment system, belonging to the field of temperature and humidity control; it solves the problem of low efficiency in heating and humidifying dry wipes; the specific system is as follows: User behavior analysis module: constructs prediction model A and prediction model B for each user; Temperature and humidity adjustment module: based on prediction model A and prediction model B, determines the quantity of dry and wet wipes used by the user, as well as the temperature and humidity of the wet wipes, and generates heating and humidification instructions according to different heating and humidification modes of the intelligent wet wipe dispenser; generates heat preservation instructions, and obtains real-time default instructions; Remote control module: detects whether the user has issued new instructions; if so, adjusts the heating, humidification, and heat preservation modes of the intelligent wet wipe dispenser according to the newly issued instructions; if not, no action is taken; This invention improves the efficiency of heating and humidifying dry wipes by analyzing users' dry and wet wipe usage habits and adjusting the working mode of the intelligent wet wipe dispenser.

Description

Technical Field

[0001] This invention relates to a mobile-based intelligent wet wipe dispenser temperature and humidity coordinated adjustment system, and pertains to the field of temperature and humidity regulation. Background Technology

[0002] Existing systems for temperature and humidity control in wet wipe dispensers have the following shortcomings: Lack of personalized service: Some existing wet wipe machines can only provide standardized services. Regardless of the user's usage habits and preferences, the machine operates in a fixed mode, such as fixed wet wipe temperature and humidity, and fixed supply time. This makes it impossible for many users to obtain a user experience that meets their needs.

[0003] Inaccurate temperature and humidity control: Some existing wet wipe dispensers do not control the heating and humidification process of wet wipes accurately enough, and cannot adjust the temperature and humidity in a timely manner according to the user's real-time needs and environmental changes, resulting in unstable temperature and humidity of the wet wipes.

[0004] Lack of intelligent heat preservation: Traditional smart wet wipe dispensers lack intelligent heat preservation design. When the user leaves home, the machine cannot adjust according to the user's status. After the wet wipes are left for a period of time, the temperature and humidity will gradually change. When the user returns home, they need to be reheated and humidified before they can be used. This not only wastes the user's time, but also increases energy consumption. Summary of the Invention

[0005] In view of the shortcomings of existing technologies, the purpose of this invention is to provide a mobile-based intelligent wet wipe dispenser temperature and humidity coordinated adjustment system, which aims to solve the problem of low efficiency in heating and humidifying dry wipes.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a mobile-based intelligent wet wipe dispenser temperature and humidity coordinated adjustment system comprising: User behavior analysis module: used to obtain the number of dry and wet wipes used by each user in different time periods, the temperature and humidity of the wet wipes when each user uses wet wipes in different time periods, as well as the ambient temperature and humidity, and to build a prediction model A for the number of dry and wet wipes used by users in each time period and a prediction model B for the temperature and humidity of the wet wipes used by users in each time period. Temperature and humidity control module: Used to obtain the current time and determine the number of dry and wet wipes used by the user, as well as the temperature and humidity of the wet wipes, based on prediction model A and prediction model B. It generates heating and humidification instructions according to the different heating and humidification methods of the smart wet wipe machine; it obtains the user's real-time location to determine whether the user is returning home; if the user is returning home, it estimates the return time and generates a keep-warm instruction; if the user is not returning home, it does not process the request; it sets the heating and humidification instructions and keep-warm instructions corresponding to each user at the current time as the real-time default instructions for each user. Remote control module: Used to detect whether the user has issued new heating and humidification commands and new heat preservation commands; if so, the heating, humidification and heat preservation modes of the smart wet wipes machine are adjusted according to the user's newly issued heating and humidification commands and heat preservation commands; if not, the smart wet wipes machine operates with the real-time default commands.

[0007] Furthermore, the steps for constructing prediction model A are as follows: Obtain the original time series and construct the time lag term Y. （t-1） To Y （t-h） ; For the lagged term Y （t-1） To Y （t-h） Standardize the data in the time trend term, and then introduce the lag term Y. （t-1） To Y （t-h） The characteristic coefficient β （1） ~β （h） and the time trend coefficient β （h+1） , Based on the Poisson distribution, a predictive model is constructed to predict the number of dry towels used by users in each time period: ; Among them, u （t） This represents the user's expected dry towel usage at time t; tr （t） The time trend term at time t; β （0） Indicates the intercept; β （0） ~β （h+1） As model parameters, and given initial values, the log-likelihood function of the Poisson distribution and the L2 regularization term are used as the loss function of the prediction model; β is calculated based on the initial values ​​of the model parameters. （0） ~β （h+1） The gradient of the loss function is calculated using the mini-batch gradient descent algorithm, and the learning rate is adjusted along the gradient descent direction during model parameter optimization until the model parameters converge or the model parameters reach the maximum number of iterations, thus obtaining the prediction model. Construct a prediction model to predict the number of wet wipes used by each user in each time period, and obtain prediction model A.

[0008] Furthermore, the steps for constructing prediction model B are as follows: Let the vector of wet wipes for a user during time period t be Ob. （t） The environment vector is En. （t） ; Let Hb be the vector of wet wipes corresponding to a time interval t with a lag of 1 to h hours. （t-1） ~Hb （t-h） Construct the initial model b （1） : ; Among them, la （1） ~la （h） , respectively representing the wet wipe vector Hb （t-1） ~Hb （t-h） The observed coefficients, where vi represents the environmental coefficients and Co represents the constant term; Substitute the wet wipe vector and environment vector corresponding to each user into the initial model b. （1） In the process, the values ​​of the observation coefficients and the environmental coefficients are calculated to obtain the initial model b. （2） ; Based on the initial model b （2） In the process, the estimated wet wipe vector for each user in each time period is calculated; The error vector is obtained by subtracting the estimated wet wipe vector from the actual wet wipe vector for each user. Determine whether the mean vector of the error vector is zero; If the value is zero, then check if the error vector is white noise; if it is white noise, then in the initial model b （2） In the middle, add the error vector to obtain the prediction model B; if it is not white noise, adjust the observation coefficient and the environmental coefficient. If it is not zero, calculate the difference vector between the error vector and the mean vector for each user, and determine whether the autocorrelation coefficient between the difference vectors is zero. If it is zero, then in the initial model b （2） Subtract the mean vector matrix from the original constant term Co, and then add the error vector to obtain the prediction model B; If it is not zero, then adjust the observation coefficient and the environmental coefficient.

[0009] Furthermore, the steps for generating the heating and humidification command are as follows: Predict the dry wipe usage and wet wipe usage for each user based on prediction model A corresponding to each user; If a user's wipe usage is zero in the current time period, no action will be taken; If a user's current wipe usage is not zero, a heating and humidification command will be generated based on the different heating and humidification methods of the smart wipe dispenser. Obtain the weight m of the dry towel （p） And specific heat capacity Cp; assuming the ambient temperature of the residence at the current time is T. （env） The ambient humidity is H （env） The predicted usage of wet wipes is n. （wet） ; Scenario 1: The smart wet wipe dispenser uses a hot water spray humidification and heating mode; Scenario 2: The smart wet wipe dispenser uses a hot water atomization humidification and heating mode.

[0010] Furthermore, the processing steps for scenario one are as follows: Given the spray flow rate (mw) of the smart wet wipe dispenser per unit time, construct the differential equation A for the temperature change of the dry wipes at time t: ; Among them, ka （1） T represents （p） The coefficient, ka （2） Represents a constant term; Calculate the stable temperature T of the wet wipes （equ） : ; Get the temperature Tp of the wet wipes before heating. （ini） T （p） The initial state is Tp （ini） The final state is T （equ） Construct the temperature change function T of the wet wipes （p） (t): ; Let η be the water absorption efficiency of the dry towel when sprayed with hot water. （s） The amount of water absorbed by the dry towel at time t is W. （in） ; Obtain the evaporation coefficient of water, and the amount of water that evaporates naturally due to ambient temperature at time t is W. （evap） Because the natural evaporation rate of hot water is W （hot） ; After a dry towel is humidified, during the process of temperature changing from the initial state to the final state, construct the humidity change function H. （hum） (t): ; Get the response time ti of the smart wet wipe dispenser （res） Let the user's target temperature be T. （tar） The predetermined target humidity is H （tar） ; First, heat the water in the smart wet wipe dispenser to T temperature. （tar） Celsius; Obtain the ambient temperature of the user's residence, and then... （res） As t, T （tar） As T （equ） Substitute the change function T in reverse （p） In (t), calculate the ideal temperature t of the dry towel before heating. （ea） ; Compare the temperatures t of the dry towel before heating. （pra） With t （ea） The size of the value determines the generation of different heating and humidification commands.

[0011] Furthermore, the step of generating the heating and humidification command is as follows: Branch a: If t（pra） ≥t （ea） Calculate the heating time tr of the dry towel （1） ; Obtain the ambient humidity of the user's residence and calculate the spray temperature of the dry towel at a flow rate of mw. （1） After the humidity h （r1） ; Determine h （r1） With H （tar） Is the difference within 3%? Branch a1: If it is within 3%, the heating and humidification command is: [T] （tar） Hot water at a temperature of [temperature value], flow rate of [flow rate value], to dry the towel using [method / method （1） A spray of water every second; Branch a2: If it is not within 3%, then calculate and compare function T. （p） (t) and function H （hum） (t) The magnitude of the first derivative; Branch a21: If function T （p） The first derivative of (t) is relatively small, so the hot water spray flow rate (mw) for calculating the dry towel is calculated. （r1） ; The heating and humidification command is: (T) （tar） hot water, mw （r1） The flow rate, to ti （res） A spray of water every second; Branch a22: If function H （hum） The first derivative of (t) is relatively small, which is useful for calculating the hot water temperature Tw. （r1） ; The heating and humidification command is: Tw （r1） hot water, mw （r1） The flow rate, to ti （res） A spray of water every second; Branch b: If t （pra） <t （ea） Then, the temperature during the hot water spray heating of the dry towel is calculated from t. （pra） Change to t （ea） time ti （r2） ; According to time ti （r2） Calculate the new flow mw （r2） ; Obtain the ambient temperature of the user's residence and calculate the hot water temperature Tw when heating with a dry towel. （r2） ; The heating and humidification command is: Tw （r2） hot water, mw （r2） The flow rate, to ti （res） A spray every second.

[0012] Furthermore, the processing steps for scenario two are as follows: Calculate the equivalent atomization flow rate md, and construct the differential equation B for the temperature change of the dry towel at time t: ; Among them, kb （1） T represents （p） The new coefficient, kb （2） Indicates the new constant term; Calculate the stable temperature of the wet wipes To （equ） : ; Get the temperature Tp of the wet wipes before heating. （ini） T （p） The initial state is Tp （ini） The final state is T （equ） Construct the temperature change function T of the wet wipes （o） (t): ; Calculate the amount of water absorbed by the dry towel at time t as Wo. （in） The amount of wet wipes that evaporates naturally due to ambient temperature at time t is Wo. （evap） Since water has no temperature, its natural evaporation rate is Wo. （hot） ; Construct the humidity change function H （o） (t): ; Branch c: Do not change the value of mb, but set T. （tar） As T （equ） , will ti （res） Substituting t into the change function H （o） (t), calculate the humidity h of the dry towel after it has been humidified by hot water mist. （r3） ; Determine h （r3） With H （tar） Is the difference within 3%? If the humidity is below 3%, the heating and humidification command for the smart wet wipe dispenser is: to use 100-degree Celsius hot water, without changing the water flow rate of the atomizer, to humidify the dry wipes in the form of water mist. （res） Water mist humidification and heating in seconds; If it is not within 3%, then compare h. （r3） With H （tar） Different heating and humidification instructions are formulated based on the size of the object.

[0013] Furthermore, the steps for formulating the heating and humidification command are as follows: Branch c1: If h （r3） >H （tar） Then calculate the heating time ti （r3）; Calculate the temperature To after heating and humidifying the dry towel. （r3） ; If To （r3） ≥T （tar） The heating and humidification command is as follows: use 100-degree hot water, without changing the water flow rate of the atomizer, to humidify the dry towel. （r3） Water mist humidification and heating in seconds; If To （r3） <T （tar） Then calculate the equivalent atomization flow rate md （r3） ; md （r3） Substitute the equivalent atomizing flow rate md in reverse to calculate the new effluent flow rate Q of the atomizer. （r3） ; The heating and humidification instructions are: use 100-degree hot water, and the atomizer should operate at Q... （r3） The water flow rate is used to tidy up the dry towels. （res） Water mist humidification and heating in seconds; Branch c2: If h （r3） <H （tar） Then calculate the heating time ti （r4） Equivalent atomization flow rate md （r4） ; md （r4） Substitute the equivalent atomizing flow rate md in reverse to calculate the new effluent flow rate Q of the atomizer. （r4） ; The heating and humidification instructions are: use 100-degree hot water, and the atomizer should operate at Q... （r4） The water flow rate is used to tidy up the dry towels. （res） Water mist humidification and heating in seconds.

[0014] Compared with the prior art, the beneficial effects of the present invention are: Personalized service: Through the user behavior analysis module, the smart wet wipe machine can record and analyze in detail the amount of dry and wet wipes used by each user at different times, as well as information such as preferred temperature and humidity. Based on this data, the machine can build a personalized usage model for the user, thereby automatically providing wet wipes that meet the user's preferences when needed.

[0015] More precise temperature and humidity control: This invention can accurately control the heating and humidification process of wet wipes based on prediction models and real-time user needs; it can adjust the heating and humidification commands in real time according to user requirements to ensure that the temperature and humidity of the wet wipes are always kept within the range set by the user, thus avoiding the problem of wet wipes deterioration or damage caused by improper temperature and humidity.

[0016] Insulation Design: This invention combines user behavior analysis and real-time location judgment, enabling the smart wet wipe dispenser to intelligently generate insulation commands; when the user leaves home, the machine can automatically enter insulation mode to keep the temperature and humidity of the wet wipes constant, avoiding the need for reheating and humidification when the user returns home. Attached Figure Description

[0017] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a schematic diagram of the system of the present invention; Figure 2 This is a schematic diagram of a 3D model of the intelligent wet wipe dispenser of the present invention; Figure 3 This is a schematic diagram of the system function flow of the present invention. Detailed Implementation

[0018] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] Please see Figure 1 and Figure 2 The mobile-based smart wet wipe dispenser temperature and humidity control system includes: User behavior analysis module: used to obtain the number of dry and wet wipes used by each user in different time periods, the temperature and humidity of the wet wipes when each user uses wet wipes in different time periods, as well as the ambient temperature and humidity, and to build a prediction model A for the number of dry and wet wipes used by users in each time period and a prediction model B for the temperature and humidity of the wet wipes used by users in each time period. The workflow of the user behavior analysis module is as follows: Get the number of dry and wet wipes used by each user in each time period (one hour per time period) over the past week, as well as the temperature and humidity when the user uses the wet wipes; The number of dry towels used by each user in each time period (one hour per time period) over the past week is used as the original time series; If the original time series contains missing or outlier values ​​(i.e., the usage of dry or wet wipes suddenly increases or decreases to more than three times or less than the historical average during a certain period), then a linear interpolation algorithm is used to fill in or correct the missing or outlier values, and then the lag order h of the original time series is determined by information criteria (AIC / BIC) or cross-validation. The earliest time node in the original time series is taken as the starting time t. （sta） ; Each element in the original time series that is not less than (t) （sta）+1) is a type 1 time node. The feature value of each type 1 time node in the original time series, lagged by one hour, is constructed point by point to obtain the time lag term Y. （t-1） ; Each element in the original time series that is not less than (t) （sta） +2) The time nodes are of two types. The feature values ​​of each type 2 time node in the original time series are constructed point by point, lagged by 2 hours, to obtain the time lag term Y. （t-2） ; And so on, taking each element in the original time series that is not less than (t) （sta） The time nodes of +h) are classified as h-type time nodes. The feature values ​​of each h-type time node in the original time series, lagged by h hours, are constructed point-by-point to obtain the time lag term Y. （t-h） ; The time index of each time point in the original time series is used as the time trend term, and hourly and day-of-week dummy variables are added to each data point in the time trend term to represent the periodicity of time. For the lagged term Y （t-1） To Y （t-h） The data in the time trend term is standardized using the Z-score, and then a lag term Y is introduced. （t-1） To Y （t-h） The characteristic coefficient β （1） ~β （h） and the time trend coefficient β （h+1） , Based on the Poisson distribution, a predictive model is constructed to predict the number of dry towels used by users in each time period: ; Among them, u （t） This represents the user's expected dry towel usage at time t; tr （t） The time trend term at time t; β （0） Indicates the intercept; β （0） ~β （h+1） As model parameters, and given initial values, the log-likelihood function of the Poisson distribution and the L2 regularization term are used as the loss function of the prediction model; β is calculated based on the initial values ​​of the model parameters. （0） ~β （h+1） The gradient of the loss function is calculated using the mini-batch gradient descent algorithm, and the learning rate is adjusted along the gradient descent direction to optimize the model parameters until the model parameters converge or the model parameters reach the maximum number of iterations, thus obtaining a prediction model (predicting the number of dry towels used by users in each time period). Using the number of wet wipes used by each user in each time period (one hour is one time period) within the past week as the original time series, repeat the above steps to construct a prediction model for the number of dry wipes used by the user in each time period, construct a prediction model for the number of wet wipes used by each user in each time period, and use the prediction model for the number of dry wipes used by each user in each time period as prediction model A. Organize the wet wipe temperature and humidity of each user at different time periods into a wet wipe vector in the form of a (2×1) matrix; also organize the ambient temperature and humidity of the user at different time periods into an environment vector in the form of a (2×1) matrix. Let the vector of wet wipes for a user during time period t be Ob. （t） The environment vector is En. （t） ; Let the vector of wet wipes corresponding to time period t, lag of 1 hour, 2 hours, up to h hours, be Hb. （t-1） Hb （t-2） ~Hb （t-h） Based on the VARX model, construct an initial model b (to predict the temperature and humidity of the wipes used by the user at different time periods). （1） : ; Among them, la （1） ,la （2） ~la （h） , respectively representing the wet wipe vector Hb （t-1） Hb （t-2） ~Hb （t-h） The observed coefficients, where vi represents the environmental coefficients; Represents matrix multiplication; Co represents the constant term, namely the default wet wipe temperature and default wet wipe humidity of the smart wet wipe machine. Co is also in the form of a (2×1) determinant. Substitute the wet wipe vector and environment vector corresponding to each user into the initial model b. （1） In this process, the values ​​of the observation coefficients and environmental coefficients are calculated using OLS (Ordinary Least Squares) or MLE (Maximum Likelihood Estimation) algorithms to obtain the initial model b. （2） ; Substitute the wet wipe vector and environment vector corresponding to each user into the initial model b. （2） In the process, the estimated wet wipe vector for each user in each time period is calculated; The error vector is obtained by subtracting the estimated wet wipe vector for each user in each time period from the actual wet wipe vector for each user in each time period. Determine whether the mean vector of the error vectors corresponding to each user (all) is zero; If the mean vector is zero, then check if the error vector is white noise; if it is white noise, then in the initial model b (for each user)... （2） In the middle, add the error vector to obtain the prediction model B (defined by each user); if it is not white noise, use the gradient descent algorithm to continue adjusting the observation coefficients and environmental coefficients; If the mean vector is not zero, calculate the difference vector between the error vector and the mean vector for each user, and determine whether the autocorrelation coefficient between the difference vectors for each user is zero. If the (autocorrelation coefficient) is zero, then in the initial model b （2） Subtract the mean vector matrix from the original constant term Co, and then add the error vector to obtain the prediction model B (defined by each user); If the autocorrelation coefficient is not zero, continue to adjust the observation coefficient and the environment coefficient using the gradient descent algorithm.

[0020] Temperature and humidity control module: Used to obtain the current time and, based on prediction model A and prediction model B, determine the number of dry and wet wipes used by the user (at the current time), as well as the temperature and humidity of the wet wipes; generate heating and humidification instructions (for the smart wet wipe machine) according to different heating and humidification methods; obtain the user's real-time location to determine whether the user is returning home; if returning home, estimate the return time and generate a keep-warm instruction; if not returning home, no action is taken; the heating and humidification instructions and keep-warm instructions corresponding to each user at the current time are used as the real-time default instructions for each user. Please see Figure 3 The functions of the temperature and humidity control module are as follows: Get the current time t （now） And the ambient temperature and humidity of each user's residence at the current time, and then obtain the data for each user at (t) （now） -1) The usage of dry wipes and wet wipes within a time period, based on the prediction model A corresponding to each user, predicts the usage of each user in (current) t. （now） Dry wipe usage and wet wipe usage over a period of time; If a user is at (current) t （now） If the amount of wet wipes used during a time period is zero, no action will be taken (i.e., no heating / humidifying or heat preservation commands will be generated). If a user is at (current) t （now） If the amount of wet wipes used during a given time period is not zero, then a heating and humidification instruction will be generated for the smart wet wipe machine based on its different heating and humidification methods. Obtain the weight m of the dry towel （p） And the specific heat capacity (of the dry towel) Cp; assuming the ambient temperature of the residence at the current time is T. （env） The ambient humidity is H （env）The predicted usage of wet wipes is n. （wet） ; Scenario 1: The smart wet wipe dispenser uses a hot water spray humidification and heating mode; Let mw be the spray flow rate of the smart wet wipe dispenser per unit time (default), and let Tw be the temperature of the hot water. （w） Construct the differential equation A for the temperature change of the dry towel at time t. （1） : ; Among them, T （p） The temperature of the wet wipe is indicated by Cw, the specific heat capacity of water is indicated by hc. （w） This represents the radiative heat dissipation coefficient of water. Differential equation A （1） Simplified to differential equation A （2） : ; Among them, ka （1） T represents （p） The coefficient, ka （2） Represent (differential equation A) （2） (of) constant term; Let the differential equation A （2） The left side is zero (meaning the rate of change of the wet wipe is zero, or the temperature of the wet wipe has reached a steady state). Calculate the steady-state temperature T of the wet wipe. （equ） : ; Get the temperature Tp of the wet wipes before heating. （ini） T （p） The initial state is Tp （ini） The final state is T （equ） (Based on differential equation A) （2） (Solution form), construct the function T of the change in temperature of the wet wipe (in the following case). （p） (t): ; Let η be the water absorption efficiency of the dry towel when sprayed with hot water. （s） The amount of water absorbed by the dry towel at time t is W. （in） : ; The evaporation coefficient of water is obtained as ke. （w） The amount of water evaporated from a wet wipe (i.e., a dry wipe that has absorbed water and become a wet wipe) at time t due to the ambient temperature is: W （evap） : ; The amount of water that evaporates naturally from the hot water at time t (i.e., a dry towel that has absorbed water and become a wet towel) is: W （hot） : ; After the dry towel is humidified, during the process of the temperature changing from the initial state to the final state, construct the function H of the change in humidity (i.e., the moisture content of the wet basis) (in the following case). （hum） (t): ; Get the response time ti of a smart wet wipe dispenser (dispensing only one wet wipe at a time). （res） Let the user's target temperature be T. （tar） The predetermined target humidity is H （tar） ; First, heat the water in the smart wet wipe dispenser to T temperature. （tar） Celsius (When performing numerical calculations, pay attention to the conversion between Celsius and Kelvin temperatures); Obtain the ambient temperature of the user's residence, and then... （res） As t, T （tar） As T （equ） Substitute the change function T in reverse （p） In (t), calculate (when the function T changes) （p） The value of (t) is T. （tar） The ideal temperature (t) for the dry towel before heating （ea） ; Based on the temperature t of the dry towel before heating （pra） This generates different heating and humidification commands for the (smart wipes dispenser); Branch a: If t （pra） ≥t （ea） Then t （pra） As Tp （ini） , will T （tar） As T （equ） Substitute the change function T in reverse （p） In (t), calculate (when the function T changes) （p） The value of (t) is T. （tar） Heating time of dry towel (tr) （1） ; Obtain the ambient humidity of the user's residence and calculate the spray temperature of the dry towel at a flow rate of mw. （1） After the humidity h （r1） ; Determine h （r1） With H （tar） Is the difference within 3% (where 3% represents the humidity error range; users or relevant technicians can adjust the humidity error range according to actual needs)? Branch a1: If (h （r1） With H （tar） If the difference is within 3%, the heating and humidification command for the smart wet wipes dispenser will be: (T...) （tar）Hot water at 100 degrees Celsius, at a flow rate of mw, is used to dry the towel. （1） A spray of water every second; Branch a2: If (h （r1） With H （tar） If the difference is not within 3%, then T will be... （tar） As T （equ） The constant value of t （pra） As Tp （ini） Given a constant value, without changing the value of mw, calculate and compare t with ti. （res） Time-varying function T （p） (t) and the function of change H （hum） (t) corresponds to the magnitude of the first derivative; Branch a21: If the change function T （p） The first derivative of (t) is relatively small (i.e., the change function T) （p） The first derivative of (t) is less than or equal to the value of the change function H. （hum） If the first derivative value of (t) is given, it indicates that the temperature change rate of the dry towel is relatively slow during hot water spraying (i.e., time has a greater impact on the humidity of the wet towel). t （pra） As Tp （ini） , will T （tar） As T （equ） , will ti （res） Substituting t into the transformation function H in reverse... （hum） In (t), calculate (when function H) （hum） The value of (t) is H （tar） (hourly) Hot water spray flow rate for dry towels (mw) （r1） ; The heating and humidification command for the smart wet wipe dispenser is: (in T...) （tar） Hot water at Celsius, in mw （r1） The flow rate, to ti （res） A spray of water every second; Branch a22: If the change function H （hum） The first derivative of (t) is relatively small (i.e., the change function T) （p） The first derivative value of (t) > the change function H （hum） If the first derivative value of (t) is given, it indicates that the rate of change of humidity of the dry towel during hot water spraying is relatively slow (i.e., time has a greater impact on the humidity of the wet towel). mw （r1） As mw, ti （res） As t, t （pra） As Tp （ini） , will T （tar） As T （equ） Substituting the second inverse into the transformation function T （p） In (t), calculate (when the function T)（p） The value of (t) is T. （tar） (Time) Hot water temperature Tw （r1） ; The heating and humidification command for the smart wet wipe dispenser is: in Tw （r1） Hot water at Celsius, in mw （r1） The flow rate, to ti （res） A spray of water every second; Branch b: If t （pra） <t （ea） Then t （pra） As Tp （ini） , will t （ea） As T （equ） Without changing the value of mw, substitute it into the change function T in reverse. （p） In (t), calculate (when the function T changes) （p） The value of (t) is T. （tar） When the dry towel is heated by hot water spray, the temperature is changed from t. （pra） Change to t （ea） time ti （r2） ; According to time ti （r2） Calculate the new flow rate (mw) of the smart water heater （r2） : ; Get the ambient temperature of the user's residence, and then... （r2） As mw, ti （res） As t, T （tar） As T （equ） , will t （pra） As Tp （ini） Substitute the change function H in reverse （hum） In (t), calculate (when the change function H) （hum） The value of (t) is H （tar） (Time) Hot water temperature Tw during hot water spray heating of dry towel （r2） ; The heating and humidification command for the smart wet wipe dispenser is: in Tw （r2） Hot water at Celsius, in mw （r2） The flow rate, to ti （res） A spray of water every second; It should be noted that because the temperature and humidity of the wet wipes are affected by the hot water temperature, spray flow rate, and spray time, changing any one of these three variables—hot water temperature, spray flow rate, and spray time—will affect the user's other humidity or temperature requirement, provided that one temperature or humidity requirement is met. Therefore, this application utilizes the method of "calculating and comparing the change function T". （p） (t) and the function of change H （hum）(t) The form of the first derivative, prioritizing the requirement of smaller rate of change (i.e., the change function T) （p） (t) and the function of change H （hum） (t) The smaller of the first derivative values ​​corresponds to the temperature or humidity requirements. Scenario 2: The smart wet wipe dispenser uses a hot water atomization humidification and heating mode; Obtain the capture efficiency η of the dry towel for the arriving droplets. （dep） The absorption efficiency η of droplets on the surface of a dry towel. （ads） To obtain the mass fraction φ of the liquid reaching the water mist from the smart wet wipe dispenser (hot water). （surv） The water flow rate Q of the atomizer （o） (That is, the flow rate of water produced per second in the form of "water mist" by the atomizer of the smart wet wipe dispenser). Calculate the equivalent atomization flow rate (md) of the surface (hot water atomization) of the smart wet wipe dispenser per unit time: Where A(p) represents the area of ​​the dry towel; (the hot water mist in this invention is assumed to be 100 degrees Celsius). Construct the differential equation B for the temperature change of the dry towel at time t. （1） : ; Where Lv represents the latent heat of water vaporization, hc （o） It represents the total heat dissipation coefficient of water (i.e., the radiative heat dissipation coefficient of water + the convective heat dissipation coefficient of water vapor). Differential equation B （1） Simplified to differential equation B （2） : ; Among them, kb （1） T represents （p） The new coefficient, kb （2） Represent (differential equation A) （2） The new constant term; Let differential equation B （2） The left side is zero (meaning the rate of change of the wet wipe is zero, or the temperature of the wet wipe has reached a steady state). Calculate the steady-state temperature To of the wet wipe. （equ） : ; Get the temperature Tp of the wet wipes before heating. （ini） T （p） The initial state is Tp （ini） The final state is T （equ） (Based on differential equation A) （2） (Solution form), construct the function T for the change in temperature of the wet wipes (in case two). （o） (t): ; Calculate the amount of water absorbed by the dry towel at time t as Wo. （in） : ; The amount of water evaporated from a wet wipe (i.e., a dry wipe that has absorbed water and become a wet wipe) at time t due to ambient temperature is: Wo （evap） : ; The amount of water that evaporates naturally at time t due to the absence of temperature on the wet wipe (i.e., a dry wipe that has absorbed water and become a wet wipe) is: Wo （hot） : ; After the dry towel is humidified, during the process of the temperature changing from the initial state to the final state, construct the function H of the change in humidity (i.e., the moisture content of the wet basis) (in the following case). （o） (t): ; Branch c: Do not change the value of mb, but set T. （tar） As T （equ） , will ti （res） Substituting t into the change function H （o） (t), calculate the humidity h of the dry towel after it has been humidified by hot water mist. （r3） ; Determine h （r3） With H （tar） Is the difference within 3%? If (h) （r3） With H （tar） If the difference is within 3%, the heating and humidification command of the smart wet wipes dispenser is: to use 100-degree Celsius hot water, without changing the water flow rate of the atomizer, to humidify the dry wipes in the form of water mist. （res） Water mist humidification and heating in seconds; If (h) （r3） With H （tar） If the difference is not within 3%, then compare h. （r3） With H （tar） Different heating and humidification instructions are formulated based on the size of the object; Branch c1: If h （r3） >H （tar） Then the value of mb will not be changed, and T will be... （tar） As T （equ） Substitute the change function H in reverse （o） In (t), calculate (the change function H) （o） The function value of (t) is H. （tar） Heating time (ti) （r3） ; ti （r3） Substitute the change function T（o） (t), calculate the heating and humidification of the dry towel (ti) （r3） Temperature after (seconds) To （r3） ; If To （r3） ≥T （tar） The heating and humidification command for the smart wet wipes dispenser is: to use 100-degree Celsius hot water, without changing the water flow rate of the atomizer, to humidify the dry wipes in the form of water mist. （r3） Water mist humidification and heating in seconds; If To （r3） <T （tar） Then ti （res） As t, T （tar） As T （equ） Substitute the change function T in reverse （o） In (t), calculate (the function of change T) （o） The function value of (t) is T. （tar） (At time) equivalent atomization flow rate md （r3） ; md （r3） Substitute the equivalent atomizing flow rate md in reverse to calculate the new effluent flow rate Q of the atomizer. （r3） ; The heating and humidification instructions for the smart wet wipe dispenser are: use 100-degree Celsius hot water, and the atomizer will operate at Q... （r3） The water flow rate, in the form of water mist, is used to spray the dry towel. （res） Water mist humidification and heating in seconds; Branch c2: If h （r3） <H （tar） Then the value of mb will not be changed, and T will be... （tar） As T （equ） Substitute the change function H in reverse （o） In (t), calculate (the change function H) （o） The function value of (t) is H. （tar） Heating time (ti) （r4） ; ti （r4） As t, T （tar） As T （equ） Substitute the change function T in reverse （o） In (t), calculate (the function of change T) （o） The function value of (t) is T. （tar） (At time) equivalent atomization flow rate md （r4） ; md （r4） Substitute the equivalent atomizing flow rate md in reverse to calculate the new effluent flow rate Q of the atomizer. （r4） ; The heating and humidification instructions for the smart wet wipe dispenser are: use 100-degree Celsius hot water, and the atomizer will operate at Q...（r4） The water flow rate, in the form of water mist, is used to spray the dry towel. （res） Water mist humidification and heating in seconds; Obtain the user's real-time location to determine if the user has returned home; If they do not return home, then nothing will be done; If returning home, estimate the return time and generate a temperature control instruction. The specific steps are as follows: The system acquires the user's location and movement speed in real time, and then calculates the user's return time t based on the location of the smart wipe dispenser. （home） ; Based on the different heating and humidification commands of the smart wet wipe dispenser, the water in the smart humidifier is kept at a constant temperature. （home） Second; If the smart wet wipe dispenser uses a hot water mist heating and humidification mode, then maintain the water level in the smart humidifier at 100%. o The temperature of C lasts for t （home） Second; The system monitors the remaining number of dry wipes in the smart wet wipe dispenser in real time. Based on the predicted number of dry wipes and wet wipes used by each user according to the prediction model A, it determines whether the remaining number of dry wipes in the smart wet wipe dispenser is sufficient. If there is sufficient supply, then no action is taken; If the amount is insufficient, the user will be prompted to replenish it.

[0021] Remote control module: Used to detect whether the user has issued new heating and humidification commands and new heat preservation commands for the (smart wipes machine); if so, the heating, humidification and heat preservation modes of the smart wipes machine will be adjusted according to the user's newly issued heating and humidification commands and heat preservation commands; if not, the smart wipes machine will run with the real-time default commands.

[0022] The above formulas are all dimensionless calculations. The formulas are derived from software simulations using a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation. For example, there are weighting coefficients and proportional coefficients. The values ​​set are to quantify each parameter to obtain a specific value, which is convenient for subsequent comparison. The values ​​of the weighting coefficients and proportional coefficients are only required to not affect the proportional relationship between the parameters and the quantified values.

[0023] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A mobile-based intelligent wet wipe dispenser temperature and humidity coordinated adjustment system, characterized in that, include: User behavior analysis module: used to obtain the number of dry and wet wipes used by each user in different time periods, the temperature and humidity of the wet wipes when using them in different time periods, as well as the ambient temperature and humidity, and to build a prediction model A for the number of dry and wet wipes used by users in different time periods and a prediction model B for the temperature and humidity of the wet wipes used by users in different time periods. Temperature and humidity control module: used to obtain the current time, and based on prediction model A and prediction model B, determine the number of dry and wet wipes used by the user, as well as the temperature and humidity of the wet wipes; generate heating and humidification instructions according to different heating and humidification methods of the smart wet wipe machine; obtain the user's real-time location to determine whether the user has returned home; If returning home, estimate the return time and generate a keep-warm instruction; If the user does not return home, no action will be taken; the heating and humidification commands and heat preservation commands corresponding to each user at the current time will be used as the real-time default commands for each user. Remote control module: Used to detect whether the user has issued new heating and humidification commands and new heat preservation commands; If specified, the heating, humidification, and heat preservation modes of the smart wipes dispenser will be adjusted according to the user's newly specified heating, humidification, and heat preservation commands; if not specified, the smart wipes dispenser will operate with the real-time default commands.

2. The mobile-based intelligent wet wipe dispenser temperature and humidity coordinated adjustment system according to claim 1, characterized in that, The steps for constructing prediction model A are as follows: Obtain the original time series and construct the time lag term Y. （t-1） To Y （t-h） ; For the lagged term Y （t-1） To Y （t-h） Standardize the data in the time trend term, and then introduce the lag term Y. （t-1） To Y （t-h） The characteristic coefficient β （1） ~β （h） and the time trend coefficient β （h+1） , Based on the Poisson distribution, a predictive model is constructed to predict the number of dry towels used by users in each time period: ； Among them, u （t） This represents the user's expected dry towel usage at time t; tr （t） The time trend term at time t; β （0） Indicates the intercept; β （0） ~β （h+1） As model parameters, and given initial values, the log-likelihood function of the Poisson distribution and the L2 regularization term are used as the loss function of the prediction model; β is calculated based on the initial values ​​of the model parameters. （0） ~β （h+1） The gradient of the loss function is calculated using the mini-batch gradient descent algorithm, and the learning rate is adjusted along the gradient descent direction during model parameter optimization until the model parameters converge or the model parameters reach the maximum number of iterations, thus obtaining the prediction model. Construct a prediction model to predict the number of wet wipes used by each user in each time period, and obtain prediction model A.

3. The mobile-based intelligent wet wipe dispenser temperature and humidity coordinated adjustment system according to claim 1, characterized in that, The steps for constructing prediction model B are as follows: Let the vector of wet wipes for a user during time period t be Ob. （t） The environment vector is En. （t） ; Let Hb be the vector of wet wipes corresponding to a time interval t with a lag of 1 to h hours. （t-1） ~Hb （t-h） Construct the initial model b （1） : ； Among them, la （1） ~la （h） , respectively representing the wet wipe vector Hb （t-1） ~Hb （t-h） The observed coefficients, where vi represents the environmental coefficients and Co represents the constant term; Substitute the wet wipe vector and environment vector corresponding to each user into the initial model b. （1） In the process, the values ​​of the observation coefficients and the environmental coefficients are calculated to obtain the initial model b. （2） ; Based on the initial model b （2） In the process, the estimated wet wipe vector for each user in each time period is calculated; The error vector is obtained by subtracting the estimated wet wipe vector from the actual wet wipe vector for each user. Determine whether the mean vector of the error vector is zero; If the value is zero, then check if the error vector is white noise; if it is white noise, then in the initial model b （2） In the middle, add the error vector to obtain the prediction model B; if it is not white noise, adjust the observation coefficient and the environmental coefficient. If it is not zero, calculate the difference vector between the error vector and the mean vector for each user, and determine whether the autocorrelation coefficient between the difference vectors is zero. If it is zero, then in the initial model b （2） Subtract the mean vector matrix from the original constant term Co, and then add the error vector to obtain the prediction model B; If it is not zero, then adjust the observation coefficient and the environmental coefficient.

4. The mobile-based intelligent wet wipe dispenser temperature and humidity coordinated adjustment system according to claim 1, characterized in that, The steps for generating the heating and humidification command are as follows: Predict the dry wipe usage and wet wipe usage for each user based on prediction model A corresponding to each user; If a user's wipe usage is zero in the current time period, no action will be taken; If a user's current wipe usage is not zero, a heating and humidification command will be generated based on the different heating and humidification methods of the smart wipe dispenser. Obtain the weight m of the dry towel （p） And specific heat capacity Cp; assuming the ambient temperature of the residence at the current time is T. （env） The ambient humidity is H （env） The predicted usage of wet wipes is n. （wet） ; Scenario 1: The smart wet wipe dispenser uses a hot water spray humidification and heating mode; Scenario 2: The smart wet wipe dispenser uses a hot water atomization humidification and heating mode.

5. The mobile-based intelligent wet wipe dispenser temperature and humidity coordinated adjustment system according to claim 4, characterized in that, The steps for handling scenario one are as follows: Given the spray flow rate (mw) of the smart wet wipe dispenser per unit time, construct the differential equation A for the temperature change of the dry wipes at time t: ； Among them, ka （1） T represents （p） The coefficient, ka （2） Represents a constant term; Calculate the stable temperature T of the wet wipes （equ） : ; Get the temperature Tp of the wet wipes before heating. （ini） T （p） The initial state is Tp （ini） The final state is T （equ） Construct the temperature change function T of the wet wipes （p） (t): ； Let η be the water absorption efficiency of the dry towel when sprayed with hot water. （s） The amount of water absorbed by the dry towel at time t is W. （in） ; Obtain the evaporation coefficient of water, and the amount of water that evaporates naturally due to ambient temperature at time t is W. （evap） Because the natural evaporation rate of hot water is W （hot） ; After a dry towel is humidified, during the process of temperature changing from the initial state to the final state, construct the humidity change function H. （hum） (t): ； Get the response time ti of the smart wet wipe dispenser （res） Let the user's target temperature be T. （tar） The predetermined target humidity is H （tar） ; First, heat the water in the smart wet wipe dispenser to T temperature. （tar） Celsius; Obtain the ambient temperature of the user's residence, and then... （res） As t, T （tar） As T （equ） Substitute the change function T in reverse （p） In (t), calculate the ideal temperature t of the dry towel before heating. （ea） ; Compare the temperatures t of the dry towel before heating. （pra） With t （ea） The size of the value determines the generation of different heating and humidification commands.

6. The mobile-based intelligent wet wipe dispenser temperature and humidity coordinated adjustment system according to claim 5, characterized in that, The steps for generating the heating and humidification command are as follows: Branch a: If t （pra） ≥t （ea） Calculate the heating time tr of the dry towel （1） ; Obtain the ambient humidity of the user's residence and calculate the spray temperature of the dry towel at a flow rate of mw. （1） After the humidity h （r1） ; Determine h （r1） With H （tar） Is the difference within 3%? Branch a1: If it is within 3%, the heating and humidification command is: [T] （tar） Hot water at a temperature of [temperature value], flow rate of [flow rate value], to dry the towel using [method / method]. （1） A spray of water every second; Branch a2: If it is not within 3%, then calculate and compare function T. （p） (t) and function H （hum） (t) The magnitude of the first derivative; Branch a21: If function T （p） The first derivative of (t) is relatively small, so the hot water spray flow rate (mw) for calculating the dry towel is calculated. （r1） ; The heating and humidification command is: (T) （tar） hot water, mw （r1） The flow rate, to ti （res） A spray of water every second; Branch a22: If function H （hum） The first derivative of (t) is relatively small, which is useful for calculating the hot water temperature Tw. （r1） ; The heating and humidification command is: Tw （r1） hot water, mw （r1） The flow rate, to ti （res） A spray of water every second; Branch b: If t （pra） <t （ea） Then, the temperature during the hot water spray heating of the dry towel is calculated from t. （pra） Change to t （ea） time ti （r2） ; According to time ti （r2） Calculate the new flow mw （r2） ; Obtain the ambient temperature of the user's residence and calculate the hot water temperature Tw when heating with a dry towel. （r2） ; The heating and humidification command is: Tw （r2） hot water, mw （r2） The flow rate, to ti （res） A spray every second.

7. The mobile-based intelligent wet wipe dispenser temperature and humidity coordinated adjustment system according to claim 4, characterized in that, The steps for handling scenario two are as follows: Calculate the equivalent atomization flow rate md, and construct the differential equation B for the temperature change of the dry towel at time t: ； Among them, kb （1） T represents （p） The new coefficient, kb （2） Indicates the new constant term; Calculate the stable temperature of the wet wipes To （equ） : ; Get the temperature Tp of the wet wipes before heating. （ini） T （p） The initial state is Tp （ini） The final state is T （equ） Construct the temperature change function T of the wet wipes （o） (t): ； Calculate the amount of water absorbed by the dry towel at time t as Wo. （in） The amount of wet wipes that evaporates naturally due to ambient temperature at time t is Wo. （evap） Since water has no temperature, its natural evaporation rate is Wo. （hot） ; Construct the humidity change function H （o） (t): ； Branch c: Do not change the value of mb, but set T. （tar） As T （equ） , will ti （res） Substituting t into the change function H （o） (t), calculate the humidity h of the dry towel after it has been humidified by hot water mist. （r3） ; Determine h （r3） With H （tar） Is the difference within 3%? If the humidity is below 3%, the heating and humidification command for the smart wet wipe dispenser is: to use 100-degree Celsius hot water, without changing the water flow rate of the atomizer, to humidify the dry wipes in the form of water mist. （res） Water mist humidification and heating in seconds; If it is not within 3%, then compare h. （r3） With H （tar） Different heating and humidification instructions are formulated based on the size of the object.

8. The mobile-based intelligent wet wipe dispenser temperature and humidity coordinated adjustment system according to claim 7, characterized in that, The steps to formulate a heating and humidification instruction are as follows: Branch c1: If h （r3） >H （tar） Then calculate the heating time ti （r3） ; Calculate the temperature To after heating and humidifying the dry towel. （r3） ; If To （r3） ≥T （tar） The heating and humidification command is as follows: use 100-degree hot water, without changing the water flow rate of the atomizer, to humidify the dry towel. （r3） Water mist humidification and heating in seconds; If To （r3） <T （tar） Then calculate the equivalent atomization flow rate md （r3） ; md （r3） Substitute the equivalent atomizing flow rate md in reverse to calculate the new effluent flow rate Q of the atomizer. （r3） ; The heating and humidification instructions are: use 100-degree hot water, and the atomizer should operate at Q... （r3） The water flow rate is used to tidy up the dry towels. （res） Water mist humidification and heating in seconds; Branch c2: If h （r3） <H （tar） Then calculate the heating time ti （r4） Equivalent atomization flow rate md （r4） ; md （r4） Substitute the equivalent atomizing flow rate md in reverse to calculate the new effluent flow rate Q of the atomizer. （r4） ; The heating and humidification instructions are: use 100-degree hot water, and the atomizer should operate at Q... （r4） The water flow rate is used to tidy up the dry towels. （res） Water mist humidification and heating in seconds.

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